Miracle-dz/UniFAR-FTUs huggingface dataset:
On percolation and NP ‐hardness

The edge‐percolation and vertex‐percolation random graph models start with an arbitrary graph G, and randomly delete edges or vertices of G with some fixed probability. We study the computational complexity of problems whose inputs are obtained by applying percolation to worst‐case instances. Specifically, we show that a number of classical NP ‐hard problems on graphs remain essentially as hard on percolated instances as they are in the worst‐case (assuming NP⊈BPP ). We also prove hardness results for other NP ‐hard problems such as Constraint Satisfaction Problems and Subset‐Sum, with suitable definitions of random deletions. Along the way, we establish that for any given graph G the independence number α(G) and the chromatic number χ(G) are robust to percolation in the following sense. Given a graph G, let G′ be the graph obtained by randomly deleting edges of G with some probability p∈(0,1) . We show that if α(G) is small, then α(G′) remains small with probability at least 0.99. Similarly, we show that if χ(G) is large, then χ(G′) remains large with probability at least 0.99. We believe these results are of independent interest.
Action Recognition by Hierarchical Mid-level Action Elements Supplementary Material S 1 . Action Proposals : Hierarchical Spatiotemporal Segments

In order to select foreground segments among the region proposals, we first use the method of [1] to select an initial set of foreground segments. Each region proposal is scored with both appearance and motion cues, and we look for regions that have generic object-like appearance and distinct motion patterns relative to their surroundings. We refer to [2] for the details of the scoring function. We take the top N highest-scoring region proposals in each frame to form an initial set of foreground segments. Given these candidate segments, we train a linear SVM classifier for each video independently, using these candidate foreground segments as positive examples along with randomly sampled from the background as negative examples. For simplicity, we only use color histogram as the feature descriptor for each segment. The segments with scores above a threshold (−1) are considered as foreground segments.
The pre-log of Gaussian broadcast with feedback can be two

A generic intuition says that the pre-log, or multi-plexing gain, cannot be larger than the minimum of the number of transmit and receive dimensions. This suggests that for the scalar broadcast channel, the pre-log cannot exceed one. By contrast, in this note, we show that when the noises are anti-correlated and feedback is present, then a pre-log of two can be attained. In other words, in this special case, in the limit of high SNR, the scalar Gaussian broadcast channel turns into two parallel AWGN channels. Achievability is established via a coding strategy due to Schalkwijk, Kailath, and Ozarow.
The Contribution of Astragalus adsurgens Roots and Canopy to Water Erosion Control in the Water–Wind Crisscrossed Erosion Region of the Loess Plateau, China

Simulated rainfall experiments were conducted to investigate the effect of Astragalus adsurgens roots and canopy on water erosion yield, erosion processes and soil resistance to erosion. Experiments were conducted on grass, root and bare slopes, with sandy soil from a water–wind crisscrossed erosion region of the Loess Plateau, China. A. adsurgens coverage on grass slopes was approximately 40%. There were three rainfall intensities of 30, 60 and 90 mm h–1 and four slope gradients of 3, 6, 9 and 12°. A. adsurgens had a significant effect on soil erosion control; soil loss was reduced by ~70% on slopes with the grass compared with bare slopes. The grass roots reduced soil loss more than its canopy, particularly in high‐intensity rainfall, which reduced soil loss by 82%. The presence of the grass and its roots changed the soil erosion process, reducing soil erodibility (Kr) and increasing the critical shear stress (τc). The soil erosion rate on the bare slope increased steadily over time; on the grass and root slopes, its rate initially increased, then decreased and then finally stabilized. Kr on the grass and root slopes was reduced by 96% and 89%, respectively, compared with the bare slope, while the corresponding τc increased by 92% and 195% respectively. These results provide insights into the mechanisms of grass on soil and water conservation and may help to improve vegetation construction in water–wind crisscrossed erosion regions of the Loess Plateau. Copyright © 2016 John Wiley & Sons, Ltd.
Edge computing for Vehicle to Everything: a short review [version 2; peer review: 1 approved with reservations, 1 not approved]

Vehicle to Everything (V2X) communications and services have sparked considerable interest as a potential component of future Intelligent Transportation Systems. V2X serves to organise communication and interaction between vehicle to vehicle (V2V), vehicle to infrastructure (V2I), vehicle to pedestrians (V2P), and vehicle to networks (V2N). However, having multiple communication channels can generate a vast amount of data for processing and distribution. In addition, V2X services may be subject to performance requirements relating to dynamic handover and low latency communication channels. Good throughput, lower delay, and reliable packet delivery are the core requirements for V2X services. Edge Computing (EC) may be a feasible option to address the challenge of dynamic handover and low latency to allow V2X information to be transmitted across vehicles. Currently, existing comparative studies do not cover the applicability of EC for V2X. This review explores EC approaches to determine the relevance for V2X communication and services. EC allows devices to carry out part or all of the data processing at the point where data is collected. The emphasis of this review is on
Robust Downstream Communication and Storage for Computational RFIDs

