Miracle-dz/UniFAR-FTUs huggingface dataset:
GeV γ-Ray Emission from M33 and Arp 299

Star-forming galaxies are huge reservoirs of cosmic rays (CRs) and these CRs convert a significant fraction of their energy into γ-rays by colliding with the interstellar medium (ISM). Several nearby star-forming galaxies have been detected in GeV–TeV γ-rays. It is also found that the γ-ray luminosities in 0.1–100 GeV correlate well with indicators of the star-formation rates of the galaxies, such as the total infrared (IR) luminosity. In this paper, we report a systematic search for possible γ-ray emission from galaxies in the IRAS Revised Bright Galaxies Sample, using 11.4 yr of γ-ray data taken by the Fermi Large Area Telescope. Two new galaxies, M33 and Arp 299, are detected significantly. The two galaxies are consistent with the empirical correlation between the γ-ray luminosity and IR luminosity, suggesting that their γ-ray emissions should mainly originate from CRs interacting with ISM. Nevertheless, there is tentative evidence that the flux of the γ-ray emission from Arp 299 is variable. If the variability is true, part of the emission from Arp 299 should originate from the obscured active galactic nucleus in this interacting galaxy system. In addition, we find that the γ-ray excess from M33 is located at the northeast region of the galaxy, where a supergiant H ii region, NGC 604, resides. This indicates that some bright star-forming regions in spiral galaxies could play a dominant role in the galaxy in producing γ-ray emission.
Human-Centered Collaborative Robots With Deep Reinforcement Learning

We present a reinforcement learning based framework for human-centered collaborative systems. The framework is proactive and balances the benefits of timely actions with the risk of taking improper actions by minimizing the total time spent to complete the task. The framework is learned end-to-end in an unsupervised fashion addressing the perception uncertainties and decision making in an integrated manner. The framework is shown to provide more time-efficient coordination between human and robot partners on an example task of packaging compared to alternatives for which perception and decision-making systems are learned independently, using supervised learning. Two important benefits of the proposed approach are that tedious annotation of motion data is avoided, and the learning is performed on-line.
Implementing a Fast Unbounded Quantum Fanout Gate Using Power-Law Interactions

The standard circuit model for quantum computation presumes the ability to directly perform gates between arbitrary pairs of qubits, which is unlikely to be practical for large-scale experiments. Power-law interactions with strength decaying as $1/r^\alpha$ in the distance $r$ provide an experimentally realizable resource for information processing, whilst still retaining long-range connectivity. We leverage the power of these interactions to implement a fast quantum fanout gate with an arbitrary number of targets. Our implementation allows the quantum Fourier transform (QFT) and Shor's algorithm to be performed on a $D$-dimensional lattice in time logarithmic in the number of qubits for interactions with $\alpha \le D$. As a corollary, we show that power-law systems with $\alpha \le D$ are difficult to simulate classically even for short times, under a standard assumption that factoring is classically intractable. Complementarily, we develop a new technique to give a general lower bound, linear in the size of the system, on the time required to implement the QFT and the fanout gate in systems that are constrained by a linear light cone. This allows us to prove an asymptotically tighter lower bound for long-range systems than is possible with previously available techniques.
ADD

We present a differentiable dynamics solver that is able to handle frictional contact for rigid and deformable objects within a unified framework. Through a principled mollification of normal and tangential contact forces, our method circumvents the main difficulties inherent to the non-smooth nature of frictional contact. We combine this new contact model with fully-implicit time integration to obtain a robust and efficient dynamics solver that is analytically differentiable. In conjunction with adjoint sensitivity analysis, our formulation enables gradient-based optimization with adaptive trade-offs between simulation accuracy and smoothness of objective function landscapes. We thoroughly analyse our approach on a set of simulation examples involving rigid bodies, visco-elastic materials, and coupled multi-body systems. We furthermore showcase applications of our differentiable simulator to parameter estimation for deformable objects, motion planning for robotic manipulation, trajectory optimization for compliant walking robots, as well as efficient self-supervised learning of control policies.
Bimonotone subdivisions of point configurations in the plane

Bimonotone subdivisions in two dimensions are subdivisions all of whose sides are either vertical or have nonnegative slope. They correspond to statistical estimates of probability distributions of strongly positively dependent random variables. The number of bimonotone subdivisions compared to the total number of subdivisions of a point configuration provides insight into how often the random variables are positively dependent. We give recursions as well as formulas for the numbers of bimonotone and total subdivisions of $2\times n$ grid configurations in the plane. Furthermore, we connect the former to the large Schroder numbers. We also show that the numbers of bimonotone and total subdivisions of a $2\times n$ grid are asymptotically equal. We then provide algorithms for counting bimonotone subdivisions for any $m \times n$ grid. Finally, we prove that all bimonotone triangulations of an $m \times n$ grid are connected by flips. This gives rise to an algorithm for counting the number of bimonotone (and total) triangulations of an $m\times n$ grid.
AutoBayes: Automated Inference via Bayesian Graph Exploration for Nuisance-Robust Biosignal Analysis

Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning, in particular for biosignal processing. We introduce an automated Bayesian inference framework, called AutoBayes, that explores different graphical models linking classifier, encoder, decoder, estimator and adversary network blocks to optimize nuisance-invariant machine learning pipelines. AutoBayes also enables justifying disentangled representation, which splits the latent variable into multiple pieces to impose different relation with subject/session-variation and task labels. We benchmark the framework on a series of physiological datasets, where we have access to subject and class labels during training, and provide analysis of its capability for subject transfer learning with/without variational modeling and adversarial training. The framework can be effectively utilized in semi-supervised multi-class classification, and reconstruction tasks for datasets in different domains as well.
A Semantic Web Framework for Automated Smart Assistants: COVID-19 Case Study

COVID-19 pandemic elucidated that knowledge systems will be instrumental in cases where accurate information needs to be communicated to a substantial group of people with different backgrounds and technological resources. However, several challenges and obstacles hold back the wide adoption of virtual assistants by public health departments and organizations. This paper presents the Instant Expert, an open-source semantic web framework to build and integrate voice-enabled smart assistants (i.e. chatbots) for any web platform regardless of the underlying domain and technology. The component allows non-technical domain experts to effortlessly incorporate an operational assistant with voice recognition capability into their websites. Instant Expert is capable of automatically parsing, processing, and modeling Frequently Asked Questions pages as an information resource as well as communicating with an external knowledge engine for ontology-powered inference and dynamic data utilization. The presented framework utilizes advanced web technologies to ensure reusability and reliability, and an inference engine for natural language understanding powered by deep learning and heuristic algorithms. A use case for creating an informatory assistant for COVID-19 based on the Centers for Disease Control and Prevention (CDC) data is presented to demonstrate the framework's usage and benefits.
Changes of oxalate oxidase and -1, 3 glucanase transcripts under Fusarium graminearum infection in wheat by quantitative real time PCR.

Fusarium ear rot (scab) is one of the most important disease in wheat and other small grain crops, which influenced strongly by weather conditions. The purpose of this study was investigation of changes in oxalate oxidase (OXO) and �-(1, 3)-glucanase transcripts to determine the initiation time point of wheat defense response to Fusarium graminearum. For this purpose, two wheat cultivars including Falat and Sumi3 as susceptible and tolerant cultivar respectively were planted in field at Gorgan agricultural research station. Total RNA was extracted from infected spikes sampled at 1, 3 and 7 days after inoculation. First standard cDNA was synthesized by using total RNA and used as template in quantitative real-time PCR (qRT-PCR). The result showed that the expression of OXO gene in susceptible variety (Falat) was decreased at all time points after inoculation. But the expression of this gene in tolerant variety increased in 7 days after inoculation however it was decreased at 1, 3 days after inoculation. The expression of �� -1, 3 glucanase gene in tolerant variety increased in 7 days after inoculation but in susceptible variety, the expression of this gene was much lower than tolerant one at this ) time.
Dynamic Competing Risk Modeling COVID-19 in a Pandemic Scenario

The emergence of coronavirus disease 2019 (COVID-19) in the United States has forced federal and local governments to implement containment measures Moreover, the severity of the situation has sparked engagement by both the research and clinical community with the goal of developing effective treatments for the disease This article proposes a time dynamic prediction model with competing risks for the infected individual and develops a simple tool for policy makers to compare different strategies in terms of when to implement the strictest containment measures and how different treatments can increase or suppress infected cases Two types of containment strategies are compared: (1) a constant containment strategy that could satisfy the needs of citizens for a long period;and (2) an adaptive containment strategy whose strict level changes across time We consider how an effective treatment of the disease can affect the dynamics in a pandemic scenario For illustration we consider a region with population 2 8 million and 200 initial infectious cases assuming a 4% mortality rate compared with a 2% mortality rate if a new drug is available Our results show compared with a constant containment strategy, adaptive containment strategies shorten the outbreak length and reduce maximum daily number of cases This, along with an effective treatment plan for the disease can minimize death rate
RGB-D-Based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments †

This research aims to improve dietetic-nutritional treatment using state-of-the-art RGB-D sensors and virtual reality (VR) technology. Recent studies show that adherence to treatment can be improved using multimedia technologies. However, there are few studies using 3D data and VR technologies for this purpose. On the other hand, obtaining 3D measurements of the human body and analyzing them over time (4D) in patients undergoing dietary treatment is a challenging field. The main contribution of the work is to provide a framework to study the effect of 4D body model visualization on adherence to obesity treatment. The system can obtain a complete 3D model of a body using low-cost technology, allowing future straightforward transference with sufficient accuracy and realistic visualization, enabling the analysis of the evolution (4D) of the shape during the treatment of obesity. The 3D body models will be used for studying the effect of visualization on adherence to obesity treatment using 2D and VR devices. Moreover, we will use the acquired 3D models to obtain measurements of the body. An analysis of the accuracy of the proposed methods for obtaining measurements with both synthetic and real objects has been carried out.
The effect of changes in temperature and food on the development of Calanus finmarchicus and Calanus helgolandicus populations

We studied the response in development times of Calanus finmarchicus and Calanus helgolandicus to changes in temperature and food conditions. Grazing experiments were performed at different temperatures for both species, and the results were implemented in a stage‐resolved zooplankton population model that predicted development times from biomass increments controlled by ingestion and metabolic losses. Predictions were validated against development data from the literature, and show that C. finmarchicus develops faster than C. helgolandicus below 11°C and slower above. The different relative development rates of the species are related to different temperature responses in ingestion rates. A temperature increase of 1°C to 2°C may have consequences for the relative contribution of C. helgolandicus and C. finmarchicus to the copepod community, and both seasonal and spatial displacements of the Calanus populations can be expected under climate change.
On O+ Ion Heating by BBELF Waves at Low Altitude: Test Particle Simulations

We investigate mechanisms of wave particle heating of ionospheric O+ ions resulting from broadband extremely low frequency (BBELF) waves using numerical test particle simulations that take into account ion‐neutral collisions, in order to explain observations from the Enhanced Polar Outflow Probe (e‐POP) satellite at low altitudes (∼400 km) (Shen et al., 2018, https://doi.org/10.1002/2017JA024955). We argue that in order to reproduce ion temperatures observed at e‐POP altitudes, the most effective ion heating mechanism is through cyclotron acceleration by short‐scale electrostatic ion cyclotron (EIC) waves with perpendicular wavelengths λ⊥ ≤ 200 m. The interplay between finite perpendicular wavelengths, wave amplitudes, and ion‐neutral collision frequencies collectively determine the ionospheric ion heating limit, which begins to decrease sharply with decreasing altitude below approximately 500 km, where the ratio νcfci becomes larger than 10−3, νc and fci denoting the O+‐O collision frequency and ion cyclotron frequency. We derive, both numerically and analytically, the ion gyroradius limit from heating by an EIC wave at half the cyclotron frequency. The limit is 0. 28λ⊥. The ion gyroradius limit from an EIC wave can be surpassed either through adding waves with different λ⊥ or through stochastic “breakout,” meaning ions diffuse in energy beyond the gyroradius limit due to stochastic heating from large‐amplitude waves. Our two‐dimensional simulations indicate that small‐scale (<1 km) Alfvén waves cannot account for the observed ion heating through trapping or stochastic heating.
Climbing the WOL: Training for Cheaper Inference

Efficient inference for wide output layers (WOLs) is an essential yet challenging task in large scale machine learning. Most approaches reduce this problem to approximate maximum inner product search (MIPS), which relies heavily on the observation that for a given model, ground truth labels correspond to logits of highest value during full model inference. However, such an assumption is restrictive in practice. In this paper, we argue that approximate MIPS subroutines, despite having sub-linear computation time, are sub-optimal because they are tailored for retrieving large inner products with high recall instead of retrieving the correct labels. With WOL, the labels often have moderate inner products, which makes approximate MIPS more challenging. We propose an alternative problem formulation, called Label Superior Sampling (LSS), where the objective is to tailor the system to ensure retrieval of the correct label. Accordingly, we propose a novel learned hash approach, which is significantly more efficient and sufficient for high inference accuracy than MIPS baselines. Our extensive evaluation indicates that LSS can match or even outperform full inference accuracy with around 5x speed up and 87% energy reduction.
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation

Graph convolutional networks (GCN) have been recently applied to semi-supervised classification problems with fewer labeled data and higher-dimensional features. Existing GCNs mostly rely on a first-order Chebyshev approximation of the graph wavelet-kernels. Such a generic propagation model may not always be well suited for the datasets. This work revisits the fundamentals of graph wavelet and explores the utility of spectral wavelet-kernels to signal propagation in the vertex domain. We first derive the conditions for representing the graph wavelet-kernels via vertex propagation. We next propose alternative propagation models for GCN layers based on Taylor expansions. We further analyze the choices of detailed propagation models. We test the proposed Taylor-based GCN (TGCN) in citation networks and 3D point clouds to demonstrate its advantages over traditional GCN methods.
DAPPLE: a pipelined data parallel approach for training large models

