SUBROUTINE CTRSEN( JOB, COMPQ, SELECT, N, T, LDT, Q, LDQ, W, M, S, $ SEP, WORK, LWORK, INFO ) * * -- LAPACK routine (version 3.1) -- * Univ. of Tennessee, Univ. of California Berkeley and NAG Ltd.. * November 2006 * * Modified to call CLACN2 in place of CLACON, 10 Feb 03, SJH. * * .. Scalar Arguments .. CHARACTER COMPQ, JOB INTEGER INFO, LDQ, LDT, LWORK, M, N REAL S, SEP * .. * .. Array Arguments .. LOGICAL SELECT( * ) COMPLEX Q( LDQ, * ), T( LDT, * ), W( * ), WORK( * ) * .. * * Purpose * ======= * * CTRSEN reorders the Schur factorization of a complex matrix * A = Q*T*Q**H, so that a selected cluster of eigenvalues appears in * the leading positions on the diagonal of the upper triangular matrix * T, and the leading columns of Q form an orthonormal basis of the * corresponding right invariant subspace. * * Optionally the routine computes the reciprocal condition numbers of * the cluster of eigenvalues and/or the invariant subspace. * * Arguments * ========= * * JOB (input) CHARACTER*1 * Specifies whether condition numbers are required for the * cluster of eigenvalues (S) or the invariant subspace (SEP): * = 'N': none; * = 'E': for eigenvalues only (S); * = 'V': for invariant subspace only (SEP); * = 'B': for both eigenvalues and invariant subspace (S and * SEP). * * COMPQ (input) CHARACTER*1 * = 'V': update the matrix Q of Schur vectors; * = 'N': do not update Q. * * SELECT (input) LOGICAL array, dimension (N) * SELECT specifies the eigenvalues in the selected cluster. To * select the j-th eigenvalue, SELECT(j) must be set to .TRUE.. * * N (input) INTEGER * The order of the matrix T. N >= 0. * * T (input/output) COMPLEX array, dimension (LDT,N) * On entry, the upper triangular matrix T. * On exit, T is overwritten by the reordered matrix T, with the * selected eigenvalues as the leading diagonal elements. * * LDT (input) INTEGER * The leading dimension of the array T. LDT >= max(1,N). * * Q (input/output) COMPLEX array, dimension (LDQ,N) * On entry, if COMPQ = 'V', the matrix Q of Schur vectors. * On exit, if COMPQ = 'V', Q has been postmultiplied by the * unitary transformation matrix which reorders T; the leading M * columns of Q form an orthonormal basis for the specified * invariant subspace. * If COMPQ = 'N', Q is not referenced. * * LDQ (input) INTEGER * The leading dimension of the array Q. * LDQ >= 1; and if COMPQ = 'V', LDQ >= N. * * W (output) COMPLEX array, dimension (N) * The reordered eigenvalues of T, in the same order as they * appear on the diagonal of T. * * M (output) INTEGER * The dimension of the specified invariant subspace. * 0 <= M <= N. * * S (output) REAL * If JOB = 'E' or 'B', S is a lower bound on the reciprocal * condition number for the selected cluster of eigenvalues. * S cannot underestimate the true reciprocal condition number * by more than a factor of sqrt(N). If M = 0 or N, S = 1. * If JOB = 'N' or 'V', S is not referenced. * * SEP (output) REAL * If JOB = 'V' or 'B', SEP is the estimated reciprocal * condition number of the specified invariant subspace. If * M = 0 or N, SEP = norm(T). * If JOB = 'N' or 'E', SEP is not referenced. * * WORK (workspace/output) COMPLEX array, dimension (MAX(1,LWORK)) * On exit, if INFO = 0, WORK(1) returns the optimal LWORK. * * LWORK (input) INTEGER * The dimension of the array WORK. * If JOB = 'N', LWORK >= 1; * if JOB = 'E', LWORK = max(1,M*(N-M)); * if JOB = 'V' or 'B', LWORK >= max(1,2*M*(N-M)). * * If LWORK = -1, then a workspace query is assumed; the routine * only calculates the optimal size of the WORK array, returns * this value as the first entry of the WORK array, and no error * message related to LWORK is issued by XERBLA. * * INFO (output) INTEGER * = 0: successful exit * < 0: if INFO = -i, the i-th argument had an illegal value * * Further Details * =============== * * CTRSEN first collects the selected eigenvalues by computing a unitary * transformation Z to move them to the top left corner of T. In other * words, the selected eigenvalues are the eigenvalues of T11 in: * * Z'*T*Z = ( T11 T12 ) n1 * ( 0 T22 ) n2 * n1 n2 * * where N = n1+n2 and Z' means the conjugate transpose of Z. The first * n1 columns of Z span the specified invariant subspace of T. * * If T has been obtained from the Schur factorization of a matrix * A = Q*T*Q', then the reordered Schur factorization of A is given by * A = (Q*Z)*(Z'*T*Z)*(Q*Z)', and the first n1 columns of Q*Z span the * corresponding invariant subspace of A. * * The reciprocal condition number of the average of the eigenvalues of * T11 may be returned in S. S lies between 0 (very badly conditioned) * and 1 (very well conditioned). It is computed as follows. First we * compute R so that * * P = ( I R ) n1 * ( 0 0 ) n2 * n1 n2 * * is the projector on the invariant subspace associated with T11. * R is the solution of the Sylvester equation: * * T11*R - R*T22 = T12. * * Let F-norm(M) denote the Frobenius-norm of M and 2-norm(M) denote * the two-norm of M. Then S is computed as the lower bound * * (1 + F-norm(R)**2)**(-1/2) * * on the reciprocal of 2-norm(P), the true reciprocal condition number. * S cannot underestimate 1 / 2-norm(P) by more than a factor of * sqrt(N). * * An approximate error bound for the computed average of the * eigenvalues of T11 is * * EPS * norm(T) / S * * where EPS is the machine precision. * * The reciprocal condition number of the right invariant subspace * spanned by the first n1 columns of Z (or of Q*Z) is returned in SEP. * SEP is defined as the separation of T11 and T22: * * sep( T11, T22 ) = sigma-min( C ) * * where sigma-min(C) is the smallest singular value of the * n1*n2-by-n1*n2 matrix * * C = kprod( I(n2), T11 ) - kprod( transpose(T22), I(n1) ) * * I(m) is an m by m identity matrix, and kprod denotes the Kronecker * product. We estimate sigma-min(C) by the reciprocal of an estimate of * the 1-norm of inverse(C). The true reciprocal 1-norm of inverse(C) * cannot differ from sigma-min(C) by more than a factor of sqrt(n1*n2). * * When SEP is small, small changes in T can cause large changes in * the invariant subspace. An approximate bound on the maximum angular * error in the computed right invariant subspace is * * EPS * norm(T) / SEP * * ===================================================================== * * .. Parameters .. REAL ZERO, ONE PARAMETER ( ZERO = 0.0E+0, ONE = 1.0E+0 ) * .. * .. Local Scalars .. LOGICAL LQUERY, WANTBH, WANTQ, WANTS, WANTSP INTEGER IERR, K, KASE, KS, LWMIN, N1, N2, NN REAL EST, RNORM, SCALE * .. * .. Local Arrays .. INTEGER ISAVE( 3 ) REAL RWORK( 1 ) * .. * .. External Functions .. LOGICAL LSAME REAL CLANGE EXTERNAL LSAME, CLANGE * .. * .. External Subroutines .. EXTERNAL CLACN2, CLACPY, CTREXC, CTRSYL, XERBLA * .. * .. Intrinsic Functions .. INTRINSIC MAX, SQRT * .. * .. Executable Statements .. * * Decode and test the input parameters. * WANTBH = LSAME( JOB, 'B' ) WANTS = LSAME( JOB, 'E' ) .OR. WANTBH WANTSP = LSAME( JOB, 'V' ) .OR. WANTBH WANTQ = LSAME( COMPQ, 'V' ) * * Set M to the number of selected eigenvalues. * M = 0 DO 10 K = 1, N IF( SELECT( K ) ) $ M = M + 1 10 CONTINUE * N1 = M N2 = N - M NN = N1*N2 * INFO = 0 LQUERY = ( LWORK.EQ.-1 ) * IF( WANTSP ) THEN LWMIN = MAX( 1, 2*NN ) ELSE IF( LSAME( JOB, 'N' ) ) THEN LWMIN = 1 ELSE IF( LSAME( JOB, 'E' ) ) THEN LWMIN = MAX( 1, NN ) END IF * IF( .NOT.LSAME( JOB, 'N' ) .AND. .NOT.WANTS .AND. .NOT.WANTSP ) $ THEN INFO = -1 ELSE IF( .NOT.LSAME( COMPQ, 'N' ) .AND. .NOT.WANTQ ) THEN INFO = -2 ELSE IF( N.LT.0 ) THEN INFO = -4 ELSE IF( LDT.LT.MAX( 1, N ) ) THEN INFO = -6 ELSE IF( LDQ.LT.1 .OR. ( WANTQ .AND. LDQ.LT.N ) ) THEN INFO = -8 ELSE IF( LWORK.LT.LWMIN .AND. .NOT.LQUERY ) THEN INFO = -14 END IF * IF( INFO.EQ.0 ) THEN WORK( 1 ) = LWMIN END IF * IF( INFO.NE.0 ) THEN CALL XERBLA( 'CTRSEN', -INFO ) RETURN ELSE IF( LQUERY ) THEN RETURN END IF * * Quick return if possible * IF( M.EQ.N .OR. M.EQ.0 ) THEN IF( WANTS ) $ S = ONE IF( WANTSP ) $ SEP = CLANGE( '1', N, N, T, LDT, RWORK ) GO TO 40 END IF * * Collect the selected eigenvalues at the top left corner of T. * KS = 0 DO 20 K = 1, N IF( SELECT( K ) ) THEN KS = KS + 1 * * Swap the K-th eigenvalue to position KS. * IF( K.NE.KS ) $ CALL CTREXC( COMPQ, N, T, LDT, Q, LDQ, K, KS, IERR ) END IF 20 CONTINUE * IF( WANTS ) THEN * * Solve the Sylvester equation for R: * * T11*R - R*T22 = scale*T12 * CALL CLACPY( 'F', N1, N2, T( 1, N1+1 ), LDT, WORK, N1 ) CALL CTRSYL( 'N', 'N', -1, N1, N2, T, LDT, T( N1+1, N1+1 ), $ LDT, WORK, N1, SCALE, IERR ) * * Estimate the reciprocal of the condition number of the cluster * of eigenvalues. * RNORM = CLANGE( 'F', N1, N2, WORK, N1, RWORK ) IF( RNORM.EQ.ZERO ) THEN S = ONE ELSE S = SCALE / ( SQRT( SCALE*SCALE / RNORM+RNORM )* $ SQRT( RNORM ) ) END IF END IF * IF( WANTSP ) THEN * * Estimate sep(T11,T22). * EST = ZERO KASE = 0 30 CONTINUE CALL CLACN2( NN, WORK( NN+1 ), WORK, EST, KASE, ISAVE ) IF( KASE.NE.0 ) THEN IF( KASE.EQ.1 ) THEN * * Solve T11*R - R*T22 = scale*X. * CALL CTRSYL( 'N', 'N', -1, N1, N2, T, LDT, $ T( N1+1, N1+1 ), LDT, WORK, N1, SCALE, $ IERR ) ELSE * * Solve T11'*R - R*T22' = scale*X. * CALL CTRSYL( 'C', 'C', -1, N1, N2, T, LDT, $ T( N1+1, N1+1 ), LDT, WORK, N1, SCALE, $ IERR ) END IF GO TO 30 END IF * SEP = SCALE / EST END IF * 40 CONTINUE * * Copy reordered eigenvalues to W. * DO 50 K = 1, N W( K ) = T( K, K ) 50 CONTINUE * WORK( 1 ) = LWMIN * RETURN * * End of CTRSEN * END