Computational RFID (CRFID) devices are emerging platforms that can enable perennial computation and sensing by eliminating the need for batteries. Although much research has been devoted to improving upstream (CRFID to RFID reader) communication rates, the opposite direction has so far been neglected, presumably due to the difficulty in guaranteeing fast and error-free transfer amidst frequent power interruptions at the CRFID. With growing interest in the market where CRFIDs are forever-embedded in various structures, it is necessary for this void to be filled. Therefore, we propose Wisent—a robust downstream communication protocol for CRFIDs that operates on top of the legacy UHF RFID communication protocol: EPC C1G2. The novelty of Wisent is its ability to adaptively change the frame length sent by the reader, based on a length throttling mechanism, to minimize the transfer times at varying channel conditions. We present an implementation of Wisent for the WISP 5 and an off-the-shelf RFID reader. Our experiments show that Wisent allows transfer up to 16 times faster than a baseline, non-adaptive shortest frame case, i.e. single word length, at sub-meter distance. As a case study, we show how Wisent enables wireless CRFID reprogramming, demonstrating the world’s first wirelessly reprogrammable (software defined) CRFID.
Patterns of i.i.d. sequences and their entropy

Bounds on the entropy of patterns of sequences generated by independently identically distributed (i.i.d.) sources are derived. A pattern is a sequence of indices that contains all consecutive integer indices in increasing order of first occurrence. If the alphabet of a source that generated a sequence is unknown, the inevitable cost of coding the unknown alphabet symbols can be exploited to create the pattern of the sequence. This pattern can in turn be compressed by itself. The bounds derived here are functions of the i.i. d. source entropy, alphabet size, and letter probabilities. It is shown that for large alphabets, the pattern entropy must decrease from the i.i. d. one. The decrease is in many cases more significant than the universal coding redundancy bounds derived in prior works. The pattern entropy is confined between two bounds that depend on the arrangement of the letter probabilities in the probability space. For very large alphabets whose size may be greater than the coded pattern length, all low probability letters are packed into one symbol. The pattern entropy is upper and lower bounded in terms of the i.i. d. entropy of the new packed alphabet. Correction terms are provided for both upper and lower bounds. The bounds are used to approximate the pattern entropy for various specific distributions, with focus on uniform and monotonic ones. Tight bounds are obtained on the pattern entropy even for distributions that have infinite i.i. d. entropy rates.
Exploiting Constructive Interference for Scalable Flooding in Wireless Networks

Constructive interference-based flooding (CIBF) is a latency-optimal flooding protocol, which can realize millisecond network flooding latency and submicrosecond time synchronization accuracy, require no network state information, and be adapted to topology changes. However, constructive interference (CI) has a precondition to function, i.e., the maximum temporal displacement Δ of concurrent packet transmissions should be less than a given hardware constrained threshold (e.g., 0.5 μs, for the IEEE 802.15.4 radio). In this paper, we derive the closed-form packet reception ratio (PRR) formula for CIBF and theoretically disclose that CIBF suffers the scalability problem. The packet reception performance of intermediate nodes degrades significantly as the density or the size of the network increases. We analytically show that CIBF has a PRR lower bound (94.5%) in the grid topology. Based on this observation, we propose the spine constructive interference-based flooding (SCIF) protocol for an arbitrary uniformly distributed topology. Extensive simulations show that SCIF floods the entire network much more reliably than the state-of- the-art Glossy protocol does in high-density or large-scale networks. We further explain the root cause of CI with waveform analysis, which is mainly examined in simulations and experiments.
A ug 2 00 7 Nearly Optimal Patchy Feedbacks for Minimization Problems with Free Terminal Time

The paper is concerned with a general optimization problem for a nonlinear control system, in the presence of a running cost and a terminal cost, with free terminal time. We prove the existence of a patchy feedback whose trajectories are all nearly optimal solutions, with pre-assigned accuracy. 1 Introduction Consider a general optimization problem min T,u(·) {
2 Componentwise LS-SVMs and Primal-Dual Formulations

This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of nonlinear components. The primal-dual derivations characterizing LS-SVMs for the estimation of the additive model result in a single set of linear equations with size growing in the number of data-points. The derivation is elaborated for the classification as well as the regression case. Furthermore, different techniques are proposed to discover structure in the data by looking for sparse components in the model based on dedicated regularization schemes on the one hand and fusion of the componentwise LS-SVMs training with a validation criterion on the other hand.
Summarizing Reports on Evolving Events Part II: Non-Linear Evolution

