Miracle-dz/UniFAR-FTUs huggingface dataset:
Spontaneous Symmetry Breaking in Interdependent Networked Game

Spatial evolution game has traditionally assumed that players interact with neighbors on a single network, which is isolated and not influenced by other systems. We introduce the simple game model into the interdependent networks composed of two networks, and show that when the interdependent factor $\alpha$ is smaller than a particular value $\alpha_C$, homogeneous cooperation can be guaranteed. However, as interdependent factor exceeds $\alpha_C$, spontaneous symmetry breaking of fraction of cooperators presents itself between different networks. In addition, our results can be well predicted by the strategy-couple pair approximation method.
Cooperative Energy Trading in CoMP Systems Powered by Smart Grids

This paper studies the energy management in coordinated multipoint (CoMP) systems powered by smart grids, where each base station (BS) with local renewable energy generation is allowed to implement two-way energy trading with the grid. Due to the uneven renewable energy supply and communication energy demand over distributed BSs, as well as the difference in the prices for their buying/selling energy from/to the grid, it is beneficial for the cooperative BSs to jointly manage their energy trading with the grid and energy consumption in CoMP-based communication for reducing the total energy cost. Specifically, we consider the downlink transmission in one CoMP cluster by jointly optimizing the BSs' purchased/sold energy units from/to the grid and their cooperative transmit precoding to minimize the total energy cost subject to the given quality-of-service (QoS) constraints for the users. First, we obtain the optimal solution to this problem by developing an algorithm based on techniques from convex optimization and uplink-downlink duality. Next, we propose a suboptimal solution of lower complexity than the optimal solution, where zero-forcing (ZF)-based precoding is implemented at the BSs. Finally, through extensive simulations, we show the performance gain achieved by our proposed joint energy trading and communication cooperation schemes in terms of energy cost reduction, as compared with conventional schemes that separately design communication cooperation and energy trading.
G T ] 2 2 A ug 2 01 7 Game Efficiency through Linear Programming Duality Nguyen

The efficiency of a game is typically quantified by the price of anarchy (PoA), defined as the worst ratio of the objective function value of an equilibrium — solution of the game — and that of an optimal outcome. Given the tremendous impact of tools from mathematical programming in the design of algorithms and the similarity of the price of anarchy and different measures such as the approximation and competitive ratios, it is intriguing to develop a duality-based method to characterize the efficiency of games. In the paper, we present an approach based on linear programming duality to study the efficiency of games. We show that the approach provides a general recipe to analyze the efficiency of games and also to derive concepts leading to improvements. The approach is particularly appropriate to bound the PoA. Specifically, in our approach the dual programs naturally lead to competitive PoA bounds that are (almost) optimal for several classes of games. The approach indeed captures the smoothness framework and also some current non-smooth techniques/concepts. We show the applicability to the wide variety of games and environments, from congestion games to Bayesian welfare, from full-information settings to incomplete-information ones. ∗Research supported by the ANR project OATA n ANR-15-CE40-0015-01
Big Data Mining and Analytics Big Data Mining and Analytics

: Microblogging, a popular social media service platform, has become a new information channel for users to receive and exchange the most up-to-date information on current events. Consequently, it is a crucial platform for detecting newly emerging events and for identifying inﬂuential spreaders who have the potential to actively disseminate knowledge about events through microblogs. However, traditional event detection models require human intervention to detect the number of topics to be explored, which signiﬁcantly reduces the efﬁciency and accuracy of event detection. In addition, most existing methods focus only on event detection and are unable to identify either inﬂuential spreaders or key event-related posts, thus making it challenging to track momentous events in a timely manner. To address these problems, we propose a Hypertext-Induced Topic Search (HITS) based Topic-Decision method (TD-HITS), and a Latent Dirichlet Allocation (LDA) based Three-Step model (TS-LDA). TD-HITS can automatically detect the number of topics as well as identify associated key posts in a large number of posts. TS-LDA can identify inﬂuential spreaders of hot event topics based on both post and user information. The experimental results, using a Twitter dataset, demonstrate the effectiveness of our proposed methods for both detecting events and identifying inﬂuential spreaders.
Query dependent ranking using K-nearest neighbor

