Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization and Proteomic Analysis of Plasma EVs Recovered from Healthy and Diseased Dogs with Canine Leishmaniosis

Dogs are highly valued companions and work animals that are susceptible to many life-threatening conditions such as canine leishmaniosis (CanL). Plasma-derived extracellular vesicles (EVs), exploited extensively in biomarker discovery, constitute a mostly untapped resource in veterinary sciences. Thus, the definition of proteins associated with plasma EVs recovered from healthy and diseased dogs with a relevant pathogen would be important for biomarker development. For this, we recovered, using size-exclusion chromatography (SEC), EVs from 19 healthy and 20 CanL dogs’ plasma and performed proteomic analysis by LC-MS/MS to define their core proteomic composition and search for CanL-associated alterations. EVs-specific markers were identified in all preparations and also non-EVs proteins. Some EVs markers such as CD82 were specific to the healthy animals, while others, such as the Integrin beta 3 were identified in most samples. The EVs-enriched preparations allowed the identification of 529 canine proteins that were identified in both groups, while 465 and 154 were only identified in healthy or CanL samples, respectively. A GO enrichment analysis revealed few CanL-specific terms. Leishmania spp. protein identifications were also found, although with only one unique peptide. Ultimately, CanL-associated proteins of interest were identified and a core proteome was revealed that will be available for intra- and inter-species comparisons.
Shape Recognition Based on Fuzzy Shape Context

In the process of shape matching based on shape context,each of the samples is directly classified into one bin in the histogram,which leads to the inaccurate feature representation and matching bias results.With fuzzy membership function introduced into log-polar coordinate,fuzzy shape context is constructed based on the histogram which represents the fuzzy partition on the distribution of samples,and shapes can be accurately represented by the descriptor.The sample set is divided into subsets in polar coordinates,and segment matching is proposed for shape matching with incorrect matches eliminated.On this basis,the circular shift matching is adopted for resolving the problem of shape matching in different postures.Experimental results prove that shape recognition and retrieval can be effectively achieved by using the proposed method.
Effect of behavioural activation for individuals with post-stroke depression: A systematic review and meta-analysis

Objectives: Post-stroke depression (PSD) affects one third of stroke survivors and substantially impacts recovery, so it is important to identify effective treatments for PSD. Behavioural activation (BA), a relatively simple intervention, has demonstrated efficacy in the treatment of adults with unipolar depression. However, its use and efficacy in the treatment of PSD has not been documented in a systematic review. This systematic review and meta-analysis considers: What effect does BA have on reducing depression symptoms in individuals diagnosed with PSD? Method: MEDLINE, Embase, Emcare, Cochrane Library, and PsycINFO, were searched for randomised controlled trials (RCTs) published in English, on 13 July 2021. To chart the data, we employed a customized extraction sheet. The Cochrane Collaboration risk-of-bias 2 tool was used to determine study quality.Results: Two papers, written by the same lead authors, met the inclusion criteria. The Communication and Low Mood trial and the Behavioural Activation Therapy for Post-Stroke Depression trial were conducted in the United Kingdom and published in 2012 and 2019, respectively. Meta-analysis showed BA was associated with a decrease in depression symptoms in patients with PSD relative to treatment as usual (standard mean difference (SMD) -0.54; 95% confidence interval (CI) -0.90 to -0.18). Conclusion: BA may be more effective when compared to treatment as usual in reducing depression symptoms in individuals with PSD. Although our meta-analysis found positive effects of BA, the evidence is inconclusive due to the small number of studies. High-quality RCTs are needed to confirm the benefits of BA in PSD.
Chirality and polarization-dependent characteristics of dielectric single gyroid metamaterials

Gyroid is a type of three-dimensional chiral structures, which have attracted much research attention recently. A dielectric single gyroid (SG) can be a candidate for providing new means of guiding light because it has been shown to exhibit complete photonic band gaps. Owing to the chiral nature, the SG metamaterials may exhibit circular polarization-dependent properties, leading to new types of polarization-sensitive devices. In this work, we present studies based on finite-difference time-domain (FDTD) method for analyzing the polarization-dependent characteristics of dielectric SG. We show that the operation frequency of SG metamaterials can be advanced from microwave to visible region by varying its material, lattice constant and volume fraction. The corresponding band structures, transmission spectra for right circularly polarized (RCP) light and left circularly polarized (LCP) light, and circular dichroism (CD) indices are examined. According to our analysis, a circular polarization gap is found in the visible region. In particular, the correlation between the volume fraction of dielectric SG and the frequency range of circular polarization band gaps is also investigated. These results are crucial for the design of functional polarization-sensitive devices at the visible wavelength based on dielectric single gyroid metamaterials.
Reduced-order observers for linear discrete-time systems

