Miracle-dz/UniFAR-FTUs huggingface dataset:
$d$ -Imbalance WOM Codes for Reduced Inter-Cell Interference in Multi-Level NVMs

In recent years, due to the spread of multi-level nonvolatile memories (NVMs), q-ary write-once memory (WOM) codes have been extensively studied. By using WOM codes, it is possible to rewrite NVMs t times before erasing the cells. Use of WOM codes enables the improvement of the performance of the storage device; however, it may also increase errors caused by inter-cell interference (ICI). This paper presents WOM codes that restrict the imbalance between code symbols throughout the write sequence, hence decreasing ICI. We first specify the imbalance model as a bound d on the difference between codeword levels. Then, a two-cell code construction for general q and input size is proposed. An upper bound on the write count is also derived, showing the optimality of the proposed construction. In addition to direct WOM constructions, we derive closed form optimal write regions for codes constructed with continuous lattices. On the coding side, the proposed codes are shown to be competitive with known codes not adhering to the bounded imbalance constraint. On the memory side, we show how the codes can be deployed within flash wordlines, and quantify their bit-error rate advantage using accepted ICI models.
The through porosity of soils as the control of hydraulic conductivity.

The prominent contribution of macropores to water flow and solute transport points to the need for thorough characterization of soil void structure. Undisturbed soil columns need to be studied to infer topological properties of macropores. Three undisturbed soil columns (7.5 cm ID, 16 cm length) of the Taylor soil were taken at a grassed floodplain in Franklin County, PA. The FlashCT 420 kV system and supplied software (HYTEC Inc.) were used for X-ray CT scanning and image reconstruction in the columns. A MatLab® software was developed for the 3D reconstruction and analysis of pores larger than 110 μm in diameter. The saturated hydraulic conductivity was measured on the 16 cm long columns and then on 8, 4, and 2 cm-thick columns obtained by consecutive slicing of the original columns. The macropore network was reconstructed from the imagery and the through pores were identified in each section as voids open to top and bottom of columns and column sections. Saturated hydraulic conductivity was affected by the column length and the minimum size of through pores. The increase in overall macroporosity did not necessarily translate into large hydraulic conductivity of columns. Introducing a novel parameter the through macroporosity was useful to quantify the effect of differences in pore space structure on the differences in hydraulic conductivity.
What do class comments tell us? An investigation of comment evolution and practices in Pharo

Previous studies have characterized code comments in different programming languages. However, very few studies have focused on the analysis of the information embedded in code comments. None of them compared the developer's practices to write the comments to the standard guidelines and analyzed these characteristics in the Pharo Smalltalk environment. The class commenting practices have their origins in Smalltalk-80, going back 40 years. Smalltalk offers a brief template for entering a comment for newly-created classes. These templates have evolved over the years, particularly in the Pharo environment. This paper reports the first empirical study investigating commenting practices in Pharo Smalltalk. We quantitatively and qualitatively analyze class comments of the most recent version of Pharo, to investigate the information types of Pharo comments. Finally, we study the adherence of developer commenting practices to the class template over Pharo versions. The results show a rapid increase in class comments in the initial three Pharo versions, while in subsequent versions developers added comments to both new and old classes, thus maintaining a similar ratio. Additionally, we found that 23 information types are typically embedded in class comments and only seven of them are present in the latest Pharo class comment template. However, the information types proposed by the standard template tend to be present more often than other types of information. Additionally, we find that a substantial proportion of comments follow the writing style of the template in writing these information types, but written and formatted in a non-uniform way. This suggests the need to standardize the commenting guidelines for formatting the text to ensure a consistent style, and to identify the information easily.
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus .

The evaluative character of a word is cal led i ts semantic orientation. A positive semantic ori entation impl ies desirabi l i ty (e.g., “ honest” , “ intrepid” ) and a negative semantic orientation impl ies undesi rabi l i ty (e.g., “ disturbing” , “ superf luous” ). This paper introduces a simple algori thm for unsupervised learning of semantic orientation f rom extremely large corpora. The method involves issuing queries to a Web search engine and using pointwise mutual information to analyse the resul ts. The algori thm is empirical ly evaluated using a training corpus of approximately one hundred bi l l ion words — the subset of the Web that is indexed by the chosen search engine. Tested with 3,596 words (1,614 positive and 1,982 negative), the algorithm attains an accuracy of 80%. The 3,596 test words include adjectives, adverbs, nouns, and verbs. The accuracy is comparable with the resul ts achieved by Hatzivassi loglou and McKeown (1997), using a complex four-stage supervised learning algori thm that is restricted to determining the semantic orientation of adjectives. 1. Semantic Orientation from Association Turney and Littman
Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus

The evaluative character of a word is cal led i ts semantic orientation. A positive semantic ori entation impl ies desirabi l i ty (e.g., “ honest” , “ intrepid” ) and a negative semantic orientation impl ies undesi rabi l i ty (e.g., “ disturbing” , “ superf luous” ). This paper introduces a simple algori thm for unsupervised learning of semantic orientation f rom extremely large corpora. The method involves issuing queries to a Web search engine and using pointwise mutual information to analyse the resul ts. The algori thm is empirical ly evaluated using a training corpus of approximately one hundred bi l l ion words — the subset of the Web that is indexed by the chosen search engine. Tested with 3,596 words (1,614 positive and 1,982 negative), the algorithm attains an accuracy of 80%. The 3,596 test words include adjectives, adverbs, nouns, and verbs. The accuracy is comparable with the resul ts achieved by Hatzivassi loglou and McKeown (1997), using a complex four-stage supervised learning algori thm that is restricted to determining the semantic orientation of adjectives. 1. Semantic Orientation from Association Turney and Littman
Deconvolution ( a ) A common CNN architecture BI BI ( b ) CNN with Bilateral

In this paper we propose a CNN architecture for image segmentation. We introduce a new “bilateral inception” layer that is used on top of a fully convolutional architecture. The bilateral inception performs a filtering between superpixels in an image. This addresses two problems that arise with CNN segmentation architectures. First, this layer propagates label information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a fully-convolutional network. In the experiments we replace the de-convolution networks and Dense-CRF that have previously been proposed to address these problems with bilateral inception layers. The reduction to superpixels reduces the amount of computations and simplifies the network design. Further, we report better empirical results by replacing De-convolutional and CNN+Dense-CRF steps in previous models even without re-training CNN filter weights.
Logarithmic regret in the dynamic and stochastic knapsack problem

We study a dynamic and stochastic knapsack problem in which a decision maker is sequentially presented with n items with unitary rewards and independent weights that are drawn from a known continuous distribution F . The decision maker seeks to maximize the expected number of items that she includes in the knapsack while satisfying a capacity constraint, and while making terminal decisions as soon as each item weight is revealed. Under mild regularity conditions on the weight distribution F , we prove that the regret—the expected difference between the performance of the best sequential algorithm and that of a prophet who sees all of the weights before making any decision—is, at most, logarithmic in n. Our proof is constructive. We devise a re-optimized heuristic that achieves this regret bound.
Unsupervised Learning of Invariant Representations in Hierarchical Architectures

Representations that are invariant to translation, scale and other transformations, can considerably reduce the sample complexity of learning, allowing recognition of new object classes from very few examples|a hallmark of human recognition. Empirical estimates of one-dimensional projections of the distribution induced by a group of ane transformations are proven to represent a unique and invariant signature associated with an image. We show how projections yielding invariant signatures for future images can be learned automatically, and updated continuously, during unsupervised visual experience. A module performing ltering and pooling, like simple and complex cells as proposed by Hubel and Wiesel, can compute such estimates. Under this view, a pooling stage estimates a one-dimensional probability distribution. Invariance from observations through a restricted window is equivalent to a sparsity property w.r. t. to a transformation, which yields templates that are a) Gabor for optimal simultaneous invariance to translation and scale or b) very specic for complex, class-dependent transformations such as rotation in depth of faces. Hierarchical architectures consisting of this basic Hubel-Wiesel module inherit its properties of invariance, stability, and discriminability while capturing the compositional organization of the visual world in terms of wholes and parts, and are invariant to complex transformations that may only be locally ane. The theory applies to several existing deep learning convolutional architectures for image and speech recognition. It also suggests that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and discriminative for recognition|and that this representation may be continuously learned in an unsupervised way during development and natural visual experience.
Riding on the primary: A new spectrum sharing paradigm for wireless-powered IoT devices

In this paper, a new spectrum sharing model referred to as riding on the primary (RoP) is proposed for wireless- powered IoT devices with ambient backscatter communication capabilities. The key idea of RoP is that the secondary transmitter harvests energy from the primary signal, then modulates its information bits to the primary signal, and reflects the modulated signal to the secondary receiver without violating the primary system's interference requirement. Compared with the conventional spectrum sharing model, the secondary system in the proposed RoP not only utilizes the spectrum of the primary system but also takes advantage of the primary signal to harvest energy and to carry its information. In this paper, we investigate the performance of such a spectrum sharing system under fading channels. To be specific, we maximize the ergodic capacity of the secondary system by jointly optimizing the transmit power of the primary signal and the reflection coefficient of the secondary ambient backscatter. Different (ideal/practical) energy consumption models, different (peak/average) transmit power constraints, different types (fixed/dynamically adjustable) reflection coefficient are considered. Optimal power allocation and reflection coefficient are obtained for each scenario.
Pac-learning Recursive Logic Programs: Eecient Algorithms