Many ranking models have been proposed in information retrieval, and recently machine learning techniques have also been applied to ranking model construction. Most of the existing methods do not take into consideration the fact that significant differences exist between queries, and only resort to a single function in ranking of documents. In this paper, we argue that it is necessary to employ different ranking models for different queries and onduct what we call query-dependent ranking. As the first such attempt, we propose a K-Nearest Neighbor (KNN) method for query-dependent ranking. We first consider an online method which creates a ranking model for a given query by using the labeled neighbors of the query in the query feature space and then rank the documents with respect to the query using the created model. Next, we give two offline approximations of the method, which create the ranking models in advance to enhance the efficiency of ranking. And we prove a theory which indicates that the approximations are accurate in terms of difference in loss of prediction, if the learning algorithm used is stable with respect to minor changes in training examples. Our experimental results show that the proposed online and offline methods both outperform the baseline method of using a single ranking function.
The Symmetric Group Defies Strong Fourier Sampling: Part II

AbstractPart I of this paper showed that the hidden subgroup problem over the symmetric group—includingthe special case relevant to Graph Isomorphism—cannot be eﬃciently solved by strong Fourier sampling,even if one may perform an arbitrary POVM on the coset state. In this paper, we extend these resultsto entangled measurements. Speciﬁcally, we show that the case of the hidden subgroup problem whichis relevant to Graph Isomorphism cannot be solved by any polynomial number of experiments on one-or two-register coset states. 1 Introduction: the hidden subgroup problem Many problems of interest in quantum computing can be reduced to an instance of the Hidden SubgroupProblem (HSP). This is the problem of determining a subgoup H of a group Ggiven oracle access to afunction f: G→ Swith the property thatf(g) = f(hg) ⇔ h∈ H . Equivalently, fis constant on the cosets of Hand takes distinct values on distinct cosets.All known eﬃcient solutions to the problem rely on the standard method or the method of
On the Capacity of Free-Space Optical Intensity Channels

Upper and lower bounds are derived on the capacity of the free-space optical intensity channel. This channel has a nonnegative input (representing the transmitted optical intensity), which is corrupted by additive white Gaussian noise. To preserve the battery and for safety reasons, the input is constrained in both its average and its peak power. For a fixed ratio of the allowed average power to the allowed peak power, the difference between the upper and the lower bound tends to zero as the average power tends to infinity and their ratio tends to one as the average power tends to zero. When only an average power constraint is imposed on the input, the difference between the bounds tends to zero as the allowed average power tends to infinity, and their ratio tends to a constant as the allowed average power tends to zero.
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking

Figure 3. Comparison of the sample and communication complexities for a number of decentralized methods. Existing deterministic methods enjoy lower sample complexity at smaller sample sizes, but such complexity scales linearly when the number of samples increases. Stochastic methods generally suffer from high communication complexity. The proposed D-GET bridges the gap between existing deterministic and stochastic methods, and achieves the optimal sample and communication complexities. Note that online methods can also be applied for finite sum problems, thus the actual sample complexity of D-GET is the minimum rate of both cases. A. Choices of Mixing Matrices Note that many choices of mixing matrices satisfy the above condition. Here we give three commonly used mixing matrices (Xiao & Boyd, 2004; Boyd et al., 2004), where di denotes the degree of node i, and dmax = maxi{di}:
Polar Write Once Memory Codes

