Miracle-dz/UniFAR-FTUs huggingface dataset:
Associations between D3Cr muscle mass and MR thigh muscle volume with strength, power, physical performance, fitness, and limitations in older adults in the SOMMA study

Background: Different measures to assess muscle size - magnetic resonance (MR) derived thigh muscle volume and d3-creatine dilution derived muscle mass (D3Cr muscle mass) - may have similar associations with strength, power, physical performance, fitness, and functional limitations in older adults. Methods: Men (N=345) and women (N=482) aged [≥]70 years from the Study of Muscle, Mobility and Aging completed exams including leg extension strength (1-repetition max) and cardiopulmonary exercise testing to assess fitness (VO2peak). Correlations and adjusted regression models stratified by sex were used to assess the association between muscle size measures and study outcomes; we tested for sex interactions. Results: D3Cr muscle mass and MR thigh muscle volume were correlated (men: r=0.62, women: r=0.51, p<.001). Lower D3Cr muscle mass and lower MR thigh muscle volume were associated with lower strength and lower VO2peak in both men and women; D3Cr muscle mass was more strongly associated with strength in men than in women (p-int<0.05). There were correlations, though less consistent, between muscle size or mass with physical performance and function. Associations between the muscle size measures and the study outcomes occasionally varied by sex, and associations of MR thigh muscle volume were, at times, slightly more strongly associated with the study outcomes than was D3Cr muscle mass. Conclusions: Less muscle - measured by either D3Cr muscle mass or MR thigh muscle volume - was associated with lower strength and worse performance. Varied associations by sex and assessment method suggest consideration be given to which measurement to use in future studies.
Variable Selection and Sensitivity Analysis via Dynamic Trees with an Application to Computer Code Performance Tuning

A BSTRACT : We show how the newly developed dynamic tree model can support variable selection and a sensitivity analysis of inputs, two tasks usually requiring disparate model structure. To this end, we adapt methods used in conjunction with static tree models and Gaussian process models (GPs). Compared with static trees, this approach allows for dynamic (sequential) variable selection with fully Bayesian evidence and sensitivity indices not previously enjoyed. Compared with GPs, it facilitates sensitivity analysis and variable selection on problems that are several orders of magnitude larger in size than previously possible. Importantly, this allows a unifying approach to mixed data applications. The same computational techniques can be used, for example, in regression and classiﬁcation applications with trivial modiﬁcation. We illustrate our methodology on several instructive data sets and then analyze automatic computer code tuning that combines classiﬁcation and regression elements, demanding both variable selection and input sensitivity analysis on large data sets
DAVE: A Deep Audio-Visual Embedding for Dynamic Saliency Prediction

This paper presents a conceptually simple and effective Deep Audio-Visual Eembedding for dynamic saliency prediction dubbed “DAVE”. Several behavioral studies have shown a strong relation between auditory and visual cues for guiding gaze during scene free viewing. The existing video saliency models, however, only consider visual cues for predicting saliency over videos and neglect the auditory information that is ubiquitous in dynamic scenes. We propose a multimodal saliency model that utilizes audio and visual information for predicting saliency in videos. Our model consists of a two-stream encoder and a decoder. First, auditory and visual information are mapped into a feature space using 3D Convolutional Neural Networks (3D CNNs). Then, a decoder combines the features and maps them to a final saliency map. To train such model, data from various eye tracking datasets containing video and audio are pulled together. We further categorised videos into ‘social’, ‘nature’, and ‘miscellaneous’ classes to analyze the models over different content types. Several analyses show that our audio-visual model outperforms video-based models significantly over all scores; overall and over individual categories. Contextual analysis of the model performance over the location of sound source reveals that the audiovisual model behaves similar to humans in attending to the location of sound source. Our endeavour demonstrates that audio is an important signal that can boost video saliency prediction and help getting closer to human performance.
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract)

