Miracle-dz/UniFAR-FTUs huggingface dataset:
Fisher Market Model based Resource Allocation for 5G Network Slicing

Network slicing (NS) is a key technology in 5G networks that enables the customization and efficient sharing of network resources to support the diverse requirements of nextgeneration services. This paper proposes a resource allocation scheme for NS based on the Fisher-market model and the Trading-post mechanism. The scheme aims to achieve efficient resource utilization while ensuring multi-level fairness, dynamic load conditions, and the protection of service level agreements (SLAs) for slice tenants. In the proposed scheme, each service provider (SP) is allocated a budget representing its infrastructure share or purchasing power in the market. SPs acquire different resources by spending their budgets to offer services to different classes of users, classified based on their service needs and priorities. The scheme assumes that SPs employ the $\alpha$-fairness criteria to deliver services to their subscribers. The resource allocation problem is formulated as a convex optimization problem to find a market equilibrium (ME) solution that provides allocation and resource pricing. A privacy-preserving learning algorithm is developed to enable SPs to reach the ME in a decentralized manner. The performance of the proposed scheme is evaluated through theoretical analysis and extensive numerical simulations, comparing it with the Social Optimal and Static Proportional sharing schemes.
Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations

Data visualization (DV) has become the prevailing tool in the market due to its effectiveness into illustrating insights in vast amounts of data. To lower the barrier of using DVs, automatic DV tasks, such as natural language question (NLQ) to visualization translation (formally called text-to-vis), have been investigated in the research community. However, text-to-vis assumes the NLQ to be well-organized and expressed in a single sentence. However, in real-world settings, complex DV is needed through consecutive exchanges between the DV system and the users. In this paper, we propose a new task named CoVis, short for Conversational text-to-Visualization, aiming at constructing DVs through a series of interactions between users and the system. Since it is the task which has not been studied in the literature, we first build a benchmark dataset named Dial-NVBench, including dialogue sessions with a sequence of queries from a user and responses from the system. Then, we propose a multi-modal neural network named MMCoVisNet to answer these DV-related queries. In particular, MMCoVisNet first fully understands the dialogue context and determines the corresponding responses. Then, it uses adaptive decoders to provide the appropriate replies: (i) a straightforward text decoder is used to produce general responses, (ii) an SQL-form decoder is applied to synthesize data querying responses, and (iii) a DV-form decoder tries to construct the appropriate DVs. We comparatively evaluate MMCoVisNet with other baselines over our proposed benchmark dataset. Experimental results validate that MMCoVisNet performs better than existing baselines and achieves a state-of-the-art performance.
Multi-Gait Locomotion Planning and Tracking for Tendon-Actuated Terrestrial Soft Robot (TerreSoRo)

The adaptability of soft robots makes them ideal candidates to maneuver through unstructured environments. However, locomotion challenges arise due to complexities in modeling the body mechanics, actuation, and robot-environment dynamics. These factors contribute to the gap between their potential and actual autonomous field deployment. A closed-loop path planning framework for soft robot locomotion is critical to close the real-world realization gap. This paper presents a generic path planning framework applied to TerreSoRo (Tetra-Limb Terrestrial Soft Robot) with pose feedback. It employs a gait-based, lattice trajectory planner to facilitate navigation in the presence of obstacles. The locomotion gaits are synthesized using a data-driven optimization approach that allows for learning from the environment. The trajectory planner employs a greedy breadth-first search strategy to obtain a collision-free trajectory. The synthesized trajectory is a sequence of rotate-then-translate gait pairs. The control architecture integrates high-level and low-level controllers with real-time localization (using an overhead webcam). Terre-SoRo successfully navigates environments with obstacles where path re-planning is performed. To best of our knowledge, this is the first instance of real-time, closed-loop path planning of a non-pneumatic soft robot.
Metric@CustomerN: Evaluating Metrics at a Customer Level in E-Commerce

Accuracy measures such as Recall, Precision, and Hit Rate have been a standard way of evaluating Recommendation Systems. The assumption is to use a fixed Top-N to represent them. We propose that median impressions viewed from historical sessions per diner be used as a personalized value for N. We present preliminary exploratory results and list future steps to improve upon and evaluate the efficacy of these personalized metrics.
Recommendation Unlearning via Matrix Correction

