| 1 | package fr.sii.ogham.template.thymeleaf.common.buider; | |
| 2 | ||
| 3 | import java.util.HashMap; | |
| 4 | import java.util.HashSet; | |
| 5 | import java.util.Map; | |
| 6 | import java.util.Set; | |
| 7 | ||
| 8 | import org.thymeleaf.TemplateEngine; | |
| 9 | import org.thymeleaf.cache.ICacheManager; | |
| 10 | import org.thymeleaf.dialect.IDialect; | |
| 11 | import org.thymeleaf.messageresolver.IMessageResolver; | |
| 12 | import org.thymeleaf.templateresolver.ITemplateResolver; | |
| 13 | ||
| 14 | import fr.sii.ogham.core.builder.AbstractParent; | |
| 15 | import fr.sii.ogham.core.builder.Builder; | |
| 16 | ||
| 17 |
1
1. build : All method instructions replaced by: return null; → NO_COVERAGE |
public abstract class AbstractThymeleafEngineConfigBuilder<MYSELF extends AbstractThymeleafEngineConfigBuilder<MYSELF, P>, P> extends AbstractParent
 implements Builder<TemplateEngine> { |
| 18 | protected final MYSELF myself; | |
| 19 | protected Set<IDialect> dialects; | |
| 20 | protected Map<String, IDialect> dialectsByPrefix; | |
| 21 | protected Set<ITemplateResolver> templateResolvers; | |
| 22 | protected ICacheManager cacheManager; | |
| 23 | protected Set<IMessageResolver> messageResolvers; | |
| 24 | ||
| 25 | @SuppressWarnings("unchecked") | |
| 26 | public AbstractThymeleafEngineConfigBuilder(Class<?> selfType, P parent) { | |
| 27 | super(parent); | |
| 28 | myself = (MYSELF) selfType.cast(this); | |
| 29 | } | |
| 30 | | |
| 31 | ||
| 32 | /** | |
| 33 | *
 | |
| 34 | * Sets a new unique dialect for this template engine. | |
| 35 | *
 | |
| 36 | *
 | |
| 37 | * This operation is equivalent to removing all the currently configured | |
| 38 | * dialects and then adding this one. | |
| 39 | *
 | |
| 40 | *
 | |
| 41 | * This operation can only be executed before processing templates for the | |
| 42 | * first time. Once a template is processed, the template engine is | |
| 43 | * considered to be initialized, and from then on any attempt to | |
| 44 | * change its configuration will result in an exception. | |
| 45 | *
 | |
| 46 | * | |
| 47 | * @param dialect | |
| 48 | * the new unique {@link IDialect} to be used. | |
| 49 | * @return this for fluent use | |
| 50 | */ | |
| 51 | public MYSELF setDialect(final IDialect dialect) { | |
| 52 |
1
1. setDialect : All method instructions replaced by: return null; → NO_COVERAGE |
dialects().clear(); |
| 53 | dialects().add(dialect); | |
| 54 | return myself; | |
| 55 | } | |
| 56 | ||
| 57 | /** | |
| 58 | *
 | |
| 59 | * Adds a new dialect for this template engine, using the specified prefix. | |
| 60 | *
 | |
| 61 | *
 | |
| 62 | * This dialect will be added to the set of currently configured ones. | |
| 63 | *
 | |
| 64 | *
 | |
| 65 | * This operation can only be executed before processing templates for the | |
| 66 | * first time. Once a template is processed, the template engine is | |
| 67 | * considered to be initialized, and from then on any attempt to | |
| 68 | * change its configuration will result in an exception. | |
| 69 | *
 | |
| 70 | * | |
| 71 | * @param prefix | |
| 72 | * the prefix that will be used for this dialect | |
| 73 | * @param dialect | |
| 74 | * the new {@link IDialect} to be added to the existing ones. | |
| 75 | * @return this for fluent use | |
| 76 | */ | |
| 77 | public MYSELF addDialect(final String prefix, final IDialect dialect) { | |
| 78 |
1
1. addDialect : All method instructions replaced by: return null; → NO_COVERAGE |
this.dialectsByPrefix().put(prefix, dialect); |
| 79 | return myself; | |
| 80 | } | |
| 81 | ||
| 82 | /** | |
| 83 | *
 | |
| 84 | * Adds a new dialect for this template engine, using the dialect's | |
| 85 | * specified default dialect. | |
| 86 | *
 | |
| 87 | *
 | |
| 88 | * This dialect will be added to the set of currently configured ones. | |
| 89 | *
 | |
| 90 | *
 | |
| 91 | * This operation can only be executed before processing templates for the | |
| 92 | * first time. Once a template is processed, the template engine is | |
| 93 | * considered to be initialized, and from then on any attempt to | |
| 94 | * change its configuration will result in an exception. | |
| 95 | *
 | |
| 96 | * | |
| 97 | * @param dialect | |
| 98 | * the new {@link IDialect} to be added to the existing ones. | |
| 99 | * @return this for fluent use | |
| 100 | */ | |
| 101 | public MYSELF addDialect(final IDialect dialect) { | |
| 102 |
1
1. addDialect : All method instructions replaced by: return null; → NO_COVERAGE |
dialects().add(dialect); |
| 103 | return myself; | |
| 104 | } | |
| 105 | ||
| 106 | /** | |
| 107 | *
 | |
| 108 | * Sets a new set of dialects for this template engine, referenced by the | |
| 109 | * prefixes they will be using. | |
| 110 | *
 | |
| 111 | *
 | |
| 112 | * This operation can only be executed before processing templates for the | |
| 113 | * first time. Once a template is processed, the template engine is | |
| 114 | * considered to be initialized, and from then on any attempt to | |
| 115 | * change its configuration will result in an exception. | |
| 116 | *
 | |
| 117 | * | |
| 118 | * @param dialects | |
| 119 | * the new map of {@link IDialect} objects to be used, referenced | |
| 120 | * by their prefixes. | |
| 121 | * @return this for fluent use | |
| 122 | */ | |
| 123 | public MYSELF setDialectsByPrefix(final Map<String, IDialect> dialects) { | |
| 124 |
1
1. setDialectsByPrefix : All method instructions replaced by: return null; → NO_COVERAGE |
dialectsByPrefix().clear(); |
| 125 | dialectsByPrefix().putAll(dialects); | |
| 126 | return myself; | |
| 127 | } | |
| 128 | ||
| 129 | /** | |
| 130 | *
 | |
| 131 | * Sets a new set of dialects for this template engine, all of them using | |
| 132 | * their default prefixes. | |
| 133 | *
 | |
| 134 | *
 | |
| 135 | * This operation can only be executed before processing templates for the | |
| 136 | * first time. Once a template is processed, the template engine is | |
| 137 | * considered to be initialized, and from then on any attempt to | |
| 138 | * change its configuration will result in an exception. | |
| 139 | *
 | |
| 140 | * | |
| 141 | * @param dialects | |
| 142 | * the new set of {@link IDialect} objects to be used. | |
| 143 | * @return this for fluent use | |
| 144 | */ | |
| 145 | public MYSELF setDialects(final Set<IDialect> dialects) { | |
| 146 |
1
1. setDialects : All method instructions replaced by: return null; → NO_COVERAGE |
this.dialects().clear(); |
| 147 | this.dialects().addAll(dialects); | |
| 148 | return myself; | |
| 149 | } | |
| 150 | ||
| 151 | /** | |
| 152 | *
 | |
| 153 | * Sets an additional set of dialects for this template engine, all of them | |
| 154 | * using their default prefixes. | |
| 155 | *
 | |
| 156 | *
 | |
| 157 | * This operation can only be executed before processing templates for the | |
| 158 | * first time. Once a template is processed, the template engine is | |
| 159 | * considered to be initialized, and from then on any attempt to | |
| 160 | * change its configuration will result in an exception. | |
| 161 | *
 | |
| 162 | * | |
| 163 | * @param additionalDialects | |
| 164 | * the new set of {@link IDialect} objects to be used. | |
| 165 | * | |
| 166 | * @since 2.0.9 | |
| 167 | * @return this for fluent use | |
| 168 | */ | |
| 169 | public MYSELF setAdditionalDialects(final Set<IDialect> additionalDialects) { | |
| 170 |
1
1. setAdditionalDialects : All method instructions replaced by: return null; → NO_COVERAGE |
dialects().addAll(additionalDialects); |
| 171 | return myself; | |
| 172 | } | |
| 173 | ||
| 174 | /** | |
| 175 | *
 | |
| 176 | * Removes all the currently configured dialects. | |
| 177 | *
 | |
| 178 | *
 | |
| 179 | * This operation can only be executed before processing templates for the | |
| 180 | * first time. Once a template is processed, the template engine is | |
| 181 | * considered to be initialized, and from then on any attempt to | |
| 182 | * change its configuration will result in an exception. | |
| 183 | *
 | |
| 184 | * | |
| 185 | * @return this for fluent use | |
| 186 | */ | |
| 187 | public MYSELF clearDialects() { | |
| 188 |
1
1. clearDialects : All method instructions replaced by: return null; → NO_COVERAGE |
dialects().clear(); |
| 189 | return myself; | |
| 190 | } | |
| 191 | ||
| 192 | /** | |
| 193 | *
 | |
| 194 | * Sets the entire set of template resolvers. | |
| 195 | *
 | |
| 196 | * | |
| 197 | * @param templateResolvers | |
| 198 | * the new template resolvers. | |
| 199 | * @return this for fluent use | |
| 200 | */ | |
| 201 | public MYSELF setTemplateResolvers(final Set<? extends ITemplateResolver> templateResolvers) { | |
| 202 |
