Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Optofluidic Sensor for Coagulation Risk Monitoring in COVID-19 Patients at Point-of-Care

While the pathophysiology underlying the COVID-19 infection remains incompletely understood, there is growing evidence to indicate that it is closely correlated to hypercoagulation among severely ill patients. Doctors may choose for use anti-coagulation doses to treat the patients at intensive care units. A rapid, easy, and low-cost solution to monitor the coagulation status at the point-of-care may help with treatment by enabling the administration of controlled doses of medication to patients and to understand the disease's underlying pathophysiology. Thromboelastography, the clinical standard is accurate; it suffers from limited portability and low sensitivity when miniaturized to handheld form factor. In the article, we summarize research helping to advance towards an integrated optofluidic device combining microfluidics and photonic sensor technology. Microfluidics are used to perform blood pre-processing, and a photonic sensor measures blood coagulation status in real-time readout on the device itself. These techniques make it portable and scalable, potentially serving as technology foundation for the development of a disposable sensor for point-of-care diagnostics in COVID-19 patients and coagulopathy in general.
Sample weighting as an explanation for mode collapse in generative adversarial networks

Generative adversarial networks were introduced with a logistic MiniMax cost formulation, which normally fails to train due to saturation, and a Non-Saturating reformulation. While addressing the saturation problem, NS-GAN also inverts the generator's sample weighting, implicitly shifting emphasis from higher-scoring to lower-scoring samples when updating parameters. We present both theory and empirical results suggesting that this makes NS-GAN prone to mode dropping. We design MM-nsat, which preserves MM-GAN sample weighting while avoiding saturation by rescaling the MM-GAN minibatch gradient such that its magnitude approximates NS-GAN's gradient magnitude. MM-nsat has qualitatively different training dynamics, and on MNIST and CIFAR-10 it is stronger in terms of mode coverage, stability and FID. While the empirical results for MM-nsat are promising and favorable also in comparison with the LS-GAN and Hinge-GAN formulations, our main contribution is to show how and why NS-GAN's sample weighting causes mode dropping and training collapse.
Adversarial Attack and Defense of Structured Prediction Models

Building an effective adversarial attacker and elaborating on countermeasures for adversarial attacks for natural language processing (NLP) have attracted a lot of research in recent years. However, most of the existing approaches focus on classification problems. In this paper, we investigate attacks and defenses for structured prediction tasks in NLP. Besides the difficulty of perturbing discrete words and the sentence fluency problem faced by attackers in any NLP tasks, there is a specific challenge to attackers of structured prediction models: the structured output of structured prediction models is sensitive to small perturbations in the input. To address these problems, we propose a novel and unified framework that learns to attack a structured prediction model using a sequence-to-sequence model with feedbacks from multiple reference models of the same structured prediction task. Based on the proposed attack, we further reinforce the victim model with adversarial training, making its prediction more robust and accurate. We evaluate the proposed framework in dependency parsing and part-of-speech tagging. Automatic and human evaluations show that our proposed framework succeeds in both attacking state-of-the-art structured prediction models and boosting them with adversarial training.
NLP Service APIs and Models for Efficient Registration of New Clients

State-of-the-art NLP inference uses enormous neural architectures and models trained for GPU-months, well beyond the reach of most consumers of NLP. This has led to one-size-fits-all public API-based NLP service models by major AI companies, serving millions of clients. They cannot afford traditional fine tuning for individual clients. Many clients cannot even afford significant fine tuning, and own little or no labeled data. Recognizing that word usage and salience diversity across clients leads to reduced accuracy, we initiate a study of practical and lightweight adaptation of centralized NLP services to clients. Each client uses an unsupervised, corpus-based sketch to register to the service. The server modifies its network mildly to accommodate client sketches, and occasionally trains the augmented network over existing clients. When a new client registers with its sketch, it gets immediate accuracy benefits. We demonstrate the proposed architecture using sentiment labeling, NER, and predictive language modeling.
Holistic static and animated 3D scene generation from diverse text descriptions