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Magenta is a block cipher with a complex and deeply nested design. However, the S-box has a simple structure, and there are also weaknesses in the key schedule. This led to cryptanalytic results being obtained against it shortly after it was disclosed. Although it has been claimed that there are other weaknesses in the design, it still seems to me that the design contains some useful principles.
The S-box used in Magenta is the following:
1 2 4 8 16 32 64 128 101 202 241 135 107 214 201 247 139 115 230 169 55 110 220 221 223 219 211 195 227 163 35 70 140 125 250 145 71 142 121 242 129 103 206 249 151 75 150 73 146 65 130 97 194 225 167 43 86 172 61 122 244 141 127 254 153 87 174 57 114 228 173 63 126 252 157 95 190 25 50 100 200 245 143 123 246 137 119 238 185 23 46 92 184 21 42 84 168 53 106 212 205 255 155 83 166 41 82 164 45 90 180 13 26 52 104 208 197 239 187 19 38 76 152 85 170 49 98 196 237 191 27 54 108 216 213 207 251 147 67 134 105 210 193 231 171 51 102 204 253 159 91 182 9 18 36 72 144 69 138 113 226 161 39 78 156 93 186 17 34 68 136 117 234 177 7 14 28 56 112 224 165 47 94 188 29 58 116 232 181 15 30 60 120 240 133 111 222 217 215 203 243 131 99 198 233 183 11 22 44 88 176 5 10 20 40 80 160 37 74 148 77 154 81 162 33 66 132 109 218 209 199 235 179 3 6 12 24 48 96 192 229 175 59 118 236 189 31 62 124 248 149 79 158 89 178 0
Start with 1, and shift one position left to obtain the next entry: when 1 is shifted out, XOR the contents with 101. This obtains the first 255 entries of the table; use 0 as the last entry.
In the Magenta documentation, f(x) is defined as entry x in this S-box. On that basis, other functions are defined in a step by step manner:
A(x,y) = f(x xor f(y)) [x and y are both bytes]
PE(x,y) = (A(x,y),A(y,x)) [that is, PE(x,y) is A(x,y) catenated with A(y,x)]
pi(x(0), x(1), ... x(15)) = ( PE(x(0),x(8)), PE(x(1),x(9)), PE(x(2),x(10)), ... PE(x(7),x(15)) )
To help keep track of where we are so far, this diagram illustrates how pi(X) operates on a 16-byte bit string:
T(w) = pi(pi(pi(pi(w)))) [where w is a 16-byte vector]
S(x(0),x(1),x(2),...x(15)) = (x(0),x(2),x(4),...x(14),x(1),x(3),x(5),...x(15))
Our last preparatory definition is that of C(n,w), where n is a number, and w a 16-bit vector, as the following:
C(1,w) = T(w)
C(n+1,w) = T(w xor S(C(n,w)))
Note that n is not a piece of the key or of the block being encrypted; it is a parameter giving the depth of recursion used.
Finally, with all these definitions, Magenta is a Feistel cipher.
Each Feistel round is expressed as taking (X(1),X(2)), where each X is half the block, 64 bits in length, and replacing it with (X(2),X(1) xor F(X(2),SK(n))), where n is the round number, and SK(n) the n-th subkey.
The F function equals the first eight bytes of S(C(3,(X(2),SK(n)))).
Thus, a round of Magenta may be pictured as follows:
Since the Magenta f-function consists of a non-invertible function applied to one half of the block with the key appended, the Magenta block cipher is a multi-round example of a Luby-Rackoff construction.
The key schedule is as follows:
It appears that, except for swapping halves, Magenta is reciprocal. The first paper giving a cryptanalysis of Magenta incorrectly gave the key schedule as K1 K2 K1 K1 K2 K1, it appears.
Originally, the F function was going to be the first eight bytes of S(C(7,(X(2),SK(n)))), but the number of rounds inside the f-function was reduced because of a possible weakness. Even so, Magenta is deeply nested with complexity.


As we saw in the last post the boot process is a series of steps that change the state of your computer from a system at rest to a running machine that can do real work.
Now one of the steps in this boot process is to find the actual boot device. To clarify the boot device is a storage medium on which your bootloader and operating system files are located. To further clarify a boot device is different from the BIOS firmware unit that comes installed on the hardware.
For most computers this default boot device is the hard disk. And on majority of computers the hard disk contains an operating system image (usually paid for by the manufacturer) which means that you almost never have to worry about the boot order of your devices. If however you want to install a new operating system on your computer or you have to replace your existing hard drive due to a failure it becomes important to understand how a boot device is chosen by a BIOS.
Well first of all BIOS recognizes multiple boot devices. It can boot from a cd/dvd drive. It can boot from a storage medium attached to a usb whether that is a pendrive or an external hard disk. It can also boot over a network that is the BIOS can download files from a connected remote machine and run it locally. It used to even boot from the floppy drive but since floppies disks are no longer manufactured this feature is mostly useless.
Although it can boot from multiple devices at any give time only one device can be used. It may boot from a hard disk or it may boot from a usb drive. But not from both of them at the same time.
But what happens when you have multiple boot devices present on a system. Consider for example a case when computer with a built in hard disk a DVD ROM and a plugged in pendrive is turned on? How would the BIOS determine on which device the OS files are located?
By simply looking into each of these devices on by one until it finds a booloader. It does not sound very effective but its really quick and works well and considering you can have only a finite number of boot devices on computer normally there is hardly a reason to change this approach. But then the question arises in which device the BIOS looks first?
Well that is determined by the boot order in your BIOS. In your BIOS you can give priority to different devices. For example you can instruct your BIOS to first try accessing the bootloader from the DVD ROM and if that fails try the hard disk. Or from the USB device or any another order that you can think of.
But the boot order feature is more of an "automated bootloader selection " ( or ABS for short) from a boot device. Most computers allow you to override this boot device selection process altogether by manually specifying the device to load from at the time of startup.
Pressing a certain key eg esc or del ( it's different for each computer) at the time of boot brings up a device selection menu which lists down all the devices on which bootloader is present. You can then choose which one you want to load your os from.
As a user you have a lot of flexibility to choose from various operating systems. sparvk pods are portable distributions of opensource operating systems that allow you to exercise that choice by providing you with ready made os for suited for different occasions. All you have to do is plug in the usb stick and choose to boot from it at the time of startup.