Directed acyclic graphs (DAGs) are commonly used to model causal relationships between random variables. Markov equivalence of DAGs indicates to which extent these causal influences are identifiable from the observational density of the random variables. In this paper, we extend the notion of Markov equivalence of DAGs to the case of interventional distributions arising from multiple intervention experiments which are crucial for improved causal inference, and we generalize the greedy equivalence search (GES) algorithm in order to process observational and interventional data (or data from different interventions) simultaneously.
Performance of polar codes on wireless communication channels

Polar codes are a class of codes proposed currently, which can achieve the capacity of binary symmetric channel with low encoding and decoding algorithm. By generalizing the definition of Bhattacharyya parameter in discrete memoryless channel, we discuss the performance of polar codes on wireless communication channels in this paper. We present the special expressions of the parameters for continuous channels (Gaussian and Rayleigh fading channels), including the recursive formulas and the initial values, and we discuss the construction of polar codes for Gaussian and Rayleigh fading channels. We analyze the application of polar codes with the defined parameter over Rayleigh fading channel by transmitting image signals. The simulation results show that polar codes have better performance than that of low density parity-check codes (LDPC) codes with the same simulation condition. It is shown that polar codes will be a good candidate for wireless communication channels.
A framework for designing mimo systems with decision feedback equalization or tomlinson-harashima precoding

We consider joint transceiver design for point-to-point Multiple-Input Multiple-Output communication systems that implement interference (pre-)subtraction; i.e., Decision Feedback Equalization (DFE) or Tomlinson-Harashima precoding (THP). We develop a unified framework for joint transceiver design of these two dual systems by considering design criteria that are expressed as functions of the (logarithm of the) Mean Square Error (MSE) of the individual data streams. By deriving two inequalities that involve the logarithms of the individual MSEs, we obtain optimal designs for two broad classes of communication objectives, namely those that are Schur-convex and Schur-concave functions of these logarithms. These two classes embrace several design criteria for which the optimal transceiver design has remained an open problem. For Schur-convex objectives, the optimal design results in data streams with equal MSEs. In addition to other desirable properties, this design simultaneously minimizes the total MSE and the average bit error rate, and maximizes the Gaussian mutual information; a property that is not achieved by a linear transceiver. Moreover, we show that the optimal design yields objective values that are superior to the corresponding optimal objective value for a linear transceiver. For Schur-concave objectives, the optimal DFE design results in linear equalization and the optimal THP design results in linear precoding. The proposed design framework can be regarded as a counterpart of the existing framework for linear transceiver design.
Automated question generation and question answering from Turkish texts

While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience and resources. Automatic question generation (QG) techniques can be utilized to satisfy the need for a continuous supply of new questions by streamlining their generation. However, compared to automatic question answering (QA), QG is a more challenging task. In this work, we fine-tune a multilingual T5 (mT5) transformer in a multi-task setting for QA, QG and answer extraction tasks using Turkish QA datasets. To the best of our knowledge, this is the first academic work that performs automated text-to-text question generation from Turkish texts. Experimental evaluations show that the proposed multi-task setting achieves state-of-the-art Turkish question answering and question generation performance on TQuADv1, TQuADv2 datasets and XQuAD Turkish split. The source code and the pre-trained models are available at https://github.com/obss/turkish-question-generation.
A Multi-task Joint Framework for Real-time Person Search

Person search generally involves three important parts: person detection, feature extraction and identity comparison. However, person search integrating detection, extraction and comparison has the following drawbacks. Firstly, the accuracy of detection will affect the accuracy of comparison. Secondly, it is difficult to achieve real-time in real-world applications. To solve these problems, we propose a Multi-task Joint Framework for real-time person search (MJF), which optimizes the person detection, feature extraction and identity comparison respectively. For the person detection module, we proposed the YOLOv5-GS model, which is trained with person dataset. It combines the advantages of the Ghostnet and the Squeeze-and-Excitation (SE) block, and improves the speed and accuracy. For the feature extraction module, we design the Model Adaptation Architecture (MAA), which could select different network according to the number of people. It could balance the relationship between accuracy and speed. For identity comparison, we propose a Three Dimension (3D) Pooled Table and a matching strategy to improve identification accuracy. On the condition of 1920*1080 resolution video and 500 IDs table, the identification rate (IR) and frames per second (FPS) achieved by our method could reach 93.6% and 25.7,
Journal of Digital Forensics, Journal of Digital Forensics, Security and Law Security and Law

