Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Spatio-Temporal Data Mining: A Survey

With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important to many real-world applications including human mobility understanding, smart transportation, urban planning, public safety, health care and environmental management. As the number, volume and resolution of spatio-temporal data increase rapidly, traditional data mining methods, especially statistics-based methods for dealing with such data are becoming overwhelmed. Recently deep learning models such as recurrent neural network (RNN) and convolutional neural network (CNN) have achieved remarkable success in many domains due to the powerful ability in automatic feature representation learning, and are also widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, anomaly detection and classification. In this paper, we provide a comprehensive review of recent progress in applying deep learning techniques for STDM. We first categorize the spatio-temporal data into five different types, and then briefly introduce the deep learning models that are widely used in STDM. Next, we classify existing literature based on the types of spatio-temporal data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, on-demand service, climate & weather analysis, human mobility, location-based social network, crime analysis, and neuroscience. Finally, we conclude the limitations of current research and point out future research directions.
Different Approaches for Human Activity Recognition: A Survey

Human activity recognition has gained importance in recent years due to its applications in various fields such as health, security and surveillance, entertainment, and intelligent environments. A significant amount of work has been done on human activity recognition and researchers have leveraged different approaches, such as wearable, object-tagged, and device-free, to recognize human activities. In this article, we present a comprehensive survey of the work conducted over the period 2010-2018 in various areas of human activity recognition with main focus on device-free solutions. The device-free approach is becoming very popular due to the fact that the subject is not required to carry anything, instead, the environment is tagged with devices to capture the required information. We propose a new taxonomy for categorizing the research work conducted in the field of activity recognition and divide the existing literature into three sub-areas: action-based, motion-based, and interaction-based. We further divide these areas into ten different sub-topics and present the latest research work in these sub-topics. Unlike previous surveys which focus only on one type of activities, to the best of our knowledge, we cover all the sub-areas in activity recognition and provide a comparison of the latest research work in these sub-areas. Specifically, we discuss the key attributes and design approaches for the work presented. Then we provide extensive analysis based on 10 important metrics, to give the reader, a complete overview of the state-of-the-art techniques and trends in different sub-areas of human activity recognition. In the end, we discuss open research issues and provide future research directions in the field of human activity recognition.
In-sample forecasting: A brief review and new algorithms

Statistical methods often distinguish between in-sample and out-of-sample approaches. In particular this is the case when time is involved. Then often time series methods are proposed that extrapolate past patterns into the future via complicated recursion formulas. Standard statistical inference is on the other hand concerned with estimating parameters within the given sample. This review paper is about a statistical methodology, where all parameters are estimated in-sample while producing a forecast out-of-sample without recursion or extrapolation. A new super-simulation algorithm ensures a faster implementation of the simplest and perhaps most important version of in-sample forecasting.
Task-Aware Deep Sampling for Feature Generation

The human ability to imagine the variety of appearances of novel objects based on past experience is crucial for quickly learning novel visual concepts based on few examples. Endowing machines with a similar ability to generate feature distributions for new visual concepts is key to sample-efficient model generalization. In this work, we propose a novel generator architecture suitable for feature generation in the zero-shot setting. We introduce task-aware deep sampling (TDS) which injects task-aware noise layer-by-layer in the generator, in contrast to existing shallow sampling (SS) schemes where random noise is only sampled once at the input layer of the generator. We propose a sample efficient learning model composed of a TDS generator, a discriminator and a classifier (e.g., a soft-max classifier). We find that our model achieves state-of-the-art results on the compositional zero-shot learning benchmarks as well as improving upon the established benchmarks in conventional zero-shot learning with a faster convergence rate.
Architecture And Design Process Of The Individualized Assesment System Integrable To Distance Education Softwares

Assessment is one of the methods used for evaluation of the learning outputs. Nowadays, use of adaptive assessment systems estimating ability level and abilities of the students is becoming widespread instead of traditional assessment systems. Adaptive assessment system evaluates students not only according to their marks that they take in test exams but also according to their ability levels. In this study, we dealt with theoretical background of the adaptive assessment systems. Study covers the structure, characteristics and architecture of the computerized adaptive test systems in detail. It dwells on the Item Response theory which is used for improving computerized adaptive test systems and the models with which this theory is used. Besides, processing steps which are required to realize a computerized adaptive test system and algorithms used for the validation of the test systems are introduced in detail. Briefly, study introduces the structure and the improvement processes of the computerized adaptive testing systems which can be integrated to distance education software.
Active Distribution Grids Offering Ancillary Services in Islanded and Grid-Connected Mode