1
1. setTemplateResolvers : All method instructions replaced by: return null; → NO_COVERAGE |
this.templateResolvers().clear(); |
| 203 | this.templateResolvers().addAll(templateResolvers); | |
| 204 | return myself; | |
| 205 | } | |
| 206 | ||
| 207 | /** | |
| 208 | *
 | |
| 209 | * Adds a new template resolver to the current set. | |
| 210 | *
 | |
| 211 | * | |
| 212 | * @param templateResolver | |
| 213 | * the new template resolver. | |
| 214 | * @return this for fluent use | |
| 215 | */ | |
| 216 | public MYSELF addTemplateResolver(final ITemplateResolver templateResolver) { | |
| 217 |
1
1. addTemplateResolver : All method instructions replaced by: return null; → NO_COVERAGE |
templateResolvers().add(templateResolver); |
| 218 | return myself; | |
| 219 | } | |
| 220 | ||
| 221 | /** | |
| 222 | *
 | |
| 223 | * Sets a single template resolver for this template engine. | |
| 224 | *
 | |
| 225 | *
 | |
| 226 | * Calling this method is equivalent to calling | |
| 227 | * {@link #setTemplateResolvers(Set)} passing a Set with only one template | |
| 228 | * resolver. | |
| 229 | *
 | |
| 230 | * | |
| 231 | * @param templateResolver | |
| 232 | * the template resolver to be set. | |
| 233 | * @return this for fluent use | |
| 234 | */ | |
| 235 | public MYSELF setTemplateResolver(final ITemplateResolver templateResolver) { | |
| 236 |
1
1. setTemplateResolver : All method instructions replaced by: return null; → NO_COVERAGE |
templateResolvers().clear(); |
| 237 | templateResolvers().add(templateResolver); | |
| 238 | return myself; | |
| 239 | } | |
| 240 | ||
| 241 | /** | |
| 242 | *
 | |
| 243 | * Sets the Cache Manager to be used. If set to null, no caches will be used | |
| 244 | * throughout the engine. | |
| 245 | *
 | |
| 246 | *
 | |
| 247 | * By default, an instance of | |
| 248 | * {@link org.thymeleaf.cache.StandardCacheManager} is set. | |
| 249 | *
 | |
| 250 | *
 | |
| 251 | * This operation can only be executed before processing templates for the | |
| 252 | * first time. Once a template is processed, the template engine is | |
| 253 | * considered to be initialized, and from then on any attempt to | |
| 254 | * change its configuration will result in an exception. | |
| 255 | *
 | |
| 256 | * | |
| 257 | * @param cacheManager | |
| 258 | * the cache manager to be set. | |
| 259 | * @return this for fluent use | |
| 260 | * | |
| 261 | */ | |
| 262 | public MYSELF setCacheManager(final ICacheManager cacheManager) { | |
| 263 |
1
1. setCacheManager : All method instructions replaced by: return null; → NO_COVERAGE |
this.cacheManager = cacheManager; |
| 264 | return myself; | |
| 265 | } | |
| 266 | ||
| 267 | /** | |
| 268 | *
 | |
| 269 | * Sets the message resolvers to be used by this template engine. | |
| 270 | *
 | |
| 271 | *
 | |
| 272 | * This operation can only be executed before processing templates for the | |
| 273 | * first time. Once a template is processed, the template engine is | |
| 274 | * considered to be initialized, and from then on any attempt to | |
| 275 | * change its configuration will result in an exception. | |
| 276 | *
 | |
| 277 | * | |
| 278 | * @param messageResolvers | |
| 279 | * the Set of template resolvers. | |
| 280 | * @return this for fluent use | |
| 281 | */ | |
| 282 | public MYSELF setMessageResolvers(final Set<? extends IMessageResolver> messageResolvers) { | |
| 283 |
1
1. setMessageResolvers : All method instructions replaced by: return null; → NO_COVERAGE |
this.messageResolvers().clear(); |
| 284 | this.messageResolvers().addAll(messageResolvers); | |
| 285 | return myself; | |
| 286 | } | |
| 287 | ||
| 288 | /** | |
| 289 | *
 | |
| 290 | * Adds a message resolver to the set of message resolvers to be used by the | |
| 291 | * template engine. | |
| 292 | *
 | |
| 293 | *
 | |
| 294 | * This operation can only be executed before processing templates for the | |
| 295 | * first time. Once a template is processed, the template engine is | |
| 296 | * considered to be initialized, and from then on any attempt to | |
| 297 | * change its configuration will result in an exception. | |
| 298 | *
 | |
| 299 | * | |
| 300 | * @param messageResolver | |
| 301 | * the new message resolver to be added. | |
| 302 | * @return this for fluent use | |
| 303 | */ | |
| 304 | public MYSELF addMessageResolver(final IMessageResolver messageResolver) { | |
| 305 |
1
1. addMessageResolver : All method instructions replaced by: return null; → NO_COVERAGE |
messageResolvers().add(messageResolver); |
| 306 | return myself; | |
| 307 | } | |
| 308 | ||
| 309 | /** | |
| 310 | *
 | |
| 311 | * Sets a single message resolver for this template engine. | |
| 312 | *
 | |
| 313 | *
 | |
| 314 | * Calling this method is equivalent to calling | |
| 315 | * {@link #setMessageResolvers(Set)} passing a Set with only one message | |
| 316 | * resolver. | |
| 317 | *
 | |
| 318 | *
 | |
| 319 | * This operation can only be executed before processing templates for the | |
| 320 | * first time. Once a template is processed, the template engine is | |
| 321 | * considered to be initialized, and from then on any attempt to | |
| 322 | * change its configuration will result in an exception. | |
| 323 | *
 | |
| 324 | * | |
| 325 | * @param messageResolver | |
| 326 | * the message resolver to be set. | |
| 327 | * @return this for fluent use | |
| 328 | */ | |
| 329 | public MYSELF setMessageResolver(final IMessageResolver messageResolver) { | |
| 330 |
1
1. setMessageResolver : All method instructions replaced by: return null; → NO_COVERAGE |
messageResolvers().clear(); |
| 331 | messageResolvers().add(messageResolver); | |
| 332 | return myself; | |
| 333 | } | |
| 334 | ||
| 335 | | |
| 336 | @Override | |
| 337 | public TemplateEngine build() { | |
| 338 |
1
1. build : All method instructions replaced by: return null; → NO_COVERAGE |
TemplateEngine engine = new TemplateEngine(); |
| 339 | configureDialects(engine); | |
| 340 | configureMessageResolvers(engine); | |
| 341 | configureTemplateResolvers(engine); | |
| 342 | configureCacheManager(engine); | |
| 343 | return engine; | |
| 344 | } | |
| 345 | ||
| 346 | protected void configureTemplateResolvers(TemplateEngine engine) { | |
| 347 |
1
1. configureTemplateResolvers : All method instructions removed → NO_COVERAGE |
if (templateResolvers != null) { |
| 348 | engine.setTemplateResolvers(templateResolvers); | |
| 349 | } | |
| 350 | } | |
| 351 | ||
| 352 | protected void configureMessageResolvers(TemplateEngine engine) { | |
| 353 |
1
1. configureMessageResolvers : All method instructions removed → NO_COVERAGE |
if (messageResolvers != null) { |
| 354 | engine.setMessageResolvers(messageResolvers); | |
| 355 | } | |
| 356 | } | |
| 357 | ||
| 358 | protected void configureCacheManager(TemplateEngine engine) { | |
| 359 |
1
1. configureCacheManager : All method instructions removed → NO_COVERAGE |
if (cacheManager != null) { |
| 360 | engine.setCacheManager(cacheManager); | |
| 361 | } | |
| 362 | } | |
| 363 | ||
| 364 | protected void configureDialects(TemplateEngine engine) { | |
| 365 |
1
1. configureDialects : All method instructions removed → NO_COVERAGE |
if (dialects != null) { |
| 366 | engine.setDialects(dialects); | |
| 367 | } | |
| 368 | if (dialectsByPrefix != null) { | |
| 369 | engine.setDialectsByPrefix(dialectsByPrefix); | |
| 370 | } | |
| 371 | } | |
| 372 | ||
| 373 | protected Set<IDialect> dialects() { | |
| 374 |
1
1. dialects : All method instructions replaced by: return null; → NO_COVERAGE |
if (dialects == null) { |
| 375 | dialects = new HashSet<>(); | |
| 376 | } | |
| 377 | return dialects; | |
| 378 | } | |
| 379 | ||
| 380 | protected Map<String, IDialect> dialectsByPrefix() { | |
| 381 |
1
1. dialectsByPrefix : All method instructions replaced by: return null; → NO_COVERAGE |
if (dialectsByPrefix == null) { |
| 382 | dialectsByPrefix = new HashMap<>(); | |
| 383 | } | |
| 384 | return dialectsByPrefix; | |
| 385 | } | |
| 386 | ||
| 387 | protected Set<IMessageResolver> messageResolvers() { | |
| 388 |
1
1. messageResolvers : All method instructions replaced by: return null; → NO_COVERAGE |
if (messageResolvers == null) { |
| 389 | messageResolvers = new HashSet<>(); | |
| 390 | } | |
| 391 | return messageResolvers; | |
| 392 | } | |
| 393 | ||
| 394 | protected Set<ITemplateResolver> templateResolvers() { | |
| 395 |
1
1. templateResolvers : All method instructions replaced by: return null; → NO_COVERAGE |
if (templateResolvers == null) { |
| 396 | templateResolvers = new HashSet<>(); | |
| 397 | } | |
| 398 | return templateResolvers; | |
| 399 | } | |
| 400 | } | |
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1.1 |