A coding scheme for write once memory (WOM) using polar codes is presented. It is shown that the scheme achieves the capacity region of noiseless WOMs when an arbitrary number of multiple writes is permitted. The encoding and decoding complexities scale as O(N log N), where N is the blocklength. For N sufficiently large, the error probability decreases subexponentially in N. The results can be generalized from binary to generalized WOMs, described by an arbitrary directed acyclic graph, using nonbinary polar codes. In the derivation, we also obtain results on the typical distortion of polar codes for lossy source coding. Some simulation results with finite length codes are presented.
Web Services Supply Chains: A Literature Reivew

The aim of this review paper is to bring into light a potential area i.e., web services supply chains for research by analyzing the existing state of art in this. It is observed from the review process that there seems to be much less work done in the area of web service supply chains as compared to e-commerce and product oriented service supply chains. The service quality assurance models, end to end Quality of Service (QoS) models, attempts made to QoS attributes are also found to be from individual perspectives of participating entities in a service process rather than a collective perspective considering individual QoS attributes rather than multiple QoS attributes. In light of these gaps we highlight the comparison between product oriented and pure online/ web service supply chains, a need for quality driven optimization in the web services supply chains, perceived complexities in the existing work and propose a conceptual model.
Tight Performance Bounds for Compressed Sensing With Conventional and Group Sparsity

In this paper, we study the problem of recovering a group sparse vector from a small number of linear measurements. In the past, the common approach has been to use various “group sparsity-inducing” norms such as the Group LASSO norm for this purpose. By using the theory of convex relaxations, we show that it is also possible to use <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-norm minimization for group sparse recovery. We introduce a new concept called group robust null space property (GRNSP), and show that, under suitable conditions, a group version of the restricted isometry property (GRIP) implies the GRNSP, and thus leads to group sparse recovery. When all groups are of equal size, our bounds are sometimes less conservative than known bounds. Moreover, our results apply even to situations where the groups have different sizes. When specialized to conventional sparsity, our bounds reduce to one of the well-known “best possible” conditions for sparse recovery. This relationship between GRNSP and GRIP is new even for conventional sparsity, and substantially streamlines the proofs of some known results. Using this relationship, we derive bounds on the <inline-formula><tex-math notation="LaTeX">$\ell _p$</tex-math></inline-formula>-norm of the residual error vector for all <inline-formula><tex-math notation="LaTeX">$p \in [1,2]$</tex-math></inline-formula>, and not just when <inline-formula><tex-math notation="LaTeX">$p = 2$</tex-math></inline-formula>. When the measurement matrix consists of random samples of a sub-Gaussian random variable, we present bounds on the number of measurements, which are sometimes less conservative than currently known bounds.
Bridgeland stability conditions on threefolds I: Bogomolov-Gieseker type inequalities

We construct new t-structures on the derived category of coherent sheaves on smooth projective threefolds. We conjecture that they give Bridgeland stability conditions near the large volume limit. We show that this conjecture is equivalent to a BogomolovGieseker type inequality for the third Chern character of certain stable complexes. We also conjecture a stronger inequality, and prove it in the case of projective space, and for various examples. Finally, we prove a version of the classical Bogomolov-Gieseker inequality, not involving the third Chern character, for stable complexes.
Performance Evaluation of Information Retrieval Models in Bug Localization

A great deal of research work has been dedicated to empirically compare and analyze the performance of text models in information retrieval based bug localization. However, obtaining the required empirical data is typically computationally expensive. Our study uses the P-value test to compare the performance of three text models that have been extensively used in information retrieval based bug localization: Vector Space Model (VSM), Latent Semantic Indexing (LSI), and Latent Dirichlet Analysis (LDA) on the method level. Using statistical significance, our study proves that VSM outperforms LSI and LDA in retrieval of relevant methods to bug reports on the method level. We then examine the factors affecting the behavior of VSM in method retrieval. The factors analyzed include, but are not limited to: methods’ lengths, queries’ lengths, methods’ documentation comments, products’ names and components’ names mentioned in bug reports. The analysis shows a strong correlation between the behavior of VSM and most of the factors examined. Our results can be generalized over any dataset that exhibit similar characteristics to those of our sample.
Evidence for the Lower Palaeozoic age of the Tay Nappe: the timing and nature of Grampian events in the Scottish Highland sector of the Laurentian Margin

