Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library

The Advanced LIGO and Advanced Virgo gravitational wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the ﬁrst detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star (BNS), a neutron star – black hole binary (NSBH) and a binary black hole (BBH), where we show a cross-comparison of results obtained using three independent sampling algorithms. These systems were analysed with non-spinning, aligned spin and generic spin conﬁgurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin conﬁgurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist conﬁdence intervals under correct prior assumptions by analysing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-speciﬁc sampling techniques we have used to improve the eﬃciency of sampling the compact binary coalescence (CBC) parameter space.
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs

Although deep neural networks (DNNs) are known to be fragile, no one has studied the effects of zooming-in and zooming-out of images in the physical world on DNNs performance. In this paper, we demonstrate a novel physical adversarial attack technique called Adversarial Zoom Lens ( AdvZL ), which uses a zoom lens to zoom in and out of pictures of the physical world, fooling DNNs without changing the characteristics of the target object. The proposed method is so far the only adversarial attack technique that does not add physical adversarial perturbation attack DNNs. In a digital environment, we construct a data set based on AdvZL to verify the antagonism of equal-scale enlarged images to DNNs. In the physical environment, we manipulate the zoom lens to zoom in and out of the target object, and generate adversarial samples. The experimental results demonstrate the effectiveness of AdvZL in both digital and physical environments. We further analyze the antagonism of the proposed data set to the improved DNNs. On the other hand, we provide a guideline for defense against AdvZL by means of adversarial training. Finally, we look into the threat possibilities of the proposed approach to future autonomous driving and variant attack ideas similar to the proposed attack.
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among 9600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.
SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method

Recent years have witnessed the rapid growth of Small Private Online Courses (SPOC) which is able to highly customized and personalized to adapt variable educational requests, in which machine learning techniques are explored to summarize and predict the learner's performance, mostly focus on the final grade. However, the problem is that the final grade of learners on SPOC is generally seriously imbalance which handicaps the training of prediction model. To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper. First, a combined indicator is defined to measure the distribution of the data, then a rule is established to guide the sampling process. Second, the batch normalization (BN) modified layers are embedded into full connected neural network to solve the data imbalanced problem. Experimental results with other three deep learning methods demonstrates the superiority of the proposed method.
Rapid mixing for lattice colourings with fewer colours

We provide an optimally mixing Markov chain for 6-colourings of the square lattice on rectangular regions with free, fixed, or toroidal boundary conditions. This implies that the uniform distribution on the set of such colourings has strong spatial mixing, so the six-state Potts antiferromagnet has a finite correlation length and a unique Gibbs measure at zero temperature. Four and five are now the only remaining values of q for which it is not known whether there exists a rapidly mixing Markov chain for q-colourings of the square lattice.
PackLib 2 : An Integrated Library of Multi-Dimensional Packing Problems Sándor

We present PackLib, the first fully integrated benchmark library for multi-dimensional packing instances. PackLib combines a systematic collection of all benchmark instances from previous literature with a well-organized set of new and challenging large instances. The XML format allows linking basic benchmark data with other important properties, like bibliographic information, origin, best known solutions, runtimes, etc. Transforming instances into a variety of existing input formats is also quite easy, as the XML format lends itself to easy conversion; for this purpose, a number of parsers are provided. Thus, PackLib aims at becoming a one-stop location for the packing and cutting community: In addition to fair and easy comparison of algorithmic work and ongoing measurement of scientific progress, it poses numerous challenges for future research.
A System for Keyword-based Searching in Databases

The web, through many search engine sites, has popularized the keyword-based search paradigm, where a user can specify a string of keywords and expect to retrieve relevant documents, possibly ranked by their relevance to the query. Since a lot of information is stored in databases (and not as HTML documents), it is important to provide a similar search paradigm for databases, where users can query a database without knowing the database schema and database query languages such as SQL. In this paper, we propose such a database search system, which accepts a free-form query as a collection of keywords, translates it into queries on the database using the database metadata, and presents query results in a well-structured and browsable form. The system maps keywords onto the database schema and uses inter-relationships (i.e., data semantics) among the referred tables to generate meaningful query results. We also describe our prototype for database search, called Mragyati. The approach proposed here is scalable, as it does not build an in-memory graph of the entire database for searching for relationships among the objects selected by the users query. Index Terms : database search, keyword search, metadata, relevance ranking, web-based search
Low-fi Skin Vision: a Case Study in Rapid Prototyping a Sensory Substitution System Conference Item Low-fi Skin Vision: a Case Study in Rapid Prototyping a Sensory Substitution System

(2009). Low-fi skin vision: A case study in rapid proto-typing a sensory substitution system. Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. ABSTRACT We describe the design process we have used to develop a minimal, twenty vibration motor Tactile Vision Sensory Substitution (TVSS) system which enables blindfolded subjects to successfully track and bat a rolling ball and thereby experience 'skin vision'. We have employed a lo-fi rapid pro-totyping approach to build this system and argue that this methodology is particularly effective for building embedded interactive systems. We support this argument in two ways. First, by drawing on theoretical insights from robotics, a discipline that also has to deal with the challenge of building complex embedded systems that interact with their environments ; second, by using the development of our TVSS as a case study: describing the series of prototypes that led to our successful design and highlighting what we learnt at each stage.
A many-sorted polyadic modal logic

