Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

SUMMARY Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. AVAILABILITY AND IMPLEMENTATION PyBNF version 1.1. 9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g., numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch? v=2aRqpqFOiS4&t=63s). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Robust recovery of bandlimited graph signals via randomized dynamical sampling

Heat diffusion processes have found wide applications in modelling dynamical systems over graphs. In this paper, we consider the recovery of a $k$-bandlimited graph signal that is an initial signal of a heat diffusion process from its space-time samples. We propose three random space-time sampling regimes, termed dynamical sampling techniques, that consist in selecting a small subset of space-time nodes at random according to some probability distribution. We show that the number of space-time samples required to ensure stable recovery for each regime depends on a parameter called the spectral graph weighted coherence, that depends on the interplay between the dynamics over the graphs and sampling probability distributions. In optimal scenarios, no more than $\mathcal{O}(k \log(k))$ space-time samples are sufficient to ensure accurate and stable recovery of all $k$-bandlimited signals. In any case, dynamical sampling typically requires much fewer spatial samples than the static case by leveraging the temporal information. Then, we propose a computationally efficient method to reconstruct $k$-bandlimited signals from their space-time samples. We prove that it yields accurate reconstructions and that it is also stable to noise. Finally, we test dynamical sampling techniques on a wide variety of graphs. The numerical results support our theoretical findings and demonstrate the efficiency.
Real Space Quantum Cluster Formulation for the Typical Medium Theory of Anderson Localization

We develop a real space cluster extension of the typical medium theory (cluster-TMT) to study Anderson localization. By construction, the cluster-TMT approach is formally equivalent to the real space cluster extension of the dynamical mean field theory. Applying the developed method to the 3D Anderson model with a box disorder distribution, we demonstrate that cluster-TMT successfully captures the localization phenomena in all disorder regimes. As a function of the cluster size, our method obtains the correct critical disorder strength for the Anderson localization in 3D, and systematically recovers the re-entrance behavior of the mobility edge. From a general perspective, our developed methodology offers the potential to study Anderson localization at surfaces within quantum embedding theory. This opens the door to studying the interplay between topology and Anderson localization from first principles.
PAC-Bayes Information Bottleneck

Understanding the source of the superior generalization ability of NNs remains one of the most important problems in ML research. There have been a series of theoretical works trying to derive non-vacuous bounds for NNs. Recently, the compression of information stored in weights (IIW) is proved to play a key role in NNs generalization based on the PAC-Bayes theorem. However, no solution of IIW has ever been provided, which builds a barrier for further investigation of the IIW's property and its potential in practical deep learning. In this paper, we propose an algorithm for the efficient approximation of IIW. Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB. From PIB, we can empirically identify the fitting to compressing phase transition during NNs' training and the concrete connection between the IIW compression and the generalization. Besides, we verify that IIW is able to explain NNs in broad cases, e.g., varying batch sizes, over-parameterization, and noisy labels. Moreover, we propose an MCMC-based algorithm to sample from the optimal weight posterior characterized by PIB, which fulfills the potential of IIW in enhancing NNs in practice.
Creating quantum correlations in generalized entanglement swapping

We study how di ﬀ erent types of quantum correlations can be established as the consequence of a generalized entanglement swapping protocol where starting from two Bell pairs (1 , 2) and (3 , 4), a general quantum measurement (denoted by a positive operator-valued measure or POVM) is performed on the pair (2 , 3), which results in creating quantum correlation in (1 , 4) shared between two spatially separated observers. Contingent upon using di ﬀ erent kinds of POVMs, we show generation or destruction of di ﬀ erent quantum correlations in the pairs (1 , 4), (1 , 2) and (3 , 4). This thus reﬂects non-trivial transfer of quantum correlations from the pairs (1 , 2) and (3 , 4) to the pair (1 , 4). As an o ﬀ shoot, this study provides an operational tool to generate di ﬀ erent types of single parameter families of quantum correlated states (for example, entangled but not EPR steerable, or EPR steerable but not Bell nonlocal, or Bell nonlocal) by choosing di ﬀ erent quantum measurements in the basic entanglement swapping setup. We further extend our study by taking mixed initial states shared by the pairs (1,2) and (3,4). Finally, we study network nonlocality in our scenario. Here, we ﬁnd out appropriate POVM measurement for which the generated correlation demonstrates / does not demonstrate network nonlocality for the whole range of the measurement parameter.
DORA: Distributed Online Risk-Aware Explorer