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This command implements simple pointer and keyboard grabs for Tk.
Two forms of grabs are possible: local and global.
- A local grab affects only the grabbing application: events will be reported to other applications as if the grab had never occurred. Grabs are local by default.
- A global grab locks out all applications on the screen, so that only the given subtree of the grabbing application will be sensitive to pointer events (mouse button presses, mouse button releases, pointer motions, window entries, and window exits). During global grabs the window manager will not receive pointer events either.
- During local grabs, keyboard events (key presses and key releases) are delivered as usual: the window manager controls which application receives keyboard events, and if they are sent to any window in the grabbing application then they are redirected to the focus window.
- During a global grab Tk grabs the keyboard so that all keyboard events are always sent to the grabbing application. The focus command is still used to determine which window in the application receives the keyboard events. The keyboard grab is released when the grab is released.
Grabs apply to particular displays. If an application has windows on multiple displays then it can establish a separate grab on each display. The grab on a particular display affects only the windows on that display. It is possible for different applications on a single display to have simultaneous local grabs, but only one application can have a global grab on a given display at once.
The tk/grab command can take any of the following forms:
- (tk/grab ?#:global? window)
- Same as tk/grab set, described below.
- (tk/grab current ?window?)
- If window is specified, returns the name of the current grab window in this application for window's display, or an empty string if there is no such window. If window is omitted, the command returns a list whose elements are all of the windows grabbed by this application for all displays, or an empty string if the application has no grabs.
- (tk/grab release window)
- Releases the grab on window if there is one, otherwise does nothing. Returns an empty string.
- (tk/grab set ?#:global? window)
- Sets a grab on window. If #:global is specified then the grab is global, otherwise it is local. If a grab was already in effect for this application on window's display then it is automatically released. If there is already a grab on window and it has the same global/local form as the requested grab, then the command does nothing. Returns an empty string.
- (tk/grab status window)
- Returns none if no grab is currently set on window, local if a local grab is set on window, and global if a global grab is set.
Corresponding Tcl/Tk command:
grab ...
Bugs
It took an incredibly complex and gross implementation to produce the simple grab effect described above. Given the current implementation, it isn't safe for applications to use the Xlib grab facilities at all except through the Tk grab procedures. If applications try to manipulate X's grab mechanisms directly, things will probably break.
If a single process is managing several different Tk applications, only one of those applications can have a local grab for a given display at any given time. If the applications are in different processes, this restriction doesn't exist.
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In this page, we introduce the Market-1501 dataset as well as the implementation of the BoW baseline.
If you use this dataset in your research, please kindly cite our work as,
Market-1501 Dataset
The Market-1501 dataset is collected in front of a supermarket in Tsinghua University. A total of six cameras were used, including 5 high-resolution cameras, and one low-resolution camera. Field-of-view overlap exists among different cameras. Overall, this dataset contains 32,668 annotated bounding boxes of 1,501 identities. In this open system, images of each identity are captured by at most six cameras. We make sure that each annotated identity is present in at least two cameras, so that cross-camera search can be performed. The Market-1501 dataset has three featured properties:
First, our dataset uses the Deformable Part Model (DPM) as pedestrian detector.
Second, in addition to the true positive bounding boxes, we also provde false alarm detection results.
Third, each identify may have multiple images under each camera. During cross-camera search, there are multiple queries and multiple ground truths for each identity.
The Market-1501 dataset is annotated using the following rules. For each detected bounding box to be annotated, we manually draw a ground truth bounding box that contains the pedestrian. Then, for the detected and hand-drawn bounding boxes, we calculate the ratio of the overlapping area to the union area. If the ratio is larger than 50%, the DPM bounding box is marked as "good"; if the ratio is smaller than 20%, the bounding boxe is marked as "distractor"; otherwise, it is marked as "junk", meaning that this image is of zero influence to the re-identification accuracy.
| Dataset | Market-1501 | RAiD [1] | CUHK03 [2] | VIPeR [3] | i-LIDS [4] | CUHK01 [5] | CUHK02 [6] | CAVIAR [7] |
| # identities | 1,501 | 43 | 1,360 | 632 | 119 | 971 | 1,816 | 72 |
| # BBoxes | 32,668 | 6,920 | 13,164 | 1,264 | 476 | 1,942 | 7,264 | 610 |
| # distractors | 2,793 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| # cam. per ID | 6 | 4 | 2 | 2 | 2 | 2 | 2 | 2 |
| DPM/Hand | DPM | hand | DPM | hand | hand | hand | hand | hand |
| Evaluation | mAP | CMC | CMC | CMC | CMC | CMC | CMC | CMC |
New!
We are organizing 2nd Workshop and Challenge on
Target Re-identification and Multi-Target Multi-Camera Tracking in CVPR 2019. Market-1501 will be used as the source training set.
We have annotated 27 ID-level attributes for Market-1501.
We have summarized current state of the art methods on Market-1501.
The dataset package is can be downloaded from any of the following links:
[Link 1] (Google Drive)
[Link 2] (Baidu Disk)
[Link 3] (A Server, Many thanks to Julian Tanke)
The package contains four folders.
1) "bounding_box_test". There are 19,732 images in this folder used for testing.
2) "bounding_box_train". There are 12,936 images in this folder used for training.
3) "query". There are 750 identities. We randomly select one query image for each camera. So the maximum number of query images is 6 for an identity. In total, there are 3,368 query images in this folder.
4) "gt_query". This folder contains the ground truth annotations. For each query, the relevant images are marked as "good" or "junk". "junk" has zero impact on search accuracy. "junk" images also include those in the same camera with the query.
5) "gt_bbox". We also provide the hand-drawn bounding boxes. They are used to judge whether a DPM bounding box is good.
Naming Rule of the bboxes
In bbox "0001_c1s1_001051_00.jpg", "c1" is the first camera (there are totally 6 cameras).
"s1" is sequence 1 of camera 1. Here, a sequence was defined automatically by the camera. We suppose that the camera cannot store a whole video that is quite large, so it splits the video into equally large sequences. Two sequences, namely, "c1s1" and "c2s1" do not happen exactly at the same time. This is mainly because the starting time of the 6 cameras are not exactly the same (it takes time to turn on them). But, "c1s1" and "c2s1" are roughly at the same time period.
"001051" is the 1051th frame in the sequence "c1s1". The frame rate is 25 frames per sec.
As with the last two digits, remember we use the DPM detector. Then, for identity "0001", there may be multiple detected bounding boxes in the frame "c1s1_001051". In other words, a pedestrian in the image may have several bboxes by DPM. So, "00" means that this bounding box is the first one among the several.
Market-1501+500k Dataset
We have released the 500k bboxes as distractors. Please download it from BaiduDisk
or GoogleDrive. The results used in Fig. 10 is also provided here.
We have summarized current state of the art methods on Market-1501+500k.
Please feel free contact us<EMAIL_ADDRESS>if you have any comment on our dataset.
Baseline Codes
For the baseline, the Bag-of-Words (BoW) model is used. Then, a number of improvements are added. In the following, we provide codes for 1) feature extraction given a pedestrian image 2) performance evaluation.
1) Feature Extraction
A classic BoW model is constructed. We use dense sampling and extract a 11-dim Color Names [8] vector for each patch. The descriptor is l1-normalized followed by square root operator [10]. A codebook is trained on the irrelevant TUD-Brussels dataset [9]. Then, a given feature vector is quantized to its nearest neighbor under Euclidean distance. We employ Multiple Assignment (MA) [11] and set MA = 10. Moreover, we also integrate Burstiness weighting [12] and Negative Evidence [13] into the BoW model.
MATLAB code for feature extraction is provided on GoogleDrive here, or Baidu Disk here.
To repeat our result on the VIPeR dataset, we provide the code of feature extraction with different parameters from the code above.
This code is also used in our CVPR15 paper.
Code on Baidu Disk is here, and on GoogleDrive here.
2) Performance Evaluation
We evaluate our algorithm on Market-1501 dataset. The evaluation package in Google Drive is here, and in Baidu Disk is here.
In this implementation, we have the following components:
a. baseline: bow descriptor + linear scan. You will obtain mAP = 14.75%, r1 accuracy = 35.84%;
b. baseline + multiple query by max/avg pooling;
c. baseline + multiple query by max pooling + re-ranking;
d. baseline + pairwise evaluation: re-id between camera pairs is evaluated, and a confusion matrix is drawn.
e. metric learning: Using the code provide by [14], we provide the training and testing protocol on our dataset.
Note that, we updated the evaluation package (Thanks Ying-Cong!). In the current version, we do not use the "gt_query" folder provided in the dataset. Instead, the ground truths are generated automatically during evaluation.
References
[1] A. Das et al.
Consistent re-identification in a camera network. In ECCV, 2014.
[2] W.
Li et al. Deepreid: deep filter pairing neural network for person re-
identification. In CVPR, 2014.
[3] D. Gray et al. Evaluating appearance
models for recognition, reacquisition, and tracking. In IEEE International
workshop on performance evaluation of tracking and surveillance, 2007.
[4]
W.-S. Zheng et al. Associating groups of people. In BMVC, 2009.
[5] W. Li
et al. Human reidentification with transferred metric learning. In ACCV,
2013.
[6] W. Li et al. Locally aligned feature transforms across views. In
CVPR, 2013.
[7] D. S. Cheng et al. Custom pictorial structures for re-
identification. In BMVC, 2011. [8] J. Van De Weijer et al. Learning color names
for real-world applications. IEEE Transactions on Image Processing, 18(7): 1512-
1533, 2009.
[9] C. Wojek et al. Multi-cue onboard pedestrian detection.
In CVPR, 2009.
[10] A. Relja et al. Three things everyone should know to
improve object retrieval. In CVPR, 2012.
[11] H. Jegou et al. Hamming
embedding and weak geometric consistency for large scale image search. In ECCV,
2008.
[12] H. Jegou et al. On the burstiness of visual elements. In CVPR,
2009. [13] H. Jegou et al. Negative evidences and co-ocurrences in image
retrieval: the benefit of PCA and whitening. In ECCV, 2012.
[14] M.
Koestinger et al, Large scale metric learning from equivalence constraints. In
CVPR, 2012.
Frequently Asked Questions
1. What are images beginning with "0000" and "-1"?
Ans: Names beginning with "0000" are distractors produced by DPM false detection.
Names beginning with "-1" are junks that are neither good nor bad DPM bboxes.
So "0000" will have a negative impact on accuracy, while "-1" will have no impact.
During testing, we rank all the images in "bounding_box_test".
2. How exactly do we use images in the "gt_bboxes" during training and testing?
Ans: gt_bboxes are not used in training and testing now. These hand-drawn images are used to calculate the IOU (intersection over union) with the DPM bboxes, so that "good", "distractor", and "junk" can be assigned to the DPM bboxes.
3. Do you have some results for full hand labeled version of the dataset (images from gt_bboxes)?
Ans: Not yet. We only have the results on the DPM bboxes. One may want to try it. It is pretty interesting.
Thank you, Niki and Jenya!


