Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural phase transitions on the nanoscale: The crucial pattern in the phase-change materials Ge2Sb2Te5 and GeTe

Phase-change materials are of immense importance for optical recording and computer memory, but the structure of the amorphous phases and the nature of the phase transition in the nanoscale bits pose continuing challenges. Massively parallel density functional simulations have been used to characterize the amorphous structure of the prototype materials Ge2Sb2Te5 and GeTe. In both, there is long-ranged order among Te atoms and the crucial structural motif is a four-membered ring with alternating atoms of types A Ge and Sb and B Te , an “ABAB square. ” The rapid amorphous-to-crystalline phase change is a reorientation of disordered ABAB squares to form an ordered lattice. There are deviations from the “8−N rule” for coordination numbers, with Te having near threefold coordination. Ge atoms are predominantly fourfold coordinated, but—contrary to recent speculation—tetrahedral coordination is found in only approximately one-third of the Ge atoms. The average coordination number of Sb atoms is 3.7, and the local environment of Ge and Sb is usually “distorted octahedral” with AB separations from 3.2 to 4 A in the first coordination shell. The number of A–A bonds is significantly greater in GeTe than in Ge2Sb2Te5. Vacancies voids in the disordered phases of these materials provide the necessary space for the phase transitions to take place. The vacancy concentration in Ge2Sb2Te5 11.8% is greater than in GeTe 6.4% , which is consistent with the better phase-change performance of the former.
Diagnosis of Acute Q Fever in a Patient by Using Metagenomic Next-Generation Sequencing: A Case Report [Response to Letter]

1Department of Clinical Laboratory, Wuhan Asia General Hospital, Wuhan Asia General Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, Hubei Province, 430056, People’s Republic of China; 2Pulmonary and Critical Care Medicine, Hankou Hospital of Wuhan, Hankou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, Hubei Province, 430012, People’s Republic of China
A Low-Complexity Post-Weighting Predistorter in a mMIMO Transmitter Under Crosstalk

The beam-oriented digital predistortion (BO-DPD) is not sufficient to linearize the output from a subarray of power amplifiers (PAs) in different directions except the desired direction. Therefore, subsequent to the BO-DPD operation, we perform a post-weighting (PW) processing to minimize the nonlinear radiations in the wide range of directions under crosstalk. Here, the optimized PW coefficients are multiplied by the polynomial terms of the BO-DPD, then, the resultant signals are distributed to the PAs to compensate the nonlinear radiations. In this work, first, we propose fully-featured post-weighting (FF-PW) scheme, then, we derive a low-complexity post-weighting (LC-PW) scheme.
VA-learning as a more efficient alternative to Q-learning

In reinforcement learning, the advantage function is critical for policy improvement, but is often extracted from a learned Q-function. A natural question is: Why not learn the advantage function directly? In this work, we introduce VA-learning, which directly learns advantage function and value function using bootstrapping, without explicit reference to Q-functions. VA-learning learns off-policy and enjoys similar theoretical guarantees as Q-learning. Thanks to the direct learning of advantage function and value function, VA-learning improves the sample efficiency over Q-learning both in tabular implementations and deep RL agents on Atari-57 games. We also identify a close connection between VA-learning and the dueling architecture, which partially explains why a simple architectural change to DQN agents tends to improve performance.
Uniqueness, mixing, and optimal tails for Brownian line ensembles with geometric area tilt

We consider non-colliding Brownian lines above a hard wall, which are subject to geometrically growing (given by a parameter $\lambda>1$) area tilts, which we call the $\lambda$-tilted line ensemble (LE). The model was introduced by Caputo, Ioffe, Wachtel [CIW] in 2019 as a putative scaling limit for the level lines of low-temperature 3D Ising interfaces. While the LE has infinitely many lines, the case of the single line, known as the Ferrari-Spohn (FS) diffusion, is one of the canonical interfaces appearing in the Kardar-Parisi-Zhang (KPZ) universality class. In contrast with well studied models with determinantal structure such as the Airy LE constructed by Corwin and Hammond as well as the FS diffusion, the $\lambda$-tilted LE is non-integrable. [CIW] constructed a stationary infinite volume Gibbs measure (the zero boundary LE) as a limit of finite LEs on finite intervals with zero boundary conditions, and obtained control on its fluctuations in terms of first moment estimates. Subsequently, Dembo, Lubetzky, Zeitouni revisited the case of finitely many lines and established an equivalence between the free and the zero boundary LEs. In this article we develop probabilistic arguments to resolve several questions that remained open. We prove that the infinite volume zero boundary LE is mixing and hence ergodic and establish a quantitative decay of correlation. Further, we prove an optimal upper tail estimate for the top line matching that of the FS diffusion. Finally, we prove uniqueness of the Gibbs measure in the sense that any uniformly tight LE (a notion which includes all stationary $\lambda$-tilted LE) must be the zero boundary LE. This immediately implies that the LE with free boundary conditions, as the number of lines and the domain size go to infinity arbitrarily converges to this unique LE.
ProGroTrack: Deep Learning-Assisted Tracking of Intracellular Protein Growth Dynamics

