Miracle-dz/UniFAR-FTUs huggingface dataset:
The Digitalization of Bioassays in the Open Research Knowledge Graph

Background: Recent years are seeing a growing impetus in the semantification of scholarly knowledge at the fine-grained level of scientific entities in knowledge graphs. The Open Research Knowledge Graph (ORKG) https://www.orkg. org/ represents an important step in this direction, with thousands of scholarly contributions as structured, fine-grained, machine-readable data. There is a need, however, to engender change in traditional community practices of recording contributions as unstructured, non-machine-readable text. For this in turn, there is a strong need for AI tools designed for scientists that permit easy and accurate semantification of their scholarly contributions. We present one such tool, ORKG-assays. Implementation: ORKG-assays is a freely available AI micro-service in ORKG written in Python designed to assist scientists obtain semantified bioassays as a set of triples. It uses an AI-based clustering algorithm which on gold-standard evaluations over 900 bioassays with 5,514 unique property-value pairs for 103 predicates shows competitive performance. Results and Discussion: As a result, semantified assay collections can be surveyed on the ORKG platform via tabulation or chart-based visualizations of key property values of the chemicals and compounds offering smart knowledge access to biochemists and pharmaceutical researchers in the advancement of drug development.
DeepDPM: Deep Clustering With an Unknown Number of Clusters

Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric approach are well known, most deep-clustering methods are parametric: namely, they require a predefined and fixed number of clusters, denoted by K. When $K$ is unknown, however, using model-selection criteria to choose its optimal value might become computationally expensive, especially in DL as the training process would have to be repeated numerous times. In this work, we bridge this gap by introducing an effective deep-clustering method that does not require knowing the value of $K$ as it infers it during the learning. Using a split/merge framework, a dynamic architecture that adapts to the changing $K$, and a novel loss, our proposed method outperforms existing nonparametric methods (both classical and deep ones). While the very few existing deep nonparametric methods lack scalability, we demonstrate ours by being the first to report the performance of such a method on ImageNet. We also demonstrate the importance of inferring $K$ by showing how methods that fix it deteriorate in performance when their assumed $K$ value gets further from the ground-truth one, especially on imbalanced datasets. Our code is available at https://github.com/BGU-CS-VIL/DeepDPM.
Lite Unified Modeling for Discriminative Reading Comprehension

As a broad and major category in machine reading comprehension (MRC), the generalized goal of discriminative MRC is answer prediction from the given materials. However, the focuses of various discriminative MRC tasks may be diverse enough: multi-choice MRC requires model to highlight and integrate all potential critical evidence globally; while extractive MRC focuses on higher local boundary preciseness for answer extraction. Among previous works, there lacks a unified design with pertinence for the overall discriminative MRC tasks. To fill in above gap, we propose a lightweight POS-Enhanced Iterative Co-Attention Network (POI-Net) as the first attempt of unified modeling with pertinence, to handle diverse discriminative MRC tasks synchronously. Nearly without introducing more parameters, our lite unified design brings model significant improvement with both encoder and decoder components. The evaluation results on four discriminative MRC benchmarks consistently indicate the general effectiveness and applicability of our model, and the code is available at https://github.com/Yilin1111/poi-net.
Algorithmic correspondence and analytic rules

We introduce the algorithm MASSA which takes classical modal formulas in input, and, when successful, effectively generates: (a) (analytic) geometric rules of the labelled calculus G3K, and (b) cut-free derivations (of a certain `canonical' shape) of each given input formula in the geometric labelled calculus obtained by adding the rule in output to G3K. We show that MASSA successfully terminates whenever its input formula is a (definite) analytic inductive formula, in which case, the geometric axiom corresponding to the output rule is, modulo logical equivalence, the first-order correspondent of the input formula.
Zeta-Payne: A Fully Automated Spectrum Analysis Algorithm for the Milky Way Mapper Program of the SDSS-V Survey

The Sloan Digital Sky Survey (SDSS) has recently initiated its fifth survey generation (SDSS-V), with a central focus on stellar spectroscopy. In particular, SDSS-V's Milky Way Mapper program will deliver multiepoch optical and near-infrared spectra for more than 5 × 106 stars across the entire sky, covering a large range in stellar mass, surface temperature, evolutionary stage, and age. About 10% of those spectra will be of hot stars of OBAF spectral types, for whose analysis no established survey pipelines exist. Here we present the spectral analysis algorithm, ZETA-PAYNE, developed specifically to obtain stellar labels from SDSS-V spectra of stars with these spectral types and drawing on machine-learning tools. We provide details of the algorithm training, its test on artificial spectra, and its validation on two control samples of real stars. Analysis with ZETA-PAYNE leads to only modest internal uncertainties in the near-IR with APOGEE (optical with BOSS): 3%–10% (1%–2%) for T eff, 5%–30% (5%–25%) for vsini , 1.7–6.3 km s−1 (0.7–2.2 km s−1) for radial velocity, <0.1 dex (<0.05 dex) for logg , and 0.4–0.5 dex (0.1 dex) for [M/H] of the star, respectively. We find a good agreement between atmospheric parameters of OBAF-type stars when inferred from their high- and low-resolution optical spectra. For most stellar labels, the APOGEE spectra are (far) less informative than the BOSS spectra of these stars, while logg , vsini , and [M/H] are in most cases too uncertain for meaningful astrophysical interpretation. This makes BOSS low-resolution optical spectra better for stellar labels of OBAF-type stars, unless the latter are subject to high levels of extinction.
Infinite-Dimensional Sparse Learning in Linear System Identification

