Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Compartment Variational Online Learning for Spiking Neural Networks

Spiking Neural Networks (SNNs) offer a novel computational paradigm that captures some of efficiency of biological brains for inference and learning via recursive processing and binary neural activations. Most existing training algorithms for SNNs assume spiking neuron models in which a neuron outputs individual spikes as a function of the dynamics of an internal state variable known as membrane potential. This paper explores a more general model in which each spiking neuron contains multiple compartments, each tracking the dynamics of a distinct membrane potential, while sharing the same synaptic weights across compartments. It is demonstrated that learning rules based on probabilistic generalized linear neural models can leverage the presence of multiple compartments through modern variational inference based on importance weighting or generalized expectation-maximization. The key idea is to use the neural compartments to sample multiple independent spiking signals from hidden neurons so as to obtain better statistical estimates of the likelihood training criterion. The derived online learning algorithms follow three-factor rules with global learning signals. Experimental results on a structured output memorization task and classification task with a standard neuromorphic data set demonstrate significant improvements in terms of accuracy and calibration with an increasing number of compartments.
CVR-Net: A deep convolutional neural network for coronavirus recognition from chest radiography images

The novel Coronavirus Disease 2019 (COVID-19) is a global pandemic disease spreading rapidly around the world. A robust and automatic early recognition of COVID-19, via auxiliary computer-aided diagnostic tools, is essential for disease cure and control. The chest radiography images, such as Computed Tomography (CT) and X-ray, and deep Convolutional Neural Networks (CNNs), can be a significant and useful material for designing such tools. However, designing such an automated tool is challenging as a massive number of manually annotated datasets are not publicly available yet, which is the core requirement of supervised learning systems. In this article, we propose a robust CNN-based network, called CVR-Net (Coronavirus Recognition Network), for the automatic recognition of the coronavirus from CT or X-ray images. The proposed end-to-end CVR-Net is a multi-scale-multi-encoder ensemble model, where we have aggregated the outputs from two different encoders and their different scales to obtain the final prediction probability. We train and test the proposed CVR-Net on three different datasets, where the images have collected from different open-source repositories. We compare our proposed CVR-Net with state-of-the-art methods, which are trained and tested on the same datasets. We split three datasets into five different tasks, where each task has a different number of classes, to evaluate the multi-tasking CVR-Net. Our model achieves an overall F1-score & accuracy of 0.997 & 0.998; 0.963 & 0.964; 0.816 & 0.820; 0.961 & 0.961; and 0.780 & 0.780, respectively, for task-1 to task-5. As the CVR-Net provides promising results on the small datasets, it can be an auspicious computer-aided diagnostic tool for the diagnosis of coronavirus to assist the clinical practitioners and radiologists. Our source codes and model are publicly available at this https URL.
Discovering Traveling Companions using Autoencoders

With the wide adoption of mobile devices, today's location tracking systems such as satellites, cellular base stations and wireless access points are continuously producing tremendous amounts of location data of moving objects. The ability to discover moving objects that travel together, i.e., traveling companions, from their trajectories is desired by many applications such as intelligent transportation systems and location-based services. Existing algorithms are either based on pattern mining methods that define a particular pattern of traveling companions or based on representation learning methods that learn similar representations for similar trajectories. The former methods suffer from the pairwise point-matching problem and the latter often ignore the temporal proximity between trajectories. In this work, we propose a generic deep representation learning model using autoencoders, namely, ATTN-MEAN, for the discovery of traveling companions. ATTN-MEAN collectively injects spatial and temporal information into its input embeddings using skip-gram, positional encoding techniques, respectively. Besides, our model further encourages trajectories to learn from their neighbours by leveraging the Sort-Tile-Recursive algorithm, mean operation and global attention mechanism. After obtaining the representations from the encoders, we run DBSCAN to cluster the representations to find travelling companion. The corresponding trajectories in the same cluster are considered as traveling companions. Experimental results suggest that ATTN-MEAN performs better than the state-of-the-art algorithms on finding traveling companions.
An Invitation to Sequential Monte Carlo Samplers

ABSTRACT Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits. Supplementary materials for this article are available online.
Formalizing Nakamoto-Style Proof of Stake

Fault-tolerant distributed systems move the trust in a single party to a majority of parties participating in the protocol. This makes blockchain based crypto-currencies possible: they allow parties to agree on a total order of transactions without a trusted third party. To trust a distributed system, the security of the protocol and the correctness of the implementation must be indisputable. We present the first machine checked proof that guarantees both safety and liveness for a consensus algorithm. We verify a Proof of Stake (PoS) Nakamoto-style blockchain (NSB) protocol, using the foundational proof assistant Coq. In particular, we consider a PoS NSB in a synchronous network with a static set of corrupted parties. We define execution semantics for this setting and prove chain growth, chain quality, and common prefix which together imply both safety and liveness.
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming

Data cleaning is naturally framed as probabilistic inference in a generative model, combining a prior distribution over ground-truth databases with a likelihood that models the noisy channel by which the data are filtered, corrupted, and joined to yield incomplete, dirty, and denormalized datasets. Based on this view, we present PClean, a unified generative modeling architecture for cleaning and normalizing dirty data in diverse domains. Given an unclean dataset and a probabilistic program encoding relevant domain knowledge, PClean learns a structured representation of the data as a relational database of interrelated objects, and uses this latent structure to impute missing values, identify duplicates, detect errors, and propose corrections in the original data table. PClean makes three modeling and inference contributions: (i) a domain-general non-parametric generative model of relational data, for inferring latent objects and their network of latent connections; (ii) a domain-specific probabilistic programming language, for encoding domain knowledge specific to each dataset being cleaned; and (iii) a domain-general inference engine that adapts to each PClean program by constructing data-driven proposals used in sequential Monte Carlo and particle Gibbs. We show empirically that short (< 50-line) PClean programs deliver higher accuracy than state-of-the-art data cleaning systems based on machine learning and weighted logic; that PClean's inference algorithm is faster than generic particle Gibbs inference for probabilistic programs; and that PClean scales to large real-world datasets with millions of rows.
VLA Limits on Intermediate-mass Black Holes in 19 Massive Globular Clusters

The NSF’s Karl G. Jansky Very Large Array (VLA) was used at 3 cm to search for accretion signatures from intermediate-mass black holes (IMBHs) in 19 globular star clusters (GCs) in NGC 3115, an early-type galaxy at a distance of 9.4 Mpc. The 19 GCs have stellar masses M⋆ ∼ (1.1–2.7) × 106 M⊙, with a mean . None were detected. An IMBH accretion model was applied to the individual GCs and their radio stack. The radio-stacked GCs have an IMBH mass and mass fraction , with each limit being uncertain by a factor of about 2.5. The latter limit contrasts with the extremes of some stripped nuclei, suggesting that the set of stacked GCs in NGC 3115 is not a set of such nuclei. The radio luminosities of the individual GCs correspond to X-ray luminosities LX < (3.3–10) × 1038 erg s−1, with a factor of about 2.5 uncertainty. These limits predicted for putative IMBHs in the GCs are consistent with extant Chandra observations. Finally, a simulated observation with a next-generation VLA demonstrates that accretion signatures from IMBHs in GCs can be detected in a radio-only search, yet they elude detection in an X-ray-only search due to confusion from X-ray binaries in the GCs.
Influence of M/A substitution on material properties of intermetallic compounds MSn2 (M = Fe, and Co; A = Li, and Na): a first-principles study

Iron and cobalt distannides \ce{MSn2} (M = Fe, Co) are regarded as a promising conversion-type anode material for lithium- and sodium-ion batteries, but their properties are not well understood. In this work, we report a first-principles study of alkali metal (A = Li, Na) substitutional effect on the structural, mechanical, lattice vibrational, electronic and defect properties of these distannides. Special attention is paid to systematic comparison between \ce{FeSn2} and \ce{CoSn2}. Our calculations reveal that M/A substitution induces a lattice expansion and decrease of elastic constants, which is more announced with Na substitution than Li, and moreover changes the elastic property of \ce{FeSn2} from ductile to brittle whereas preserves the ductility of \ce{CoSn2}. An imaginary phonon frequency mode appears only for \ce{FeSn2} and \ce{FeNaSn2}, and M/A substitution provokes a definite gap between high and low frequency regions. We perform a careful analysis of electronic density of states, band structures and Fermi surface, providing an insight into difference of electronic structures between \ce{FeSn2} and \ce{CoSn2}. With further calculation of defect formation energies and alkali ion diffusion barriers, we believe this work can be useful to design conversion-type anode materials for alkali-ion batteries.
On Manually Reverse Engineering Communication Protocols of Linux-Based IoT Systems