Accurate tracking of cellular and subcellular structures, along with their dynamics, plays a pivotal role in understanding the underlying mechanisms of biological systems. This paper presents a novel approach, ProGroTrack, that combines the You Only Look Once (YOLO) and ByteTrack algorithms within the detection-based tracking (DBT) framework to track intracellular protein nanostructures. Focusing on iPAK4 protein fibers as a representative case study, we conducted a comprehensive evaluation of YOLOv5 and YOLOv8 models, revealing the superior performance of YOLOv5 on our dataset. Notably, YOLOv5x achieved an impressive mAP50 of 0.839 and F-score of 0.819. To further optimize detection capabilities, we incorporated semi-supervised learning for model improvement, resulting in enhanced performances in all metrics. Subsequently, we successfully applied our approach to track the growth behavior of iPAK4 protein fibers, revealing their two distinct growth phases consistent with a previously reported kinetic model. This research showcases the promising potential of our approach, extending beyond iPAK4 fibers. It also offers a significant advancement in precise tracking of dynamic processes in live cells, and fostering new avenues for biomedical research.
Generating Visual Information for Motion Sickness Reduction Using a Computational Model Based on SVC Theory

With the advancements in automated driving, there is concern that motion sickness will increase as non-driving-related tasks increase. Therefore, techniques to reduce motion sickness have drawn much attention. Research studies have attempted to estimate motion sickness using computational models for controlling it. Among them, a computational model for estimating motion sickness incidence (MSI) with visual information as input based on subjective vertical conflict theories was developed. In addition, some studies attempt to mitigate motion sickness by controlling visual information. In particular, it has been confirmed that motion sickness is suppressed by matching head movement and visual information. However, there has been no research on optimal visual information control that suppresses motion sickness in vehicles by utilizing mathematical models. We, therefore, propose a method for generating optimal visual information to suppress motion sickness caused from vehicle motion by utilizing a motion sickness model with vestibular and visual inputs. To confirm the effectiveness of the proposed method, we investigated changes in the motion sickness experienced by the participants according to the visual information displayed on the head-mounted display. The experimental results suggested that the proposed method mitigates the motion sickness of the participants.
Accurate Estimation of Diffusion Coefficients and their Uncertainties from Computer Simulation

Self-diffusion coefficients, $D^*$, are routinely estimated from molecular dynamics simulations by fitting a linear model to the observed mean-squared displacements (MSDs) of mobile species. MSDs derived from simulation suffer from statistical noise, which introduces uncertainty in the resulting estimate of $D^*$. An optimal scheme for estimating $D^*$ will minimise this uncertainty, i.e., will have high statistical efficiency, and will give an accurate estimate of the uncertainty itself. We present a scheme for estimating $D^*$ from a single simulation trajectory with high statistical efficiency and accurately estimating the uncertainty in the predicted value. The statistical distribution of MSDs observable from a given simulation is modelled as a multivariate normal distribution using an analytical covariance matrix for an equivalent system of freely diffusing particles, which we parameterise from the available simulation data. We then perform Bayesian regression to sample the distribution of linear models that are compatible with this model multivariate normal distribution, to obtain a statistically efficient estimate of $D^*$ and an accurate estimate of the associated statistical uncertainty.
Communication Efficient Distributed Newton Method with Fast Convergence Rates

We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires O (d) communication complexity, where d is the problem dimension. We also provide theoretical analysis to show the proposed method has the similar convergence rate as the classical second-order optimization algorithms. Concretely, our method can find (∈, √dLe,)-second-order stationary points for nonconvex problem by O (√dL,∈-3/2) iterations, where L is the Lipschitz constant of Hessian. Moreover, it enjoys a local superlinear convergence under the strongly-convex assumption. Experiments on both convex and nonconvex problems show that our proposed method performs significantly better than baselines.
External Language Model Integration for Factorized Neural Transducers