Avoid starting, stopping, or managing threads in any way.
Starting, stopping, or managing thread usage restriction eliminates the possibility of conflicts with the EJB container's responsibility of managing locking, threading, and concurrency issues.
REFERENCE
Enterprise JavaBeans Programming restrictions on EJB
by Sanjay Mahapatra -- JavaWorld August 2000


Our program is turning from a simple game into an expert system. In this stage, we will add a menu that will allow you not only to play with the computer but also get different information from the knowledge base.
As before, the program first greets the user. If a database has not been created yet, the program should ask the user about their favorite animal. This animal will become the basis of the newly created knowledge base. The program should then offer the user to play.
If a knowledge tree already exists, the computer loads it, greets the user as an expert system in animals, and offers the user a menu. The menu has to include at least these five items:
The greater-than symbol followed by a space >
represents the user input. Note that it's not part of the input.
Example 1: the game starts anew
Good evening!
I want to learn about animals.
Which animal do you like most?
> cat
Welcome to the animal expert system!
What do you want to do:
1. Play the guessing game
2. List of all animals
3. Search for an animal
4. Calculate statistics
5. Print the Knowledge Tree
0. Exit
> 1
You think of an animal, and I guess it.
Press enter when you're ready.
>
Is it a cat?
> No
I give up. What animal do you have in mind?
> a shark
Specify a fact that distinguishes a cat from a shark.
The sentence should be of the format: 'It can/has/is ...'.
> It is a mammal
Is this fact correct for the shark?
> No
Wonderful! I've learned so much about animals!
Do you like to play again?
> No
What do you want to do:
1. Play the guessing game
2. List of all animals
3. Search for an animal
4. Calculate statistics
5. Print the Knowledge Tree
0. Exit
> 0
Thank you and goodbye!
Example 2: an already existing knowledge base has been loaded
Good afternoon!
Welcome to the animal expert system!
What do you want to do:
1. Play the guessing game
2. List of all animals
3. Search for an animal
4. Calculate statistics
5. Print the Knowledge Tree
0. Exit
Example 3: the user wants to search for an animal
The search has to accept the animal's name with or without the article. If the animal is found, the program should print all the facts it knows about this animal.
Your choice:
3
Enter the animal:
hare
Facts about the hare:
- It is a mammal.
- It is living in the forest.
- It doesn't have a long bushy tail.
- It is a shy animal.
If the animal is not found, print this information for the user:
Your choice:
3
Enter the animal:
rabbit
No facts about the rabbit.
Example 4: the user wants to see the list of all animals
The program should traverse the knowledge tree and collect all the animal names into a list. The list of animals should be sorted in ascending order without articles.
Your choice:
2
Here are the animals I know:
- cat
- dog
- fox
- hare
- shark
- wolf
Example 5: the user wants to see the stats
The program should print the root node, the size of the knowledge tree (number of nodes), the number of animals, the number of facts, and the maximum and minimum depth of the tree which correspond to the minimum and maximum number of questions the program gets before it wins or gives up.
> 4
The Knowledge Tree stats
- root node It is a mammal
- total number of nodes 11
- total number of animals 6
- total number of statements 5
- height of the tree 4
- minimum animal's depth 1
- average animal's depth 3.0
Example 6: the user wants to print tree
The program should print the Knowledge Tree.
> 5
└ Is it a mammal?
├ Is it living in the forest?
│├ Is it a shy animal?
││├ a hare
││└ a wolf
│└ Can it climb tree?
│ ├ a cat
│ └ a dog
└ a shark


Weather - Web Visualization Dashboard
Weather factors to latitude—check it out!
Data
Humidity
Temperature
Wind
Cloudiness
Analyzing the relationship of latitude of cities to weather factors. The study consisted of looking at cloudiness, humidity, temperature, and wind.
Cloudy Map
Humidity Map
Temperature Map


INFORMATION SESSION MATERIALS
2015 PRIME AGREEMENT
PRE-DEPARTURE CULTURAL MATERIALS
RE-ENTRY MATERIALS
CULTURAL QUESTIONS
Assessment and Evaluation Forms for PRIME
+ CULTURAL OUTCOMES
+ STUDENT RESEARCH EXPERIENCE
+ UCSD MENTOR EVALUATION
+ HOST RESEARCH EVALUATION
Intercultural Development Inventory
- www.idiinventory.com


This weather application was created as a project for Wats 4000.
Created with:
Look out for future improvemnets!


Go to Customer Management
Email:
For the Month of:
Car VIN Code:
Make:
Model:
Year:
Pick Up/Drop Off Address:
Daily Rental Fee:
Show Rental History of: (Type in Car VIN Code)
Choose A Location From The Given List:
Choose the date to show all reservations in that day.
Which Month:


ED arrests ex-Bhushan Power & Steel CMD Sanjay Singal for bank loan fraud
The ED has recently attached assets worth Rs 4,025 crore of Bhushan Power and Steel Limited (BPSL).
by Press Trust of IndiaThe ED on Friday arrested former Bhushan Power and Steel Ltd CMD Sanjay Singal in connection with its probe in a multi-crore money laundering case linked to an alleged bank loan fraud, officials said.
They said Singal was placed under arrest, under the Prevention of Money Laundering Act (PMLA), after he was questioned in connection with the case.
He will be produced before a court here on Saturday for seeking further custody, the officials said.
He was arrested as he was not cooperating in the probe, they said.
The ED has recently attached assets worth Rs 4,025 crore of Bhushan Power and Steel Limited (BPSL).
"An amount of Rs 695.14 crore was introduced as capital by Sanjay Singal (the then CMD of the company) and his family members in BPSL out of artificially generated long-term capital gains (LTCG) by diversion of bank loans fund of BPSL," the ED had said.