This paper presents a many-sorted polyadic modal logic that generalizes some of the existing approaches. The algebraic semantics has led us to a many-sorted generalization of boolean algebras with operators, for which we prove the analogue of the J\'onsson-Tarski theorem. While the transition from the mono-sorted logic to many-sorted one is a smooth process, we see our system as a step towards deepening the connection between modal logic and program verification, since our system can be seen as the propositional fragment of Matching logic, a first-order logic for specifying and reasoning about programs.
An improved sum-product estimate over finite fields

This paper gives an improved sum-product estimate for subsets of a finite field whose order is not prime. It is shown, under certain conditions, that $$\max\{|A+A|,|A\cdot{A}|\}\gg{\frac{|A|^{12/11}}{(\log_2|A|)^{5/11}}}. $$ This new estimate matches, up to a logarithmic factor, the current best known bound obtained over prime fields by Rudnev (\cite{mishaSP}).
CHRONIOUS: A wearable platform for monitoring and management of patients with chronic disease

The CHRONIOUS system has been developed based on an open architecture design that consists of a set of subsystems which interact in order to provide all the needed services to the chronic disease patients. An advanced multi-parametric expert system is being implemented that fuses information effectively from various sources using intelligent techniques. Data are collected by sensors of a body network controlling vital signals while additional tools record dietary habits and plans, drug intake, environmental and biochemical parameters and activity data. The CHRONIOUS platform provides guidelines and standards for the future generations of “chronic disease management systems” and facilitates sophisticated monitoring tools. In addition, an ontological information retrieval system is being delivered satisfying the necessities for up-to-date clinical information of Chronic Obstructive pulmonary disease (COPD) and Chronic Kidney Disease (CKD). Moreover, support tools are being embedded in the system, such as the Mental Tools for the monitoring of patient mental health status. The integrated platform provides real-time patient monitoring and supervision, both indoors and outdoors and represents a generic platform for the management of various chronic diseases.
On the Importance of the Distance Measures Used to Train and Test Knowledge-Based Potentials for Proteins

Knowledge-based potentials are energy functions derived from the analysis of databases of protein structures and sequences. They can be divided into two classes. Potentials from the first class are based on a direct conversion of the distributions of some geometric properties observed in native protein structures into energy values, while potentials from the second class are trained to mimic quantitatively the geometric differences between incorrectly folded models and native structures. In this paper, we focus on the relationship between energy and geometry when training the second class of knowledge-based potentials. We assume that the difference in energy between a decoy structure and the corresponding native structure is linearly related to the distance between the two structures. We trained two distance-based knowledge-based potentials accordingly, one based on all inter-residue distances (PPD), while the other had the set of all distances filtered to reflect consistency in an ensemble of decoys (PPE). We tested four types of metric to characterize the distance between the decoy and the native structure, two based on extrinsic geometry (RMSD and GTD-TS*), and two based on intrinsic geometry (Q* and MT). The corresponding eight potentials were tested on a large collection of decoy sets. We found that it is usually better to train a potential using an intrinsic distance measure. We also found that PPE outperforms PPD, emphasizing the benefits of capturing consistent information in an ensemble. The relevance of these results for the design of knowledge-based potentials is discussed.
Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under ` p Distances ( Appendicies )

Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018. Copyright 2018 by the author(s). The first loop of the above reduction can be performed in O(1) rounds of MPC by replicating the data 2 = O(1) times and running O(1)-round MPC sorting algorithm (Goodrich et al., 2011) on all the replicas in parallel. The second loop can be performed in O(d log n) rounds total by using O(log n) connectivity algorithm in each iteration. Correctness of the above algorithm follows from the following observation: if ‖vi − vj‖0 = t then there exists a path in the graph G between i and j that uses only edges of weight at most t.
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research. Conventional approaches in ML use cross-entropy loss (CEL) that often suffers from poor margin classification. For the first time, we show that contrastive loss (CL) improves the performance of CEL especially for imbalanced EHR data and the related COVID-19 analyses. This study has been approved by the Institutional Review Board at the Icahn School of Medicine at Mount Sinai. We use EHR data from five hospitals within the Mount Sinai Health System (MSHS) to predict mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over 24 and 48 hour time windows. We train two sequential architectures (RNN and RETAIN) using two loss functions (CEL and CL). Models are tested on full sample data set which contain all available data and restricted data set to emulate higher class imbalance.CL models consistently outperform CEL models with the restricted data set on these tasks with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC. For the restricted sample, only the CL model maintains proper clustering and is able to identify important features, such as pulse oximetry. CL outperforms CEL in instances of severe class imbalance, on three EHR outcomes with respect to three performance metrics: predictive power, clustering, and feature importance. We believe that the developed CL framework can be expanded and used for EHR ML work in general.
Directed information, causal estimation, and communication in continuous time