Panagiotis Stamatopoulos Department of Informatics, University of Athens, Greece takis@di.uoa. gr Abstract We present an approach for summarization from multiple documents which report on events that evolve through time, taking into account the different document sources. We distinguish the evolution of an event into linear and non-linear. According to our approach, each document is represented by a collection of messages which are then used in order to instantiate the cross-document relations that determine the summary content. The paper presents the summarization system that implements this approach through a case study on linear evolution.
A LEVEL-SET HIT-AND-RUN SAMPLER FOR QUASI-CONCAVE DISTRIBUTIONS Supplementary Materials 1 The LSHR 1 Algorithm

In this appendix, we give pseudo-code for our LSHR1 level-set hit-and-run sampler described in our manuscript. We must pre-specify a minimum value K of the probability density f (·) as our stopping threshold. The first threshold t 1 must be pre-specified, and we arbitrarily use t 1 = 0.95 * f (x x x max). Finally, the number of hit-and-run iterations per level set, m, must be chosen. The value of m influences the accuracy of our volume ratio estimates. In Section 4, we explore appropriate choices for the number of hit-and-run iterations m. We find that a value of m = 1000 is a reasonable trade-off between accurate estimation of our volume ratios and the computational cost of more hit-and-run steps. When considering the proposal for level set k + 1, the initial t prop is set to t k − (t k−1 − t k), so that our proposal moves the same distance in t as the previous successful move. If this initial t prop is rejected for not being warm enough, we choose a new proposal t prop as the midpoint of the old proposal and t k , and so on.
SentiLR: Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis

Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, whereas we argue that such knowledge can promote language understanding in various NLP tasks. To benefit the downstream tasks in sentiment analysis, we propose a novel language representation model called SentiLR, which introduces word-level linguistic knowledge including part-of-speech tag and prior sentiment polarity from SentiWordNet. During pre-training, we first acquire the prior sentiment polarity of each word by querying the SentiWordNet dictionary with its part-of-speech tag. Then, we devise a new pre-training task called label-aware masked language model consisting of two sub-tasks: 1) word knowledge recovering given the sentence-level label; 2) sentence-level label prediction with linguistic knowledge enhanced context. Experiments show that SentiLR achieves state-of-the-art performances on sentence-level / aspect-level sentiment analysis and sentiment-aware data augmentation.
MIMO Multiple Access Channel With an Arbitrarily Varying Eavesdropper: Secrecy Degrees of Freedom

A two-transmitter Gaussian multiple access wiretap channel with multiple antennas at each of the nodes is investigated. The channel matrices of the legitimate users are fixed and revealed to all the terminals, whereas the channel matrices of the eavesdropper are arbitrarily varying and only known to the eavesdropper. The secrecy degrees of freedom (s.d.o.f.) region under a strong secrecy constraint is characterized. A transmission scheme that orthogonalizes the transmit signals of the two users at the intended receiver, and uses a single-user wiretap code for each user, is shown to achieve the s.d. o.f. region. The converse involves establishing an upper bound on a weighted-sum-rate expression. This is accomplished by using induction, where at each step one combines the secrecy and multiple-access constraints associated with an adversary eavesdropping a carefully selected group of sub-channels.
A Bayesian Model of node interaction in networks

We are concerned with modeling the strength of links in networks by taking into account how often those links are used. Link usage is a strong indicator of how closely two nodes are related, but existing network models in Bayesian Statistics and Machine Learning are able to predict only wether a link exists at all. As priors for latent attributes of network nodes we explore the Chinese Restaurant Process (CRP) and a multivariate Gaussian with fixed dimensionality. The model is applied to a social network dataset and a word coocurrence dataset.
GIBBS PHENOMENON FOR DISPERSIVE PDES

We investigate the Cauchy problem for linear, constant-coefficient evolution PDEs on the real line with discontinuous initial conditions (ICs). First, we establish sufficient conditions for any given order of differentiability of the solution for positive times. Importantly, these conditions allow for jump discontinuities in the IC. Second, we characterize the small-time behavior of the solution near such discontinuities, and we show that the leading-order solution near a discontinuity is given by a similarity solution expressed in terms of special functions which are generalizations of the classical special functions. Third, we present an efficient numerical method for the accurate evaluation of these special functions. Finally, we show that the leading-order behavior of the solution of dispersive PDEs near a discontinuity of the ICs is characterized by Gibbs-type oscillations, and we prove that, asymptotically, the overshoot of the solution near the discontinuity coincides exactly with the Wilbraham-Gibbs constant.
Analysis of Loss Networks with Routing