{% include 'multiplicity/graphs/options.html' %} {% include 'multiplicity/graphs/series.name.html' %}


Spark Streaming + Kafka Integration Guide
Apache Kafka is publish-subscribe messaging rethought as a distributed, partitioned, replicated commit log service. Here we explain how to configure Spark Streaming to receive data from Kafka.
-
Linking: In your SBT/Maven projrect definition, link your streaming application against the following artifact (see Linking section in the main programming guide for further information).
groupId = org.apache.spark artifactId = spark-streaming-kafka_2.10 version = 1.1.0
-
Programming: In the streaming application code, import
KafkaUtils
and create input DStream as follows.import org.apache.spark.streaming.kafka._ val kafkaStream = KafkaUtils.createStream( streamingContext, [zookeeperQuorum], [group id of the consumer], [per-topic number of Kafka partitions to consume])
Points to remember:
-
Topic partitions in Kafka does not correlate to partitions of RDDs generated in Spark Streaming. So increasing the number of topic-specific partitions in the
KafkaUtils.createStream()
only increases the number of threads using which topics that are consumed within a single receiver. It does not increase the parallelism of Spark in processing the data. Refer to the main document for more information on that. -
Multiple Kafka input DStreams can be created with different groups and topics for parallel receiving of data using multiple receivers.
-
-
Deploying: Package
spark-streaming-kafka_2.10
and its dependencies (exceptspark-core_2.10
andspark-streaming_2.10
which are provided byspark-submit
) into the application JAR. Then usespark-submit
to launch your application (see Deploying section in the main programming guide).


Apache Lucene® is a widely used Java full-text search engine. This section describes how Apache Geode integrates with Apache Lucene. We assume that the reader is familiar with Apache Lucene’s indexing and search functionalities.
The Apache Lucene integration:
- Enables users to create Lucene indexes on data stored in Geode
- Provides high availability of indexes using Geode’s HA capabilities to store the indexes in memory
- Colocates indexes with data
- For persistent regions, persists Lucene indexes to disk
- Updates the indexes asynchronously to minimize impacting write latency
- Provides scalability by partitioning index data
For more details, see Javadocs for the classes and interfaces that implement Apache Lucene indexes and searches, including
LuceneService
, LuceneSerializer
, LuceneIndexFactory
, LuceneQuery
, LuceneQueryFactory
, LuceneQueryProvider
, and LuceneResultStruct
.
Using the Apache Lucene Integration
You can interact with Apache Lucene indexes through a Java API,
through the gfsh
command-line utility, or by means of the cache.xml
configuration file.
Key Points
- Apache Lucene indexes are supported only on partitioned regions. Replicated region types are not supported.
- Lucene indexes reside on servers. You cannot create a Lucene index on a client.
- A Lucene index applies to only one region. Multiple indexes can be defined for a single region.
- Heterogeneous objects in a single region are supported.
Creating a Lucene Index
Note: Create the Lucene index before creating the region.
When you create a Lucene index, you must provide three pieces of information:
- The name of the index you wish to create
- The name of the region to be indexed and searched
- The names of the fields you wish to index
You must specify at least one field to be indexed.
If the object value for the entries in the region comprises a primitive type value without a field name,
then use __REGION_VALUE_FIELD
to specify the field to be indexed. __REGION_VALUE_FIELD
serves as the field name for entry values of all
primitive types, including String
, Long
, Integer
, Float
, and Double
.
Each field has a corresponding analyzer to extract terms from text. When no analyzer is specified,
the org.apache.lucene.analysis.standard.StandardAnalyzer
is used.
The index has an associated serializer that renders the indexed object as a Lucene document comprised of searchable fields. The default serializer is a simple one that handles top-level fields, but does not render collections or nested objects.
Geode supplies a built-in serializer, FlatFormatSerializer()
, that handles
collections and nested objects. See Using FlatFormatSerializer to Index Fields within Nested Objects for more information
regarding Lucene indexes for nested objects.
As a third alternative, you can create your own serializer, which must implement the LuceneSerializer
interface.
Creating a Lucene Index: Java API Example
The following example uses the Java API to create a Lucene index with two fields. No analyzers are specified, so the default analyzer handles both fields. No serializer is specified, so the default serializer is used.
// Get LuceneService
LuceneService luceneService = LuceneServiceProvider.get(cache);
// Create the index on fields with default analyzer
// prior to creating the region
luceneService.createIndexFactory()
.addField("name")
.addField("zipcode")
.create(indexName, regionName);
Region region = cache.createRegionFactory(RegionShortcut.PARTITION)
.create(regionName);
Creating a Lucene Index: Gfsh Example
In gfsh, use the create lucene index command to create Lucene indexes.
The following example creates an index with two fields. The default analyzer handles both fields, and the default serializer is used.
gfsh>create lucene index --name=indexName --region=/orders --field=customer,tags
The next example creates an index, specifying a custom analyzer for the second field. “DEFAULT” in the first analyzer position specifies that the default analyzer will be used for the first field.
gfsh>create lucene index --name=indexName --region=/orders
--field=customer,tags --analyzer=DEFAULT,org.apache.lucene.analysis.bg.BulgarianAnalyzer
Creating a Lucene Index: XML Example
This XML configuration file specifies a Lucene index with three fields and three analyzers:
<cache
xmlns="http://geode.apache.org/schema/cache"
xmlns:lucene="http://geode.apache.org/schema/lucene"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://geode.apache.org/schema/cache
http://geode.apache.org/schema/cache/cache-1.0.xsd
http://geode.apache.org/schema/lucene
http://geode.apache.org/schema/lucene/lucene-1.0.xsd"
version="1.0">
<region name="region" refid="PARTITION">
<lucene:index name="myIndex">
<lucene:field name="a"
analyzer="org.apache.lucene.analysis.core.KeywordAnalyzer"/>
<lucene:field name="b"
analyzer="org.apache.lucene.analysis.core.SimpleAnalyzer"/>
<lucene:field name="c"
analyzer="org.apache.lucene.analysis.standard.ClassicAnalyzer"/>
<lucene:field name="d" />
</lucene:index>
</region>
</cache>
Using FlatFormatSerializer to Index Fields within Nested Objects
Geode supplies a built-in serializer, org.apache.geode.cache.lucene.FlatFormatSerializer
that renders collections and nested objects as searchable fields, which you can access using the syntax
fieldnameAtLevel1.fieldnameAtLevel2
for both indexing and querying.
For example, in the following data model, the Customer object contains both a Person object and a collection of Page objects. The Person object also contains a Page object.
public class Customer implements Serializable {
private String name;
private Collection<String> phoneNumbers;
private Collection<Person> contacts;
private Page[] myHomePages;
......
}
public class Person implements Serializable {
private String name;
private String email;
private int revenue;
private String address;
private String[] phoneNumbers;
private Page homepage;
.......
}
public class Page implements Serializable {
private int id; // search integer in int format
private String title;
private String content;
......
}
The FlatFormatSerializer
creates one document for each parent object, adding an indexed field for each data field
in a nested object, identified by its qualified name. Similarly, collections are flattened and
treated as tokens in a single field. For example, the FlatFormatSerializer
could convert a
Customer object, with the structure described above, into a document containing fields such as name
, contacts.name
,
and contacts.homepage.title
based on the indexed fields specified at index creation. Each segment is a field name, not a field type,
because a class (such as Customer) could have more than one field of the same type (such as Person).
The serializer creates and indexes the fields you specify when you request index creation.
The example below demonstrates how to index the name
field and the nested fields contacts.name
, contacts.email
,
contacts.address
, contacts.homepage.title
.
// Get LuceneService
LuceneService luceneService = LuceneServiceProvider.get(cache);
// Create Index on fields, some are fields in nested objects:
luceneService.createIndexFactory().setLuceneSerializer(new FlatFormatSerializer())
.addField("name")
.addField("contacts.name")
.addField("contacts.email")
.addField("contacts.address")
.addField("contacts.homepage.title")
.create("customerIndex", "Customer");
// Create region
Region CustomerRegion = ((Cache)cache).createRegionFactory(shortcut).create("Customer");
The gfsh equivalent of the above Java code uses the create lucene index
command, with options
specifying the index name, region name, field names, and the FlatFormatSerializer
, specified
using its fully qualified name,org.apache.geode.cache.lucene.FlatFormatSerializer
:
gfsh>create lucene index --name=customerIndex --region=Customer
--field=name,contacts.name,contacts.email,contacts.address,contacts.homepage.title
--serializer=org.apache.geode.cache.lucene.FlatFormatSerializer
The syntax for querying a nested field is the same as for a top level field, but with the
additional qualifying parent field name, such as contacts.name:Jones77*
. This distinguishes which
“name” field is intended when there can be more than one “name” field at different hierarchical
levels in the object.
Java query:
LuceneQuery query = luceneService.createLuceneQueryFactory()
.create("customerIndex", "Customer", "contacts.name:Jones77*", "name");
PageableLuceneQueryResults<K,Object> results = query.findPages();
gfsh query:
gfsh>search lucene --name=customerIndex --region=Customer
--queryString="contacts.name:Jones77*"
--defaultField=name
Queries
Querying a Lucene Index: Gfsh Example
For details, see the gfsh search lucene command reference page.
gfsh>search lucene --name=indexName --region=/orders --queryString="Jones*"
--defaultField=customer
Querying a Lucene Index: Java API Example
LuceneQuery<String, Person> query = luceneService.createLuceneQueryFactory()
.create(indexName, regionName, "name:John AND zipcode:97006", defaultField);
Collection<Person> results = query.findValues();
Destroying an Index
Since a region-destroy operation does not cause the destruction of any Lucene indexes, destroy any Lucene indexes prior to destroying the associated region.
Destroying a Lucene Index: Java API Example
luceneService.destroyIndex(indexName, regionName);
An attempt to destroy a region with a Lucene index will result in
an IllegalStateException
,
issuing an error message similar to:
java.lang.IllegalStateException: The parent region [/orders] in colocation chain
cannot be destroyed, unless all its children [[/indexName#_orders.files]] are
destroyed
...
Destroying a Lucene Index: Gfsh Example
For details, see the gfsh destroy lucene index command reference page.
The error message that results from an attempt to destroy a region prior to destroying its associated Lucene index will be similar to:
Region /orders cannot be destroyed because it defines Lucene index(es)
[/ordersIndex]. Destroy all Lucene indexes before destroying the region.
Changing an Index
Changing an index requires rebuilding it. Implement these steps to change an index:
- Export all region data.
- Destroy the Lucene index.
- Destroy the region.
- Create a new index.
- Create a new region without the user-defined business logic callbacks.
Import the region data with the option to turn on callbacks. The callbacks will be to invoke a Lucene async event listener to index the data. The
gfsh import data
command will be of the form:gfsh>import data --region=myReg --member=M3 --file=myReg.gfd --invoke-callbacks=true
If the API is used to import data, the code to set the option to invoke callbacks will be similar to this code fragment:
Region region = ...; File snapshotFile = ...; RegionSnapshotService service = region.getSnapshotService(); SnapshotOptions options = service.createOptions(); options.invokeCallbacks(true); service.load(snapshotFile, SnapshotFormat.GEMFIRE, options);
Alter the region to add the user-defined business logic callbacks.
Additional Gfsh Commands
See the gfsh describe lucene index command reference page for the command that prints details about a specific index.
See the gfsh list lucene index command reference page for the command that prints details about the Lucene indexes created for all members.
Requirements and Caveats
- Join queries between regions are not supported.
- Lucene indexes are stored in on-heap memory only.
- Lucene queries from within transactions are not supported.
On an attempt to query from within a transaction,
a
LuceneQueryException
is thrown, issuing an error message on the client (accessor) similar to:
Exception in thread "main" org.apache.geode.cache.lucene.LuceneQueryException:
Lucene Query cannot be executed within a transaction
...
- Lucene indexes must be created prior to creating the region. If an attempt is made to create a Lucene index after creating the region, the error message is similar to:
Member | Status
---------------------------- | ------------------------------------------------------
<IP_ADDRESS>(s2:97639)<v2>:1026 | Failed: The lucene index must be created before region
<IP_ADDRESS>(s3:97652)<v3>:1027 | Failed: The lucene index must be created before region
<IP_ADDRESS>(s1:97626)<v1>:1025 | Failed: The lucene index must be created before region
- The order of server creation with respect to index and region creation
is important.
The cluster configuration service cannot work if servers are created
after index creation, but before region creation,
as Lucene indexes are propagated to the cluster configuration after
region creation.
To start servers at multiple points within the start-up process,
use this ordering:
- start server(s)
- create Lucene index
- create region
- start additional server(s)
- An invalidate operation on a region entry does not invalidate a corresponding Lucene index entry. A query on a Lucene index that contains values that have been invalidated can return results that no longer exist. Therefore, do not combine entry invalidation with queries on Lucene indexes.
- Lucene indexes are not supported for regions that have eviction configured
with a local destroy.
Eviction can be configured with overflow to disk,
but only the region data is overflowed to disk,
not the Lucene index.
On an attempt to create a region with eviction configured to do local destroy
(with a Lucene index),
an
UnsupportedOperationException
is thrown, issuing an error message similar to:
[error 2017/05/02 16:12:32.461 PDT <main> tid=0x1]
java.lang.UnsupportedOperationException:
Exception in thread "main" java.lang.UnsupportedOperationException:
Lucene indexes on regions with eviction and action local destroy are not supported
...
Be aware that using the same field name in different objects where the field has different data types may have unexpected consequences. For example, if an index on the field SSN has the following entries
Object_1 object_1
has String SSN = “1111”Object_2 object_2
has Integer SSN = 1111Object_3 object_3
has Float SSN = 1111.0
Integers and floats will not be converted into strings. They remain as
IntPoint
andFloatPoint
within Lucene. The standard analyzer will not try to tokenize these values. The standard analyzer will only try to break up string values. So, a string search for “SSN: 1111” will returnobject_1
. AnIntRangeQuery
forupper limit : 1112
andlower limit : 1110
will returnobject_2
, and aFloatRangeQuery
withupper limit : 1111.5
andlower limit : 1111.0
will returnobject_3
.Backups should only be made for regions with Lucene indexes when there are no puts, updates, or deletes in progress. A backup might cause an inconsistency between region data and a Lucene index. Both the region operation and the associated index operation cause disk operations, yet those disk operations are not done atomically. Therefore, if a backup were taken between the persisted write to a region and the resulting persisted write to the Lucene index, then the backup represents inconsistent data in the region and Lucene index.