We present algorithms that learn certain classes of function-free recursive logic programs in polynomial time from equivalence queries. In particular, we show that a single k-ary recursive constant-depth determinate clause is learnable. Two-clause programs consisting of one learnable recursive clause and one constant-depth determinate non-recursive clause are also learnable, if an additional \basecase" oracle is assumed. These results immediately imply the pac-learnability of these classes. Although these classes of learnable recursive programs are very constrained, it is shown in a companion paper that they are maximally general, in that generalizing either class in any natural way leads to a compu-tationally diicult learning problem. Thus, taken together with its companion paper, this paper establishes a boundary of eecient learnability for recursive logic programs.
MODELING AND CHARACTERIZATION OF TRAFFIC IN A PUBLIC SAFETY WIRELESS NETWORK

Reliable communication and interoperability between public safety agencies play vital role for public safety. We analyze traffic data from a deployed trunked radio communication network operated by public safety wireless network service provider E-created to evaluate the utilization of network resources and to locate network bottlenecks. Our analysis may be used to address existing and future network congestion problems. We also present statistical analysis of traffic data. We examine statistical distribution and autocorrelation function of call inter-arrival and call holding times during several busy hours. We find that call inter-arrival times are long-range dependent and may be modelled by both Weibull and gamma distributions. Call holding times follow the lognormal distribution and are uncorrelated. These findings indicate that traditional Erlang models for voice traffic may not be suitable for evaluating the performance of trunked radio networks. At that time, Jesus spoke these words: " I thank thee, Father, Lord of heaven and earth, for hiding these things from the learned and wise, and revealing them to the simple . " The Gospel according to Matthew, ch. 11 / Â Â Â ÂN N N No matter what you' ve done for yourself or for humanity, if you can' t look back on having given love and attention to your own family, what have you really accomplished? "-Lee Iacocca v ACKNOWLEDGEMENTS
Opinion Formation by Informed Agents

Opinion formation and innovation diffusion have gained lots of attention in the last decade due to its application in social and political science. Control of the diffusion process usually takes place using the most influential people in the society, called opinion leaders or key players. But the opinion leaders can hardly be accessed or hired for spreading the desired opinion or information. This is where informed agents can play a key role. Informed agents are common people, not distinguishable from the other members of the society that act in coordination. In this paper we show that informed agents are able to gradually shift the public opinion toward a desired goal through microscopic interactions. In order to do so they pretend to have an opinion similar to others, but while interacting with them, gradually and intentionally change their opinion toward the desired direction. In this paper a computational model for opinion formation by the informed agents based on the bounded confidence model is proposed. The effects of different parameter settings including population size of the informed agents, their characteristics, and network structure, are investigated. The results show that social and open-minded informed agents are more efficient than selfish or closed-minded agents in control of the public opinion.
The Generalized Stochastic Likelihood Decoder: Random Coding and Expurgated Bounds

The likelihood decoder is a stochastic decoder that selects the decoded message at random, using the posterior distribution of the true underlying message given the channel output. In this paper, we study a generalized version of this decoder, where the posterior is proportional to a general function that depends only on the joint empirical distribution of the output vector and the code word. This framework allows both mismatched versions and universal versions of the likelihood decoder, as well as the corresponding ordinary deterministic decoders, among many others. We provide a direct analysis method that yields the exact random coding exponent (as opposed to separate upper bounds and lower bounds that turn out to be compatible, which were derived earlier by Scarlett et al.). We also extend the result from pure channel coding to combined source and channel coding (random binning followed by random channel coding) with side information available to the decoder. Finally, returning to pure channel coding, we derive also an expurgated exponent for the stochastic likelihood decoder, which turns out to be at least as tight (and in some cases, strictly so) as the classical expurgated exponent of the maximum likelihood decoder, even though the stochastic likelihood decoder is suboptimal.
Network synchronization in a noisy environment with time delays