This paper analyzes stochastic networks consisting of finite capacity nodes with different classes of requests which move according to some routing policy. The Markov processes describing these networks do not have, in general, reversibility properties so that the explicit expression of their invariant distribution is not known. A heavy traffic limit regime is considered: the arrival rates of calls as well as the capacities of the nodes are proportional to a factor going to infinity. It is proved that, in the limit, the associated rescaled Markov process converges to a deterministic dynamical system with a unique equilibrium point characterized by a non-standard fixed point equation.
A ug 1 99 7 Quantum Entanglement and the Communication Complexity of the Inner Product Function ∗

We consider the communication complexity of the binary inner product function in a variation of the two-party communication complexity scenario where the parties have an a priori supply of particles in an entangled quantum state. Previous proofs of lower bounds for the inner product function exist in scenarios where there is no initial entanglement, but they do not apply in the scenario with prior entanglement. In fact, there exist functions whose communication complexity with prior entanglement is provably less than that in models of communication without entanglement [5, 7]. We show that the communication complexity of the inner product function with prior entanglement is at least n/2. Our proof employs a novel kind of “quantum” reduction between protocols
PR ] 1 2 A pr 2 00 7 LARGE CLOSED QUEUEING NETWORKS IN SEMI-MARKOV ENVIRONMENT AND ITS APPLICATION VYACHESLAV

The paper studies closed queueing networks containing a server station and k client stations. The server station is an infinite server queueing system, and client stations are single server queueing systems with autonomous service, i.e. every client station serves customers (units) only at random instants generated by strictly stationary and ergodic sequence of random variables. The total number of units in the network is N . The expected times between departures in client stations are (Nμj) . After a service completion in the server station a unit is transmitted to the jth client station with probability pj (j = 1, 2, . . . , k), and being processed in the jth client station the unit returns to server station. The network is assumed to be in semi-Markov environment. A semi-Markov environment is defined by a finite or countable infinite Markov chain as well as by sequences of independent and identically distributed random variables. Then the routing matrix pj as well as transmission rates (which are expressed via parameters of the network) depend on a Markov state of the environment. The paper studies the queue-length processes in client stations of this network, and is aimed to the analysis of performance measures associated with this network. The questions risen in this paper have immediate relation to quality control of complex telecommunication networks, and the obtained results are expected to lead to solutions for many practical problems of this area of research.
Integrating literature-constrained and data-driven inference of signalling networks with CNORfeeder

4 Example 3 4.1 A. Inference CNORfeeder . 3 4.2 B. Compression CellNOptR . 4 4.3 C. Integration CNORfeeder . 4 4.4 D. Weighting CNORfeeder . 5 4.5 E. Training CellNOptR . 5
Confidence Calibration with Bounded Error Using Transformations

As machine learning techniques become widely adopted in new domains, especially in safety-critical systems such as autonomous vehicles, it is crucial to provide accurate output uncertainty estimation. As a result, many approaches have been proposed to calibrate neural networks to accurately estimate the likelihood of misclassi-ﬁcation. However, while these methods achieve low expected calibration error (ECE), few techniques provide theoretical performance guarantees on the calibration error (CE). In this paper, we introduce Hoki, a novel calibration algorithm with a theoretical bound on the CE. Hoki works by transforming the neural network logits and/or inputs and recursively performing calibration leveraging the information from the corresponding change in the output. We provide a PAC-like bounds on CE that is shown to decrease with the number of samples used for calibration, and increase proportionally with ECE and the number of discrete bins used to calculate ECE. We perform experiments on multiple datasets, including ImageNet, and show that the proposed approach generally outperforms state-of-the-art calibration algorithms across multiple datasets and models – providing nearly an order or magnitude improvement in ECE on ImageNet. In addition, Hoki is fast algorithm which is comparable to temperature scaling in terms of learning time.
BDDC for Mixed-Hybrid Formulation of Flow in Porous Media with Combined Mesh Dimensions Research

We extend the Balancing Domain Decomposition by Constraints (BDDC) method to mixed-hybrid finite element discretization of flow in porous media with combined mesh dimensions. It is assumed that the modeler desires to incorporate into the finite element model a detailed description of the reservoir geometry, including a possibly complex structure of vugs, cavities and fractures with various topology and sizes. Use of the mixed-hybrid formulation is motivated by an effort to modify the saddle-point problem into the one which is symmetric, positive definite, so that conjugate gradients with the BDDC preconditioner can be used. The preconditioner then takes an advantage of the special structure of the global finite element matrix. We apply our solver to several academic and real world problems, and the numerical experiments indicate that our approach allows for an efficient and scalable iterative solution of the resulting large systems of equations. MSC: 65F08, 65F10, 65M55, 65N55, 65Y05