Luenberger's observer is considered as an alternate to the Kalman filter for obtaining state estimates in linear discrete-time stochastic systems. An interesting new solution to the problem of constructing optimal reduced-order observers is presented. The solution contains as special cases both Kalman's optimal filter and the optimal minimal-order observer of Leondes and Novak. Also, the Tse and Athans observer is obtained as a special case of the reduced-order observer solution.
Mechanism of dynamical phase transitions: The complex-time survival amplitude

Dynamical phase transitions are defined through non-analyticities of the survival probability of an out-of-equilibrium time-evolving state at certain critical times. They ensue from zeros of the corresponding survival amplitude. By extending the time variable onto the complex domain, we formulate the complex-time survival amplitude. The complex zeros of this quantity near the time axis correspond, in the infinite-size limit, to non-analytical points where the survival probability abruptly vanishes. Our results are numerically exemplified in the fully-connected transverse-field Ising model, which displays a symmetry-broken phase delimited by an excited-state quantum phase transition. A detailed study of the behavior of the complex-time survival amplitude when the characteristics of the out-of-equilibrium protocol changes is presented. The influence of the excited-state quantum phase transition is also put into context.
Randomized Primal-Dual Methods with Adaptive Step Sizes

In this paper we propose a class of randomized primal-dual methods to contend with large-scale saddle point problems defined by a convex-concave function $\mathcal{L}(\mathbf{x},y)\triangleq\sum_{i=1}^m f_i(x_i)+\Phi(\mathbf{x},y)-h(y)$. We analyze the convergence rate of the proposed method under mere convexity and strong convexity assumptions of $\mathcal{L}$ in $\mathbf{x}$-variable. In particular, assuming $\nabla_y\Phi(\cdot,\cdot)$ is Lipschitz and $\nabla_\mathbf{x}\Phi(\cdot,y)$ is coordinate-wise Lipschitz for any fixed $y$, the ergodic sequence generated by the algorithm achieves the convergence rate of $\mathcal{O}(M/k)$ in the expected primal-dual gap. Furthermore, assuming that $\mathcal{L}(\cdot,y)$ is strongly convex for any $y$, and that $\Phi(\mathbf{x},\cdot)$ is affine for any $\mathbf{x}$, the scheme enjoys a faster rate of $\mathcal{O}(M/k^2)$ in terms of primal solution suboptimality. We implemented the proposed algorithmic framework to solve kernel matrix learning problem, and tested it against other state-of-the-art first-order methods
Evaluation of multilevel sequentially semiseparable preconditioners on computational fluid dynamics benchmark problems using Incompressible Flow and Iterative Solver Software

This paper studies a new preconditioning technique for sparse systems arising from discretized partial differential equations in computational fluid dynamics problems. This preconditioning technique exploits the multilevel sequentially semiseparable (MSSS) structure of the system matrix. MSSS matrix computations give a data‐sparse way to approximate the LU factorization of a sparse matrix from discretized partial differential equations in linear computational complexity with respect to the problem size. In contrast to the standard block diagonal and block upper‐triangular preconditioners, we exploit the global MSSS structure of the 2×2 block system from the discretized Stokes equation and linearized Navier‐Stokes equation. This avoids approximating the Schur complement explicitly, which is a big advantage over standard block preconditioners. Through numerical experiments on standard computational fluid dynamics benchmark problems in Incompressible Flow and Iterative Solver Software, we show the performance of the MSSS preconditioners. They indicate that the global MSSS preconditioner not only yields mesh size independent convergence but also gives viscosity parameter and Reynolds number independent convergence. Compared with the algebraic multigrid (AMG) method and the geometric multigrid (GMG) method for block preconditioners, the MSSS preconditioning technique is more robust than both the AMG method and GMG method, and considerably faster than the AMG method. Copyright © 2015 John Wiley & Sons, Ltd.
LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment

The first important step in extracting DNA characters is using the output data of MinION devices in the form of electrical current signals. Various cutting-edge base callers use this data to detect the DNA characters based on the input. In this paper, we discuss several shortcomings of prior base callers in the case of time-critical applications, privacy-aware design, and the problem of catastrophic forgetting. Next, we propose the LRDB model, a lightweight open-source model for private developments with a better read-identity (0.35% increase) for the target bacterial samples in the paper. We have limited the extent of training data and benefited from the transfer learning algorithm to make the active usage of the LRDB viable in critical applications. Henceforth, less training time for adapting to new DNA samples (in our case, Bacterial samples) is needed. Furthermore, LRDB can be modified concerning the user constraints as the results show a negligible accuracy loss in case of using fewer parameters. We have also assessed the noise-tolerance property, which offers about a 1.439% decline in accuracy for a 15dB noise injection, and the performance metrics show that the model executes in a medium speed range compared with current cutting-edge models.
First Results from the JWST Early Release Science Program Q3D: The Warm Ionized Gas Outflow in z ∼ 1.6 Quasar XID 2028 and Its Impact on the Host Galaxy