Regularized methods have been widely applied to system identification problems without known model structures. This paper presents an infinite-dimensional sparse learning algorithm based on atomic norm regularization. Atomic norm regularization decomposes the transfer function into first-order atomic models and solves a group lasso problem that selects a sparse set of poles and identifies the corresponding coefficients. The difficulty in solving the problem lies in the fact that there are an infinite number of possible atomic models. This work proposes a greedy algorithm that generates new candidate atomic models maximizing the violation of the optimality conditions of the existing problem. This algorithm is able to solve the infinite-dimensional group lasso problem with high precision. The algorithm is further extended to reduce the bias and reject false positives in pole location estimation by iteratively reweighted adaptive group lasso and complementary pairs stability selection respectively. Numerical results demonstrate that the proposed algorithm performs better than benchmark parameterized and regularized methods in terms of both impulse response fitting and pole location estimation.
SMP-PHAT: Lightweight DoA Estimation by Merging Microphone Pairs

This paper introduces SMP-PHAT, which performs direction of arrival (DoA) of sound estimation with a microphone array by merging pairs of microphones that are parallel in space. This approach reduces the number of pairwise cross-correlation computations, and brings down the number of ﬂops and mem-ory lookups when searching for DoA. Experiments on low-cost hardware with commonly used microphone arrays show that the proposed method provides the same accuracy as the former SRP-PHAT approach, while reducing the computational load by 39% in some cases.
Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data

Recommender systems based on collaborative filtering play a vital role in many E-commerce applications as they guide the user in finding their items of interest based on the user's past transactions and feedback of other similar customers. Data Sparsity is one of the major drawbacks with collaborative filtering technique arising due to the less number of transactions and feedback data. In order to reduce the sparsity problem, techniques called transfer learning/cross-domain recommendation has emerged. In transfer learning methods, the data from other dense domain(s) (source) is considered in order to predict the missing ratings in the sparse domain (target). In this paper, we come up with a novel transfer learning approach for cross-domain recommendation, wherein the cluster-level rating pattern(codebook) of the source domain is obtained via a co-clustering technique. Thereafter we apply the Maximum Margin Matrix factorization (MMMF) technique on the codebook in order to learn the user and item latent features of codebook. Prediction of the target rating matrix is achieved by introducing these latent features in a novel way into the optimisation function. In the experiments we demonstrate that our model improves the prediction accuracy of the target matrix on benchmark datasets.
Spin Physics Detector at NICA

The Spin Physics Detector is one of two large setups at the NICA collider under construction at JINR (Dubna). The ultimate goal of the studies at SPD is the measurement of different spin observables in polarized proton-proton and deuteron-deuteron collisions sensitive to the polarized gluon structure of the nucleon at the luminosity up to 10$^{32}$ $cm^{-2}\cdot s^{-1}$ and $\sqrt{s}$ up to 27 GeV. SPD will consist of the superconducting magnetic system, silicon tracker, straw mini-drift tubes tracker, time-of-flight system, electromagnetic"shashlyk"- type calorimeter, muon (range) and local-polarimetry systems. The high performance free-streaming DAQ system will be able to operate at the collision rate up to 4 MHz.
Lindblad evolutions, Born rule, Heralding and cloning

We discuss an apparent diﬃculty in computing the radiation emitted by a system undergoing Lindblad evolution. The diﬃculty is resolved by viewing the problem as Born rule for conserved currents deﬁned by the appropriate terms in the adjoint Lindbladian. In Heralding Alice prepares Bob’s system in a state that mirrors her test. We show that heralding is consistent with no-cloning. This follows from the observation that heralding is not a completely positive map.
Intermunicipal Travel Networks of Mexico (2020-2021)