Stratigraphical and structural evidence for latest Proterozoic–Phanerozoic deposition of the Dalradian Supergroup in the Scottish Highlands sector of the Laurentian margin is incompatible with geochronological data suggesting c. 750 Ma (Knoydartian) and c. 600 Ma (Grampian) orogenesis. The age of the 590 ± 2 Ma Ben Vuirich Granite is critical to the dating of Grampian orogenesis. Fabric evidence indicates that the granite predates D2 while its relationship to D1 is unclear. In this paper the D1–D2 overprint during the evolution of the Tay Nappe, a regional Grampian structure, is examined. Documentation of the distribution, style and orientation of D1 and D2 structures from well exposed sections near the Highland Border has allowed the development of a kinematic model for the D1/D2 deformation. In this model early crustal shortening resulted in D1 upright structures that were subsequently overturned in a D2, SE‐directed shear regime associated with underthrusting of a southern landmass. The model is best explained in terms of a single continental collision event with no evidence of a time gap between D1 and D2. This analysis is consistent with pre‐regional D1 intrusion of the Ben Vuirich Granite and an Early Palaeozoic Grampian Orogeny.
Improving Data Driven Wordclass Tagging by System Combination

In this paper we examine how the diierences in modelling between diierent data driven systems performing the same NLP task can be exploited to yield a higher accuracy than the best individual system. We do this by means of an experiment involving the task of morpho-syntactic wordclass tagging. Four well-known tagger generators (Hidden Markov Model, Memory-Based, Transformation Rules and Maximum Entropy) are trained on the same corpus data. After comparison, their outputs are combined using several voting strategies and second stage classiiers. All combination taggers outperform their best component, with the best combination showing a 19.1% lower error rate than the best individual tagger.
2-2013 Differentially Private Data Analysis of Social Networks via Restricted Sensitivity

We introduce the notion of restricted sensitivity as an alternative to global and smooth sensitivity to improve accuracy in differentially private data analysis. The definition of restricted sensitivity is similar to that of global sensitivity except that instead of quantifying over all possible datasets, we take advantage of any beliefs about the dataset that a querier may have, to quantify over a restricted class of datasets. Specifically, given a query f and a hypothesis H about the structure of a dataset D, we show generically how to transform f into a new query fH whose global sensitivity (over all datasets including those that do not satisfy H) matches the restricted sensitivity of the query f . Moreover, if the belief of the querier is correct (i.e., D ∈ H) then fH(D) = f(D). If the belief is incorrect, then fH(D) may be inaccurate. We demonstrate the usefulness of this notion by considering the task of answering queries regarding social-networks, which we model as a combination of a graph and a labeling of its vertices. In particular, while our generic procedure is computationally inefficient, for the specific definition of H as graphs of bounded degree, we exhibit efficient ways of constructing fH using different projection-based techniques. We then analyze two important query classes: subgraph counting queries (e.g., number of triangles) and local profile queries (e.g., number of people who know a spy and a computer-scientist who know each other). We demonstrate that the restricted sensitivity of such queries can be significantly lower than their smooth sensitivity. Thus, using restricted sensitivity we can maintain privacy whether or not D ∈ H, while providing more accurate results in the event that H holds true. This research was supported in part by CyLab at Carnegie Mellon University under grants DAAD19-02-1-0389 and W911NF-09-1-0273 from the Army Research Office, the National Science Foundation under grants CCF-1101215 and CCF-1116892, the NSF Science and Technology Center TRUST, and an NSF Graduate Fellowship, as well as by the MSR-CMU Center for Computational Thinking.
Optimal Lower Bounds for Locality Sensitive Hashing ( except when q is tiny )