Quasar feedback may regulate the growth of supermassive black holes, quench coeval star formation, and impact galaxy morphology and the circumgalactic medium. However, direct evidence for quasar feedback in action at the epoch of peak black hole accretion at z ≈ 2 remains elusive. A good case in point is the z = 1.6 quasar WISEA J100211.29+013706.7 (XID 2028), where past analyses of the same ground-based data have come to different conclusions. Here, we revisit this object with the integral-field unit of the Near Infrared Spectrograph on board the JWST as part of Early Release Science program Q3D. The excellent angular resolution and sensitivity of the JWST data reveal new morphological and kinematic substructures in the outflowing gas plume. An analysis of the emission-line ratios indicates that photoionization by the central quasar dominates the ionization state of the gas with no obvious sign for a major contribution from hot young stars anywhere in the host galaxy. The rest-frame near-UV emission aligned along the wide-angle cone of outflowing gas is interpreted as a scattering cone. The outflow has cleared a channel in the dusty host galaxy, through which some of the quasar ionizing radiation is able to escape and heat the surrounding interstellar and circumgalactic media. Although the warm ionized outflow is not powerful enough to impact the host galaxy via mechanical feedback, radiative feedback by the active galactic nucleus, aided by the outflow, may help to explain the unusually small molecular gas mass fraction in the galaxy host.
Fitting time-dependent Markovian dynamics to noisy quantum channels

Understanding how to characterise and mitigate errors is a key challenge in developing reliable quantum architecture for near-term applications. Recent work (arXiv:2103.17243) provides an efficient set of algorithms for analysing unknown noise processes requiring only tomographic snapshots of the quantum operator under consideration, without the need of any a-priori information on the noise model, nor necessitating a particular experimental setup. The only assumption made is that the observed channel can be approximated by a time-independent Markovian map, which is a typically reasonable framework when considering short time scales. In this note we lift the time-independent assumption, presenting an extension of the scheme now able to analyse noisy dynamics with time-dependent generators from a sequence of snapshots. We hence provide a diagnostic tool for a wider spectrum of instances while inheriting all the favourable features from the previous protocol. On the theoretical side, the problem of characterising time-dependent Markovian channels has been an open problem for many decades. This work gives an approach to tackle this characterisation problem rigorously.
TOWARDS ROBUST BANGLA COMPLEX NAMED ENTITY RECOGNITION

Named Entity Recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying named entities in text. But much work hasn’t been done for complex named entity recognition in Bangla, despite being the seventh most spoken language globally. CNER is a more challenging task than traditional NER as it involves identifying and classifying complex and compound entities, which are not common in Bangla language. In this paper, we present the winning solution of Bangla Complex Named Entity Recognition Challenge - addressing the CNER task on BanglaCoNER dataset using two different approaches, namely Conditional Random Fields (CRF) and ﬁnetuning transformer based Deep Learning models such as BanglaBERT. Thedataset consisted of 15300 sentences for training and 800 sentences for validation, in the . conll format. Exploratory Data Analysis (EDA) on the dataset revealed that the dataset had 7 different NER tags, with notable presence of English words, suggesting that the dataset is synthetic and likely a product of translation. We experimented with a variety of feature combinations including Part of Speech (POS) tags, word sufﬁxes, Gazetteers, and cluster information from embeddings, while also ﬁnetuning the BanglaBERT (large) model for NER. We found that not all linguistic patterns are immediately apparent or even intuitive to humans, which is why Deep Learning based models has proved to be the more effective model in NLP, including CNER task. Our ﬁne tuned BanglaBERT (large) model achieves an F1 Score of 0.79 on the validation set. Overall, our study highlights the importance of Bangla Complex Named Entity Recognition, particularly in the context of synthetic datasets. Our ﬁndings also demonstrate the efﬁcacy of Deep Learning models such as BanglaBERT for NER in Bangla language
Measuring the Impact of Explanation Bias: A Study of Natural Language Justifications for Recommender Systems

Despite the potential impact of explanations on decision making, there is a lack of research on quantifying their effect on users’ choices. This paper presents an experimental protocol for measuring the degree to which positively or negatively biased explanations can lead to users choosing suboptimal recommendations. Key elements of this protocol include a preference elicitation stage to allow for personalizing recommendations, manual identification and extraction of item aspects from reviews, and a controlled method for introducing bias through the combination of both positive and negative aspects. We study explanations in two different textual formats: as a list of item aspects and as fluent natural language text. Through a user study with 129 participants, we demonstrate that explanations can significantly affect users’ selections and that these findings generalize across explanation formats.
ShabbyPages: A Reproducible Document Denoising and Binarization Dataset