We present a collection of networks that describe the travel patterns between municipalities in Mexico between 2020 and 2021. Using anonymized mobile device geo-location data we constructed directed, weighted networks representing the (normalized) volume of travels between municipalities. We analysed changes in global (graph total weight sum), local (centrality measures), and mesoscale (community structure) network features. We observe that changes in these features are associated with factors such as Covid-19 restrictions and population size. In general, events in early 2020 (when initial Covid-19 restrictions were implemented) induced more intense changes in network features, whereas later events had a less notable impact in network features. We believe these networks will be useful for researchers and decision makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation

A fundamental and challenging problem in deep learning is catastrophic forgetting, the tendency of neural networks to fail to preserve the knowledge acquired from old tasks when learning new tasks. This problem has been widely investigated in the research community and several Incremental Learning approaches have been proposed in the past years. While earlier works in computer vision have mostly focused on image classification and object detection, more recently some IL approaches for semantic segmentation have been introduced. These previous works showed that, despite its simplicity, knowledge distillation can be effectively employed to alleviate catastrophic forgetting. In this paper, we follow this research direction and, inspired by recent literature on contrastive learning, we propose a novel distillation framework, Uncertainty-aware Contrastive Distillation. In a nutshell, is operated by introducing a novel distillation loss that takes into account all the images in a mini-batch, enforcing similarity between features associated to all the pixels from the same classes, and pulling apart those corresponding to pixels from different classes. Our experimental results demonstrate the advantage of the proposed distillation technique, which can be used in synergy with previous IL approaches, and leads to state-of-art performance on three commonly adopted benchmarks.
Energy Efficient VM Placement in a Heterogeneous Fog Computing Architecture

— Recent years have witnessed a remarkable development in communication and computing systems, mainly driven by the increasing demands of data and processing intensive applications such as virtual reality, M2M, connected vehicles, IoT services, to name a few. Massive amounts of data will be collected by various mobile and fixed terminals that will need to be processed in order to extract knowledge from the data. Traditionally, a centralized approach is taken for processing the collected data using large data centers connected to a core network. However, due to the scale of the Internet-connected things, transporting raw data all the way to the core network is costly in terms of the power consumption, delay, and privacy. This has compelled researchers to propose different decentralized computing paradigms such as fog computing to process collected data at the network edge close to the terminals and users. In this paper, we study, in a Passive Optical Network (PON)-based collaborative-fog computing system, the impact of the heterogeneity of the fog units’ capacity and energy-efficiency on the overall energy-efficiency of the fog system. We optimized the virtual machine (VM) placement in this fog system with three fog cells and formulated the problem as a mixed integer linear programming (MILP) optimization model with the objective of minimizing the networking and processing power consumption of the fog system. The results indicate that in our proposed architecture, the processing power consumption is the crucial element to achieve energy efficient VMs placement.
Temporal and Spectral Properties of the Auditory Mismatch Negativity and P3a Responses in Schizophrenia

The mismatch negativity (MMN) event-related potential (ERP) indexes relatively automatic detection of changes in sensory stimuli and is typically attenuated in individuals with schizophrenia. However, contributions of different frequencies of electroencephalographic (EEG) activity to the MMN and the later P3a attentional orienting response in schizophrenia are poorly understood and were the focus of the present study. Participants with a schizophrenia-spectrum disorder (n = 85) and non-psychiatric control participants (n = 74) completed a passive auditory oddball task containing 10% 50 ms “deviant” tones and 90% 100 ms “standard” tones. EEG data were analyzed using spatial principal component analysis (PCA) applied to wavelet-based time-frequency analysis and MMN and P3a ERPs. The schizophrenia group compared to the control group had smaller MMN amplitudes and lower deviant-minus-standard theta but not alpha event-related spectral perturbation (ERSP) after accounting for participant age and sex. Larger MMN and P3a amplitudes but not latencies were correlated with greater theta and alpha time-frequency activity. Multiple linear regression analyses revealed that control participants showed robust relationships between larger MMN amplitudes and greater deviant-minus-standard theta inter-trial coherence (ITC) and between larger P3a amplitudes and greater deviant-minus-standard theta ERSP, whereas these dynamic neural processes were less tightly coupled in participants with a schizophrenia-spectrum disorder. Study results help clarify frequency-based contributions of time-domain (ie, ERP) responses and indicate a potential disturbance in the neural dynamics of detecting change in sensory stimuli in schizophrenia. Overall, findings add to the growing body of evidence that psychotic illness is associated with widespread neural dysfunction in the theta frequency band.
Distinction Between Hepatocellular Carcinoma and Hypervascular Liver Metastases in Non-cirrhotic Patients Using Gadoxetate Disodium-Enhanced Magnetic Resonance Imaging

