Miracle-dz/UniFAR-FTUs huggingface dataset:
Competing effects in fourth-order aggregation-diffusion equations

We give sharp conditions for global in time existence of gradient flow solutions to a Cahn-Hilliard-type equation, with backwards second order degenerate diffusion, in any dimension and for general initial data. Our equation is the 2-Wasserstein gradient flow of a free energy with two competing effects: the Dirichlet energy and the power-law internal energy. Homogeneity of the functionals reveals critical regimes that we analyse. Sharp conditions for global in time solutions, constructed via the minimising movement scheme, also known as JKO scheme, are obtained. Furthermore, we study a system of two Cahn-Hilliard-type equations exhibiting an analogous gradient flow structure.
360VOT: A New Benchmark Dataset for Omnidirectional Visual Object Tracking

360° images can provide an omnidirectional field of view which is important for stable and long-term scene perception. In this paper, we explore 360° images for visual object tracking and perceive new challenges caused by large distortion, stitching artifacts, and other unique attributes of 360° images. To alleviate these problems, we take advantage of novel representations of target localization, i.e., bounding field-of-view, and then introduce a general 360 tracking framework that can adopt typical trackers for omnidirectional tracking. More importantly, we propose a new large-scale omnidirectional tracking benchmark dataset, 360VOT, in order to facilitate future research. 360VOT contains 120 sequences with up to 113K high-resolution frames in equirectangular projection. The tracking targets cover 32 categories in diverse scenarios. Moreover, we provide 4 types of unbiased ground truth, including (rotated) bounding boxes and (rotated) bounding field-of-views, as well as new metrics tailored for 360° images which allow for the accurate evaluation of omnidirectional tracking performance. Finally, we extensively evaluated 20 state-of-the-art visual trackers and provided a new baseline for future comparisons. Homepage: https://360vot.hkustvgd. com
Insufficient Gibbs Sampling

In some applied scenarios, the availability of complete data is restricted, often due to privacy concerns; only aggregated, robust and inefficient statistics derived from the data are made accessible. These robust statistics are not sufficient, but they demonstrate reduced sensitivity to outliers and offer enhanced data protection due to their higher breakdown point. We consider a parametric framework and propose a method to sample from the posterior distribution of parameters conditioned on various robust and inefficient statistics: specifically, the pairs (median, MAD) or (median, IQR), or a collection of quantiles. Our approach leverages a Gibbs sampler and simulates latent augmented data, which facilitates simulation from the posterior distribution of parameters belonging to specific families of distributions. A by-product of these samples from the joint posterior distribution of parameters and data given the observed statistics is that we can estimate Bayes factors based on observed statistics via bridge sampling. We validate and outline the limitations of the proposed methods through toy examples and an application to real-world income data.
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models

Abstract Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development.
Vibrations and Heat Transfer in Glasses: The Role Played by Disorder

Amorphous materials are also distinguished from crystals by their thermal properties. The structural disorder seems to be responsible both for a significant increase in heat capacity compared to crystals of the same composition, but also for a significant decrease in thermal conductivity. The temperature dependence of thermal conductivity, unusual for common interpretations of solid-state physics, gave rise to a lot of debates. We review in this article different interpretations of thermal conductivity in amorphous materials. We show finally that the temperature dependence of thermal conductivity in dielectric materials can be understood by relating it to the disorder-dependent harmonic vibrational eigenmodes.
Quantum Computer Simulations at Warp Speed: Assessing the Impact of GPU Acceleration: A Case Study with IBM Qiskit Aer, Nvidia Thrust & cuQuantum

Quantum computer simulators are crucial for the development of quantum computing. This work investigates GPU and multi-GPU systems' suitability and performance impact on a widely used simulation tool – the state vector simulator Qiskit Aer. In particular, we evaluate the performance of both Qiskit's default Nvidia Thrust backend and the recent Nvidia cuQuantum backend on Nvidia A100 GPUs. We provide a benchmark suite of representative quantum applications for characterization. For simulations with a large number of qubits, the two GPU backends can provide up to 14× speedup over the CPU backend, with Nvidia cuQuantum providing a further 1.5–3× speedup over the default Thrust backend. Our evaluation on a single GPU identifies the most important functions in Nvidia Thrust and cuQuantum for different quantum applications and their compute and memory bottlenecks. We also evaluate the gate fusion and cache-blocking optimizations on different quantum applications. Finally, we evaluate large-number qubit quantum applications on multi-GPU and identify data movement between host and GPU as the limiting factor for the performance.
Disturbance Preview for Non-Linear Model Predictive Trajectory Tracking of Underwater Vehicles in Wave Dominated Environments