Document denoising and binarization are fundamental problems in the document processing space, but current datasets are often too small and lack sufficient complexity to effectively train and benchmark modern data-driven machine learning models. To fill this gap, we introduce ShabbyPages, a new document image dataset designed for training and benchmarking document denoisers and binarizers. ShabbyPages contains over 6,000 clean"born digital"images with synthetically-noised counterparts ("shabby pages") that were augmented using the Augraphy document augmentation tool to appear as if they have been printed and faxed, photocopied, or otherwise altered through physical processes. In this paper, we discuss the creation process of ShabbyPages and demonstrate the utility of ShabbyPages by training convolutional denoisers which remove real noise features with a high degree of human-perceptible fidelity, establishing baseline performance for a new ShabbyPages benchmark.
Multi-Competitive Virus Spread over a Time-Varying Networked SIS Model with an Infrastructure Network

We study the spread of multi-competitive viruses over a (possibly) time-varying network of individuals accounting for the presence of shared infrastructure networks that further enables transmission of the virus. We establish a sufficient condition for exponentially fast eradication of a virus for: 1) time-invariant graphs, 2) time-varying graphs with symmetric interactions between individuals and homogeneous virus spread across the network (same healing and infection rate for all individuals), and 3) directed and slowly varying graphs with heterogeneous virus spread (not necessarily same healing and infection rates for all individuals) across the network. Numerical examples illustrate our theoretical results and indicate that, for the time-varying case, violation of the aforementioned sufficient conditions could lead to the persistence of a virus.
Learning Spatio-Temporal Aggregations for Large-Scale Capacity Expansion Problems

Effective investment planning decisions are crucial to ensure that critical cyber-physical infrastructures satisfy performance requirements over an extended time horizon. Computing these decisions often requires solving Capacity Expansion Problems (CEPs). In the context of regional-scale energy systems, these problems are prohibitively expensive to solve owing to large network sizes, heterogeneous node characteristics (e.g., electric power and natural gas energy vectors), and a large number of operational periods. To maintain tractability, traditional approaches resort to aggregating network nodes and/or selecting a set of representative time periods. Often, these reductions do not capture the supply-demand variations that crucially impact the CEP costs and constraints, leading to suboptimal decisions. Here, we propose a novel graph convolutional autoencoder approach for spatiotemporal aggregation of a generic CEP with heterogeneous nodes (CEPHN). Our autoencoder architecture leverages graph pooling to identify nodes with similar characteristics and minimizes a multi-objective loss function. This loss function is specifically tailored to induce desirable spatial and temporal aggregations in terms of tractability and optimality of CEPHN. In particular, the output of the graph pooling provides a spatial aggregation while clustering the low-dimensional encoded representations yields a temporal aggregation. We apply our approach to generation expansion planning of coupled power and natural gas system in New England. The resulting spatiotemporal aggregation leads to a simpler CEPHN with 6 nodes (as opposed to 88 nodes in the original system) and a small set of representative days selected from a full year. We evaluate aggregation outcomes over a range of hyperparameters governing the loss function, and compare resulting upper bounds on the original problem with those obtained using previously known methods. The results from our case study show that this approach provides solutions that are 33% (resp. 10%) better those than obtained from standard spatial (resp. temporal) aggregation approaches.
NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes

With the introduction of Neural Radiance Fields (NeRFs), novel view synthesis has recently made a big leap forward. At the core, NeRF proposes that each 3D point can emit radiance, allowing to conduct view synthesis using differentiable volumetric rendering. While neural radiance fields can accurately represent 3D scenes for computing the image rendering, 3D meshes are still the main scene representation supported by most computer graphics and simulation pipelines, enabling tasks such as real time rendering and physics-based simulations. Obtaining 3D meshes from neural radiance fields still remains an open challenge since NeRFs are optimized for view synthesis, not enforcing an accurate underlying geometry on the radiance field. We thus propose a novel compact and flexible architecture that enables easy 3D surface reconstruction from any NeRF-driven approach. Upon having trained the radiance field, we distill the volumetric 3D representation into a Signed Surface Approximation Network, allowing easy extraction of the 3D mesh and appearance. Our final 3D mesh is physically accurate and can be rendered in real time on an array of devices.
Tail-GAN: Learning to Simulate Tail Risk Scenarios

The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components, with particular importance devoted to the simulation of tail risk scenarios. We propose a novel data-driven approach that utilizes Generative Adversarial Network (GAN) architecture and exploits the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES). Our proposed approach is capable of learning to simulate price scenarios that preserve tail risk features for benchmark trading strategies, including consistent statistics such as VaR and ES. We prove a universal approximation theorem for our generator for a broad class of risk measures. In addition, we show that the training of the GAN may be formulated as a max-min game, leading to a more effective approach for training. Our numerical experiments show that, in contrast to other data-driven scenario generators, our proposed scenario simulation method correctly captures tail risk for both static and dynamic portfolios.