One of the greatest challenges facing modern society is the rising tide of cyber crimes. These crimes, since they rarely fit the model of conventional crimes, are difficult to investigate, hard to analyze, and difficult to prosecute. Collecting data in a unified framework is a mandatory step that will assist the investigator in sorting through the mountains of data. In this paper, we explore designing a dimensional model for a data warehouse that can be used in analyzing cyber crime data. We also present some interesting queries and the types of cyber crime analyses that can be performed based on the data warehouse. We discuss several ways of utilizing the data warehouse using OLAP and data mining technologies. We finally discuss legal issues and data population issues for the data warehouse.
Analyzing the Noise Robustness of Deep Neural Networks

Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial examples. These examples are intentionally designed by making imperceptible perturbations and often mislead a DNN into making an incorrect prediction. This phenomenon means that there is significant risk in applying DNNs to safety-critical applications, such as driverless cars. To address this issue, we present a visual analytics approach to explain the primary cause of the wrong predictions introduced by adversarial examples. The key is to analyze the datapaths of the adversarial examples and compare them with those of the normal examples. A datapath is a group of critical neurons and their connections. To this end, we formulate the datapath extraction as a subset selection problem and approximately solve it based on back-propagation. A multi-level visualization consisting of a segmented DAG (layer level), an Euler diagram (feature map level), and a heat map (neuron level), has been designed to help experts investigate datapaths from the high-level layers to the detailed neuron activations. Two case studies are conducted that demonstrate the promise of our approach in support of explaining the working mechanism of adversarial examples.
A Simple Polynomial-time Rescaling Algorithm for Solving Linear Programs

The perceptron algorithm, developed mainly in the machine learning literature, is a simple greedy method for finding a feasible solution to a linear program (alternatively, for learning a threshold function). In spite of its exponential worst-case complexity, it is often quite useful, in part due to its noise-tolerance and also its overall simplicity. In this paper, we show that a randomized version of the perceptron algorithm along with periodic rescaling runs in polynomial-time. The resulting algorithm for linear programming has an elementary description and analysis.
Hunter in the Dark: Discover Anomalous Network Activities Using Deep Ensemble Networks

—Machine learning (ML)-based network intrusion detection system (NIDS) plays a critical role in discovering unknown and novel threats in a large-scale cyberspace. It has been widely adopted as a mainstream hunting method in many organizations, such as ﬁnancial institutes, manufacturing companies and government agencies. However, there are two challenging issues in the existing designs: 1) achieving excellent performance of threat detection is often at the cost of a large number of false positives, leading to the problem of alert fatigue and 2) the interpretability of detection results is low, making it difﬁcult for the security analyst to obtain the insight of threats and take promptly actions against the attacks. To tackle the above issues, in this paper we propose a defense mechanism, DarkHunter, that includes three parts: stream processor, detection engine and incident analyzer. The stream processor converts raw network packet streams into data records of a set of statistical features that can be effectively used for learning; The detection engine leverages an efﬁcient ensemble neural network (EnsembleNet) to identify anomalous network trafﬁc; The incident analyzer applies a correlation analysis to ﬁlter out the mis-predictions from EnsembleNet, traces each detected threat from its statistical representation back to its source trafﬁc ﬂow to enhance its intelligibility and prioritizes the threats to be responded to minimize security risks. Our evaluations, based on the UNSW-NB15 testbed, show that DarkHunter signiﬁcantly outperforms state-of-the-art ML-based NIDS designs by achieving higher accuracy, higher detection rate, higher precision, higher F 1 score while keeping lower false alarm rate.
Fast hamiltonicity checking via bases of perfect matchings