We propose an adaptation method for factorized neural transducers (FNT) with external language models. We demonstrate that both neural and n-gram external LMs add significantly more value when linearly interpolated with predictor output compared to shallow fusion, thus confirming that FNT forces the predictor to act like regular language models. Further, we propose a method to integrate class-based n-gram language models into FNT framework resulting in accuracy gains similar to a hybrid setup. We show average gains of 18% WERR with lexical adaptation across various scenarios and additive gains of up to 60% WERR in one entity-rich scenario through a combination of class-based n-gram and neural LMs.
Mitigating Catastrophic Forgetting in Long Short-Term Memory Networks

Continual learning on sequential data is critical for many machine learning (ML) deployments. Unfortunately, LSTM networks, which are commonly used to learn on sequential data, suffer from catastrophic forgetting and are limited in their ability to learn multiple tasks continually. We discover that catastrophic forgetting in LSTM networks can be overcome in two novel and readily-implementable ways -- separating the LSTM memory either for each task or for each target label. Our approach eschews the need for explicit regularization, hypernetworks, and other complex methods. We quantify the benefits of our approach on recently-proposed LSTM networks for computer memory access prefetching, an important sequential learning problem in ML-based computer system optimization. Compared to state-of-the-art weight regularization methods to mitigate catastrophic forgetting, our approach is simple, effective, and enables faster learning. We also show that our proposal enables the use of small, non-regularized LSTM networks for complex natural language processing in the offline learning scenario, which was previously considered difficult.
Learning from Children: Improving Image-Caption Pretraining via Curriculum

Image-caption pretraining has been quite successfully used for downstream vision tasks like zero-shot image classification and object detection. However, image-caption pretraining is still a hard problem -- it requires multiple concepts (nouns) from captions to be aligned to several objects in images. To tackle this problem, we go to the roots -- the best learner, children. We take inspiration from cognitive science studies dealing with children's language learning to propose a curriculum learning framework. The learning begins with easy-to-align image caption pairs containing one concept per caption. The difficulty is progressively increased with each new phase by adding one more concept per caption. Correspondingly, the knowledge acquired in each learning phase is utilized in subsequent phases to effectively constrain the learning problem to aligning one new concept-object pair in each phase. We show that this learning strategy improves over vanilla image-caption training in various settings -- pretraining from scratch, using a pretrained image or/and pretrained text encoder, low data regime etc.
Instabilities of Decelerating Flow

This paper numerically investigates the instability characteristics of decelerating flows. The flow dynamics and temporal evolution of coherent structures in a diverging section with mild spatial pressure gradient are analyzed using numerical experiments with Incompact3d code. The unsteady nature of flow and adverse pressure gradient is the reason for inflectional velocity profiles, results into boundary layer separation and instability with reverse flow and later on it leads to vortex formation. Vortex formation time is found by vortex Reynold's number. Time of first vortex formation, non-dimensional vortex formation times with Reynold's number etc. are validated with an experimental results.
A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition

In this paper we consider a composite optimization problem that minimizes the sum of a weakly smooth function and a convex function with either a bounded domain or a uniformly convex structure. In particular, we first present a parameter-dependent conditional gradient method for this problem, whose step sizes require prior knowledge of the parameters associated with the H\"older continuity of the gradient of the weakly smooth function, and establish its rate of convergence. Given that these parameters could be unknown or known but possibly conservative, such a method may suffer from implementation issue or slow convergence. We therefore propose a parameter-free conditional gradient method whose step size is determined by using a constructive local quadratic upper approximation and an adaptive line search scheme, without using any problem parameter. We show that this method achieves the same rate of convergence as the parameter-dependent conditional gradient method. Preliminary experiments are also conducted and illustrate the superior performance of the parameter-free conditional gradient method over the methods with some other step size rules.
Machine Learning Architectures for Price Formation Models with Common Noise

We propose a machine learning method to solve a mean-field game price formation model with common noise. This involves determining the price of a commodity traded among rational agents subject to a market clearing condition imposed by random supply, which presents additional challenges compared to the deterministic counterpart. Our approach uses a dual recurrent neural network encoding noise dependence and a particle approximation of the mean-field model with a single loss function optimized by adversarial training. We provide a posteriori estimates for convergence and illustrate our method through numerical experiments.
Toward Fine Contact Interactions: Learning to Control Normal Contact Force with Limited Information