Future Active Distribution Grids (ADGs) will incorporate a plethora of Distributed Generators (DGs) and other Distributed Energy Resources (DERs), allowing them to provide ancillary services in grid-connected mode and, if necessary, operate in an islanded mode to increase reliability and resilience. In this paper, we investigate the ability of an ADG to provide Frequency Control (FC) in grid-connected mode and ensure reliable islanded operation for a pre-specified time period. First, we formulate the operation of the grid participating in European-type FC markets as a centralized multi-period optimal power flow problem with a rolling horizon of 24 hours. Then, we include constraints to the grid-connected operational problem to guarantee the ability to switch to islanded operation at every time instant. Finally, we explore the technical and economic feasibility of offering these services on a balanced low-voltage distribution network. The results show that the proposed scheme is able to offer and respond to different FC products, while ensuring that there is adequate energy capacity at every time step to satisfy critical load in the islanded mode.
The Discovery of Six Recycled Pulsars from the Arecibo 327 MHz Drift-Scan Pulsar Survey

Recycled pulsars are old (≳108 yr) neutron stars that are descendants from close, interacting stellar systems. In order to understand their evolution and population, we must find and study the largest possible number of recycled pulsars in a way that is as unbiased as possible. In this work, we present the discovery and timing solutions of five recycled pulsars in binary systems (PSRs J0509+0856, J0709+0458, J0732+2314, J0824+0028, and J2204+2700) and one isolated millisecond pulsar (PSR J0154+1833). These were found in data from the Arecibo 327 MHz Drift-Scan Pulsar Survey (AO327). All these pulsars have a low dispersion measure (DM; ), and have a DM-determined distance of ≲3 kpc. Their timing solutions, with data spans ranging from 1 to ∼7 yr, include precise estimates of their spin and astrometric parameters, and for the binaries, precise estimates of their Keplerian binary parameters. Their orbital periods range from about 4 to 815 days and the minimum companion masses (assuming a pulsar mass of 1.4 M⊙) range from ∼0.06 to 1.11 M⊙. For two of the binaries we detect post-Keplerian parameters; in the case of PSR J0709+0458 we measure the component masses but with a low precision, in the not too distant future the measurement of the rate of advance of periastron and the Shapiro delay will allow very precise mass measurements for this system. Like several other systems found in the AO327 data, PSRs J0509+0854, J0709+0458, and J0732+2314 are now part of the NANOGrav timing array for gravitational wave detection.
Warping Resilient Time Series Embeddings

Time series are ubiquitous in real world problems and computing distance between two time series is often required in several learning tasks. Computing similarity between time series by ignoring variations in speed or warping is often encountered and dynamic time warping (DTW) is the state of the art. However DTW is not applicable in algorithms which require kernel or vectors. In this paper, we propose a mechanism named WaRTEm to generate vector embeddings of time series such that distance measures in the embedding space exhibit resilience to warping. Therefore, WaRTEm is more widely applicable than DTW. WaRTEm is based on a twin auto-encoder architecture and a training strategy involving warping operators for generating warping resilient embeddings for time series datasets. We evaluate the performance of WaRTEm and observed more than $20\%$ improvement over DTW in multiple real-world datasets.
On the universality of noiseless linear estimation with respect to the measurement matrix

In a noiseless linear estimation problem, the goal is to reconstruct a vector from the knowledge of its linear projections . There have been many theoretical works concentrating on the case where the matrix is a random i.i. d. one, but a number of heuristic evidence suggests that many of these results are universal and extend well beyond this restricted case. Here we revisit this problem through the prism of development of message passing methods, and consider not only the universality of the -transition, as previously addressed, but also the one of the optimal Bayesian reconstruction. We observed that the universality extends to the Bayes-optimal minimum mean-squared (MMSE) error, and to a range of structured matrices.
Chiral perturbation theory, the 1/N_c expansion and Regge behavior determine the structure of the lightest scalar meson