Hosted Compiler
This tutorial demonstrates how to host the F# compiler.
NOTE: The FSharp.Compiler.Service API is subject to change when later versions of the nuget package are published
NOTE: There are several options for hosting the F# compiler. The easiest one is to use the
fsc.exe
process and pass arguments.
NOTE: By default compilations using FSharp.Compiler.Service reference FSharp.Core <IP_ADDRESS> (matching F# 3.0). You can override this choice by passing a reference to FSharp.Core for <IP_ADDRESS> or later explicitly in your command-line arguments.
First, we need to reference the libraries that contain F# interactive service:
1: 2: 3: 4: 5: 6: |
|
Now write content to a temporary file:
1: 2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: |
|
Now invoke the compiler:
1:
|
|
If errors occur you can see this in the 'exitCode' and the returned array of errors:
1: 2: 3: 4: 5: 6: 7: |
|
Compiling to a dynamic assembly
You can also compile to a dynamic assembly, which uses the F# Interactive code generator. This can be useful if you are, for example, in a situation where writing to the file system is not really an option.
You still have to pass the "-o" option to name the output file, but the output file is not actually written to disk.
The 'None' option indicates that the initiatlization code for the assembly is not executed.
1: 2: 3: 4: 5: 6: 7: 8: |
|
Full name: Compiler.checker
Full name: Compiler.fn
static val DirectorySeparatorChar : char
static val AltDirectorySeparatorChar : char
static val VolumeSeparatorChar : char
static val InvalidPathChars : char[]
static val PathSeparator : char
static member ChangeExtension : path:string * extension:string -> string
static member Combine : [<ParamArray>] paths:string[] -> string + 3 overloads
static member GetDirectoryName : path:string -> string
static member GetExtension : path:string -> string
static member GetFileName : path:string -> string
...
Full name: System.IO.Path
Full name: Compiler.fn2
Full name: Compiler.fn3
static member AppendAllLines : path:string * contents:IEnumerable<string> -> unit + 1 overload
static member AppendAllText : path:string * contents:string -> unit + 1 overload
static member AppendText : path:string -> StreamWriter
static member Copy : sourceFileName:string * destFileName:string -> unit + 1 overload
static member Create : path:string -> FileStream + 3 overloads
static member CreateText : path:string -> StreamWriter
static member Decrypt : path:string -> unit
static member Delete : path:string -> unit
static member Encrypt : path:string -> unit
static member Exists : path:string -> bool
...
Full name: System.IO.File
File.WriteAllText(path: string, contents: string, encoding: System.Text.Encoding) : unit
Full name: Compiler.errors1
Full name: Compiler.exitCode1
Full name: Compiler.errors1b
Full name: Compiler.exitCode1b
Full name: Compiler.errors2
Full name: Compiler.exitCode2
Full name: Compiler.dynAssembly2
Full name: Compiler.errors3
Full name: Compiler.exitCode3
Full name: Compiler.dynAssembly3
Full name: Microsoft.FSharp.Core.Operators.stdout
Full name: Microsoft.FSharp.Core.Operators.stderr


Coding Quiz Challenge
Try to answer the following code-relatated questions within the time limit.
Keep in mind that incorrect answers will penalize your score/time by ten seconds!
Time remaining:60
Try to answer the following code-relatated questions within the time limit.
Keep in mind that incorrect answers will penalize your score/time by ten seconds!