Suppose we want to walk from one corner of the CBD to the opposite corner, without ever walking along the same street segment twice.
The street segments form a graph.
Then a path that never visits the same vertex twice is a self-avoiding walk.
For our purposes a lattice is an infinite, connected, vertex-transitive graph.
A self-avoiding walk (SAW) $\omega$ is a sequence of lattice vertices $(\omega_0, \omega_1, \dots, \omega_n)$ such that $\omega_i \neq \omega_j$ for $i\neq j$, and $\omega_i,\omega_{i+1}$ are adjacent on the lattice for each $i=0,\dots,n-1$. We say $\omega$ has size $|\omega| = n$.
Since the lattice is vertex-transitive, if one SAW is a translation of another we consider them to be the same. Equivalently assume $\omega_0 = 0$.
For a given lattice take $c_n$ to be the number of SAWs of size $n$. Then eg. on the square lattice \[ (c_n)_{n\geq0} = (1, 4, 12, 36, 100, 284, 780, \dots) \]
A self-avoiding polygon (SAP) $\pi$ is a sequence of lattice vertices $(\pi_1, \pi_1, \dots, \pi_{n})$ such that $\pi_i \neq \pi_j$ for $i\neq j$, and $\pi_i,\pi_{i+1}$ are adjacent on the lattice, and so too are $\pi_1,\pi_n$. The size of $\pi$ is $n$.
Equivalently a SAP is a SAW which ends adjacent to its initial vertex.
For our purposes two SAPs are the same if they are translates of one another. So there are $2n$ SAWs (of length $n-1$) corresponding to each SAP of size $n$. Let $p_n$ be the number of size-$n$ SAPs. Then eg. on the square lattice \[ (p_n)_{n\geq0} = (0,0,0,0,1,0,2,0,7,0,28,0,124,\dots) \] (On some other lattices can have SAPs of odd size.)
Two flavours: bond animals and vertex animals.
A bond animal is a collection of lattice edges (ie. pairs of adjacent vertices) such that the resulting graph is connected. Every SAW or SAP is a bond animal.
A site animal is a collection of lattice vertices such that the induced graph (ie. draw an edge between any pair of vertices which are adjacent on the lattice) is connected. These are also called polyominoes.
In both cases the size is the number of edges and the order is the number of vertices. (For SAPs and SAWs these are equivalent.) As usual, counted up to translation.
There are many many other objects of study, eg.
These objects are interesting in their own right - lots of work dedicated to counting and analysing them.
But also lots of connections to other fields.
Conception for SAWs is usually credited to Paul Flory (J. Chem Phys. 1949). He was interested in a theoretical model for long linear polymer chains in solution. In particular he wanted a model which accounted for the excluded volume principle: the idea that two monomers in a chain cannot occupy the same space.
| poly(2-vinylpyridine) (Y. Roiter & S. Minko, J. Amer. Chem. Soc. 2005) | square lattice SAW of length $2^{25}$ (N. Clisby 201?) |
SAWs (or SAPs, trees, etc., depending on the model) turn out to be a good model for polymers in a good solvent (at least for long ones). They display the same metric properties - ie. how much they "spread out".
In addition to polymers floating in solution, polymers can interact with
These systems undergo phase transitions.
Animals (vertex or bond) are equivalent to percolation clusters, and SAWs and SAPs are also closely connected: eg. for bond percolation in $d\geq2$ dimensions \[ \frac{1}{\mu_d} \leq p_c(d) \] where $\mu_d$ is the connective constant of the lattice (defined later in this talk).
The $N$-vector model is a generalisation of the Ising model of ferromagnetism. For the Ising model the Hamiltonian is \[ \mathcal{H} = -\sum_{x\sim y} S^{(x)}S^{(y)} \] where the sum is over all adjacent pairs of vertices, and the spins are $\pm1$. For some function $F$ of the spins, the expected value of $F$ is \[ \langle F\rangle = \frac1Z \mathbb{E}(F e^{-\beta \mathcal{H}}) \] where the expectation is with respect to the product of the Bernoulli measures on the spins, $\beta\geq0$ is inverse temperature, and $Z$ is the partition function \[ Z = \mathbb{E}(e^{-\beta\mathcal{H}}).\]
The two-point function is \[ \langle F\rangle = \langle F(0,x)\rangle = \langle S^{(0)} S^{(x)}\rangle \] For small $\beta$ (large $T$) this decays exponentially: \[ \langle S^{(0)} S^{(x)}\rangle \bowtie e^{-r(\beta)|x|} \]
The correlation length is $\xi(\beta) = \frac{1}{r(\beta)}$. For $d\geq2$ there is a critical (inverse) temperature $\beta_c$ such that \[ \xi(\beta) \sim \text{const.}(\beta_c-\beta)^{-\nu} \quad \text{as }\beta\nearrow\beta_c. \] Similarly the susceptibility is \[ \chi(\beta) = \frac1d\sum_x \langle S^{(0)}S^{(x)}\rangle \] and \[ \chi(\beta) \sim \text{const.}(\beta_c-\beta)^{-\gamma} \quad \text{as }\beta\nearrow\beta_c. \]
For the $N$-vector model, the $\pm1$ spins are replaced by values in the $N$-dimensional sphere of radius $\sqrt{N}$. The product of spins becomes a dot product, and the measure becomes the product of uniform measures on the sphere. Quantities like two-point functions and susceptibilities are similarly defined.
P.G. de Gennes (Phys. Lett. A 1972) used (non-rigorous) renormalisation group methods to analyse the $N$-vector model, and then showed that one can (in a sense) take $N\to0$. This leads to a generating function for SAWs: \[ \lim_{N\to0}\langle S^{(0)}_i \cdot S^{(x)}_j\rangle = \delta_{i,j} \sum_{\omega\,:\,0\to x}\beta^{|\omega|} \] where $S^{(x)}_i$ is the $i$-th component of $S^{(x)}$ and the sum is over all SAWs from 0 to $x$.
Those renormalisation groups methods give exact (conjectural) values for critical exponents like $\nu$ and $\gamma$ in the $N\to0$ limit, which we will encounter again later. eg. in $d=2$ \[ \nu = \frac34 \qquad\qquad \gamma = \frac{43}{32}.\]
Headphone cables, shoelaces, necklaces, and even polymer molecules can get tangled up in knots and links.
H.L. Frisch and E. Wasserman (J. Amer. Chem. Soc. 1961) and M. Delbruck (Proc. Symp. Appl. Math. 1962) conjectured that random simple closed curves in $\mathbb{R}^3$ will be knotted with high probability. More precisely, the probability of being unknotted decays exponentially with the length of the curve.
| (S.A. Wasserman et al, Science 1985) |
The FWD conjecture was first proved by D.W. Sumners and S.G. Whittington (J. Phys. A: Math. Gen. 1988) for SAPs on the cubic lattice. Their method used pattern theorems.
Subsequently proved for a number of other models (on and off the lattice).
The definitions of SAWs, SAPs etc. are easy to understand, but actually proving anything is a different story.
An explicit expression for $c_n$, $p_n$, etc. will almost certainly never be found.
A few important results have been proved, and more are widely expected to be true but remain unproved.
A SAW of length $m+n$ can be split into two SAWs of lengths $m$ and $n$, implying \[ c_{m+n} \leq c_m c_n \quad \Rightarrow \quad \log c_{m+n} \leq \log c_m + \log c_n.\]
The value $\mu$ is called the connective constant of the lattice. Follows that \[ c_n = e^{o(n)}\mu^n \]
In $d\geq2$ dimensions the value of $\mu$ is not known, except one special case:
Otherwise only have numerical estimates (later).
SAPs cannot be split in two like SAWs - but instead they can be concatenated: \[ p_{m+n} \geq p_m p_m \] In fact SAPs and SAWs are closely related.
Fekete's lemma can be used to show that the limit exists, then SAPs can be split into SAWs to show it is the same $\mu$.
Animals are also superadditive $\Rightarrow$ they have growth rates. Not the same values as SAWs/SAPs.
Recall that $c_n = e^{o(n)}\mu^n$. For $d=2,3$ it is believed that \[ c_n = \text{const.}n^{\gamma-1}\mu^n(1+o(1)) \] with same $\gamma$ as for the susceptibility of the $N$-vector model near criticality. Value of $\gamma$ depends on dimension, but not the specific lattice (unlike $\mu$). So for $d=2$ expected that $\gamma = \frac{43}{32}$.
For $d=4$ expected that $\gamma=1$ with an additional $(\log n)^{\frac14}$ factor.
Similar story for SAPs: expected that \[ p_n = \text{const.}n^{\alpha-3}\mu^n(1+o(1)) \] with $(\log n)^{\frac14}$ correction when $d=4$. For $d=2$ expected that $\alpha=\frac12$.
Lattice animals (bond and site) and trees are all believed to be in the same universality class, for all dimensions. Expect \[ a_n = \text{const.}n^{-\theta}\lambda^n(1+o(1)) \] where $\lambda$ depends on the model and/or lattice, but $\theta$ depends only on the dimension.
In 2D and 3D expect (G. Parisi & N. Sourlas, Phys. Rev. Lett. 1981) $\theta=1$ and $\theta=\frac32$ respectively. For $d \geq 9$ expect mean-field behaviour $\Rightarrow$ $\theta=\frac52$. Logarithmic correction when $d=8$.
Different ways of measuring this, but often want to know how "spread out" they are. eg. radius of gyration $R_g$ defined by \[ R^2_g(\omega) = \frac{1}{2n^2}\sum_{\omega_i,\omega_j \in \omega} |\omega_i-\omega_j|^2 \] where the sum is over all pairs of vertices in $\omega$.
Average over all objects of size $n$. Expect a power law \[ \langle R_g\rangle_n = \text{const.}n^\nu(1+o(1)) \] with a logarithmic correction for the upper critical dimension. Depends only on dimension, not the lattice.
For SAWs and SAPs this is the same $\nu$ as for the two-point function of the $N$-vector model, ie. $\nu=\frac34$ for $d=2$. For $d \geq 5$ it is the same as for random walks, ie. $\nu=\frac12$ (T. Hara & G. Slade, Comm. Math. Phys. 1992)
For animals and trees $\nu$ expect $\nu=\frac12$ when $d=3$ and $\nu=\frac14$ when $d>8$.
Let $G_z(0,x)$ be the generating functions of SAWs which start at $0$ and end at $x$: \[ G_z(0,x) = \sum_{\omega\,:\,0\to x}z^{|\omega|} \] Then with $z_c = \frac{1}{\mu}$, it is expected that \[ G_{z_c}(0,x) \sim \frac{\text{const.}}{|x|^{d-2+\eta}} \quad\text{as }|x|\to\infty \] for a constant $\eta$. The exponents $\gamma, \nu, \eta$ are then believed to obey Fisher's scaling relation \[ \gamma = (2-\eta)\nu. \]
There also exist hyperscaling relations in which the dimension $d$ appears: \begin{align*} d\nu -2 + \alpha &= 0 \\ d\nu -2\Delta_4 + \gamma &= 0 \end{align*} ($\Delta_4$ is another exponent related to pairs of SAWs.) However these are known to fail for $d\geq5$.
These generally fall into two categories: series analysis and Monte Carlo (random sampling) methods.
Typically (but not always) series analysis gives better results in $d=2$ and Monte Carlo does better for $d\geq3$. Main reason is that the numbers grow too fast in $d\geq3$ to get long enough series.
Recently some other transfer matrix methods have been developed for $d=2$.
Many different methods for enumeration. In 2D the most powerful is the finite lattice method - counts configurations in boxes by building them one "slice" at a time:
Originally developed by I. Enting (J. Phys. A 1980) and subsequently improved by several others, most prominently I. Jensen.
For SAWs and SAPs, reduces computational complexity from $O(\mu^n)$ to about $O(1.2^n)$.
In higher dimensions the FLM does not work well - there are other algorithms which offer (much more modest) improvements over direct enumeration.
Basic idea: compute the coefficients of a sequence (eg. $c_n$) up to some $n=N$, then try to "extrapolate". A.J. Guttmann world expert in series analysis.
For example on the square lattice $p_n$ is known up to $n=130$ (N. Clisby & I. Jensen, J. Phys. A 2012). The best way to analyse the series is to use differential approximants, where one searches for DEs satisfied by the truncated power series \[ C_N(z) = \sum_{n=0}^N c_n z^n \] and then looks for singular points.
Many many different algorithms for random sampling configurations. Each has its own strengths and weaknesses.
One of the most powerful for SAWs & SAPs (in $\geq3$D) is the pivot algorithm. Essentially: choose a section of the object at random, and attempt to apply a random lattice symmetry (rotation or reflection). If the result is still self-avoiding, keep it; otherwise, reject.
Proposed moves are rarely successful, but when they do succeed they make large changes. N. Clisby's algorithm (J. Stat. Phys. 2010) requires only $O(\log n)$ time per attempted move. He has used the algorithm to sample SAWs with billions of steps.
One weakness is that for SAPs pivots can change the topology.
For the square lattice a completely different method using the eigenvalues of a "Topological Transfer Matrix" has recently been found (J.L. Jacobsen et al, J. Phys. A 2016) to be more efficient. The current best estimate is now \[ \mu_\text{sq} = 2.63815853032790(3) \]
For the triangular lattice, series analysis does best (I. Jensen, J. Stat. Mech. 2004): \[ \mu_\text{tri} = 4.150797226(9) \]
In 3D \begin{align} \mu_\text{sc} &= 4.684039931(27) \\ \mu_\text{fcc} &= 10.037075(20) \\ \mu_\text{bcc} &= 6.530520(20) \end{align} The sc value is due to a very sophisticated Monte Carlo study (N. Clisby, J. Phys. A 2013); the fcc and bcc values come from series analysis (Schram et al, J. Stat. Mech. 2017).
(Estimates exist for growth rates of animals too.)
All numerical work strongly supports the conjectures $\gamma=\frac{43}{32}$ and $\nu = \frac34$ in 2D.
In 3D there is no reason to believe exponents are rational. Most precise estimates for SAWs are due to Clisby (J. Phys. A 2017 and Phys. Rev. Lett. 2010) \[ \gamma = 1.15695300(95) \qquad\text{and}\qquad \nu = 0.587597(7) \]