We study lower bounds for Locality Sensitive Hashing (LSH) in the strongest setting: point sets in {0, 1}d under the Hamming distance. Recall that H is said to be an (r, cr, p, q)-sensitive hash family if all pairs x, y ∈ {0, 1}d with dist(x, y) 6 r have probability at least p of collision under a randomly chosen h ∈ H, whereas all pairs x, y ∈ {0, 1}d with dist(x, y) > cr have probability at most q of collision. Typically, one considers d →∞, with c > 1 fixed and q bounded away from 0. For its applications to approximate nearest neighbor search in high dimensions, the quality of an LSH family H is governed by how small its “ρ parameter” ρ = ln(1/p)/ ln(1/q) is as a function of the parameter c. The seminal paper of Indyk and Motwani showed that for each c > 1, the extremely simple family H = {x 7→ xi : i ∈ [d]} achieves ρ 6 1/c. The only known lower bound, due to Motwani, Naor, and Panigrahy, is that ρ must be at least (e − 1)/(e + 1) > . 46/c (minus od(1)). The contribution of our paper is twofold: 1. We show the “optimal” lower bound for ρ: it must be at least 1/c (minus od(1)). Our proof is very simple, following almost immediately from the observation that the noise stability of a boolean function at time t is a log-convex function of t. 2. We raise and discuss the following issue: neither the application of LSH to nearest neighbor search, nor the known LSH lower bounds, hold as stated if the q parameter is tiny. Here “tiny” means q = 2−Θ(d), a parameter range we believe is natural.
Structured mutation inspired by evolutionary theory enriches population performance and diversity

Grammar-Guided Genetic Programming (GGGP) employs a variety of insights from evolutionary theory to autonomously design solutions for a given task. Recent insights from evolutionary biology can lead to further improvements in GGGP algorithms. In this paper, we apply principles from the theory of Facilitated Variation and knowledge about heterogeneous mutation rates and mutation eﬀects to improve the variation operators. We term this new method of variation Facilitated Mutation (FM). We test FM performance on the evolution of neural network optimizers for image classiﬁcation, a relevant task in evolutionary computation, with important implications for the ﬁeld of machine learning. We compare FM and FM combined with crossover (FMX) against a typical mutation setup to assess the beneﬁts of the approach. We ﬁnd that FMX in particular provides statistical improvements in key metrics, creating a superior optimizer overall (+0.48% average test accuracy), improving the average quality of solutions (+50% average population ﬁtness) and discovering more diverse high quality behaviors (+400 high quality solutions discovered per run on average). Additionally, FM and FMX can reduce the number of ﬁtness evaluations in an evolutionary run, reducing computational costs in some scenarios.
Depositional facies and characteristic of trace fossils in the upper Triassic Taishanmiao Formation of Liushan basin,Henan Province

Based on the research of sedimentology and ichnology,the upper Triassic Taishanmiao Formation in Li-ushan basin of Henan province is considered to be a set of terrestrial fluvial—lacustrine and coal-bearing deposi-tional system under a warm and moist climate conditions.During the late Triassic,the environmental evolution of this basin was a process as fluvial—lucustrine and swamp—lake shore and shallow lacustrine—deep lacustrine—lake shore and shallow lacustrine—semi-deep lacustrine—lake shore and shallow lacustrine.Therefore,the basin went through such two evolutionary stages of sedimentary cycles as "subside—rise—subside—rise". In this work,4 ichnogenera and 4 ichnospecies,such as Beaconites coronus,Cochlichnus surpuliformis,Palaeophycus tubularis and Planolites montanus,have been identified in the Taishanmiao Formation.This paper especially discusses the developing features of the Beaconites coronus in different micro-facies,and speculates that the trace-makers of Beaconites adapt best to the shore lake environment.The research result provides the theoretical support to not only the compilation of late Triassic lithofacies palaeogeography map but also the evaluation and development of poten-tial mineral resources in this area.