IoT security and privacy has raised grave concerns. Efforts have been made to design tools to identify and understand vulnerabilities of IoT systems. Most of the existing protocol security analysis techniques rely on a well understanding of the underlying communication protocols. In this article, we systematically present the first manual reverse engineering framework for discovering communication protocols of embedded Linux-based IoT systems. We have successfully applied our framework to reverse engineer a number of IoT systems. As an example, we present a detailed use of the framework reverse engineering the WeMo smart plug communication protocol by extracting the firmware from the flash, performing static and dynamic analysis of the firmware, and analyzing network traffic. The discovered protocol exposes severe design flaws that allow attackers to control or deny the service of victim plugs. Our manual reverse engineering framework is generic and can be applied to both read-only and writable embedded Linux filesystems.
Gossypium raimondii: a source of fertility restorer for Cytoplasmic Male Sterility of Gossypium hirsutum L

Abstract Comparative studies of pollen fertility restoration in different triploid hybrids using cytoplasmic male sterile (CMS) line of Gossypium hirsutum 76 IH (2n = 4x=52; AhAhDhDh), wild species and perennial cottons were studied. Triploid hybrid (3n = 39; AhDhD5) involving G. raimondii (2n = 26; D5 D5) was vigorous growing and partially fertile. Restoration of pollen fertility observed in this hybrid was attributed to bivalent formation (11.13II/ PMC) leading to formation of 40 % fertile pollen. In contrast to the CMS female parent, arrestation of meiosis during early prophase and degeneration of PMCs was not observed in triploid hybrid that indicated existence of fertility restorer factor in G. raimondii. Efforts were made to induce chromosome doubling as well as induce boll setting in this hybrid with the help of different treatments of colchicine and growth hormones, respectively. The fertility restorer factor reported in present report will be an alternative source of restorer gene in G. hirsutum L. cotton added with tolerance to sucking pests, drought and desired fibre properties.
The Effects of Habitat Fragmentation on Midwestern Grassland Bird Communities

The influence of area and vegetation structure on breeding bird communities associated with 24 Illinois grassland fragments (0.5—600 ha) was studied between 1987 and 1989 to document the effects of habitat fragmentation in a severely fragmented midwestern landscape. Fragment area strongly influenced bird communities within grasslands and accounted for a high percentage of the variation in mean breeding bird species richness among fragments (R2 = 0.84). Breeding bird species richness patterns within 4.5—ha subsections of these grasslands also significantly increased with fragment size. Eight of the 15 (53%) most common bird species had distributions among fragments that were significantly influenced by habitat area, whereas six species (40%) had distributions within fragments that were significantly influenced by vegetation structure only. The Dickcissel (Spiza americana) was the only species with a distribution within fragments that was not significantly associated with either habitat area or vegetation structure. Four groups of birds were identified by an analysis of habitat area and vegetation structure preferences of individual species: area—sensitive species (5 species), edge species (3), vegetation—restricted species (6), and the Dickcissel. Estimates of minimal area requirements for the five area—sensitive species ranged from 5 to 55 ha. Discriminant analyses of habitat suitability within fragments suggests that the absence of area—sensitive grassland bird species form some small fragments may result, in part, from limited habitat availability. All five area—sensitive species, however, also regularly avoided structurally suitable habitat on small grassland fragments. As a result of the considerable extent to which native and, more recently, agricultural grasslands have declined in the Midwest, habitat fragmentation is likely to have caused midwestern grassland bird declines, especially for area—sensitive species.
Ferrofluid Microdroplet Splitting for Population‐Based Microfluidics and Interfacial Tensiometry

Ferrofluids exhibit a unique combination of liquid properties and strong magnetic response, which leads to a rich variety of interesting functional properties. Here, the magnetic‐field‐induced splitting of ferrofluid droplets immersed in an immiscible liquid is presented, and related fascinating dynamics and applications are discussed. A magnetic field created by a permanent magnet induces instability on a mother droplet, which divides into two daughter droplets in less than 0.1 s. During the splitting process, the droplet undergoes a Plateau–Rayleigh‐like instability, which is investigated using high‐speed imaging. The dynamics of the resulting satellite droplet formation is shown to depend on the roughness of the supporting surface. Further increasing the field results in additional splitting events and self‐assembly of microdroplet populations, which can be magnetically actuated. The effects of magnetization and interfacial tension are systematically investigated by varying magnetic nanoparticles and surfactant concentrations, and a variety of outcomes from labyrinthine patterns to discrete droplets are observed. As the splitting process depends on interfacial tension, the droplet splitting can be used as a measure for interfacial tension as low as 0.1 mN m−1. Finally, a population‐based digital microfluidics concept based on the self‐assembled microdroplets is presented.
Book Reviews