For an even integer t ≥ 2, the Matching Connectivity matrix H<sub>t</sub> is a matrix that has rows and columns both labeled by all perfect matchings of the complete graph K<sub>t</sub> on t vertices; an entry H<sub>t</sub>[M<sub>1</sub>,M<sub>2</sub>] is 1 if M<sub>1</sub>∪ M<sub>2</sub> is a Hamiltonian cycle and 0 otherwise. Motivated by the computational study of the Hamiltonicity problem, we present three results on the structure of H<sub>t</sub>: We first show that H<sub>t</sub> has rank exactly 2<sup>t/2-1</sup> over GF(2) via an appropriate factorization that explicitly provides families of matchings X<sub>t</sub> forming bases for H<sub>t</sub>. Second, we show how to quickly change representation between such bases. Third, we notice that the sets of matchings X<sub>t</sub> induce permutation matrices within H<sub>t</sub>. We use the factorization to derive an 1.888<sup>n</sup> n<sup>O(1)</sup> time Monte Carlo algorithm that solves the Hamiltonicity problem in directed bipartite graphs. Our algorithm as well counts the number of Hamiltonian cycles modulo two in directed bipartite or undirected graphs in the same time bound. Moreover, we use the fast basis change algorithm from the second result to present a Monte Carlo algorithm that given an undirected graph on n vertices along with a path decomposition of width at most pw decides Hamiltonicity in (2+√2)<sup>pw</sup> n<sup>O(1)</sup> time. Finally, we use the third result to show that for every ε >0 this cannot be improved to (2+√2-ε)<sup>pw</sup>n<sup>O(1)</sup> time unless the Strong Exponential Time Hypothesis fails, i.e., a faster algorithm for this problem would imply the breakthrough result of an O((2-ε')<sup>n</sup>) time algorithm for CNF-Sat.
Conjunctive Query Answering for the Description Logic

Conjunctive queries play an important role as an expressive query language for Description Logics (DLs). Although modern DLs usually provide for transitive roles, conjunctive query answering over DL knowledge bases is only poorly understood if transitive roles are admitted in the query. In this paper, we consider unions of conjunctive queries over knowledge bases formulated in the prominent DL SHIQ and allow transitive roles in both the query and the knowledge base. We show decidability of query answering in this setting and establish two complexity bounds: regarding combined complexity, we prove that there is a deterministic algorithm for query answering that needs time single exponential in the size of the KB and double exponential in the size of the query, which is optimal. Regarding data complexity, we prove co-NP-completeness.
C A ] 1 9 A pr 2 00 6 Random Sampling of Sparse Trigonometric Polynomials II-Orthogonal Matching Pursuit versus Basis Pursuit

We continue investigating the problem of reconstructing a multivariate trigonometric polynomial having only few non-zero coefficients from few random samples. Both for a continuous and a discrete probability model for the sampling points we prove theoretical results on the success probability of reconstruction when using Orthogonal Matching Pursuit (OMP) or Basis Pursuit (BP). Although our theoretical estimates are the same for both methods, our numerical experiments indicate that OMP outperforms BP slightly. Moreover, OMP is significantly faster than BP in practice.
General non-asymptotic and asymptotic formulas in channel resolvability and identification capacity and its application to wiretap channel

Several non-asymptotic formulas are established in channel resolvability and identification capacity. They are applied to wire-tap channel. By using them, theǫ capacities of the above three problems are considered in the most general setting, w here no structural assumptions such as the stationary memoryles s property are made on a channel. As a result, we solve an open problem proposed in Han & Verdú[2] and Han [3]. Moreover, we obtain lower bounds of exponents of error probability and the wire-tapper’s information in wire-tap channel.
BAYESIAN NUMERICAL HOMOGENIZATION