Operating in the near-vicinity of marine energy devices poses significant challenges to the control of underwater vehicles, predominantly due to the presence of large magnitude wave disturbances causing hazardous state perturbations. Approaches to tackle this problem have varied, but one promising solution is to adopt predictive control methods. Given the predictable nature of ocean waves, the potential exists to incorporate disturbance estimations directly within the plant model; this requires inclusion of a wave predictor to provide online preview information. To this end, this paper presents a Non-linear Model Predictive Controller with an integrated Deterministic Sea Wave Predictor for trajectory tracking of underwater vehicles. State information is obtained through an Extended Kalman Filter, forming a complete closed-loop strategy and facilitating online wave load estimations. The strategy is compared to a similar feed-forward disturbance mitigation scheme, showing mean performance improvements of 51% in positional error and 44.5% in attitude error. The preliminary results presented here provide strong evidence of the proposed method's high potential to effectively mitigate disturbances, facilitating accurate tracking performance even in the presence of high wave loading.
A Note on the Equidistribution of 3-Colour Partitions

In this short note, we prove equidistribution results regarding three families of three-colour partitions recently introduced by Schlosser and Zhou. To do so, we prove an asymptotic formula for the infinite product $F_{a,c}(\zeta ; {\rm e}^{-z}) := \prod_{n \geq 0} \big(1- \zeta {\rm e}^{-(a+cn)z}\big)$ ($a,c \in \mathbb{N}$ with $0 <a\leq c$ and $\zeta$ a root of unity) when $z$ lies in certain sectors in the right half-plane, which may be useful in studying similar problems. As a corollary, we obtain the asymptotic behaviour of the three-colour partition families at hand.
Systematic scheme and key parameters of moon-based imaging spectrometer

With the development of aerospace industry, the detection of the earth itself is no longer limited to satellites outside the atmosphere. As the natural satellite of the Earth, terrestrial observation of the moon has unique advantages such as longterm consistency, integrity and stability. Imaging spectrometer based on the Moon, can improve the ability to observe the earth comprehensively and promote the in-depth study on scientific issues of global change. In this work, in viewing of the perspective of exploring the energy balance of the earth, the location between the earth and the moon as well as observation effect, the advantages and disadvantages of different schemes of the spectrometer component are compared and the system scheme is demonstrated and analyzed. Consequently, with the sub-satellite point ground resolution (5, 10, 20 km) and waveband (8.4-8.9 μm, 10.3-11.4 μm, and 11.5-12.5 μm), the spatial data acquisition method is determined as staring, the spectral data acquisition method is applied to a filter wheel with HgCdTe photoconductive area array detector (15 μm*15 μm) and the optical system aperture (936.96, 468.48, 234.24 mm) focal length (1152, 576, 288 mm) respectively. This work is an initial exploration of the moon-to-earth observation imaging spectrometer, which provides a theoretical basis for the following system implementation.
Das Merkelzellkarzinom

The Merkel cell carcinoma is a rare tumor of the subdermal tissue. Its origin are Merkel cells or the subcutaneous pluripotential stem cells. The etiology of the tumor is unknown, however, exposition to UV radiation and immunosuppression are relevant predisposing factors. Clinically, a subcutaneous rapidly growing tumor with or without enlarged lymph nodes is seen. Beside excisional biopsy and various imaging modalities, somatostatin-receptor-scintigraphy plays an important role in diagnostic. Surgical resection of the tumor combined with neck dissection followed by radiation therapy is the current therapy of choice. Different chemotherapy protocols are available for patients with distant metastasis.
Transient response for a half-space with variable thermal conductivity and diffusivity under thermal and chemical shock

ABSTRACT The present work aims to investigate the transient thermoelastic diffusive response for a half-space with variable thermal conductivity and diffusivity in the context of the generalized thermoelastic diffusion theory. The boundary plane of the half-space is assumed to be traction free and subjected to a time-dependent thermal and chemical shock. The governing equations of the problem are formulated by using Kirchhoff’s transformation. Due to the complexity of the equations, Laplace transformation method is applied to solve them. Numerical results are obtained and illustrated graphically. Parameter studies are performed to evaluate the effects of variable thermal conductivity and diffusivity on the response. The present investigation could be helpful for better understanding the multifield coupling effect of mechanical and thermal fields in real materials.
Inconsistent Finite Differencing Errors in the Variational Adjustment of Horizontal Wind Components

Inconsistency in writing a set of partial differential equations in finite difference form results in error in the numerical solution. An example is shown in which horizontal wind components are variationally adjusted to remove the surface pressure tendency. Three Euler-Lagrange equations are solved simultaneously using two different finite difference forms, one of which is consistent and the other inconsistent. Derived quantities (divergence and *-velocity) are examined using both consistently and inconsistently adjusted horizontal wind components.
Least-squares Fitting of a Polynomial Constrained to be either Non-negative, Non-decreasing or Convex