The leading 1/N{sub c} behavior of Unitarized Chiral Perturbation Theory distinguishes the nature of the {rho} and the {sigma}. At one loop order the {rho} is a {bar q}q meson, while the {sigma} is not. However, semi-local duality between resonances and Regge behaviour cannot be satisfied for larger N{sub c}, if such a distinction holds. While the {sigma} at N{sub c}= 3 is inevitably dominated by its di-pion component, Unitarised Chiral Perturbation Theory beyond one loop order reveals that as N{sub c} increases above 6-8, the {sigma} has a sub-dominant {bar q}q fraction up at 1.2 GeV. Remarkably this ensures semi-local duality is fulfilled for the range of N{sub c} {approx}< 15-30, where the unitarization procedure adopted applies.
Indoor Image Representation by High-Level Semantic Features

Indoor image features extraction is a fundamental problem in multiple fields such as image processing, pattern recognition, robotics, and so on. Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e.g., object association). These techniques, therefore, involve undesired classification performance. To tackle this issue, we propose the notion of high-level semantic features and design four steps to extract them. Specifically, we first construct the objects pattern dictionary through extracting raw objects in the images, and then retrieve and extract semantic objects from the objects pattern dictionary. We finally extract our high-level semantic features based on the calculated probability and delta parameter. The experiments on three publicly available datasets (MIT-67, Scene15, and NYU V1) show that our feature extraction approach outperforms the state-of-the-art feature extraction methods for indoor image classification, given a lower dimension of our features than those methods.
Space lower bounds for linear prediction

We show that fundamental learning tasks, such as finding an approximate linear separator or linear regression, require memory at least \emph{quadratic} in the dimension, in a natural streaming setting. This implies that such problems cannot be solved (at least in this setting) by scalable memory-efficient streaming algorithms. Our results build on a memory lower bound for a simple linear-algebraic problem -- finding orthogonal vectors -- and utilize the estimates on the packing of the Grassmannian, the manifold of all linear subspaces of fixed dimension.
Snake-Like Robots for Minimally Invasive, Single Port, and Intraluminal Surgeries

The surgical paradigm of Minimally Invasive Surgery (MIS) has been a key driver to the adoption of robotic surgical assistance. Progress in the last three decades has led to a gradual transition from manual laparoscopic surgery with rigid instruments to robot-assisted surgery. In the last decade, the increasing demand for new surgical paradigms to enable access into the anatomy without skin incision (intraluminal surgery) or with a single skin incision (Single Port Access surgery - SPA) has led researchers to investigate snake-like flexible surgical devices. In this chapter, we first present an overview of the background, motivation, and taxonomy of MIS and its newer derivatives. Challenges of MIS and its newer derivatives (SPA and intraluminal surgery) are outlined along with the architectures of new snake-like robots meeting these challenges. We also examine the commercial and research surgical platforms developed over the years, to address the specific functional requirements and constraints imposed by operations in confined spaces. The chapter concludes with an evaluation of open problems in surgical robotics for intraluminal and SPA, and a look at future trends in surgical robot design that could potentially address these unmet needs.
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation

We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation. We achieve this tractability by (i) decomposing ReLU nonlinearities into the product of an identity and a Heaviside step function, (ii) introducing a separate path that decomposes the neural net expectation from its variance. We demonstrate formally that introducing separate latent binary variables to the activations allows representing the neural network likelihood as a chain of linear operations. Performing variational inference on this construction enables a sampling-free computation of the evidence lower bound which is a more effective approximation than the widely applied Monte Carlo sampling and CLT related techniques. We evaluate the model on a range of regression and classification tasks against BNN inference alternatives, showing competitive or improved performance over the current state-of-the-art.
Graph embedding on biomedical networks: methods, applications and evaluations

Abstract Motivation Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. Results We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug–drug interaction (DDI) prediction, protein–protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks. Availability and implementation As part of our contributions in the paper, we develop an easy-to-use Python package with detailed instructions, BioNEV, available at: https://github.com/xiangyue9607/BioNEV, including all source code and datasets, to facilitate studying various graph embedding methods on biomedical tasks. Supplementary information Supplementary data are available at Bioinformatics online.
A Methodology and Tool for Measurement-Based Dependability Evaluation of Digital Systems in Critical Applications