Numerical homogenization, i.e., the finite-dimensional approximation of solution spaces of PDEs with arbitrary rough coefficients, requires the identification of accurate basis elements. These basis elements are oftentimes found after a laborious process of scientific investigation and plain guesswork. Can this identification problem be facilitated? Is there a general recipe/decision framework for guiding the design of basis elements? We suggest that the answer to the above questions could be positive based on the reformulation of numerical homogenization as a Bayesian inference problem in which a given PDE with rough coefficients (or multiscale operator) is excited with noise (random right-hand side/source term) and one tries to estimate the value of the solution at a given point based on a finite number of observations. We apply this reformulation to the identification of bases for the numerical homogenization of arbitrary integro-differential equations and show that these bases have optimal recovery properties. In particular we show how rough polyharmonic splines can be rediscovered as the optimal solution of a Gaussian filtering problem.
Perplexity from PLM Is Unreliable for Evaluating Text Quality

Recently, amounts of works utilize perplexity~(PPL) to evaluate the quality of the generated text. They suppose that if the value of PPL is smaller, the quality(i.e. fluency) of the text to be evaluated is better. However, we find that the PPL referee is unqualified and it cannot evaluate the generated text fairly for the following reasons: (i) The PPL of short text is larger than long text, which goes against common sense, (ii) The repeated text span could damage the performance of PPL, and (iii) The punctuation marks could affect the performance of PPL heavily. Experiments show that the PPL is unreliable for evaluating the quality of given text. Last, we discuss the key problems with evaluating text quality using language models.
The Minimal Set of Ingleton Inequalities

The Ingleton inequalities are the inequality constraints known to be required of representable matroids. For a matroid of n elements, there are 16n Ingleton inequalities. In this paper, we show that many of these inequalities are redundant. We explicitly determine the unique minimal set of Ingleton inequalities, which number on the order of 6n/4- O(5n). In information theory, these inequalities are required of the entropy functions of certain random variables constructed from linear subspaces. As a result, these inequalities appear as constraints in linear programming outer bounds for the capacity region of multisource network coding where the codes are required to be linear. Ingleton inequalities have also played an instrumental role in demonstrating the insufficiency of linear codes for multisource network coding. The reduction that we obtain for Ingleton inequalities is sufficiently large to meaningfully reduce the complexity of these linear programming bounds.
Deterministic Randomness Extraction from Generalized and Distributed Santha--Vazirani Sources | SIAM Journal on Computing | Vol. 46, NO. 1 | Society for Industrial and Applied Mathematics

A Santha–Vazirani (SV) source is a sequence of random bits where the conditional distribution of each bit, given the previous bits, can be partially controlled by an adversary. Santha and Vazirani show that deterministic randomness extraction from these sources is impossible. In this paper, we study the generalization of SV sources for nonbinary sequences. We show that unlike the binary setup of Santha and Vazirani, deterministic randomness extraction in the generalized case is sometimes possible. In particular, if the adversary has access to s “nondegenerate” dice that are c-sided and can choose one die to throw based on the previous realizations of the dice, then deterministic randomness extraction is possible if s < c. We present a necessary condition and a sufficient condition for the possibility of deterministic randomness extraction. These two conditions complement each other in the nondegenerate cases. Next, we turn to a distributed setting. In this setting the SV source consists of a random sequence of pairs (a1, b1), (a2, b2), . distributed between two parties, where the first party receives ai’s and the second one receives bi’s. The goal of the two parties is to extract common randomness without communication. Using the notion of maximal correlation, we prove a necessary condition and a sufficient condition for the possibility of common randomness extraction from these sources. Based on these two conditions, the problem of common randomness extraction essentially reduces to the problem of randomness extraction from (nondistributed) SV sources. This result generalizes results of Gács and Körner, and Witsenhausen about common randomness extraction from independently and identically distributed sources to adversarial sources.