The ability of a network to synchronize can change drastically when time delays are introduced. Nonzero time delays impose a limitation on the strength/frequency of communication regarding the synchronizability of the network. When synchronization is possible, there is a fundamental limit on how well the network can synchronize, even in the most optimal circumstances. These fundamental properties are apparent even in the most basic case of uniform time delays in stochastic, linearlycoupled synchronization problems. This basic model can be expanded to include the richer behavior of networks with multiple delays. Non-uniform time delays can arise when there are multiple sources of delay, e.g. the time to transmit and the time to process information. In this particular two-delay case, the primary limitation on network synchronization does not come from restrictions in the transmission of a node’s state to its neighbors; rather it depends on the ability for each node to process and respond to the information about itself in the context of its local environment. Furthermore, given a network’s structure, there are optimal delays for which the network remains synchronizable for longer processing delays. As a result, synchronization is not always improved – and in some cases can be totally destroyed – by minimizing the transmission delays. For special cases, one can also study the scaling function that quantifies synchronization, showing the limitation of synchronization in a noisy network.
Self-Correcting Variable-Metric Algorithms for Nonsmooth Optimization

A generic algorithmic framework is proposed for minimizing nonsmooth and potentially nonconvex objective functions. The framework is variable-metric in the sense that, in each iteration, a step is computed using a symmetric positive definite matrix whose value is updated in a similar manner as in the Broyden-Fletcher-Goldfarb-Shanno (BFGS) scheme popular for minimizing smooth objectives. Unlike previously proposed variable-metric algorithms for minimizing nonsmooth functions, the proposed framework exploits the self-correcting properties of BFGS-type updating. In so doing, the framework does not overly restrict the manner in which the step computation matrices are updated, yet the scheme is controlled well enough that global convergence guarantees can be established. The results of numerical experiments for various algorithms are presented to demonstrate the self-correcting behaviors that are guaranteed by the framework.
SSS* = Alpha-Beta + TT

In 1979 Stockman introduced the SSS* minimax search algorithm that domi- nates Alpha-Beta in the number of leaf nodes expanded. Further investigation of the algorithm showed that it had three serious drawbacks, which prevented its use by practitioners: it is difficult to understand, it has large memory requirements, and it is slow. This paper presents an alternate formulation of SSS*, in which it is implemented as a series of Alpha-Beta calls that use a transposition table (AB- SSS*). The reformulation solves all three perceived drawbacks of SSS*, making it a practical algorithm. Further, because the search is now based on Alpha-Beta, the extensive research on minimax search enhancements can be easily integrated into AB-SSS*. To test AB-SSS* in practise, it has been implemented in three state-of-the- art programs: for checkers, Othello and chess. AB-SSS* is comparable in performance to Alpha-Beta on leaf node count in all three games, making it a viable alternative to Alpha-Beta in practise. Whereas SSS* has usually been regarded as being entirely different from Alpha-Beta, it turns out to be just an Alpha-Beta enhancement, like null-window searching. This runs counter to published simulation results. Our research leads to the surprising result that iterative deepening versions of Alpha-Beta can expand fewer leaf nodes than iterative deepening versions of SSS* due to dynamic move re-ordering.
Successive Convexification for Real-Time Six-Degree-of-Freedom Powered Descent Guidance with State-Triggered Constraints

In this paper, we present a real-time successive convexification algorithm for a generalized free-final-time 6-degree-of-freedom powered descent guidance problem. We build on our previous work by introducing the following contributions: (i) a free-ignition-time modification that allows the algorithm to determine the optimal engine ignition time, (ii) a tractable aerodynamics formulation that models both lift and drag, and (iii) a continuous state-triggered constraint formulation that emulates conditionally enforced constraints. In particular, contribution (iii) effectively allows constraints to be enabled or disabled by if-statements conditioned on the solution variables of the parent continuous optimization problem. To the best of our knowledge, this represents a novel formulation in the optimal control literature, and enables a number of interesting applications, including velocity-triggered angle of attack constraints and range-triggered line of sight constraints. Our algorithm converts the resulting generalized powered descent guidance problem from a non-convex free-final-time optimal control problem into a sequence of tractable convex second-order cone programming subproblems. With the aid of virtual control and trust region modifications, these subproblems are solved in succession until convergence is attained. Simulations using a third-party solver demonstrate the real-time capabilities of the proposed algorithm, with a maximum execution time of less than 0.7 seconds over a multitude of problem feature combinations.
: Coupled Diversity-Sensitive Momentum Contrastive

In this appendix, we provide additional details which were omitted in the main manuscript owing to the limited space. From the perspective of algorithm details, we first give more technical details of representation modules. For instance-level representation, the feature aggregators will be introduced. For concept-level representation, the details of concept selection and the creation of statistical commonsense graph are described. Then, more designed motivation and illustration of our proposed Diversity-sensitive Contrastive Learning (DCL) loss will be given. After that, we also report more experimental results, including the performance of models with different data encoder, influence of different diversity estimation functions, impact of hyper-parameters, data distribution visualization of joint embedding space, performance comparison with different contrastive objectives, and bidirectional image-text retrieval results.