Exploration of unknown environments is an important challenge in the field of robotics. While a single robot can achieve this task alone, evidence suggests it could be accomplished more efficiently by groups of robots, with advantages in terms of terrain coverage as well as robustness to failures. Exploration can be guided through belief maps, which provide probabilistic information about which part of the terrain is interesting to explore (either based on risk management or reward). This process can be centrally coordinated by building a collective belief map on a common server. However, relying on a central processing station creates a communication bottleneck and single point of failure for the system. In this paper, we present Distributed Online Risk-Aware (DORA) Explorer, an exploration system that leverages decentralized information sharing to update a common risk belief map. DORA-Explorer allows a group of robots to explore an unknown environment discretized as a 2D grid with obstacles, with high coverage while minimizing exposure to risk, effectively reducing robot failures.
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective

A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. With further constraints such as bit balance and code orthogonality, it is not uncommon for existing models to employ a large number (>4) of losses. This leads to difficulties in model training and subsequently impedes their effectiveness. In this work, we propose a novel deep hashing model with only a single learning objective. Specifically, we show that maximizing the cosine similarity between the continuous codes and their corresponding binary orthogonal codes can ensure both hash code discriminativeness and quantization error minimization. Further, with this learning objective, code balancing can be achieved by simply using a Batch Normalization (BN) layer and multi-label classification is also straightforward with label smoothing. The result is an one-loss deep hashing model that removes all the hassles of tuning the weights of various losses. Importantly, extensive experiments show that our model is highly effective, outperforming the state-of-the-art multi-loss hashing models on three large-scale instance retrieval benchmarks, often by significant margins. Code is available at https://github.com/kamwoh/orthohash
Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data

ABSTRACT High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology, genomics, and proteomics, the data are often functional in their nature and exhibit a degree of roughness and nonstationarity. These structures pose additional challenges to commonly used methods that rely mainly on a two-stage approach performing variable selection and classification separately. We propose in this work a novel Gaussian process discriminant analysis (GPDA) that combines these steps in a unified framework. Our model is a two-layer nonstationary Gaussian process coupled with an Ising prior to identify differentially-distributed locations. Scalable inference is achieved via developing a variational scheme that exploits advances in the use of sparse inverse covariance matrices. We demonstrate the performance of our methodology on simulated datasets and two proteomics datasets: breast cancer and SARS-CoV-2. Our approach distinguishes itself by offering explainability as well as uncertainty quantification in addition to low computational cost, which are crucial to increase trust and social acceptance of data-driven tools. Supplementary materials for this article are available online.
Large-scale Crash Localization using Multi-Task Learning

Crash localization, an important step in debugging crashes, is challenging when dealing with an extremely large number of diverse applications and platforms and underlying root causes. Large-scale error reporting systems, e.g., Windows Error Reporting (WER), commonly rely on manually developed rules and heuristics to localize blamed frames causing the crashes. As new applications and features are routinely introduced and existing applications are run under new environments, developing new rules and maintaining existing ones become extremely challenging. We propose a data-driven solution to address the problem. We start with the first large-scale empirical study of 362 𝐾 crashes and their blamed methods reported to WER by tens of thousands of applications running in the field. The analysis provides valuable insights on where and how the crashes happen and what methods to blame for the crashes. These insights enable us to develop Deep-Analyze, a novel multi-task sequence labeling approach for identifying blamed frames in stack traces. We evaluate our model with over a million real-world crashes from four popular Microsoft applications and show that DeepAnalyze, trained with crashes from one set of applications, not only accurately localizes crashes of the same applications, but also bootstrap crash localization for other applications with zero to very little additional training data.
An adapted solution of a fractional backward stochastic differential equation

