Miracle-dz/UniFAR-FTUs huggingface dataset:
Insight into molecular interaction between shrimp and white spot syndrome virus through MjsvCL-VP28 complex: an in-silico approach

Abstract White Spot disease is a devastating disease of shrimps caused by White Spot Syndrome Virus in multifarious shrimp species. At present there is no absolute medication to suppress the disease hence, there is an urgent need for development of drug against the virus. Molecular interaction between viral envelope protein VP28 and shrimp receptor protein especially chitins play a pivotal role in ingression of WSSV. In the present study, we have tried to shed light on structural aspects of lectin protein in Marsupenaeus japonicus (MjsvCL). A structural insight to the CTLD-domain of MjsvCL has facilitated the understanding of the binding mechanism between the two proteins that is responsible for entry of WSSV into shrimps. Further, incorporation of molecular dynamics simulation and MMPBSA studies revealed the affinity of binding and certain hotspot residues, which are critical for association of both the proteins. For the first time we have proposed that these amino acids are quintessential for formation of VP28-MjsvCL complex and play crucial role in entry of WSSV into shrimps. Targeting the interaction between VP28 and CTLD of MjsvCL may possibly serve as a potential drug target. The current study provides information for better understanding the interaction between VP28 and MjsvCL that could be a plausible site for future inhibitors against WSSV in shrimps. Communicated by Ramaswamy H. Sarma
Forensic applications of hyperspectral imaging technique: a narrative review

Hyperspectral imaging (HSI) collects and processes information from the entire electromagnetic spectrum to obtain the spectrum of each pixel in the image of a scene, with the aim of finding objects and identifying materials. It is a non-contact, non-destructive technology that can be used without modifying or altering the analysed target. Forensic analysis and crime scene investigations are two of the most investigated fields of application, being able to detect and analyse many types of evidence. In this paper we analysed the most commonly reported forensic science applications. The literature indicates that the fields in which HSI appears most promising are the analysis of blood traces, document forgery, gunshot residues and the identification of fingerprints.
DeepBackRib: Deep learning to understand factors associated with readmissions after rib fractures

Deep learning algorithms have promising potential to yield high predictive performance, yet clinical adoption is limited by poor explainability. We demonstrate one way to facilitate making deep learning algorithms explainable using DeepBackRib. BACKGROUND Deep neural networks yield high predictive performance, yet obscure interpretability limits clinical applicability. We aimed to build an explainable deep neural network that elucidates factors associated with readmissions after rib fractures among nonelderly adults, termed DeepBackRib. We hypothesized that DeepBackRib could accurately predict readmissions and a game theoretic approach to elucidate how predictions are made would facilitate model explainability. METHODS We queried the 2017 National Readmissions Database for index hospitalization encounters of adults aged 18 to 64 years hospitalized with multiple rib fractures. The primary outcome was 3-month readmission(s). Study cohort was split 60-20-20 into training-validation-test sets. Model input features included demographic/injury/index hospitalization characteristics and index hospitalization International Classification of Diseases, Tenth Revision, diagnosis codes. The seven-layer DeepBackRib comprised multipronged strategies to mitigate overfitting and was trained to optimize recall. Shapley additive explanation analysis identified the marginal contribution of each input feature for predicting readmissions. RESULTS A total of 20,260 patients met the inclusion criteria, among whom 11% (n = 2,185) experienced 3-month readmissions. Feature selection narrowed 3,164 candidate input features to 61, and DeepBackRib yielded 91%, 85%, and 82% recall on the training, validation, and test sets, respectively. Shapley additive explanation analysis quantified the marginal contribution of each input feature in determining DeepBackRib's predictions: underlying chronic obstructive pulmonary disease and long index hospitalization length of stay had positive associations with 3-month readmissions, while private primary payer and diagnosis of pneumothorax during index admission had negative associations. CONCLUSION We developed and internally validated a high-performing deep learning algorithm that elucidates factors associated with readmissions after rib fractures. Despite promising predictive performance, standalone deep learning algorithms are insufficient for clinical prediction tasks: a concerted effort is needed to ensure that clinical prediction algorithms remain explainable. LEVEL OF EVIDENCE Prognostic and Epidemiological; Level III.
A shift-optimized Hill-type estimator

A wide range of natural and social phenomena result in observables whose distributions can be well approximated by a power-law decay. The well-known Hill estimator of the tail exponent provides results which are in many respects superior to other estimators in case the asymptotics of the distribution is indeed a pure power-law, however,systematic errors occur if the distribution is altered by simply shifting it. We demonstrate some related problems which typically emerge when dealing with empirical data and suggest a procedure designed to extend the applicability of the Hill estimator.
Active Predictive Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems

In this article, we propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC), designing an agent completely built from predictive processing circuits that facilitate dynamic, online learning from sparse rewards, embodying the principles of planning-as-inference. Concretely, we craft an adaptive agent system, which we call active predictive coding (ActPC), that balances an internally-generated epistemic signal (meant to encourage intelligent exploration) with an internally-generated instrumental signal (meant to encourage goal-seeking behavior) to learn how to control various simulated robotic systems as well as a complex robotic arm using a realistic simulator, i.e., the Surreal Robotics Suite, for the block lifting task and the can pick-and-place problem. Notably, our results demonstrate that the proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backpropagation-based reinforcement learning approaches.
EcoFormer: Energy-Saving Attention with Linear Complexity

Transformer is a transformative framework that models sequential data and has achieved remarkable performance on a wide range of tasks, but with high computational and energy cost. To improve its efficiency, a popular choice is to compress the models via binarization which constrains the floating-point values into binary ones to save resource consumption owing to cheap bitwise operations significantly. However, existing binarization methods only aim at minimizing the information loss for the input distribution statistically, while ignoring the pairwise similarity modeling at the core of the attention. To this end, we propose a new binarization paradigm customized to high-dimensional softmax attention via kernelized hashing, called EcoFormer, to map the original queries and keys into low-dimensional binary codes in Hamming space. The kernelized hash functions are learned to match the ground-truth similarity relations extracted from the attention map in a self-supervised way. Based on the equivalence between the inner product of binary codes and the Hamming distance as well as the associative property of matrix multiplication, we can approximate the attention in linear complexity by expressing it as a dot-product of binary codes. Moreover, the compact binary representations of queries and keys enable us to replace most of the expensive multiply-accumulate operations in attention with simple accumulations to save considerable on-chip energy footprint on edge devices. Extensive experiments on both vision and language tasks show that EcoFormer consistently achieves comparable performance with standard attentions while consuming much fewer resources. For example, based on PVTv2-B0 and ImageNet-1K, Ecoformer achieves a 73% on-chip energy footprint reduction with only a 0.33% performance drop compared to the standard attention. Code is available at https://github.com/ziplab/EcoFormer.
WiForceSticker: Batteryless, Thin Sticker-like Flexible Force Sensor

Any two objects in contact with each other exert a force that could be simply due to gravity or mechanical contact, such as a robotic arm gripping an object or even the contact between two bones at our knee joints. The ability to naturally measure and monitor these contact forces allows a plethora of applications from warehouse management (detect faulty packages based on weights) to robotics (making a robotic arms' grip as sensitive as human skin) and healthcare (knee-implants). It is challenging to design a ubiquitous force sensor that can be used naturally for all these applications. First, the sensor should be small enough to fit in narrow spaces. Next, we don't want to lay cumbersome cables to read the force values from the sensors. Finally, we need to have a battery-free design to meet the in-vivo applications. We develop WiForceSticker, a wireless, battery-free, sticker-like force sensor that can be ubiquitously deployed on any surface, such as all warehouse packages, robotic arms, and knee joints. WiForceSticker first designs a tiny $4$~mm~$\times$~$2$~mm~$\times$~$0.4$~mm capacitative sensor design equipped with a $10$~mm~$\times$~$10$~mm antenna designed on a flexible PCB substrate. Secondly, it introduces a new mechanism to transduce the force information on ambient RF radiations that can be read by a remotely located reader wirelessly without requiring any battery or active components at the force sensor, by interfacing the sensors with COTS RFID systems. The sensor can detect forces in the range of $0$-$6$~N with sensing accuracy of $<0.5$~N across multiple testing environments and evaluated with over $10,000$ varying force level presses on the sensor. We also showcase two application case studies with our designed sensors, weighing warehouse packages and sensing forces applied by bone joints.
2x2 MIMO Prototype for BER and EVM Measurements in Metal Enclosure

—In this work, we present a 2x2 near-ﬁeld multi- input multiple-output (MIMO) prototype for bit-error-rate (BER) and error vector magnitude (EVM) measurements in a metal enclosure. The near-ﬁeld MIMO prototype is developed using software-deﬁned-radios (SDRs) for over-the-air transmission of QPSK modulated baseband waveforms. We check the near-ﬁeld MIMO BER and EVM measurements in three different scenarios in a highly reﬂecting metal enclosure environment. In the ﬁrst scenario, the line-of-sight (LOS) communication link is investi- gated when the mode-stirrer is stationary. In stationary channel conditions near-ﬁeld MIMO BER and EVM measurements are performed. In the second scenario, BER and EVM measurements are performed in dynamic channel conditions when the mode- stirrer is set to move continuously. In the third scenario, LOS communication near-ﬁeld MIMO BER and EVM measurements are performed in stationary channel conditions but now in the presence of MIMO interference. In three different scenarios, near-ﬁeld MIMO BER and EVM measurements are investigated at different Tx USRP gain values and in the presence of varying levels of MIMO interference.
Semi-implicit Integration and Data-Driven Model Order Reduction in Structural Dynamics with Hysteresis