We propose a framework for holistic static and animated 3D scene generation from diverse text descriptions. Prior works of scene generation rely on static rule-based entity extraction from natural language description. However, this limits the usability of a practical solution. To overcome this limitation, we use one of state-of-the-art architecture - TransformerXL. Instead of rule-based extraction, our framework leverages the rich contextual encoding which allows us to process a larger range (diverse) of possible natural language descriptions. We empirically show how our proposed mechanism generalizes even on novel combinations of object-features during inference. We also show how our framework can jointly generate static and animated 3D scene efficiently. We modify CLEVR to generate a large, scalable dataset - Integrated static and animated 3D scene (Iscene). Data preparation code and pre-trained model available at - this https URL.
Constraints on the epoch of dark matter formation from Milky Way satellites

A small fraction of thermalized dark radiation that transitions into cold dark matter (CDM) between big bang nucleosynthesis and matter-radiation equality can account for the entire dark matter relic density. Because of its transition from dark radiation, ``late-forming dark matter'' (LFDM) suppresses the growth of linear matter perturbations and imprints the oscillatory signatures of dark radiation perturbations on small scales. The cutoff scale in the linear matter power spectrum is set by the redshift ${z}_{T}$ of the phase transition; tracers of small-scale structure can therefore be used to infer the LFDM formation epoch. Here, we use a forward model of the Milky Way (MW) satellite galaxy population to address the question: How late can dark matter form? For dark radiation with strong self-interactions, which arises in theories of neutrinolike LFDM, we report ${z}_{T}g5.5\ifmmode\times\else\texttimes\fi{}{10}^{6}$ at 95% confidence based on the abundance of known MW satellite galaxies. This limit rigorously accounts for observational incompleteness corrections, marginalizes over uncertainties in the connection between dwarf galaxies and dark matter halos, and improves upon galaxy clustering and Lyman-$\ensuremath{\alpha}$ forest constraints by nearly an order of magnitude. We show that this limit can also be interpreted as a lower bound on ${z}_{T}$ for LFDM that free-streams prior to its phase transition, although dedicated simulations will be needed to analyze this case in detail. Thus, dark matter created by a transition from dark radiation must form no later than one week after the big bang.
Efficient Multicontact Pattern Generation With Sequential Convex Approximations of the Centroidal Dynamics

This article investigates the problem of efficient computation of physically consistent multicontact behaviors. Recent work showed that under mild assumptions, the problem could be decomposed into simpler kinematic and centroidal dynamic optimization problems. Based on this approach, we propose a general convex relaxation of the centroidal dynamics leading to two computationally efficient algorithms based on iterative resolutions of second-order cone programs. They optimize centroidal trajectories, contact forces, and importantly the timing of the motions. We include the approach in a kinodynamic optimization method to generate full-body movements. Finally, the approach is embedded in a mixed-integer solver to further find dynamically consistent contact sequences. Extensive numerical experiments demonstrate the computational efficiency of the approach, suggesting that it could be used in a fast receding horizon control loop. Executions of the planned motions on simulated humanoids and quadrupeds and on a real quadruped robot further show the quality of the optimized motions.
A Multi-task Learning Framework for Opinion Triplet Extraction

The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-of-the-art approaches.
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban

Intelligent robots need to achieve abstract objectives using concrete, spatiotemporally complex sensory information and motor control. Tabula rasa deep reinforcement learning (RL) has tackled demanding tasks in terms of either visual, abstract, or physical reasoning, but solving these jointly remains a formidable challenge. One recent, unsolved benchmark task that integrates these challenges is Mujoban, where a robot needs to arrange 3D warehouses generated from 2D Sokoban puzzles. We explore whether integrated tasks like Mujoban can be solved by composing RL modules together in a sense-plan-act hierarchy, where modules have well-defined roles similarly to classic robot architectures. Unlike classic architectures that are typically model-based, we use only model-free modules trained with RL or supervised learning. We find that our modular RL approach dramatically outperforms the state-of-the-art monolithic RL agent on Mujoban. Further, learned modules can be reused when, e.g., using a different robot platform to solve the same task. Together our results give strong evidence for the importance of research into modular RL designs. Project website: this https URL
Reading Comprehension as Natural Language Inference:A Semantic Analysis

In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.
Ensembles of Convolutional Neural Networks for pediatric pneumonia diagnosis