It is a challenging task to train large DNN models on sophisticated GPU platforms with diversified interconnect capabilities. Recently, pipelined training has been proposed as an effective approach for improving device utilization. However, there are still several tricky issues to address: improving computing efficiency while ensuring convergence, and reducing memory usage without incurring additional computing costs. We propose DAPPLE, a synchronous training framework which combines data parallelism and pipeline parallelism for large DNN models. It features a novel parallelization strategy planner to solve the partition and placement problems, and explores the optimal hybrid strategies of data and pipeline parallelism. We also propose a new runtime scheduling algorithm to reduce device memory usage, which is orthogonal to re-computation approach and does not come at the expense of training throughput. Experiments show that DAPPLE planner consistently outperforms strategies generated by PipeDream's planner by up to 3.23× speedup under synchronous training scenarios, and DAPPLE runtime outperforms GPipe by 1.6× speedup of training throughput and saves 12% of memory consumption at the same time.
Settlement of vegetative fragments of Ulva prolifera confirmed as an important seed source for succession of a large‐scale green tide bloom

Ulva prolifera green tides in the Yellow Sea of China in the past 3 yr are among the largest macroalgal blooms ever recorded. Satellite images, as well as wind and current patterns, provide a clear picture of the formation of these green tides; however, their place of origin remains unclear. Terrestrial investigation along the coastline of the Jiangsu Province and two shipboard surveys in the Yellow Sea in 2009 showed that filamentous Ulvaceae algae were prevalent in various environments in Jiangsu Province including floating rafts for Porphyra cultivation, land‐based bivalve culturing ponds, estuaries, embankments, intertidal stones, wharfs, and hard muddy coasts. Vegetative fragments in sediment samples were found in both shipboard surveys during the bloom and postbloom periods, and all of the samples were U. prolifera. Phylogenetic analysis of 5S spacer sequences showed that the green tide in the Yellow Sea was not of terrestrial origin. However, settled vegetative fragments of U. prolifera were an important seed source for the successive and more serious green tide blooms.
Gender Classification based on Local Binary Pattern and K-Nearest Neighbour

— Gender recognition from face image is a research area in the field of pattern recognition that determines gender based on biometric features of the face. Research has shown that the disparity between facial masculinity and femininity can be utilized to improve performances of face recognition applications in biometrics, human ‐ computer interaction, and surveillance and computer vision. There is need to develop a gender classification system for human ‐ computer interaction that recognizes the gender of an individual through the input face image. Hence, this study developed a facial gender recognition system using K ‐ Nearest Neighbour (KNN). Facial images from the FERET database were obtained from the internet. The image database is made up of several images of male and female faces which have been categorized based on gender. These databases contain images of different poses for each individual. Local Binary pattern (LBP) was used to extract features of the images and the images were classified using K ‐ Nearest Neighbour algorithm . The system was implemented using Matrix Laboratory 8.1 (MATLAB 2015a). The classification results showed that highest accuracy of 92% was achieved. The system could be adopted in classifying face images into male or female which is required in security control system or any other related systems .
Automatic Page Segmentation Without Decompressing the Run-Length Compressed Text Documents

Page segmentation is considered to be the crucial stage for the automatic analysis of documents with complex layouts. This has traditionally been carried out in uncompressed documents, although most of the documents in real life exist in a compressed form warranted by the requirement to make storage and transfer efficient. However, carrying out page segmentation directly in compressed documents without going through the stage of decompression is a challenging goal. This research paper proposes demonstrating the possibility of carrying out a page segmentation operation directly in the run-length data of the CCITT Group-3 compressed text document, which could be single- or multi-columned and might even have some text regions in the inverted text color mode. Therefore, before carrying out the segmentation of the text document into columns, each column into paragraphs, each paragraph into text lines, each line into words, and, finally, each word into characters, a pre-processing of the text document needs to be carried out. The pre-processing stage identifies the normal text regions and inverted text regions, and the inverted text regions are toggled to the normal mode. In the sequel to initiate column separation, a new strategy of incremental assimilation of white space runs in the vertical direction and the auto-estimation of certain related parameters is proposed. A procedure to realize column-segmentation employing these extracted parameters has been devised. Subsequently, what follows first is a two-level horizontal row separation process, which segments every column into paragraphs, and in turn, into text-lines. Then, there is a two-level vertical column separation process, which completes the separation into words and characters.