Recommender systems are important for providing personalized services to users, but the vast amount of collected user data has raised concerns about privacy (e.g., sensitive data), security (e.g., malicious data) and utility (e.g., toxic data). To address these challenges, recommendation unlearning has emerged as a promising approach, which allows specific data and models to be forgotten, mitigating the risks of sensitive/malicious/toxic user data. However, existing methods often struggle to balance completeness, utility, and efficiency, i.e., compromising one for the other, leading to suboptimal recommendation unlearning. In this paper, we propose an Interaction and Mapping Matrices Correction (IMCorrect) method for recommendation unlearning. Firstly, we reveal that many collaborative filtering (CF) algorithms can be formulated as mapping-based approach, in which the recommendation results can be obtained by multiplying the user-item interaction matrix with a mapping matrix. Then, IMCorrect can achieve efficient recommendation unlearning by correcting the interaction matrix and enhance the completeness and utility by correcting the mapping matrix, all without costly model retraining. Unlike existing methods, IMCorrect is a whitebox model that offers greater flexibility in handling various recommendation unlearning scenarios. Additionally, it has the unique capability of incrementally learning from new data, which further enhances its practicality. We conducted comprehensive experiments to validate the effectiveness of IMCorrect and the results demonstrate that IMCorrect is superior in completeness, utility, and efficiency, and is applicable in many recommendation unlearning scenarios.
HODLR3D: Hierarchical matrices for N-body problems in three dimensions

This article introduces HODLR3D, a class of hierarchical matrices arising out of $N$-body problems in three dimensions. HODLR3D relies on the fact that certain off-diagonal matrix sub-blocks arising out of the $N$-body problems in three dimensions are numerically low-rank. For the Laplace kernel in $3$D, which is widely encountered, we prove that all the off-diagonal matrix sub-blocks are rank deficient in finite precision. We also obtain the growth of the rank as a function of the size of these matrix sub-blocks. For other kernels in three dimensions, we numerically illustrate a similar scaling in rank for the different off-diagonal sub-blocks. We leverage this hierarchical low-rank structure to construct HODLR3D representation, with which we accelerate matrix-vector products. The storage and computational complexity of the HODLR3D matrix-vector product scales almost linearly with system size. We demonstrate the computational performance of HODLR3D representation through various numerical experiments. Further, we explore the performance of the HODLR3D representation on distributed memory systems. HODLR3D, described in this article, is based on a weak admissibility condition. Among the hierarchical matrices with different weak admissibility conditions in $3$D, only in HODLR3D did the rank of the admissible off-diagonal blocks not scale with any power of the system size. Thus, the storage and the computational complexity of the HODLR3D matrix-vector product remain tractable for $N$-body problems with large system sizes.
Can Bekenstein’s area law prevail in modified theories of gravity?

According to Bekenstein's area law, the black hole entropy is identified holographically -- with one quarter of the horizon area. However, it is commonly believed that such a law is only valid in Einstein's theory and that higher curvature corrections generically give rise to its modifications. This is for example the case of black holes in Lovelock gravities, or their four-dimensional cousins in the recently discovered 4D scalar-tensor Gauss--Bonnet gravity where one naively `finds' (classical) logarithmic corrections to the Bekenstein's law. In this Letter we argue that such logarithmic corrections originate from ignoring the shift symmetry of the 4D Gauss--Bonnet gravity. When this symmetry is properly taken into account, there is no longer any departure from the area law in this theory. Moreover, the first law remains valid upon modifying the black hole temperature, which can be derived via the Euclidean grand canonical ensemble (Brown--York) procedure, but is no longer given by the surface gravity. Interestingly, we show that upon similar modification of the black hole temperature the area law can also prevail for black holes in higher-dimensional Lovelock gravities.
Motion Degeneracy in Self-supervised Learning of Elevation Angle Estimation for 2D Forward-Looking Sonar

2D forward-looking sonar is a crucial sensor for underwater robotic perception. A well-known problem in this field is estimating missing information in the elevation direction during sonar imaging. There are demands to estimate 3D information per image for 3D mapping and robot navigation during fly-through missions. Recent learning-based methods have demonstrated their strengths, but there are still drawbacks. Supervised learning methods have achieved high-quality results but may require further efforts to acquire 3D ground-truth labels. The existing self-supervised method requires pretraining using synthetic images with 3D supervision. This study aims to realize stable self-supervised learning of elevation angle estimation without pretraining using synthetic images. Failures during self-supervised learning may be caused by motion degeneracy problems. We first analyze the motion field of 2D forward-looking sonar, which is related to the main supervision signal. We utilize a modern learning framework and prove that if the training dataset is built with effective motions, the network can be trained in a self-supervised manner without the knowledge of synthetic data. Both simulation and real experiments validate the proposed method.
Boundedness and stability analysis for impulsive stochastic differential equations driven by G-Brownian motion