{% load md %}
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This is just to let you know that {{ comment.name }} replied to the discussion of "{{comment.post.title}}". Here's what they said:
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--- layout: page title: About header-img: "img/about-bg.png" ---
Hello. My name is OJ. I am currently living in Pennsylvania but my family is originally from Liberia. I am excited to be starting this journey to become a software developer. I currently work as an Investment Accountant for a fortune 100 company.


| Level | Preparation | Cooking | Price | Quantity | ||||||||||||
| Medium | 1h30 | None | Cheap | 6 persons |
| Ingredients | Equipment used | ||
| 351g of dark chocolate | clingfilm (width: 30cm) | ||
| 2 baskets of fresh raspberries | non-stick plate 40x30cm | ||
| 1 branch of fresh mint | spatula type maryse (25cm) | ||
| 3dl of crème fraiche with a fat level of 30% | curved spatula 15cm | ||
| 70g of icing sugar | piping bag (40cm) |
| Level | Preparation | Cooking | Price | Quantity | ||||||||||||
| Easy | 1h | 15min | Cheap | 4 persons |
| Ingredients | Equipment used | ||
| 800g of potatoes | Japanese mandolin 6,5cm | ||
| 30cl of whole milk | Saucepan 28cm | ||
| 50cl of crème fraîche | Garlic cut | ||
| a big knob of butter | Spatula | ||
| 2 garlic cloves | circle hollow platter in black cast hiron | ||
| Nutmeg | |||
| Salt | |||
| Pepper |
| Level | Preparation | Cooking | Price | Quantity | ||||||||||||
| Easy | 20min | None | Quite expensive | 4 persons |
| Ingredients | Equipment used | ||
| 12 knobs of Saint Jacques | A blender | ||
| orange juice | A big bowl | ||
| lemon juice | A strainer | ||
| 2 spoons of nut oil | Spatula | ||
| 4 spoons of Colza oil | circle hollow platter in black cast hiron | ||
| mix of bays | |||
| 1/2 bunch of chives | |||
| salt, pepper |


A JavaScript implementation of the Rip-off game using arcade.js (view the source to learn more).
Use [left], [right], and [up] to move the ship. [space] to fire.
This game requires HTML 5 support.