Our aim in this paper is to deal with a new type differential equation so-called Caputo fractional backward stochastic differential equations (for short Caputo fBSDEs) and study the global existence and uniqueness of an adapted solution to Caputo fBSDEs of order α ∈ ( 1 2 , 1) whose coefficients satisfy Lipschitz condition by applying fundamental lemma which plays a crucial role in the theory of Caputo fBSDEs. The interesting point here is to use a new weighted norm in square-integrable measurable function space for fundamental lemma and therefore for the whole paper. For this class of systems, we then show the coincidence between the notion of stochastic Volterra integral equation and mild solution.
Integrating Contextual Knowledge to Visual Features for Fine Art Classification

Automatic art analysis has seen an ever-increasing interest from the pattern recognition and computer vision community. However, most of the current work is mainly based solely on digitized artwork images, sometimes supplemented with some metadata and textual comments. A knowledge graph that integrates a rich body of information about artworks, artists, painting schools, etc., in a unified structured framework can provide a valuable resource for more powerful information retrieval and knowledge discovery tools in the artistic domain. To this end, this paper presents ArtGraph: an artistic knowledge graph based on WikiArt and DBpedia. The graph, implemented in Neo4j, already provides knowledge discovery capabilities without having to train a learning system. In addition, the embeddings extracted from the graph are used to inject"contextual"knowledge into a deep learning model to improve the accuracy of artwork attribute prediction tasks.
Modularis: Modular Relational Analytics over Heterogeneous Distributed Platforms

The enormous quantity of data produced every day together with advances in data analytics has led to a proliferation of data management and analysis systems. Typically, these systems are built around highly specialized monolithic operators optimized for the underlying hardware. While effective in the short term, such an approach makes the operators cumbersome to port and adapt, which is increasingly required due to the speed at which algorithms and hardware evolve. To address this limitation, we present Modularis , an execution layer for data analytics based on sub-operators , i.e., composable building blocks resembling traditional database operators but at a finer granularity. To demonstrate the feasibility and advantages of our approach, we use Modularis to build a distributed query processing system supporting relational queries running on an RDMA cluster, a serverless cloud platform, and a smart storage engine. Modularis requires minimal code changes to execute queries across these three diverse hardware platforms, showing that the sub-operator approach reduces the amount and complexity of the code to maintain. In fact, changes in the platform affect only those sub-operators that depend on the underlying hardware (in our use cases, mainly the sub-operators related to network communication). We show the end-to-end performance of Modularis by comparing it with a framework for SQL processing (Presto), a commercial cluster database (SingleStore), as well as Query-as-a-Service systems (Athena, BigQuery). Modularis outperforms all these systems, proving that the design and architectural advantages of a modular design can be achieved without degrading performance. We also compare Modularis with a hand-optimized implementation of a join for RDMA clusters. We show that Modularis has the advantage of being easily extensible to a wider range of join variants and group by queries, all of which are not supported in the hand-tuned join.
Impact of Role Conflict and Role Ambiguity on Job Satisfaction the Mediating Effect of Job Stress and Moderating Effect of Islamic Work Ethics

The main idea behind this study is to examine the impact of role ambiguity and role conflict on job satisfaction. This study also assesses the moderating effect of Islamic work ethics, role ambiguity, role conflict and also examine the mediating effect of job stress between role ambiguity, role conflict and job satisfaction. The questionnaire was used for data collection. 200 questionnaires were distributed among respondent out of which 175 were used for data analysis statistically descriptive statistics correlation and regression analysis were used for testing hypothesis. The finding of this study reveal that the role conflict has a negative relationship with job satisfaction and job stress has an insignificant relationship with the job satisfaction. This study also reveal that the role stressor should be controlled so that an increase in job satisfaction. When both role ambiguity and role conflict increase job stress also increase and the Islamic work ethics has a negative impact on job stress. Job stress significantly partially mediate the relationship between role conflict and job satisfaction.
Simplifying the OpenFlexure microscope software with the web of things

We present the OpenFlexure Microscope software stack which provides computer control of our open source motorised microscope. Our diverse community of users needs both graphical and script-based interfaces. We split the control code into client and server applications interfaced via a web API conforming to the W3C Web of Things standard. A graphical interface is viewed either in a web browser or in our cross-platform Electron application, and gives basic interactive control including common operations such as Z stack acquisition and tiled scanning. Automated control is possible from Python and Matlab, or any language that supports HTTP requests. Network control makes the software stack more robust, allows multiple microscopes to be controlled by one computer, and facilitates sharing of equipment. Graphical and script-based clients can run simultaneously, making it easier to monitor ongoing experiments. We have included an extension mechanism to add functionality, for example controlling additional hardware components or adding automation routines. Using a Web of Things approach has resulted in a user-friendly and extremely versatile software control solution for the OpenFlexure Microscope, and we believe this approach could be generalized in the future to make automated experiments involving several instruments much easier to implement.
Intrinsic neural network dynamics in catatonia