This paper presents an overview of a measurement-based methodology for dependability evaluation of critical digital 2 systems and describes a software tool under development for it. The approach is based on measurements of operational systems and on dependability models to provide quantitative reliability and availability assessments with stated confidence levels. The methodology is described, and some of the lessons learned in its early use are discussed. The design of a software tool to implement the methodology is outlined and the current experience in applying the methodology is summarized.
Continuous-variables quantum cryptography: asymptotic and finite-size security analysis

In this thesis we study the finite-size analysis of two continuous-variables quantum key distribution schemes. The first one is the one-way protocol using Gaussian modulation of thermal states and the other is the measurement- device-independent protocol. To do so, we adopt an efficient channel parameter estimation method based on the assumption of the Gaussian variables and the central limit theorem introduced by Ruppert et al. [Phys. Rev. A 90, 062310 (2014)]. Furthermore, we present a composable security analysis of the measurement device independent protocol for coherent attacks with a channel parameter estimation that is not based on the central limit theorem. We also investigated, in the asymptotic regime, an asymmetric situation for the authenticated parties against the eavesdropper caused by fast-fading channels. Here we assume that the eavesdropper has the full control of the communication channel and can instantaneously change its transmissivity in every use of it. We assumed the simple model of a uniform fading and addressed the cases of one-way protocols, continuous-measurement-device- independent protocol in symmetric configuration and its star network extension for three users. Finally, we extended the asymptotic study of the one-way protocols using an arbitrary number of phase-encoded coherent states assuming a thermal loss channel without using a Gaussian approximation.
Primordial extremal black holes as dark matter

We show that primordial (nearly) extremal black holes with a wide range of masses from the Planck scale to around $10^9$ g could be cosmologically stable and explain dark matter, given a dark electromagnetism and a heavy dark electron. For individual black holes, Hawking radiation and Schwinger discharge processes are suppressed by near-extremality and the heaviness of the dark electron, respectively. In contrast, the merger events of binary systems provide an opportunity to directly observe Hawking radiation. Because the merger products are not extremal, they rapidly evaporate and produce transient high-energy neutrino and gamma ray signals that can be observed at telescopes like IceCube and HAWC. The relationship between the near-extremal black hole and dark electron masses could also shed light on the weak gravity conjecture.
Regulation of bacterial biomass and community structure by metazoan and protozoan predation

We performed food web manipulation experiments in three eutrophic Daphnia‐dominated ponds, to compare the predation impact on planktonic bacteria exerted by metazoan and protozoan bacterial consumers. We analyzed the bacterial morphological composition by image analysis and the taxonomic composition by fluorescent in situ hybridization with group‐specific oligonucleotide probes. The removal of Daphnia always resulted in a microbial succession in which first free‐living bacteria and then heterotrophic nanoflagellates (HNFs) increased. Distinct stages of bacterial grazing either exclusively by Daphnia or by HNFs allowed a quantitative and qualitative comparison of their predation impact on the bacterial assemblage. Both bacterial consumers showed a strong size‐selective impact that shifted the free‐living bacterial community structure toward small cells. Suppression of bacterial biomass below the carrying capacity was similar with Daphnia or HNFs in one experiment and was significantly stronger with Daphnia as bacterial consumer in two experiments. This was due to the fact that bacteria that were resistant to protozoan predation partially compensated grazing mortality. Bacteria attached to aggregates and detrital particles were more important as grazing‐resistant forms than bacterial filaments and constituted up to 50% of total bacterial biomass at the end of the experiments. Changes in predation pressure were also associated with shifts in bacterial community composition. Bacteria belonging to the beta subclass of the class Proteobacteria and to the Cytophaga/Flavobacterium group dominated originally. The latter were most strongly reduced by HNF grazing, whereas other groups, in two experiments the alpha sublass (ALF) of Proteobacteria and in one experiment bacteria hybridizing with the probe for Archaea, even increased during HNF grazing. The composition also differed between bacteria associated with aggregates and freely suspended bacteria, the most obvious being the dominance of ALF growing on detrital aggregates. The experiments demonstrated that predation is a major structuring force for planktonic bacterial communities and that changes in predation regime probably have a much stronger impact on the structure of the bacterial community than on bacterial abundance and biomass.