Note
Click here to download the full example code
Convert a pipeline with a LightGBM regressor¶
The discrepancies observed when using float and TreeEnsemble operator (see Issues when switching to float) explains why the converter for LGBMRegressor may introduce significant discrepancies even when it is used with float tensors.
Library lightgbm is implemented with double. A random forest regressor with multiple trees computes its prediction by adding the prediction of every tree. After being converting into ONNX, this summation becomes , where F is the number of trees in the forest, the output of tree i and a float addition. The discrepancy can be expressed as . This grows with the number of trees in the forest.
To reduce the impact, an option was added to split the node TreeEnsembleRegressor into multiple ones and to do a summation with double this time. If we assume the node if split into a nodes, the discrepancies then become .
Train a LGBMRegressor¶
from distutils.version import StrictVersion
import warnings
import timeit
import numpy
from pandas import DataFrame
# import matplotlib.pyplot as plt
from tqdm import tqdm
from lightgbm import LGBMRegressor
from onnxruntime import InferenceSession
from skl2onnx import to_onnx, update_registered_converter
from skl2onnx.common.shape_calculator import calculate_linear_regressor_output_shapes # noqa
from onnxmltools import __version__ as oml_version
from onnxmltools.convert.lightgbm.operator_converters.LightGbm import convert_lightgbm # noqa
N = 1000
X = numpy.random.randn(N, 20)
y = (numpy.random.randn(N) +
numpy.random.randn(N) * 100 * numpy.random.randint(0, 1, 1000))
reg = LGBMRegressor(n_estimators=1000)
reg.fit(X, y)
Out:
LGBMRegressor(n_estimators=1000)
Register the converter for LGBMClassifier¶
The converter is implemented in onnxmltools: onnxmltools…LightGbm.py. and the shape calculator: onnxmltools…Regressor.py.
def skl2onnx_convert_lightgbm(scope, operator, container):
options = scope.get_options(operator.raw_operator)
if 'split' in options:
if StrictVersion(oml_version) < StrictVersion('1.9.2'):
warnings.warn(
"Option split was released in version 1.9.2 but %s is "
"installed. It will be ignored." % oml_version)
operator.split = options['split']
else:
operator.split = None
convert_lightgbm(scope, operator, container)
update_registered_converter(
LGBMRegressor, 'LightGbmLGBMRegressor',
calculate_linear_regressor_output_shapes,
skl2onnx_convert_lightgbm,
options={'split': None})
Convert¶
We convert the same model following the two scenarios, one single TreeEnsembleRegressor node, or more. split parameter is the number of trees per node TreeEnsembleRegressor.
model_onnx = to_onnx(reg, X[:1].astype(numpy.float32),
target_opset={'': 14, 'ai.onnx.ml': 2})
model_onnx_split = to_onnx(reg, X[:1].astype(numpy.float32),
target_opset={'': 14, 'ai.onnx.ml': 2},
options={'split': 100})
Out:
/var/lib/jenkins/workspace/onnxcustom/onnxcustom_UT_39_std/_doc/examples/plot_gexternal_lightgbm_reg.py:78: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
if StrictVersion(oml_version) < StrictVersion('1.9.2'):
Discrepancies¶
sess = InferenceSession(model_onnx.SerializeToString(),
providers=['CPUExecutionProvider'])
sess_split = InferenceSession(model_onnx_split.SerializeToString(),
providers=['CPUExecutionProvider'])
X32 = X.astype(numpy.float32)
expected = reg.predict(X32)
got = sess.run(None, {'X': X32})[0].ravel()
got_split = sess_split.run(None, {'X': X32})[0].ravel()
disp = numpy.abs(got - expected).sum()
disp_split = numpy.abs(got_split - expected).sum()
print("sum of discrepancies 1 node", disp)
print("sum of discrepancies split node",
disp_split, "ratio:", disp / disp_split)
Out:
sum of discrepancies 1 node 0.00021465639434970537
sum of discrepancies split node 4.253979806128228e-05 ratio: 5.046013477555163
The sum of the discrepancies were reduced 4, 5 times. The maximum is much better too.
disc = numpy.abs(got - expected).max()
disc_split = numpy.abs(got_split - expected).max()
print("max discrepancies 1 node", disc)
print("max discrepancies split node", disc_split, "ratio:", disc / disc_split)
Out:
max discrepancies 1 node 1.85114812856213e-06
max discrepancies split node 3.228950369305039e-07 ratio: 5.732971761224528
Processing time¶
The processing time is slower but not much.
print("processing time no split",
timeit.timeit(
lambda: sess.run(None, {'X': X32})[0], number=150))
print("processing time split",
timeit.timeit(
lambda: sess_split.run(None, {'X': X32})[0], number=150))
Out:
processing time no split 3.6881782775744796
processing time split 3.6407719487324357
Split influence¶
Let’s see how the sum of the discrepancies moves against the parameter split.
res = []
for i in tqdm(list(range(20, 170, 20)) + [200, 300, 400, 500]):
model_onnx_split = to_onnx(reg, X[:1].astype(numpy.float32),
target_opset={'': 14, 'ai.onnx.ml': 2},
options={'split': i})
sess_split = InferenceSession(model_onnx_split.SerializeToString(),
providers=['CPUExecutionProvider'])
got_split = sess_split.run(None, {'X': X32})[0].ravel()
disc_split = numpy.abs(got_split - expected).max()
res.append(dict(split=i, disc=disc_split))
df = DataFrame(res).set_index('split')
df["baseline"] = disc
print(df)
Out:
0%| | 0/12 [00:00<?, ?it/s]/var/lib/jenkins/workspace/onnxcustom/onnxcustom_UT_39_std/_doc/examples/plot_gexternal_lightgbm_reg.py:78: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
if StrictVersion(oml_version) < StrictVersion('1.9.2'):
8%|8 | 1/12 [00:14<02:38, 14.38s/it]
17%|#6 | 2/12 [00:27<02:15, 13.55s/it]
25%|##5 | 3/12 [00:40<01:58, 13.18s/it]
33%|###3 | 4/12 [00:52<01:43, 12.94s/it]
42%|####1 | 5/12 [01:05<01:30, 12.98s/it]
50%|##### | 6/12 [01:18<01:16, 12.80s/it]
58%|#####8 | 7/12 [01:30<01:03, 12.68s/it]
67%|######6 | 8/12 [01:43<00:51, 12.78s/it]
75%|#######5 | 9/12 [01:55<00:37, 12.64s/it]
83%|########3 | 10/12 [02:08<00:25, 12.55s/it]
92%|#########1| 11/12 [02:21<00:12, 12.69s/it]
100%|##########| 12/12 [02:33<00:00, 12.58s/it]
100%|##########| 12/12 [02:33<00:00, 12.80s/it]
disc baseline
split
20 1.932818e-07 0.000002
40 2.141412e-07 0.000002
60 2.458757e-07 0.000002
80 3.333199e-07 0.000002
100 3.228950e-07 0.000002
120 3.369252e-07 0.000002
140 6.233323e-07 0.000002
160 4.348161e-07 0.000002
200 6.233323e-07 0.000002
300 9.991299e-07 0.000002
400 1.237549e-06 0.000002
500 1.135892e-06 0.000002
Graph.
ax = df.plot(title="Sum of discrepancies against split\n"
"split = number of tree per node")
# plt.show()
Total running time of the script: ( 3 minutes 8.918 seconds)