Dexterous manipulation of objects through fine control of physical contacts is essential for many important tasks of daily living. A fundamental ability underlying fine contact control is compliant control, i.e., controlling the contact forces while moving. For robots, the most widely explored approaches heavily depend on models of manipulated objects and expensive sensors to gather contact location and force information needed for real-time control. The models are difficult to obtain, and the sensors are costly, hindering personal robots' adoption in our homes and businesses. This study performs model-free reinforcement learning of a normal contact force controller on a robotic manipulation system built with a low-cost, information-poor tactile sensor. Despite the limited sensing capability, our force controller can be combined with a motion controller to enable fine contact interactions during object manipulation. Promising results are demonstrated in non-prehensile, dexterous manipulation experiments.
Regression analysis of mixed sparse synchronous and asynchronous longitudinal covariates with varying-coefficient models

We consider varying-coefficient models for mixed synchronous and asynchronous longitudinal covariates, where asynchronicity refers to the misalignment of longitudinal measurement times within an individual. We propose three different methods of parameter estimation and inference. The first method is a one-step approach that estimates non-parametric regression functions for synchronous and asynchronous longitudinal covariates simultaneously. The second method is a two-step approach in which synchronous longitudinal covariates are regressed with the longitudinal response by centering the synchronous longitudinal covariates first and, in the second step, the residuals from the first step are regressed with asynchronous longitudinal covariates. The third method is the same as the second method except that in the first step, we omit the asynchronous longitudinal covariate and include a non-parametric intercept in the regression analysis of synchronous longitudinal covariates and the longitudinal response. We further construct simultaneous confidence bands for the non-parametric regression functions to quantify the overall magnitude of variation. Extensive simulation studies provide numerical support for the theoretical findings. The practical utility of the methods is illustrated on a dataset from the ADNI study.
Transmit Power Optimization of IoT Devices over Incomplete Channel Information

Efficient resource allocation (RA) strategies within massive and dense Internet of Things (IoT) networks is one of the major challenges in deployment of IoT-network based smart ecosystems involving heterogeneous power-constrained IoT devices operating in varied radio and environmental conditions. In this paper, we focus on the transmit power minimization problem for IoT devices while maintaining a threshold channel throughput. The established optimization literature is not robust against the fast-fading channel and the interaction among different transmit signals in each instance. Besides, realistically, each IoT node possesses incomplete channel state information (CSI) on its neighbors, such as the channel gain being private information for the node itself. In this work, we resort to Bayesian game theoretic strategies for solving the transmit power optimization problem exploiting incomplete CSIs within massive IoT networks. We provide a steady discussion on the rationale for selecting the game theory, particularly the Bayesian scheme, with a graphical visualization of our formulated problem. We take advantage of the property of the existence and uniqueness of the Bayesian Nash equilibrium (BNE), which exhibits reduced computational complexity while optimizing transmit power and maintaining target throughput within networks comprised of heterogeneous devices.
Doing the right thing for the right reason: Evaluating artificial moral cognition by probing cost insensitivity

Is it possible to evaluate the moral cognition of complex artificial agents? In this work, we take a look at one aspect of morality: `doing the right thing for the right reasons. ' We propose a behavior-based analysis of artificial moral cognition which could also be applied to humans to facilitate like-for-like comparison. Morally-motivated behavior should persist despite mounting cost; by measuring an agent's sensitivity to this cost, we gain deeper insight into underlying motivations. We apply this evaluation to a particular set of deep reinforcement learning agents, trained by memory-based meta-reinforcement learning. Our results indicate that agents trained with a reward function that includes other-regarding preferences perform helping behavior in a way that is less sensitive to increasing cost than agents trained with more self-interested preferences.
Integrating Generative Artificial Intelligence in Intelligent Vehicle Systems

This paper aims to serve as a comprehensive guide for researchers and practitioners, offering insights into the current state, potential applications, and future research directions for generative artificial intelligence and foundation models within the context of intelligent vehicles. As the automotive industry progressively integrates AI, generative artificial intelligence technologies hold the potential to revolutionize user interactions with, delivering more immersive, intuitive, and personalised in-car experiences. We provide an overview of current applications of generative artificial intelligence in the automotive domain, emphasizing speech, audio, vision, and multimodal interactions. We subsequently outline critical future research areas, including domain adaptability, alignment, multi-modal integration and others, as well as, address the challenges and risks associated with ethics. By fostering collaboration and addressing these research areas, generative artificial intelligence can unlock its full potential, transforming the driving experience and shaping the future of intelligent vehicles.