SUMMARY An algorithm is provided to fit the (unique) least-squares polynomial Y(x) to n data points (x, y) subject to a constraint that it be either non-negative, non-positive, non-decreasing, non-increasing, convex or concave at all points x in a closed interval L Some generalization to a general linear model can be handled, and certain alternative constraints can also be dealt with. The problem is close to the classical problem of Quadratic Programming (Q.P.), and some of the known results in Q.P. may be used in fitting the curve. The algorithm terminates in at most three steps. The number of steps depends on the degree of the polynomial Y(x) and the data. When no more than two steps are required, the algorithm is very easy to carry out but when three steps are necessary, it becomes more difficult. A useful statistical application is the fitting of a polynomial to growth data. In that case, we might like the fitted curve to be non-decreasing over the range of interest.
A review of computing offloading and caching in edge computing based on reinforcement learning

Edge Computing (EC) leverages edge devices to provide computational and analytical capabilities, reducing the load and response time of cloud servers and ensuring the real-time processing of tasks. However, the capacity of edge devices is limited and requires rational allocation of computational and storage resources to reduce delay, energy consumption, and cost. AS one of the research hotspots in machine learning, Reinforcement Learning (RL) can optimize resource allocation strategies for EC to maximize task rewards and make edge devices more intelligent. This study comprehensively reviews the literature on offloading, caching, joint offloading and caching in EC by RL, as well as classifies and compares them. The works of the computational offloading problem are further divided into three parts according to optimization objectives: reducing delay, reducing energy consumption, and the trade-off between energy consumption and delay. EC for different application scenarios is also summarized. Lastly, this paper outlines the main directions for future research and the challenges faced in this field. CCS CONCEPTS • Computing Methodologies
Human Activity Classification Based on Data Analysis and Feature Extraction

Human behavior recognition is one of the most important research directions in the field of computer vision, and it plays an important role in the fields of rehabilitative medicine, auxiliary security, and scene entertainment. To address the shortcomings of traditional HAR recognition methods with tedious feature extraction and severe overfitting, we propose a human behavior recognition model based on XGBoost and feature simplification methods with a limited data set. The model uses the XGBoost algorithm to classify the collected sensor data to recognize human behaviors. In addition, to improve the efficiency and accuracy of the model, we also propose a feature simplification method to reduce the computational complexity and the risk of model overfitting by reducing the number of features. Experimental results show that the model has high accuracy and computational efficiency and can be applied to different human behavior recognition scenarios. CCS Concepts: Computing methodologies∼Machine learning∼Machine learning approaches
DrivCapsNet: A Driving Style Detection Algorithm Based on Capsule Network

Driver behavior plays a fundamental role in the driver-vehicle-environment system, where the driving style can significantly impact vehicle emissions, fuel consumption, insurance expenses, road safety, and advanced driver assistance systems (ADAS). Nonetheless, detecting driver behavior is a complex and challenging task, traditional methods require a lot of data pre-processing and there is still no research on discriminative driving behavior with capsule networks which can capture the spatial relationships of data. However, it has not been fully studied and applied for driver behavior detection. To tackle these challenges, we propose an methodology for detecting driving style using a capsule network, named DrivCapsNet, which is capable of detecting various driving styles using either inertial measurement unit (IMU) data or camera data. A crucial advantage of this method is that its dynamic routing mechanism can extract the relationships between the parts and the entity, thereby improving detection accuracy. We performed comprehensive experiments on two realistic driving datasets to substantiate the efficacy of our proposed DrivCapsNet approach. The outcomes validate that our approach performs well and achieves accurate driving style detection, highlighting its potential to contribute significantly to the field of driver behavior analysis.
PAGE: Parallel Scalable Regionalization Framework

Regionalization techniques group spatial areas into a set of homogeneous regions to analyze and draw conclusions about spatial phenomena. A recent regionalization problem, called MP-regions, groups spatial areas to produce a maximum number of regions by enforcing a user-defined constraint at the regional level. The MP-regions problem is NP-hard. Existing approximate algorithms for MP-regions do not scale for large datasets due to their high computational cost and inherently centralized approaches to process data. This article introduces a parallel scalable regionalization framework (PAGE) to support MP-regions on large datasets. The proposed framework works in two stages. The first stage finds an initial solution through randomized search, and the second stage improves this solution through efficient heuristic search. To build an initial solution efficiently, we extend traditional spatial partitioning techniques to enable parallelized region building without violating the spatial constraints. Furthermore, we optimize the region building efficiency and quality by tuning the randomized area selection to trade off runtime with region homogeneity. The experimental evaluation shows the superiority of our framework to support an order of magnitude larger datasets efficiently compared to the state-of-the-art techniques while producing high-quality solutions.
Research and implementation of network impairment simulation system