Description: Get all following siblings of each element up to but not including the element matched by the selector, DOM node, or jQuery object passed.
selector: Selector A string containing a selector expression to indicate where to stop matching following sibling elements.
filter: Selector A string containing a selector expression to match elements against.
element: Element A DOM node or jQuery object indicating where to stop matching following sibling elements.
filter: Selector A string containing a selector expression to match elements against.
Given a selector expression that represents a set of DOM elements, the .nextUntil()
method searches through the successors of these elements in the DOM tree, stopping when it reaches an element matched by the method's argument. The new jQuery object that is returned contains all following siblings up to but not including the one matched by the .nextUntil()
argument.
If the selector is not matched or is not supplied, all following siblings will be selected; in these cases it selects the same elements as the .nextAll()
method does when no filter selector is provided.
As of jQuery 1.6, A DOM node or jQuery object, instead of a selector, may be passed to the .nextUntil()
method.
The method optionally accepts a selector expression for its second argument. If this argument is supplied, the elements will be filtered by testing whether they match it.
<!DOCTYPE html>

term 1

definition 1-a

definition 1-b

definition 1-c

definition 1-d

term 2

definition 2-a

definition 2-b

definition 2-c

term 3

definition 3-a

definition 3-b


| Date | Project | Reference No. | Description | foreach($payment_types as $key => $value){ //Reset value with 0 $payment_types[$key] = 0; ?>=$value?> | } ?>Total |
|---|---|---|---|---|---|
| = $oimsSetting->convertToDisplayDate($report->context_date)?> | =$report->project->slug?> | =$report->reference_number?> | =$report->description?> | foreach($payment_types as $key => $value){ $amount = false; if($report->payment_type == $key){ $amount = $report->debit_amount != 0 ? $report->debit_amount : $report->credit_amount; $payment_types[$key] += $amount; if($report->debit_amount != 0) { $total +=$amount; $grand_total += $amount; } } ?>=$amount ?> | } ?>=$total?> |
| Total | foreach($payment_types as $key => $value){ ?>=$value?> | } ?>=$grand_total?> |
| DESCRIPTION | |
|---|---|
| Opening Balance On Date =$oimsSetting->convertToDisplayDate($from_date) ?> | = $oimsSetting->getPriceFormattedWithoutCurrency($balance_amount) ?> |
| Total Received | = $oimsSetting->getPriceFormattedWithoutCurrency($payment_types['Payment Received']) ?> |
| Total Expenses | = $oimsSetting->getPriceFormattedWithoutCurrency($grand_total) ?> |
| Closing Balance On Date =$oimsSetting->convertToDisplayDate($to_date) ?> | = $oimsSetting->getPriceFormattedWithoutCurrency($closing_balance_amount) ?> |


Incumbents Lead in Seattle City Council Races, Despite Amazon Investment
by KARINA BROWN(CN) – Early returns Tuesday night did not reveal whether Amazon’s $1 million gamble to transform Seattle’s city council would pay off, but several races showed the corporate giant’s candidates falling behind.
Last year, Seattle city council passed a per-employee tax on the state’s largest employers to build affordable housing and fund its fight against homelessness. About 500 companies grossing $20 million or more per year would have paid approximately 14 cents per hour worked by each employee – which after five years would add up to over $45 million.
But Amazon, Starbucks and the Downtown Seattle Association fought the tax and, two weeks later, the council voted 7-2 to repeal it. Now, the tech giant is trying to remove progressive council members and is spending nearly half a million to back candidates that would make the city council much more friendly to big business.
Amazon responded by spending over $1 million on this year’s election, funding opponents to try and unseat progressive council members such as Lisa Herbold and Kshama Sawant and to fight Tammy Morales.
As of Tuesday night, only about half the results had been tallied, but early returns showed that the funding campaign led by the world’s richest man might fail to install a pro-business city council. The following numbers are not final, and have not yet been certified by Washington’s secretary of state.
Incumbent Lisa Herbold squeaked ahead of Phil Tavel –who received $120,000 from Amazon – at 51% to 48%, respectively.
Amazon-backed Mark Solomon, who works for the Seattle Police Department, trailed Tammy Morales by 14 points. Morales is a community planner who has called for another head tax, or some other form of taxing Seattle’s wealthiest, such as an inheritance tax, a tax on CEOs or on second homes.
But in the council’s third district, Amazon-funded Egan Orion lead two-term incumbent Kshama Sawant, 54% to 45%. Sawant has called for taxing big businesses to pay for housing, transit improvements and environmental protections.
More results will continue to roll in throughout the week. Historically, progressive candidates get more of Seattle’s later votes.
Copyright © 2019 courthousenews.com


Before you get started
Before diving into the FlatBuffers usage in Dart, it should be noted that the Tutorial page has a complete guide to general FlatBuffers usage in all of the supported languages (including Dart). This page is designed to cover the nuances of FlatBuffers usage, specific to Dart.
You should also have read the Building documentation to build flatc
and should be familiar with Using the schema compiler and Writing a schema.
FlatBuffers Dart library code location
The code for the FlatBuffers Go library can be found at flatbuffers/dart
. You can browse the library code on the FlatBuffers GitHub page.
Testing the FlatBuffers Dart library
The code to test the Dart library can be found at flatbuffers/tests
. The test code itself is located in dart_test.dart.
To run the tests, use the DartTest.sh shell script.
Note: The shell script requires the Dart SDK to be installed.
Using the FlatBuffers Dart library
Note: See Tutorial for a more in-depth example of how to use FlatBuffers in Dart.
FlatBuffers supports reading and writing binary FlatBuffers in Dart.
To use FlatBuffers in your own code, first generate Dart classes from your schema with the --dart
option to flatc
. Then you can include both FlatBuffers and the generated code to read or write a FlatBuffer.
For example, here is how you would read a FlatBuffer binary file in Dart: First, include the library and generated code. Then read a FlatBuffer binary file into a List<int>
, which you pass to the factory constructor for Monster
:
import 'dart:io' as io;
import 'package:flat_buffers/flat_buffers.dart' as fb;
import './monster_my_game.sample_generated.dart' as myGame;
List<int> data = await new io.File('monster.dat').readAsBytes();
var monster = new myGame.Monster(data);
Now you can access values like this:
var hp = monster.hp;
var pos = monster.pos;
Differences from the Dart SDK Front End flat_buffers
The work in this repository is signfiicantly based on the implementation used internally by the Dart SDK in the front end/analyzer package. Several significant changes have been made.
- Support for packed boolean lists has been removed. This is not standard in other implementations and is not compatible with them. Do note that, like in the JavaScript implementation, null values in boolean lists will be treated as false. It is also still entirely possible to pack data in a single scalar field, but that would have to be done on the application side.
- The SDK implementation supports enums with regular Dart enums, which works if enums are always indexed at 1; however, FlatBuffers does not require that. This implementation uses specialized enum-like classes to ensure proper mapping from FlatBuffers to Dart and other platforms.
- The SDK implementation does not appear to support FlatBuffer structs or vectors of structs - it treated everything as a built-in scalar or a table. This implementation treats structs in a way that is compatible with other non-Dart implementations, and properly handles vectors of structs. Many of the methods prefixed with 'low' have been prepurposed to support this.
- The SDK implementation treats int64 and uint64 as float64s. This implementation does not. This may cause problems with JavaScript compatibility - however, it should be possible to use the JavaScript implementation, or to do a customized implementation that treats all 64 bit numbers as floats. Supporting the Dart VM and Flutter was a more important goal of this implementation. Support for 16 bit integers was also added.
- The code generation in this offers an "ObjectBuilder", which generates code very similar to the SDK classes that consume FlatBuffers, as well as Builder classes, which produces code which more closely resembles the builders in other languages. The ObjectBuilder classes are easier to use, at the cost of additional references allocated.
Text Parsing
There currently is no support for parsing text (Schema's and JSON) directly from Dart, though you could use the C++ parser through Dart Native Extensions. Please see the C++ documentation for more on text parsing (note that this is not currently an option in Flutter - follow this issue for the latest).


The mission of MMMUT Gorakhpur Robotics Club is to unite students in an educational environment and provide students a place to pursue their interest in mechanical , electronic and software design by working on large and small scale robotics projects . Having said that we take pride in talking about our projects , be it autonomous bots, humanoids , swarm of tiny robots, robotic arms or aerial vehicles , we have'nt left any stone unturned . However, the club provides much more than just technical growth to its members , from team-building to cooperation, from perseverance to sincerity, the club teaches you a myriad f interpersonal skills essential in daily life. The club is open to students with or without experience because , believe it or not , none of us had any idea of robotics when we first stepped into the clube room ,neither do we expect anything from you . You just need to show up and the club and its people will take care of the rest . The club's environment preputates the sharing of wisdom and experience amongst our members . All we need from you is enthusiasm!


Your organisations
Add the organisations that you want to hire apprentices through.
You need to sign an agreement for each organisation before you can access levy funds and hire apprentices throughthat organisation.
| Name | Agreement status | |
|---|---|---|
| ACME Ltd Coventry | Signed | View |
| Acme Ltd Bradford | Not signed | View or sign |
There are no organisations added.


The Bag of Joy Project is a service project start-up by 2 Diocesan Boys' School students who want to deliver joy to lives of those who are facing a tough time during the epidemic by delivering Bags of Joy to them. We are asking for donations in the form of supplies. The collected Bag of Joy will be distributed to children and elderlies in need through community centres and direct deliveries.