The book under review is the new addition in the series by Daniel Goleman who has earlier written best selling books like, "The Meditative Mind, Emotional Intelligence, Working with Emotional Intelligence" and has also coauthored "Primal Leadership: Learning to Lead with Emotional Intelligence". In "Social Intelligence," the author offers a perspective shift which extends the notion of social cognition to such non-cognitive capacities like empathy, concern and the impulse for compassion that connects us with other people. According to Goleman, these caring sensibilities in social intelligence help us to act wisely in human relationships, and their implications for modern living can be understood in terms of accumulating evidence in social neurosciences which explains sociability as the predominant form offunctioning ofthe human brain. Essentially, the book seeks to provide insights to such key questions like: Can we do a better job of helping our children grow up to be happy? Can relationships buffer us from disease? How can a teacher or a leader enable the brain of students or workers to do their best? What helps groups with interpersonal conflicts amongst its members to live together in peace? What do these insights suggest for the kind of society that we are able to build and for what really matters in each of our lives?
Real-Time Crowd Monitoring Using Seamless Indoor-Outdoor Localization

Human identification and monitoring are critical in many applications, such as surveillance, evacuation planning. Human identification and monitoring are not an easy task in the case of a large and densely populated crowd. However, none of the existing solutions consider seamless localization, identification, and tracking of the crowd for surveillance in both indoor and outdoor environments with significant accuracy. In this paper, we propose a novel and real-time surveillance system (named, SmartISS) which identifies, tracks and monitors individuals’ wireless equipment(s) using their MAC ids. Our trackers/sensing units (PSUs) are the portable entities comprising of Smartphone/Jetson-TK1/PC which are enough to capture users’ devices probe requests and locations. PSUs upload collected traces on the cloud server periodically where cloud server keeps finding the suspicious person(s). To retrieve the updated information, we propose an algorithm (named, LLTR) to select the optimal number of PSUs for finding the latest location(s) of the suspicious person(s). To validate and to show the usability of SmartISS, we develop a real prototype testbed and evaluate it extensively on a real-world dataset of 117,121 traces collected during the technical festival held at IIT Roorkee, India. SmartISS selects PSUs with an average selection accuracy of 95.3 percent.
A Stochastic Variance Reduced Primal Dual Fixed Point Method for Linearly Constrained Separable Optimization

In this paper we combine the stochastic variance reduced gradient (SVRG) method [17] with the primal dual fixed point method (PDFP) proposed in [7] to solve a sum of two convex functions and one of which is linearly composite. This type of problems are typically arisen in sparse signal and image reconstruction. The proposed SVRG-PDFP can be seen as a generalization of Prox-SVRG [37] originally designed for the minimization of a sum of two convex functions. Based on some standard assumptions, we propose two variants, one is for strongly convex objective function and the other is for general convex cases. Convergence analysis shows that the convergence rate of SVRG-PDFP is O(1/k) (here k is the iteration number) for general convex objective function and linear for k strongly convex case. Numerical examples on machine learning and CT image reconstruction are provided to show the effectiveness of the algorithms.
Path homology as a stronger analogue of cyclomatic complexity

Cyclomatic complexity is an incompletely specified but mathematically principled software metric that can be usefully applied to both source and binary code. We consider the application of path homology as a stronger analogue of cyclomatic complexity. We have implemented an algorithm to compute path homology in arbitrary dimension and applied it to several classes of relevant flow graphs, including randomly generated flow graphs representing structured and unstructured control flow. We also compared path homology and cyclomatic complexity on a set of disassembled binaries obtained from the grep utility. There exist control flow graphs realizable at the assembly level with nontrivial path homology in arbitrary dimension. We exhibit several classes of examples in this vein while also experimentally demonstrating that path homology gives identicial results to cyclomatic complexity for at least one detailed notion of structured control flow. We also experimentally demonstrate that the two notions differ on disassembled binaries, and we highlight an example of extreme disagreement. Path homology empirically generalizes cyclomatic complexity for an elementary notion of structured code and appears to identify more structurally relevant features of control flow in general. Path homology therefore has the potential to substantially improve upon cyclomatic complexity.
Complex coordinate rotation method based on gradient optimization

In atomic, molecular, and nuclear physics, the method of complex coordinate rotation is a widely used theoretical tool for studying resonant states. Here, we propose a novel implementation of this method based on the gradient optimization (CCR-GO). The main strength of the CCR-GO method is that it does not require manual adjustment of optimization parameters in the wave function; instead, a mathematically well-defined optimization path can be followed. Our method is proven to be very efficient in searching resonant positions and widths over a variety of few-body atomic systems, and can significantly improve the accuracy of the results. As a special case, the CCR-GO method is equally capable of dealing with bound-state problems with high accuracy, which is traditionally achieved through the usual extreme conditions of energy itself.