Pneumonia is a lung infection that causes 15% of childhood mortality (under 5 years old) around the world. This pathology is mainly caused by viruses or bacteria. Most frequent associated viruses are respiratory syncytial virus (RSV), influenza virus and human parainfluenza virus (HPIV). Most frequent bacteria are: Streptococcus pneumoniae, Haemophilus influenzae, Streptococcus pyogenes and Staphylococcus aureus, and Mycoplasma pneumoniae. Our goal is to classify chest radiographs (X-ray) into two classes: consolidation, which corresponds to alveolar pneumonia, and non-consolidation, corresponding to non-alveolar pneumonia. X-rays imaging analysis is one of the most used methods for pneumonia diagnosis. These clinical images can be analyzed using machine learning methods such as convolutional neural networks (CNN), which learn to extract visual features critical for the classification. However, the usability of these systems is limited in medicine due to the lack of interpretability in the sense that these models do not generate a simple explanation for the predictions they make. To overcome the explainability and interpretability problems of these CNN "black boxes", we have developed an AI explainable approach (XAI) based on heatmaps, which allows highlighting those areas of the image that have been more relevant to generate the final classification. A new CNN model has been designed and trained, using pediatric X-rays (950 samples of children between one month and 16 years old), with two main goals, maximizing the Area Under the Curve (AUC) and the True Positive Rate (TPR) in the dataset. From our experiments, we have obtained some promising results, with a final model that reaches an AUC of 0.81 and a TPR of 0.67, however applying ensemble techniques the performance of the model improved to an AUC of 0.92 and a TPR of 0.76.
Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences

The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences. While ODE produces the smooth hidden states between observation intervals, the RNN will trigger a hidden state jump when a new observation arrives, thus cause the interpolation discontinuity problem. To address this issue, we propose the cubic spline smoothing compensation, which is a stand-alone module upon either the output or the hidden state of ODE-RNN and can be trained end-to-end. We derive its analytical solution and provide its theoretical interpolation error bound. Extensive experiments indicate its merits over both ODE-RNN and cubic spline interpolation.
Optimal Control of a Soft CyberOctopus Arm

In this paper, we use the optimal control methodology to control a flexible, elastic Cosserat rod. An inspiration comes from stereotypical movement patterns in octopus arms, which are observed in a variety of manipulation tasks, such as reaching or fetching. To help uncover the mechanisms underlying these observed morphologies, we outline an optimal control-based framework. A single octopus arm is modeled as a Hamiltonian control system, where the continuum mechanics of the arm is modeled after the Cosserat rod theory, and internal, distributed muscle forces and couples are considered as controls. First order necessary optimality conditions are derived for an optimal control problem formulated for this infinite dimensional system. Solutions to this problem are obtained numerically by an iterative forward-backward algorithm. The state and adjoint equations are solved in a dynamic simulation environment, setting the stage for studying a broader class of optimal control problems. Trajectories that minimize control effort are demonstrated and qualitatively compared with experimentally observed behaviors.
Energy of many-particle quantum states.

We derive a functional form for the energy of interacting many-particle systems from first principles. Our main contribution is that we define the effective zero-point energy of an interacting system in terms of fluctuations of momentum and the Coulomb electric field. An uncertainty equation can be derived, relating the interaction energy in terms of the kinetic energy and local charge density. The Lieb-Thirring bound is then applied to constrain the kinetic energy which takes particle (anti-)symmetry into account, and the resulting functional form applies to fermionic as well as bosonic systems; in the uniform density case, the functional form agrees with the entire range of available Quantum Monte Carlo (QMC) data of many-particle systems, to within chemical accuracy. Furthermore, the functional form shows promising agreement with QMC phase transition data found in the literature, and in the high density case, where QMC data is not as readily available for a BCC Wigner state, predicts the stability of a BCC Wigner crystal agreeing qualitatively with Unrestricted Hartree-Fock. Seen in this way, many-particle wavefunctions are minimum uncertainty states in coherence with the electromagnetic bath, where the coupling is a self-generated scalar potential.
Episodic Memory for Learning Subjective-Timescale Models

In model-based learning, an agent's model is commonly defined over transitions between consecutive states of an environment even though planning often requires reasoning over multi-step timescales, with intermediate states either unnecessary, or worse, accumulating prediction error. In contrast, intelligent behaviour in biological organisms is characterised by the ability to plan over varying temporal scales depending on the context. Inspired by the recent works on human time perception, we devise a novel approach to learning a transition dynamics model, based on the sequences of episodic memories that define the agent's subjective timescale - over which it learns world dynamics and over which future planning is performed. We implement this in the framework of active inference and demonstrate that the resulting subjective-timescale model (STM) can systematically vary the temporal extent of its predictions while preserving the same computational efficiency. Additionally, we show that STM predictions are more likely to introduce future salient events (for example new objects coming into view), incentivising exploration of new areas of the environment. As a result, STM produces more informative action-conditioned roll-outs that assist the agent in making better decisions. We validate significant improvement in our STM agent's performance in the Animal-AI environment against a baseline system, trained using the environment's objective-timescale dynamics.
Neural-Network Quantum States for the Electronic Structure of Real Solids