Network impairment simulation is designed to simulate different Internet network environments on a local area network(LAN) and provide testing environments for different services under various network conditions (latency, bandwidth limitations, network conflicts, etc.). This verifies the application in a real network environment. In this paper, we implement a system based on the Linux platform which communicate with the Linux kernel through netlink to achieve network damage simulation using the traffic control mechanism in Linux. In this simulation system, it also implements simulation according to certain network loss policies in besides implementing network delay, packet loss, speed limit, repetition, chaos, jitter, etc. For network delay simulation, we proposed to use sparrow search algorithm-optimized long short-term memory(SSA-LSTM) algorithm to predict future delays using past network delay data. And to perform accurate simulation in the network impairment simulation system based on the results obtained from the prediction, which makes the network impairment simulation about delay more consistent with the real WAN environment. Finally, the experiment verifies the function of the network impairment simulation system.
Concurrent Multipath Transmission for ultra-Reliable and Low Latency with Deep Reinforcement Learning

Concurrent multipath transmission combining FEC is an effective approach to achieve ultra-reliable low latency communication in modern network. Rate allocation algorithm which distributing packets over multiple parallel paths plays a critical role in this architecture. However, traditional heuristic algorithm becomes less applicable due to great computational burdens and increasing demands for real-time. In this paper, we propose a deep reinforcement learning based algorithm NeuroCMT to solve this problem. Moreover, in order to deal with large discrete action space problem in NeuroCMT, we introduce a method with low complexity and high generalization ability. Furthermore, we implement prioritized experience replay technique in DDPG to attain better sample efficiency and enhanced convergence speed utilizing prioritized sampling. Extensive experiments are performed and the evaluation results demonstrate clearly that our approach achieves high reliability as well as low latency effectively.
CCG of Questions and Focus

In Combinatory Categorial Grammar (CCG) (Steedman 1996, 2000, Szabolcsi 1987), questions and focused sentences have been considered to be categories of S. In Type Logical Grammar (TLG), Jager (2005) terms questions to be a separate category q, and Barker and chieh Shan (2006) marks such types of clauses with modalities in multi-modal TLG. In spite of such treatment, questions have been considered to be sets of possible answers from semantic perspectives (Hamblin 1973). Pragmatically, focus induces a set of alternatives (Rooth 1985, 1992). A question we will consider in this paper is: should semantics of questions and focus be reflected not only in semantics and types but also in syntactic categories (Hamblin 1973, Rooth 1996)? The hypothesis of direct compositionality assumes that syntax and semantics work together in tandem, and every syntactic operation does not occur without corresponding semantic outcome (Barker and Jacobson 2007). I claim that interrogatives and focused sentences should be functions from a sentence to another sentence in view of their semantics (Hamblin 1973, Rooth 1985, 1992). Such novel categories enable combining with the following sentence in a discourse by means of functional application. While word order signals questions and exclamatives in English, as in Is Susan happy? , the presence of sentence-final particles such as ka marks questions in Japanese. What are the categories and meanings of Japanese sentence-final particles? In particular, I analyze particles such as yo and certain types of ne as verum, or polarity focus operators (Hohle 1992, Romero and Han 2004) and ka, no, ne, na, ke, and kashira as question markers. Given such semantics, their categories are S\(S\S). The treatment of focus and questions proposed in this paper no longer calls for modalities—syntax and semantics work together in tandem.
Research on Abnormal Traffic Detection of Internet of Things Based on Feature Selection

The Internet of Things (IoT) is under attack with the spread of IoT device applications. Traffic anomaly detection is critical to ensure IoT security. This paper proposes a method for abnormal traffic detection in IoT based on random forest and cascade deep learning detection model. IoT device traffic data were analyzed by random forest algorithm to screen out feature parameters with significant relevance to the detection target. To detect abnormal traffic, the feature parameters were taken as the input of convolution neural network (CNN) and long short-term memory (LSTM) detection models and to compare and analyze different dimensions, classification methods, and algorithmic model approaches. The results show that the detection accuracy reached 99.98% in dichotomous classification and 88.14% in multi-classification for high-dimensional data. The proposed CNN and LSTM cascade model detection methods were more stable than CNN and LSTM methods. Additionally, the model solved the problem of high dimensionality and nonlinearity of a large number of parameters to ensure that the main characteristics of the input variables were remembered over time. Therefore, the detection accuracy of this hybrid model is more desirable than that of the CNN and LSTM models without feature selection.