Dr. Rithvik T
Urticaria
Apply Aloe Vera gel when rashes appear.
Neem paste also can be applied externally.
Avoiding factors such as excess intake of salt, sour gruels and mustard; exposure to cold wind; improper emesis; insect bite.
Dr. Kamalesh
Diabetes
ake powder of fenugreek twice a day with hot water.
Take Triphala powder twice a day with hot water. Add lady’s finger every day in the diet.
Krish Verma
8453256314
19
Vijay Kumar
7635419654
62
Find us at:
#123, XYZ Street, ABC Colony,
Madhurovoyal, Chennai,
Tamilnadu, India
Pincode: 600094
Contact us at:
Email<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>


We aggregate users!
- architect dynamic niches
- leverage real-time infomediaries
- exploit B2B e-business
- morph leading-edge models
- innovate killer convergence
- utilize best-of-breed functionalities
- matrix synergistic functionalities
- scale revolutionary web-readiness
- transition bleeding-edge paradigms
- harness rich architectures
- benchmark revolutionary partnerships
- benchmark extensible content
- drive user-centric models
- incentivize interactive metrics


The data that we visualize here is the Auto MPG Data Set which contains 398 automobile records from 1970 to 1982 of US, Japan and Europe. Each car is described by its model, region of production, year of production and 6 attributes: MPG (miles per gallon), number of cylinders, displacement, horsepower, weight, acceleration.
Below shows the correlation between the six attributes. Clicking on one of the graph shows a bigger detailed scatter plot.
Below shows the evolution of each characteristic of car in each region along the years.
Below shows the proportion of brands of cars produced in each origin.


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Hmm, you seem to be lost
How about going to the homepage or just enjoy this beautiful picture.
Unblur
Homepage


Lego Pays Homage To Its Heritage With A Wonderful Wooden Minifig
by Mike FaheyIn 1932 Ole Kirk Christiansen, founder of The Lego Group, started making and selling wooden toys out of his shop in Billund, Denmark. The company continued making wooden toys until 1960, two years after the Lego brick as we know it today was born. Now The Lego Group celebrates old and new with a seven inch tall, $US120 ($174) minifigure made out of wood. Well, except for the hands.
The Lego Originals Wooden Minifigure, available starting November 3 at Lego.com, is not so much a toy as it is a collectible art piece. Crafted from real oak, other than the hands, which are plastic for gripability purposes, the piece neatly sums up the whole of Lego history.
The Wooden Minifigure, five times the size of the icon plastic people introduced in 1978, comes packaged in a premium collector’s box. Inside are a collection of plastic Lego bricks, so fans can build little models for the figure to hold, as well as a booklet detailing the creation of the wooden model. The booklet also includes a collection of photos showing how Lego designers used paint and props to customise the figure, creating their own unique celebrations of Lego history. Here are a few of my favourites.
My home is absolutely littered with Lego minifigures. Maybe it’s time to sequester my tiny plastic pals away in a nice, comfy drawer and replace them with something a bit more classy. Or I’ll just keep them out and arrange them around the Lego Originals Wooden Minifigure so they can worship it as their god.
One of those.


About
I graduated from the University of Texas at Dallas. I enjoy problem-solving and coding. Always strive to bring 100% to the work I do. I have worked on technologies like Python, Django, Flask, MySQL, PostgreSQL, MongoDB, HTML5, CSS, Java during my bachelor's. I have 19 months of professional work experience which helped me strengthen my experience in Python, Flask, and Django. I am passionate about developing complex applications that solve real-world problems impacting millions of users.
- Languages: Pysthon, JavaScript, C, HTML/CSS, Bash
- Databases: MySQL, PostgreSQL, MongoDB
- Libraries: NumPy, Pandas, OpenCV
- Frameworks: Flask, Django, Node.js, Keras, TensorFlow, PyTorch, Bootstrap, Apache Beam
- Tools & Technologies: Git, Docker, AWS, GCP, Heroku, JIRA
Looking for an opportunity to work in a challenging position combining my skills in Software Engineering/Data Science, which provides professional development, interesting experiences and personal growth.
Experience
- Tracked the revenue of franchise to ensure smooth transition of ownership from one franchise owner to another.
- Removed redundancy in survey questions from customer feedback platform based on correlation matrix values.
- Built intuitive dashboards in tableau to visualize the YoY sales growth, average guest check size for franchise reporting
- Generated automatic weekly performance reports using Python, sent out the franchise and reduced time by 50%
- Cleaned, mapped, and organized two years of instore data using Python scripts using NumPy, pandas libraries, etc.
- Used Alteryx and Python to automate ETL jobs for update tables inside snowflake cloud warehouse daily.
- Identified integration impact, data flows and data stewardship.
- Generated, wrote and run PL/SQL script to implement the DB changes including table update, addition or update of indexes, creation of views and store procedures.
- Consolidated and updated various data models through reverse and forward engineering.
- Tools: Python, Flask, MongoDB, Tableau, Snowflake, AWS, Microsoft Excel
- Built an automated ETL process to integrate data from multiple sources in AWS S3 and load using data integration tools for further analytics and saved time on implementation by 100 hours
- Extracted data from 20+ tables in database using PL/SQL queries for creating powerful dashboards in Tableau for reporting to Stakeholders and management
- Interacted with stakeholders and data engineers to put into production and to implement the model within AWS EC2 for live forecasting in client’s monthly and weekly revenue with over 82% accuracy
- Tools: Python, Flask, tableau, SQL,. etc
Skills
Languages and Databases
Libraries
Frameworks
Other
Education
Dallas, Texas
Degree: Master of Science in Information Technology and Management
CGPA: 3.56/4.0
- Distributed Database Systems
- Cloud Computing
- Foundations of Algorithms
Relevant Courseworks:
Mumbai, India
Degree: Bachelor of Engineering in Electronics and telecommunication engineering
CGPA: 6.56/10
- Data Structures and Algorithms
- Database Management Systems
- Operating Systems
- Machine Learning
- Computer Vision
Relevant Courseworks:


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UITableView
class UITableView : UIScrollView, NSCoding, UIDataSourceTranslating
-
Initiates a layout pass of UITableView and its items. Necessary for calculating new cell heights and animations when the internal state of a cell changes and needs to reflect them immediately.
Declaration
Swift
public func render()
-
Deselects the previously selected row, with an option to animate the deselection.
Declaration
Swift
public func deselectLastSelectedRow(animated: Bool)
Parameters
animated
true
to animate the deselection, orfalse
if the change should be immediate. -
Find the
IndexPath
for a particular view. Returnsnil
if the view is not an instance of, or a subview ofUITableViewCell
, or if that cell is not a child ofself
Declaration
Swift
public func indexPath(for view: UIView) -> IndexPath?
Parameters
view
The view to find the
IndexPath
.Return Value
The
IndexPath
of the view in theUITableView
ornil
if it could not be found.


Cats, though mostly self-sufficient, won't require as much of your attention as possible.
Cats are hunters, so most of the time, they will hunt rodents for themselves. But, to earn it's trust, give it some meat. Remember, mature cats are lactose intolerant!
Cats generally groom themselves, so this should not worry you much. Just take it to the vet occasionally.
Cats are clean creatures, so their environment isn't. Always clean their homes, and their feeding plates to prevent them from leaving.
Back To Home Page


{% extends "base.html" %} {% block content %} {% load staticfiles %}
The Hops Museum depends on the generosity of foundations, corporations and dedicated individuals to maintain the expansion of the museums enriching public programs/activities. In this section of the website you will find many ways in which you can participate in and support the Hops Museum. Join us in our efforts in bringing together community by becoming a member, volunteer, donor or partner of the Hops Museum. All or part of your gift may be tax deductible as a charitable contribution. Please check with your tax advisor. The Hop Museum, Inc. is a not-for-profit 501(c)3 Here are some ways to give/ donate...
We'd like to thank these people for their partnership