Establishing a predictive ab initio method for solid systems is one of the fundamental goals in condensed matter physics and computational materials science. We demonstrate that artificial neural networks can simulate first-principles calculations of extended periodic materials. The stochastic optimization of a highly expressive ansatz accurately provides the ground-state energy of a one-dimensional system up to the thermodynamic limit, reaching its chemical accuracy. The correlation energy is simulated precisely also for real solids in both two and three dimensions. We further show that multiple excited states corresponding to the quasiparticle band structure can be obtained with an additional small computation exploiting a technique developed from quantum simulations.
A Double Auction for Charging Scheduling among Vehicles Using DAG-Blockchains

Electric vehicle (EV) is becoming more and more popular in our daily life, which replaces the traditional fuel vehicles to reduce carbon emissions and protect the environment. The EVs need to be charged, but the number of charging piles in a charging station (CS) is limited and charging is usually more time-consuming than fueling. According to this scenario, we propose a secure and efficient charging scheduling system based on DAG-blockchain and double auction mechanism. In a smart area, it attempts to assign EVs to the available CSs in the light of their submitted charging requests and status information. First, we design a lightweight charging scheduling framework that integrates DAG-blockchain and modern cryptography technology to ensure security and scalability during performing scheduling and completing tradings. In this process, a constrained double auction problem is formulated because of the limited charging resources in a CS, which motivates the EVs and CSs in this area to participate in the market based on their preferences and statuses. Due to this constraint, our problem is more complicated and harder to achieve the truthfulness as well as system efficiency compared to the existing double auction model. To adapt to it, we propose two algorithms, namely the truthful mechanism for charging (TMC) and efficient mechanism for charging (EMC), to determine the assignments between EVs and CSs and pricing strategies. Then, both theoretical analysis and numerical simulations show the correctness and effectiveness of our proposed algorithms.
Unbounded Slice Sampling

Slice sampling is an efficient Markov Chain Monte Carlo algorithm to sample from an unnormalized density with acceptance ratio always $1$. However, when the variable to sample is unbounded, its "stepping-out" heuristic works only locally, making it difficult to uniformly explore possible candidates. This paper proposes a simple change-of-variable method to slice sample an unbounded variable equivalently from [0,1).
Identification and expression profiles of candidate chemosensory receptors in Histia rhodope (Lepidoptera: Zygaenidae)

Insect olfaction and vision play important roles in survival and reproduction. Diurnal butterflies mainly rely on visual cues whereas nocturnal moths rely on olfactory signals to locate external resources. Histia rhodope Cramer (Lepidoptera: Zygaenidae) is an important pest of the landscape tree Bischofia polycarpa in China and other Southeast Asian regions. As a diurnal moth, H. rhodope represents a suitable model for studying the evolutionary shift from olfactory to visual communication. However, only a few chemosensory soluble proteins have been characterized and information on H. rhodope chemoreceptor genes is currently lacking. In this study, we identified 45 odorant receptors (ORs), nine ionotropic receptors (IRs), eight gustatory receptors (GRs) and two sensory neuron membrane proteins (SNMPs) from our previously acquired H. rhodope antennal transcriptomic data. The number of chemoreceptors of H. rhodope was less compared with that found in many nocturnal moths. Some specific chemoreceptors such as OR co-receptor (ORco), ionotropic receptors co-receptor, CO2 receptors, sugar receptors and bitter receptors were predicted by phylogenetic analysis. Notably, two candidate pheromone receptors (PRs) were identified within a novel PR lineage. qRT-PCR results showed that almost all tested genes (22/24) were predominantly expressed in antennae, indicating that they may be important in olfactory function. Among these antennae-enriched genes, six ORs, five IRs and two GRs displayed female-biased expression, while two ORs displayed male-biased expression. Additionally, HrhoIR75q.2 and HrhoGR67 were more highly expressed in heads and legs. This study enriches the olfactory gene inventory of H. rhodope and provides the foundation for further research of the chemoreception mechanism in diurnal moths.