Purpose: This study aims to identify the hallmarks of gadoxetate disodium-enhanced magnetic resonance imaging distinguishing hepatocellular carcinoma (HCC) from hypervascular liver metastases (HLMs). Methods: Between January 2008 and October 2020, among patients who underwent gadoxetate disodium-enhanced MRI, those who met the following criteria were retrospectively included: without chronic hepatitis or liver stiffness ≤ 2.5 kPa on magnetic resonance elastography or F0/F1 on pathological assessment. Two blinded radiologists reviewed the imaging findings to judge the presence or absence of the enhancing capsule, nonperipheral washout, corona enhancement, hypointensity in the transitional/hepatobiliary phase (HBP), hyperintensity on T2-weighted/diffusion-weighted imaging (DWI), mosaic architecture, and blood products/fat in mass. The lesion-to-liver signal intensity ratios in HBP and DWI were also calculated. Univariate and multivariate analyses were performed to identify the imaging hallmarks distinguishing HCC from HLM. Interobserver agreement was calculated using kappa values and intraclass correlation coefficients (ICCs). Results: The final study cohort comprised 72 lesions in 44 patients (mean age, 65.0±11.9 years). Univariate analysis revealed higher frequencies of the following features in HCC than in HLM (P < .10): nonperipheral washout, corona enhancement, transitional phase hypointensity, mosaic architecture, and fat in mass (P = .002-.073). Multivariate analysis revealed that nonperipheral washout and mosaic architecture favored the diagnosis of HCC over that of HLM with odds ratios of 7.66 and 14.6, respectively (P = .038 and .029, respectively). The interobserver agreement for each item was moderate or substantial (kappa or ICC = .447-.792). Conclusion: Peripheral washout and mosaic architecture may be reliable imaging hallmarks for distinguishing HCC from HLM.
Raising Climate-Resilient Crops: Journey From the Conventional Breeding to New Breeding Approaches

Background In order to meet the demands of the ever-increasing human population, it has become necessary to raise climate-resilient crops. Plant breeding, which involves crossing and selecting superior gene pools, has contributed tremendously towards achieving this goal during the past few decades. The relatively newer methods of crop improvement based on genetic engineering are relatively simple, and targets can be achieved in an expeditious manner. More recently emerged genome editing technique using CRISPR has raised strong hopes among plant scientists for precise integration of valuable traits and removal of undesirable ones. Conclusion Genome editing using Site-Specific Nucleases (SSNs) is a good alternative to the plant breeding and genetic engineering approaches as it can modify the genomes specifically and precisely at the target site in the host genome. Another added advantage of the genome editing approach is the simpler biosafety regulations that have been adopted by many countries for commercialization of the products thus generated. This review provides a critical assessment of the available methods for improving the stress tolerance in crop plants. Special emphasis has been given on genome editing approach in light of the diversity of tools, which are being discovered on an everyday basis and the practical applications of the same. This information will serve as a beginner’s guide to initiate the crop improvement programs as well as giving technical insight to the expert to plan the research strategically to tackle even multigenic traits in crop plants.
Mitochondria–actin cytoskeleton crosstalk in cell migration

Mitochondria perform diverse functions in the cell and their roles during processes such as cell survival, differentiation, and migration are increasingly being appreciated. Mitochondrial and actin cytoskeletal networks not only interact with each other, but this multifaceted interaction shapes their functional dynamics. The interrelation between mitochondria and the actin cytoskeleton extends far beyond the requirement of mitochondrial ATP generation to power actin dynamics, and impinges upon several major aspects of cellular physiology. Being situated at the hub of cell signaling pathways, mitochondrial function can alter the activity of actin regulatory proteins and therefore modulate the processes downstream of actin dynamics such as cellular migration. As we will discuss, this regulation is highly nuanced and operates at multiple levels allowing mitochondria to occupy a strategic position in the regulation of migration, as well as pathological events that rely on aberrant cell motility such as cancer metastasis. In this review, we summarize the crosstalk that exists between mitochondria and actin regulatory proteins, and further emphasize on how this interaction holds importance in cell migration in normal as well as dysregulated scenarios as in cancer.
Molecular Virology of SARS-CoV-2 and Related Coronaviruses

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The global COVID-19 pandemic continues to threaten the lives of hundreds of millions of people, with a severe negative impact on the global economy. SUMMARY Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The global COVID-19 pandemic continues to threaten the lives of hundreds of millions of people, with a severe negative impact on the global economy. Although several COVID-19 vaccines are currently being administered, none of them is 100% effective. Moreover, SARS-CoV-2 variants remain an important worldwide public health issue. Hence, the accelerated development of efficacious antiviral agents is urgently needed. Coronavirus depends on various host cell factors for replication. An ongoing research objective is the identification of host factors that could be exploited as targets for drugs and compounds effective against SARS-CoV-2. In the present review, we discuss the molecular mechanisms of SARS-CoV-2 and related coronaviruses, focusing on the host factors or pathways involved in SARS-CoV-2 replication that have been identified by genome-wide CRISPR screening.