Asi nomas quedo
In love with JavaScript
All we need is love
This website was awesome
The best rapper ever?
Meet the future developer
One of the best songs...
Comments
Leonidas Esteban
Asi nomas quedo
Alan Santiago
In love with JavaScript
Tirone Jose Gonzales
All we need is love
Alan Santiago
This website was awesome


Sandra Munsamy kidnapping: Four accused abandon bail
by Kaveel SinghThe four people accused of kidnapping Durban businesswoman Sandra Munsamy will not apply to be released on bail, it emerged in court on Monday during their second appearance in the Durban Magistrate's Court.
Representing two of the accused, former national director of public prosecutions Mxolisi Nxasana said his clients would only consider applying for bail once they saw the police docket.
The court ordered that none of the accused could be named, saying there was a dispute over the location of their ID cards.
The accused say police took their cards, but prosecutor Kuveshnie Pillay disputed this, saying authorities were only in possession of their passports.
Previously, the court cited security reasons and a pending identity parade as reasons for the protection of their identities.
Some of the accused needed a Portuguese translator for the brief proceedings before Magistrate Anand Maharaj.
Finalise 'international part' of investigation
Family members of two Mozambican nationals accused of the kidnapping were also in the public gallery.
The men, who were previously held at Durban Central police station, will be moved to Westville Prison, Maharaj said.
Outside court, Hawks spokesperson Brigadier Hangwani Mulaudzi said two of the accused claimed to be South Africans.
"That is with Home Affairs now. We are waiting for their verification on that."
He added that the Hawks hoped to finalise various aspects of the case before it returned to court on January 20, 2020.
"We want to finalise the international part of the investigation. We previously announced that there are matters we are looking at in Mozambique to see if the suspects are linked to anything there."
Alleged kidnapping syndicate
Last week, the Hawks confirmed that four people had been arrested in connection with the kidnapping. They allegedly demanded R140m for her release.
Munsamy was kidnapped in May on the M13 in Stapleton Road, Pinetown, while driving her black Range Rover.
The Hawks confirmed that she had been found alive in Witbank on Thursday evening – six months after her disappearance.
Mulaudzi previously told News24 that Munsamy had been found bound in chains in Emalahleni.
Mulaudzi had said an international syndicate was allegedly behind the kidnapping, but said more information would be revealed once the case was before court.
He added that one of the men arrested could be a senior member of the syndicate, but that this could not be officially confirmed.
The syndicate allegedly behind the kidnapping may be involved in the kidnappings of Pretoria businessman Omar Carrim and Johannesburg businessman Shiraz Ghatoo, which were reportedly linked to the case through evidence, The Saturday Star reported.


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Lead Teaching Assistant
IT Trainee: IT Strategic Planning and Architecture
Research at York (RAY) Student
IT Trainee: IT Strategic Planning and Architecture
Upper-Years PHAS Lab Assistant
IT Trainee: IT Strategic Planning and Architecture
Summer Student: Information Technology & Network Services
Lifeguard and Swim Instructor


OUT OF SIGHT (1998)
Rating: 2.0 stars (out of 4.0) ******************************** Key to rating system: 2.0 stars - Debatable 2.5 stars - Some people may like it 3.0 stars - I liked it 3.5 stars - I am biased in favor of the movie 4.0 stars - I felt the movie's impact personally or it stood out ********************************* A Movie Review by David Sunga
Directed by: Steven Soderbergh
Written by: Scott Frank, based on a novel by Elmore Leonard
Starring: George Clooney, Jennifer Lopez, Ving Rhames
Ingredients: Male bank robber, female federal marshall, true love
Synopsis: A gentleman con man named Foley (George Clooney) busts out of jail with the help of his friend Buddy (Ving Rhames). Their plans are complicated by the accidental presence of federal marshall Karen Sisco (Jennifer Lopez), whom they have to hide temporarily in the trunk of the car, along with Foley.
While in the trunk, Foley and Karen realize that they are both lonely, both photogenic, and both people who enjoy romantic risks. If it weren't for the fact of being on opposites sides of the law, they could be a couple. Karen escapes, and the rest of the movie is mainly about their attraction, not so much about action. When Karen learns Foley will be in Detroit for a burglary, their attraction gets a second chance.
Will he catch her or not? Will she catch him or not? Will they have an argument? Will he fantasize about her? Will she fantasize about him? And will the ending be happy, tragic, or end with a nod and a wink?
The movie answers all.
Opinion: In OUT OF SIGHT charmers George Clooney and Jennifer Lopez stand out in marked contrast to anyone in this movie who is non-beautiful, since non-beautiful main characters (especially Hispanics) are portrayed as all dreadfully dorky, unintelligent, vindictive, and grossly incompetent. Actually Karen Sisco (Lopez) is also incompetent any time she's matching professional wits with bank robber Foley, but luckily she always suddenly regains her toughness and intellect when dealing with anyone else. Contrived, but hey, it's a romance. The thing is, I always wonder is why there always has to be a weaker sex in a formular romance.
On the other hand, fans who enjoy the sight of George Clooney or Jennifer Lopez will enjoy this movie, as it provides lots of glamorous footage of these two stars in different clothing, hair colors, and hairstyles, along with charming dialogue and flirtatious lines.
One interesting thing about the script OUT OF SIGHT is how it jumps back and forth between prison conversations in a facility in Florida and earlier conversations in a facility in Lompoc, California to show how and when Foley met various criminal acquaintances.
As long as fans know to be expecting a slow romance, rather than a hard-hitting drama or action movie, the conversation in OUT OF SIGHT should be enjoyable. The main idea ("What if a bank robber falls for cop?") is never left out of sight.
Reviewed by David Sunga July 1, 1998
Copyright © 1998 by David Sunga This review and others like it can be found at THE CRITIC ZOO: http://www.criticzoo.com email<EMAIL_ADDRESS>The review above was posted to the
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§806-8 Prosecution where indictment not essential. In criminal cases brought in the first instance in a court of record, but in which the accused may be held to answer without an indictment by a grand jury, the legal prosecutor may arraign and prosecute the accused upon an information, complaint, or an indictment at the prosecutor's election; and in all criminal cases brought in the first instance in a court of record the prosecutor may arraign and prosecute the accused by information, complaint, or indictment, as the case may be, whether there has been a previous examination, or commitment for trial by a judge, or not. [L 1903, c 39, §3; RL 1925, §4019; RL 1935, §5492; RL 1945, §10793; RL 1955, §258-4; HRS §711-8; am L 1970, c 188, §39; ren L 1972, c 9, pt of §1; gen ch 1985; am L 1991, c 147, §3]
Cross References
See Const. art. I, §10.
Use of indictment or information, see §§801-1, 806-6.
Rules of Court
Indictment, information, or complaint, see HRPP rule 7.


wincache_scache_info (PECL wincache >= 1.1.0) wincache_scache_info — Retrieves information about files cached in the session cache 说明 array wincache_scache_info ([ bool $summaryonly = false ] ) Retrieves information about session cache content and its usage. 参数 summaryonly Controls whether the returned array will contain information about individual cache entries along with the session cache summary. 返回值 Array of meta data about session cache 或者在失败时返回 FALSE The array returned by this function contains the following elements: • total_cache_uptime - total time in seconds that the session cache has been active • total_item_count - total number of elements that are currently in the session cache • is_local_cache - true is the cache metadata is for a local cache instance, false if the metadata is for the global cache • total_hit_count - number of times the data has been served from the cache • total_miss_count - number of times the data has not been found in the cache • scache_entries - an array that contains the information about all the cached items: □ key_name - name of the key which is used to store the data □ value_type - type of value stored by the key □ use_time - time in seconds since the file has been accessed in the opcode cache □ last_check - time in seconds since the file has been checked for modifications □ ttl_seconds - time remaining for the data to live in the cache, 0 meaning infinite □ age_seconds - time elapsed from the time data has been added in the cache □ hitcount - number of times data has been served from the cache 范例 Example #1 A wincache_scache_info() example
以上例程会输出： Array ( [total_cache_uptime] => 17357 [total_file_count] => 121 [total_hit_count] => 36562 [total_miss_count] => 201 [scache_entries] => Array ( [1] => Array ( [file_name] => c:\inetpub\wwwroot\checkcache.php [add_time] => 17356 [use_time] => 7 [last_check] => 10 [hit_count] => 454 [function_count] => 0 [class_count] => 1 ) [2] => Array (...iterates for each cached file) ) ) 参见 • wincache_fcache_fileinfo() - Retrieves information about files cached in the file cache • wincache_fcache_meminfo() - Retrieves information about file cache memory usage • wincache_ocache_meminfo() - Retrieves information about opcode cache memory usage • wincache_rplist_fileinfo() - Retrieves information about resolve file path cache • wincache_rplist_meminfo() - Retrieves information about memory usage by the resolve file path cache • wincache_refresh_if_changed() - Refreshes the cache entries for the cached files • wincache_ucache_meminfo() - Retrieves information about user cache memory usage • wincache_ucache_info() - Retrieves information about data stored in the user cache • wincache_scache_meminfo() - Retrieves information about session cache memory usage