Structural damping is approximately rate-independent for many materials. Popular models for rate-independent dissipation are hysteresis models. A highly popular hysteresis model is the Bouc-Wen model. If such dissipation is incorporated in a finite element model with hysteretic states at many Gauss points distributed over the structure, then many nonsmooth differential equations must be integrated alongside the dynamic equations. Such numerical integration is difficult due to both nonanalytic hysteresis and higher modal frequencies. Here, we offer two contributions. First, we present a simple semi-implicit numerical integration approach, wherein the structural part is integrated implicitly and the hysteresis part is integrated explicitly. A cantilever beam example is solved in detail using high mesh refinement. Convergence is approximately quadratic for lower damping and for a smoother hysteresis loop. For less smooth hysteresis and higher damping, convergence is roughly linear on average. Even so, time steps far larger than the smallest time period in the model give stable results. This benefit arises from the implicit part of the algorithm. This algorithm is used to compute datasets for our second contribution, which is a model order reduction approach for both vibration modes and hysteretic states. Convergence with higher numbers of retained hysteretic states is excellent.
Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation

Both in academic and industry-based research, online evaluation methods are seen as the golden standard for interactive applications like recommendation systems. Naturally, the reason for this is that we can directly measure utility metrics that rely on interventions, being the recommendations that are being shown to users. Nevertheless, online evaluation methods are costly for a number of reasons, and a clear need remains for reliable offline evaluation procedures. In industry, offline metrics are often used as a first-line evaluation to generate promising candidate models to evaluate online. In academic work, limited access to online systems makes offline metrics the de facto approach to validating novel methods. Two classes of offline metrics exist: proxy-based methods, and counterfactual methods. The first class is often poorly correlated with the online metrics we care about, and the latter class only provides theoretical guarantees under assumptions that cannot be fulfilled in real-world environments. Here, we make the case that simulation-based comparisons provide ways forward beyond offline metrics, and argue that they are a preferable means of evaluation.
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description

Most existing Time series classification (TSC) models lack interpretability and are difficult to inspect. Interpretable machine learning models can aid in discovering patterns in data as well as give easy-to-understand insights to domain specialists. In this study, we present Neuro-Symbolic Time Series Classification (NSTSC), a neuro-symbolic model that leverages signal temporal logic (STL) and neural network (NN) to accomplish TSC tasks using multi-view data representation and expresses the model as a human-readable, interpretable formula. In NSTSC, each neuron is linked to a symbolic expression, i.e., an STL (sub)formula. The output of NSTSC is thus interpretable as an STL formula akin to natural language, describing temporal and logical relations hidden in the data. We propose an NSTSC-based classifier that adopts a decision-tree approach to learn formula structures and accomplish a multiclass TSC task. The proposed smooth activation functions enable the model to be learned in an end-to-end fashion. We test NSTSC on a real-world wound healing dataset from mice and benchmark datasets from the UCR time-series repository, demonstrating that NSTSC achieves comparable performance with the state-of-the-art models. Furthermore, NSTSC can generate interpretable formulas that match domain knowledge.
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold

When training overparameterized deep networks for classification tasks, it has been widely observed that the learned features exhibit a so-called"neural collapse"phenomenon. More specifically, for the output features of the penultimate layer, for each class the within-class features converge to their means, and the means of different classes exhibit a certain tight frame structure, which is also aligned with the last layer's classifier. As feature normalization in the last layer becomes a common practice in modern representation learning, in this work we theoretically justify the neural collapse phenomenon for normalized features. Based on an unconstrained feature model, we simplify the empirical loss function in a multi-class classification task into a nonconvex optimization problem over the Riemannian manifold by constraining all features and classifiers over the sphere. In this context, we analyze the nonconvex landscape of the Riemannian optimization problem over the product of spheres, showing a benign global landscape in the sense that the only global minimizers are the neural collapse solutions while all other critical points are strict saddles with negative curvature. Experimental results on practical deep networks corroborate our theory and demonstrate that better representations can be learned faster via feature normalization.
Quantum computational finance: martingale asset pricing for incomplete markets