The notion of directed information is introduced for stochastic processes in continuous time. Properties and operational interpretations are presented for this notion of directed information, which generalizes mutual information between stochastic processes in a similar manner as Massey's original notion of directed information generalizes Shannon's mutual information in the discrete-time setting. As a key application, Duncan's theorem is generalized to estimation problems in which the evolution of the target signal is affected by the past channel noise, and the causal minimum mean squared error estimation is related to directed information from the target signal to the observation corrupted by additive white Gaussian noise. An analogous relationship holds for the Poisson channel. The notion of directed information as a characterizing of the fundamental limit on reliable communication for a wide class of continuous-time channels with feedback is discussed.
Convergence analysis of a proximal point algorithm for minimizing differences of functions

Abstract Several optimization schemes have been known for convex optimization problems. However, numerical algorithms for solving nonconvex optimization problems are still underdeveloped. A significant progress to go beyond convexity was made by considering the class of functions representable as differences of convex functions. In this paper, we introduce a generalized proximal point algorithm to minimize the difference of a nonconvex function and a convex function. We also study convergence results of this algorithm under the main assumption that the objective function satisfies the Kurdyka–ᴌojasiewicz property.
Clustering is Efficient for Approximate Maximum Inner Product Search

Locality Sensitive Hashing (LSH) techniques have recently become a popular solution for solving the approximate Maximum Inner Product Search (MIPS) problem, which arises in many situations and have in particular been used as a speed-up for the training of large neural probabilistic language models. In this paper we propose a new approach for solving approximate MIPS based on a variant of the $k$-means algorithm. We suggest using spherical $k$-means which is an algorithm that can efficiently be used to solve the approximate Maximum Cosine Similarity Search (MCSS), and basing ourselves on previous work by Shrivastava and Li we show how it can be adapted for approximate MIPS. Our new method compares favorably with LSH-based methods on a simple recall rate test, by providing a more accurate set of candidates for the maximum inner product. The proposed method is thus likely to benefit the wide range of problems with very large search spaces where a robust approximate MIPS heuristic could be of interest, such as for providing a high quality short list of candidate words to speed up the training of neural probabilistic language models.
Delft University of Technology A survey on machine learning-based performance improvement of wireless networks PHY, MAC and network layer

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-ofservice (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.
ChoiceRank : Identifying Preferences from Node Traffic in Networks Supplementary Material

The supplementary material consists of three parts. In Section A, we present a generalization of the network choice model, and we prove a) the minimal sufficiency of per-node traffic statistics and b) the well-posedness of MAP inference. In Section B, we deepen our discussion of the ML estimator. Finally, in Section C, we revisit the ChoiceRank algorithm. We give the modifications needed for the generalized network choice model, prove the convergence of the algorithm, and give an alternative derivation based on the expectation maximization perspective.
The Generalized Trust Region Subproblem Ting Kei Pong

The interval bounded generalized trust region subproblem (GTRS) consists in minimizing a general quadratic objective, q0(x) → min, subject to an upper and lower bounded general quadratic constraint, l ≤ q1(x) ≤ u. This means that there are no definiteness assumptions on either quadratic function. We first study characterizations of optimality for this implicitly convex problem under a constraint qualification and show that it can be assumed without loss of generality. We next classify the GTRS into easy case and hard case instances, and demonstrate that the upper and lower bounded general problem can be reduced to an equivalent equality constrained problem after identifying suitable generalized eigenvalues and possibly solving a sparse system. We then discuss how the Rendl-Wolkowicz algorithm proposed in [11, 29] can be extended to solve the resulting equality constrained problem, highlighting the connection between the GTRS and the problem of finding minimum generalized eigenvalues of a parameterized matrix pencil. Finally, we present numerical results to illustrate this algorithm at the end of the paper.
Distributed Inexact Damped Newton Method: Data Partitioning and Work-Balancing

In this paper, we study inexact damped Newton method implemented in a distributed environment. We are motivated by the original DiSCO algorithm [Communication-Efﬁcient Distributed Optimization of Self-Concordant Empirical Loss, Yuchen Zhang and Lin Xiao, 2015]. We show that this algo-rithm may not scale well and propose algorithmic modiﬁca-tions which lead to fewer communications and better load-balancing between nodes. Those modiﬁcations lead to a more efﬁcient algorithm with better scaling. This was made possibly by introducing our new pre-conditioner which is specially designed so that the preconditioning step can be solved exactly and efﬁciently. Numerical experiments for minimization of regularized empirical loss with a 273GB instance shows the efﬁciency of proposed algorithm.
Chaîne d'analyse et de synthèse de textures 3D basée sur une décomposition en pyramides orientées

This study deals with the extension of texture Analysis Synthesis method proposed by Portilla and Simoncelli to the 3D domain. The method is based on textures decomposition of textures, in Fourier Domain, in order to extract descriptors. The main idea is based on building a link between 3D textures and these descriptors. The goal is to use these parameters in classification and segmentation processes. The synthesis process is used to validate the analysis process.