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{% extends 'creator/index.html' %} {% block content %}
Create new events
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Before Change
def pass1():
password = raw_input("Enter the password ")
special = "!@//$%^&*()?"
if len(password) >= 8 and not password.islower() and not password.isupper() and not password.isalpha() and not password.isdigit() and any((c in special) for c in password):
print("Strong Password")
After Change
def pass1():
invalidcharacters=set(string.punctuation)
while True:
password = input("Enter the password of atleast 10 characters length \n ")
if len(password) < 10 :
print(" your password is less than 10 characters")


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This section is a quick introduction to electricity basics.
A circuit is a closed loop of conductive material that electrons flow through; That's why it is called a circuit. Usually, engineers build circuits to use the energy of these electrons racing around the track to do some kind of useful work. Electrical energy can be used to drive motors, power lights, and perform all sorts of tasks. Electrical energy can also be used to represent a value, or a logic level , where one energy level represents a "1", and another energy level represents a "0". This can be used for transmitting information across a wire, and fed as inputs into logic gates to get a predictable output.
Throughout this chapter, I will use the term electrical component, or simply component, to generically refer to gizmos wired into a circuit.
There are four concepts you will need to understand in order to do basic work with circuits. These four concepts are:
We get the electrons to go around the track by creating what is called an electric potential otherwise known as voltage. Electric Potential is a difference in electric potential energy. It is a lot like a ski lift, bringing skiers up a mountain to a position of high gravitational potential energy. The rest of the circuit then, is the ski run, with electrons racing down, moving to achieve the lowest energy state possible. Just like skiers being pulled by gravity down a mountain, the electric force pulls electrons from a place of high electric potential to a place of lower electric potential.
We call this lowest electric potential state ground. Ground is is the place where the potential energy of the electrons is zero. That isn’t to say they don’t have any energy at all. It means within the closed loop of the circuit, they have reached the “lowest” place, they have no further motivation to move around the circuit; Remember that voltage is is simply the difference between electrons in a “high place” (High electric potential) that want to fall to a “low place” (low electric potential).
Voltage is measured between two points. It is a “Difference” between two places. Between a “high place” and a “low place”.
When someone says a part is a “5 volt part” they mean it is meant to operate with a difference of 5 volts between the positive and negative terminals of the part.
When someone measures the voltage of a wire someplace in the circuit, and claims it is 7.32 Volts, they mean that part of the circuit has 7.32 Volts more than ground, or the “lowest place” in the circuit. Remember that voltage without two points is meaningless.
Voltage is usually represented in equations with the letter “V”, it is measured in Volts.
Current is a measure of the rate of electrons flowing through a particular part of the circuit.
An easy way to visualize current is to envision standing by a river. A river with a high current has a large torrent of water rushing past. A river with a low current lazily flows past.
The unit for current is Amps. I or A are also both common shorthand to refer to current. It can also described with the following equation where “Q” represents the amount of electric charge (or electrons in simpler, but less accurate terms):
I = delta Q/delta t
Or change in electric charge over some period of time, (usually 1 second).
Since current is a measure of the actual flow of electrons, measuring current is an important step in determining if an electrical circuit is “on”. Remember that electricity is described by two parts, voltage and current. A circuit can have a large amount of voltage (electric potential energy), but without current (electron flow), there is not electricity. The high voltage provides a high potential for electricity, but alone is not electricity. This relationship between voltage and current is called electrical power , which I will describe later in this section.
All components have a range of currents they will work in. Take an LED for example. If given insufficient current, it won’t light up. If given too much current, it will break. It is important to ensure all components in a system receive current within their safe operating range.
Electrical Power measures of the rate of electrical energy being used to do work over time. It is dependent upon two factors: the rate of electrons flowing (current), and the electrical potential energy they expend as they flow (voltage).
Electrical power is measured in the unit Watts or W for shorthand, and the variable used is "P". It can be found with the following equations:
P = (V*Q)/T P = VI
The equation above helps show that power in a circuit is due to the voltage and the current together.
For example, if a microcontroller is hooked up to a circuit with a fixed voltage of 2.5 volts, and very little current flows through it 0.1 mA, it is using a small amount of power.
(0.1 mA) * (2.5 v) = 0.25 mW
If the current increases to 5 mA, the power level of the microcontroller goes up.
(5 mA) * (2.5 v) = 12.5 mW
Resistance is a measure of resistance to electron flow through a wire or component. It is measured in Ohms (Ω), and commonly represented by the letter R. The following equation describes resistance behavior.
V/I = R
Nearly everything electrons flow through has some amount of resistance. Wires and other conductive materials have a very low resistance, while air, plastics, and other stuff have very high resistance. Electrons will want to follow the path of least resistance to reach the lowest potential energy state possible. So when given the option between a low resistance path like a wire, and a high resistance path like the air around a wire, the electrons will pick the wire.
Resistance is important, because most components we wire into circuits to do work have some resistance associated with them. Yet other devices are only designed to handle a certain amount of voltage or current safely, and so parts called resistors can be used to resist the electrical flow, and alter the current flow and voltage level of the whole circuit so the sensitive part can be used safely.
| Voltage: "Electric Potential"� | (Volts, V, V) | The amount of potential energy encouraging the electron to reach ground. The “steepness of the ski slope”. |
| Current: Rate of Electrons per second | (Amps, A, I) | The amount of electrons flowing through a particular part of the circuit. Think of a river, with a lot or a little bit of water flowing past. |
| Power: Rate of work per second | (Watts, P, W) | The amount of electric energy used over time. |
| Resistance: Amount of resistance to flow | (Ohms, R, Ω) | Think of water flowing through a plumbing system encountering a thick water filter. |
© 2017


Hi! I’m Yongjing, a Software Engineer with full stack experience. Now I am working in Real-Time Search team in Coupa. I love building beautiful things with usability and performance.
In 2013, I got my bachelor's degree then came to the Pennsylvania State University for my graduate study. I felt so proud of myself since I completed my major(Electrical Engineering) and minor(Computational Science) in a 2-year master of science program.
In 2015, I started working as a Research Software Engineer at VideoMining one week after my graduation ceremony, where I learned to research about indoor localization algorithm, build real-time data pipeline in Amazon Web Services ecosystem and other interesting Internet of Things projects. I really appreciate this experience in VideoMining.
In 2017, I moved to Cincinnati with my girl friend(now wife) and started working as a Full Stack Developer in Aquiire, which is the only real-time procure-to-pay (P2P) software solution in the industry. We used Java Spring related tech stack.
In 2018, Coupa bought Aquiire and I have been a Software Engineer in Real-Time Search team in Coupa since then.
Software is the ultimate interface between hardware improvement and human usage. Since users can only get benefits from the improvement of good software, I wanna be this interface with my coding skills to make people live better.


Welcome to San Joaquin Valley Town Hall.
We have some fascinating speakers for you this season!
Jeffrey Toobin
Andrew Ross Sorkin
Amy Chua


I am a Responsive Designer & Developer who thrives on visual storytelling and the best user experience. I am currently enrolled in Skillcrush's Break Into Tech Front End-Development Track. When I'm not working and learning through Skillcrush, I can be found spending time with my husband and pup, getting lost in a good movie or book and exploring all Fort Worth, TX has to offer!
I was born and raised in Crystal Lake, IL. It is a small suburb about an hour north of Chicago. I have loved a lot of the places I have lived so far, but Chicago will always have my heart. Though I can't say I miss the cold weather!
My Passion: I am in love with dance, especially ballet. I love how expressive it can be to watch. I loved it so much that it was actually one of my majors at Butler University.
Fun Fact: I have worked in the non-profit sector for over over six years. I wanted to work in the non-profit world to help make a difference in the world. My hope is to use my coding skills to help continue to make a differnce.


Various commands for folder/directory navigation:
.. [subdirectory to navigate into from the new level]
... [subdirectory to navigate into from the new level]
.... [subdirectory to navigate into from the new level]
..... [subdirectory to navigate into from the new level]
Navigates the number of directory levels up the hierarchy and from there navigates down the folders in the optional parameter.


[§245-35] Unlicensed possession or use of stamps. A person who is not licensed under this chapter and who knowingly [possesses] or uses a stamp shall be guilty of a class B felony. [L 2000, c 249, pt of §1, §20(2); am L 2002, c 94, §3]


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