--- layout: post authors: ["Areej Alsheikh"] title: "Teaching in Bali" date: 2015-08-14 time: "00:30:00" tags: ["Workshops", "DIPnet"] ---
On July 2015, I was fortunate to receive an invitation to the first international workshop of the Diversity of the Indo-Pacific Network (DIPnet), which explains the "Bali" part, in order to teach R and the Unix command line during the first two days. The weeklong workshop was co-hosted by the Hawai'i Institute of Marine Biology and Indonesia Biodiversity Research Center (IBRC), and lead by Dr. Eric Crandall from CSU Monterey Bay. The theme of the workshop was "Molecular Ecology and Bioinformatics in Developing Countries", which featured lectures and labs developed by the invited participants in their area of expertise. For this, a two-day software carpentry workshop was very much needed to begin the week and prepare all participants for the computational sessions coming up in the week.
The audience was mostly faculty members and some students from different universities around Indonesia including Universitas Makassar and Universitas Negeri Papua, around half of which were women. Almost all attendees had little or no programming experience and nearly all used "not-so-good" Windows machines. To overcome this challenge, NERC kindly provided us with BioLinux on bootable USB sticks. R and Rstudio were installed separately for each participant. Although the ResBaz awesome cloud was made available for this workshop by Tim Dettrick, we didn't need to use it as participants preferred to have the tools installed on their own computers.
Overall, the workshop was a big success. Two major challenges were encountered in this workshop. The first challenge was the language barrier. As English is not the audience's main language, they typically find difficulty keeping up with regular conversational pace and unfamiliar vocabulary. That was one thing I had to be careful about when explaining the lessons. For this, I had to pay full attention to my pace, clear pronunciation of words, and avoiding usage of possibly unfamiliar words. In fact, in order to watch my pace I introduced additional sticky note colour (blue), which means to "slow down". They loved it! The language challenge was mainly why we only chose to cover the Unix Command in one full day, and R in another full day.
The second challenge was making sure the audience was following up and understanding the material presented. The reason why this was a challenge is because in the Indonesian culture, it is considered "rude" to interrupt speakers or ask questions. Therefore, I had to frequently check in with them for any problems or if I needed to repeat something. I also constantly kept reminding them to use the sticky notes. After a few hours, the ice was broken and they were more than comfortable asking questions, requesting to repeat or slow down, and even making jokes.
For the first day, we made it through most of the Bash material except for "Shell scripts", and "Finding things". At first, they were relatively slow in understanding the content, but the more we covered, the faster they became in understanding new commands and concepts. The second day, however, some found R to be slightly more difficult to understand than the shell. A few criticized the challenges as being "not straight-forward", while others felt like the choices of R lessons were not targeted for programming novice (e.g. introducing functions so early). It felt to them that lessons focused more on developing good programming practice, rather than basic knowledge of how the R language works. It was felt that the examples in the R lesson were somewhat abstract, especially in comparison to the concrete examples in the Unix lesson for how use of the command line could speed up processing of scientific data.
Overall, everyone was very happy with the workshop and had lots of fun. They felt that they learned how to learn R/Unix. They developed some sense of abstract thinking. Most importantly, they now recognize the power of task automation and the importance of such skills in doing their respective research.


--- layout: base title: Computer Brighton ---
Now you understand. Let's go to computer Brighton
You can read the overview of computer and the rules here.
Is anyone interested in a Computer meetup in Brighton?
If you have any suggestions for venues, or if you want to help organise Computer Brighton, get in touch with @badgermind or @computebrighton via Twitter


In my role as a research assistant is was contributing to the mlr-package. This is a R-package for machine learning in R. I'm still part of the mlr-organization and contributing to the package by cleaning up the issue tracker, as well as implementimg new features into the package.
In my role as a tutor I was supervising two lectures both held by our working group Computational statistics:


{% set page = "team" %} {% extends "layout.nunjucks" %} {% block content %} {% include "sections/header.html" %} Meet the team. {% for teammate in teammates %} {{teammate.name}} {{teammate.title}} {% endfor %} {% include "sections/footer.html" %} {% endblock %}


Send us a message to know about future trainings in
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RegisterFrom the most profound fundamentals, to all the common practices.
By learning the Lean concepts that sustain many Agile frameworks, you will have a more meaningful experience and a clear path for Continuous Improvement.
What are the relationships between Lean, Kanban, Scrum, DevOps, Design Thinking, ..?
When does it makes sense to use one or another?
How to estimate, build teams, manage risk & budget, etc?
How can we lead autonomous teams, ensuring alignment, transparency and high-performance?
All these questions and much more, are going to be answered in a consistent and practical way, allowing you to understand them and become a change agent in your organization.


| drawPath | Draw a Picture Object |
| drawPath-method | Class "PictureChar" |
| drawPath-method | Class "PictureFill" |
| drawPath-method | Class "PictureStroke" |
| drawPath-method | Class "PictureText" |
| explodePaths | Explode Complex Picture Paths |
| grid.picture | Draw a Picture Object |
| grid.symbols | Draw a Picture Object |
| grobify | Convert a Picture into a Grid Grob |
| grobify-method | Class "Picture" |
| grobify-method | Class "PictureChar" |
| grobify-method | Class "PictureFill" |
| grobify-method | Class "PictureStroke" |
| grobify-method | Class "PictureText" |
| oldGrobify | Convert a Picture into a Grid Grob |
| oldGrobify-method | Class "PictureChar" |
| oldGrobify-method | Class "PictureFill" |
| oldGrobify-method | Class "PictureStroke" |
| oldGrobify-method | Class "PictureText" |
| picture | Draw a Picture Object |
| Picture-class | Class "Picture" |
| PictureChar-class | Class "PictureChar" |
| PictureFill-class | Class "PictureFill" |
| pictureGrob | Draw a Picture Object |
| PictureOp-class | Class "PictureOp" |
| picturePaths | Draw Individual Picture Paths |
| PictureStroke-class | Class "PictureStroke" |
| PictureSummary-class | Class "PictureSummary" |
| PictureText-class | Class "PictureText" |
| PostScriptTrace | Convert PostScript file to RGML file |
| readPicture | Import an RGML file |
| symbolize | Convert a Picture into a Grid Grob |
| symbolize-method | Class "Picture" |
| symbolize-method | Class "PictureChar" |
| symbolize-method | Class "PictureFill" |
| symbolize-method | Class "PictureStroke" |
| symbolize-method | Class "PictureText" |
| symbolsGrob | Draw a Picture Object |
| [-method | Class "Picture" |
| [[-method | Class "Picture" |


Full Stack Software Engineer
I am passionate, persistent, autonomous learner Full Stack Software Engineer, cope with change positively, Life for me is challenges and coding faster.
I am passionate, persistent, autonomous learner Full Stack Software Engineer, cope with change positively, Life for me is challenges and coding faster.


drush migrate-upgrade --legacy-root=http://data.agaric.com --configure-only
drush mr --all
1 * #Entities
+ 2 * #Fields
= Migration Complexity
You're not going to migrate in your schema that you had in Drupal 7 or 6 or Joomla...disco insolence, Solvognen’s Tangle Of Wires
...you'll build it exactly as you want it in Drupal 8.Les Lim
Migrate module is using a more general process called extract, transform, and load, or ETL.Brad Lowry
Migration is an Extract, Transform, Load (ETL) process. For historical reasons, in the Drupal migration tool the extract phase is called "source", the transform phase is called "process", and the load phase is called "destination". Migration API
agaric_migrate.info.yml
:
type: module name: Agaric Migrate description: "Migrate from Agaric's Drupal 7 site to its new Agaric 8 site." package: Migrate core: 8.x dependencies: - migrate - migrate_plus
For more complex processing, you can add process plugins.
In a file like src/Plugin/migrate/process/NerdUserRoles.php
You start out with:
namespace Drupal\nerdcustom\Plugin\migrate\process; use Drupal\migrate\ProcessPluginBase; use Drupal\migrate\MigrateExecutableInterface; use Drupal\migrate\Row;github.com/stevector/nerdologues-d8
/** * This plugin maps old roles to new roles. * * @MigrateProcessPlugin( * id = "nerd_user_roles" * ) */ class NerdUserRoles extends ProcessPluginBase { /** * {@inheritdoc} */ public function transform($value, MigrateExecutableInterface $migrate_executable, Row $row, $destination_property) { if ($value == 3) { return 'administrator'; } elseif ($value == 4) { return 'content_administrator'; } } }
Cory Doctorow, "The coming civil war over general purpose computing"


Father and son jailed for killing farmer
by Algoa FMNorth West police commissioner, Lieutenant General Baile Motswenyane on Tuesday, welcomed the hefty sentence meted to a father and son for killing an emerging farmer from Brits.
The 52-year-old Schalk Myburgh and his 29-year-old son Schalk Myburgh Jnr were sentenced at the High Court in Pretoria for the brutal murder of Rickson Morake Mavula.
Both were sentenced to 22 years in prison.
Mavula, a vegetable farmer in the Sandrift area, was murder on December 29, 2015.
Police spokesperson Colonel Adele Myburgh says it was revealed during court proceedings that at about 8 pm on the said day, Mavula, accompanied by his three children, drove his vehicle to look for one of his goats which drowned in the dam alongside Havana Road.
She says the Mavulas were busy rescuing their goat when two males approached them and blocked the way by parking their vehicle in front of the family's car. Mavula was then accused of theft of the goat and subsequently assaulted. It was during the assault that Mavula fell down on the ground. One of the accused, Schalk Myburgh Jnr got in the vehicle and drove over Mavula. The accused left Mavula motionless and went to the nearby farm where they related the story to the owner.
The pair went to Brits police station on December 30, 2015, where they reported that they were attacked by a group of approximately 10 people.
A preliminary investigation was conducted and revealed that the allegations were false. Consequently, the pair were arrested on Sunday, 10 January 2016 through the evidence provided by Mavula's 15-year-old son, who also witnessed the gruesome ordeal.
The duo was ultimately found guilty for murder by the North Gauteng High Court on Friday, 20 September 2019.
- African News Agency (ANA)