A derivative is a ﬁnancial security whose value is a function of underlying traded assets and market outcomes. Pricing a ﬁnancial derivative involves setting up a market model, ﬁnding a martingale (“fair game”) probability measure for the model from the given asset prices, and using that probability measure to price the derivative. When the number of underlying assets and/or the number of market outcomes in the model is large, pricing can be computationally demanding. We show that a variety of quantum techniques can be applied to the pricing problem in ﬁnance, with a particular focus on incomplete markets. We discuss three different methods that are distinct from previous works: they do not use the quantum algorithms for Monte Carlo estimation and they extract the martingale measure from market variables akin to bootstrapping, a common practice among ﬁnancial institutions. The ﬁrst two methods are based on a formulation of the pricing problem into a linear program and are using respectively the quantum zero-sum game algorithm and the quantum simplex algorithm as subroutines. For the last algorithm, we formalize a new market assumption milder than market completeness for which quantum linear systems solvers can be applied with the associated potential for large speedups. As a prototype use case, we conduct numerical experiments in the framework of the Black-Scholes-Merton model.
Towards self-attention based visual navigation in the real world

Vision guided navigation requires processing complex visual information to inform task-orientated decisions. Applications include autonomous robots, self-driving cars, and assistive vision for humans. A key element is the extraction and selection of relevant features in pixel space upon which to base action choices, for which Machine Learning techniques are well suited. However, Deep Reinforcement Learning agents trained in simulation often exhibit unsatisfactory results when deployed in the real-world due to perceptual differences known as the $\textit{reality gap}$. An approach that is yet to be explored to bridge this gap is self-attention. In this paper we (1) perform a systematic exploration of the hyperparameter space for self-attention based navigation of 3D environments and qualitatively appraise behaviour observed from different hyperparameter sets, including their ability to generalise; (2) present strategies to improve the agents' generalisation abilities and navigation behaviour; and (3) show how models trained in simulation are capable of processing real world images meaningfully in real time. To our knowledge, this is the first demonstration of a self-attention based agent successfully trained in navigating a 3D action space, using less than 4000 parameters.
NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries

We develop NL2INTERFACE to explore the potential of generating usable interactive multi-visualization interfaces from natural language queries. With NL2INTERFACE, users can directly write natural language queries to automatically generate a fully interactive multi-visualization interface without any extra effort of learning a tool or programming language. Further, users can interact with the interfaces to easily transform the data and quickly see the results in the visualizations.
OA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM

In this work, we explore the use of objects in Simultaneous Localization and Mapping in unseen worlds and propose an object-aided system (OA-SLAM). More precisely, we show that, compared to low-level points, the major benefit of objects lies in their higher-level semantic and discriminating power. Points, on the contrary, have a better spatial localization accuracy than the generic coarse models used to represent objects (cuboid or ellipsoid). We show that combining points and objects is of great interest to address the problem of camera pose recovery. Our main contributions are: (1) we improve the relocalization ability of a SLAM system using high-level object landmarks; (2) we build an automatic system, capable of identifying, tracking and reconstructing objects with 3D ellipsoids; (3) we show that object-based localization can be used to reinitialize or resume camera tracking. Our fully automatic system allows on-the-fly object mapping and enhanced pose tracking recovery, which we think, can significantly benefit to the AR community. Our experiments show that the camera can be relocalized from viewpoints where classical methods fail. We demonstrate that this localization allows a SLAM system to continue working despite a tracking loss, which can happen frequently with an uninitiated user. Our code and test data are released at gitlab.inria. fr/tangram/oa-slam.
Uncertainty Quantification of Collaborative Detection for Self-Driving

Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still have uncertainties on object detection due to practical challenges, which will affect the later modules in self-driving such as planning and control. Hence, uncertainty quantification is crucial for safety-critical systems such as CAVs. Our work is the first to estimate the uncertainty of collaborative object detection. We propose a novel uncertainty quantification method, called Double- M Quantification, which tailors a moving block bootstrap (MBB) algorithm with direct modeling of the multivariant Gaussian distribution of each corner of the bounding box. Our method captures both the epistemic uncertainty and aleatoric uncertainty with one inference pass based on the offline Double- M training process. And it can be used with different collaborative object detectors. Through experiments on the comprehensive collaborative perception dataset, we show that our Double-M method achieves more than 4× improvement on uncertainty score and more than 3% accuracy improvement, compared with the state-of-the-art uncertainty quantification methods. Our code is public on https://coperception.github. io/double-m-quantification/.