Catatonia is a transnosologic psychomotor syndrome with high prevalence in schizophrenia spectrum disorders (SSD). There is mounting neuroimaging evidence that catatonia is associated with aberrant frontoparietal, thalamic and cerebellar regions. Large‐scale brain network dynamics in catatonia have not been investigated so far. In this study, resting‐state fMRI data from 58 right‐handed SSD patients were considered. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate and test the underlying intrinsic components (ICs) in SSD patients with (NCRS total score ≥ 3; n = 30) and without (NCRS total score = 0; n = 28) catatonia. Functional network connectivity (FNC) during rest was calculated between pairs of ICs and transient changes in connectivity were estimated using sliding windowing and clustering (to capture both static and dynamic FNC). Catatonic patients showed increased static FNC in cerebellar networks along with decreased low frequency oscillations in basal ganglia (BG) networks. Catatonic patients had reduced state changes and dwelled more in a state characterized by high within‐network correlation of the sensorimotor, visual, and default‐mode network with respect to noncatatonic patients. Finally, in catatonic patients according to DSM‐IV‐TR (n = 44), there was a significant correlation between increased within FNC in cortico‐striatal state and NCRS motor scores. The data support a neuromechanistic model of catatonia that emphasizes a key role of disrupted sensorimotor network control during distinct functional states.
Near‐infrared optical spectroscopy for pancreas shrinkage estimation with multi synchrosqueezing transform and multivariate regression model

In this article, we proposed a method to estimate pancreas shrinkage with pancreas β cell insulin secretion. The β cells in the pancreas secrete insulin and digestive enzymes after food consumption. Conventionally, the pancreas structure estimation is done with magnetic resonance imaging (MRI) and ultrasound imaging techniques. However, the structure of the pancreas changes due to islet cell death. The presence of islet cells is detected through near infrared (NIR) spectroscopy signal acquired from the epigastric region (pancreas) of the abdomen. Subsequently, the NIR spectroscopy signal from the pancreas is analyzed with multi synchrosqueezing transform (MSST); whereas, the β cell insulin secretion varies for diabetic and nondiabetic persons. The existence of β cell and insulin secretion correlates with Root Mean Square (RMS) and kurtosis via a multivariate regression model to evaluate pancreas shrinkage. In terms of numerical results, NIR spectroscopy signal from the pancreas was obtained for about 20 nondiabetic and 20 diabetic persons. The pancreas shrinkage was estimated with 88% accuracy. The results are validated with MRI pancreas images for earlier detection of the apoptotic pancreas. The pancreas shrinkage causes lower insulin emission and unpredictable blood glucose in diabetic patients. Analysis of NIR spectroscopy signals of the pancreas with MSST was done to obtain higher‐order and lower‐order frequency components.
Assessment of astrocytes as a mediator of memory and learning in rodents

The classical view of astrocytes is that they provide supportive functions for neurons, transporting metabolites and maintaining the homeostasis of the extracellular milieu. This view is gradually changing with the advent of molecular genetics and optical methods allowing interrogation of selected cell types in live experimental animals. An emerging view that astrocytes additionally act as a mediator of synaptic plasticity and contribute to learning processes has gained in vitro and in vivo experimental support. Here we focus on the literature published in the past two decades to review the roles of astrocytes in brain plasticity in rodents, whereby the roles of neurotransmitters and neuromodulators are considered to be comparable to those in humans. We outline established inputs and outputs of astrocytes and discuss how manipulations of astrocytes have impacted the behavior in various learning paradigms. Multiple studies suggest that the contribution of astrocytes has a considerably longer time course than neuronal activation, indicating metabolic roles of astrocytes. We advocate that exploring upstream and downstream mechanisms of astrocytic activation will further provide insight into brain plasticity and memory/learning impairment.