ABSTRACT In this article, the pth moment globally exponential ultimate boundedness, pth moment globally exponential stability, quasi sure globally exponential boundedness and quasi sure globally exponential stability are investigated for impulsive stochastic differential equations driven by G-Brownian motion. Using G-Lyapunov function methods and inequality techniques, some sufficient conditions are derived for the boundedness and stability. Comparing with the existing methods, the obtained results allow the corresponding impulse-free systems to be unstable and unbounded. An example is provided to show the effectiveness of the theoretical results.
AsdKB: A Chinese Knowledge Base for the Early Screening and Diagnosis of Autism Spectrum Disorder

To easily obtain the knowledge about autism spectrum disorder and help its early screening and diagnosis, we create AsdKB, a Chinese knowledge base on autism spectrum disorder. The knowledge base is built on top of various sources, including 1) the disease knowledge from SNOMED CT and ICD-10 clinical descriptions on mental and behavioural disorders, 2) the diagnostic knowledge from DSM-5 and different screening tools recommended by social organizations and medical institutes, and 3) the expert knowledge on professional physicians and hospitals from the Web. AsdKB contains both ontological and factual knowledge, and is accessible as Linked Data at https://w3id.org/asdkb/. The potential applications of AsdKB are question answering, auxiliary diagnosis, and expert recommendation, and we illustrate them with a prototype which can be accessed at http://asdkb.org. cn/.
Error Localization for Sequential Effect Systems (Extended Version)

We describe a new concrete approach to giving predictable error locations for sequential (flow-sensitive) effect systems. Prior implementations of sequential effect systems rely on either computing a bottom-up effect and comparing it to a declaration (e.g., method annotation) or leaning on constraint-based type inference. These approaches do not necessarily report program locations that precisely indicate where a program may"go wrong"at runtime. Instead of relying on constraint solving, we draw on the notion of a residual from literature on ordered algebraic structures. Applying these to effect quantales (a large class of sequential effect systems) yields an implementation approach which accepts exactly the same program as an original effect quantale, but for effect-incorrect programs is guaranteed to fail type-checking with predictable error locations tied to evaluation order. We have implemented this idea in a generic effect system implementation framework for Java, and report on experiences applying effect systems from the literature and novel effect systems to Java programs. We find that the reported error locations with our technique are significantly closer to the program points that lead to failed effect checks.
Electronic transport and thermoelectricity in the selenospinel 
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We report a study of selenospinel Cu$_{6-x}$Fe$_{4+x}$Sn$_{12}$Se$_{32}$ ($x$ = 0, 1, 2) single crystals, which crystalize in a cubic structure with the $Fd\overline{3}m$ space group, and show typical semiconducting behavior. The large discrepancy between the activation energy for electrical conductivity $E_\rho$ (32.3 $\sim$ 69.8 meV), and for thermopower $E_\textrm{S}$ (3.2 $\sim$ 11.5 meV), indicates a polaronic transport mechanism between 350 and 50 K. With decreasing temperature, it evolves into variable-range hopping conduction. Furthermore, the heat capacity shows a hump around 25(5) K and diverges from the Debye $T^3$ law at low temperatures, indicating the observation of structural glassy features in these crystalline solids.
Pre-training End-to-end ASR Models with Augmented Speech Samples Queried by Text

In end-to-end automatic speech recognition system, one of the difficulties for language expansion is the limited paired speech and text training data. In this paper, we propose a novel method to generate augmented samples with unpaired speech feature segments and text data for model pre-training, which has the advantage of low cost without using additional speech data. When mixing 20,000 hours augmented speech data generated by our method with 12,500 hours original transcribed speech data for Italian Transformer transducer model pre-training, we achieve 8.7% relative word error rate reduction. The pre-trained model achieves similar performance as the model pre-trained with multilingual transcribed 75,000 hours raw speech data. When merging the augmented speech data with the multilingual data to pre-train a new model, we achieve even more relative word error rate reduction of 12.2% over the baseline, which further verifies the effectiveness of our method for speech data augmentation.
A Scalable Approach to Multi-Context Continual Learning via Lifelong Skill Encoding

Continual or lifelong learning (CL) is one of the most challenging problems in machine learning. In this paradigm, a system must learn new tasks, contexts, or data without forgetting previously learned information. We present a scalable approach to multi-context continual learning (MCCL) in which we decouple how a system learns to solve new tasks (i.e., acquires skills) from how it stores them. Our approach leverages two types of artificial networks: (1) a set of reusable, \textit{task-specific networks} (TN) that can be trained as needed to learn new skills, and (2) a lifelong, \textit{autoencoder network} (EN) that stores all learned skills in a compact, latent space. To learn a new skill, we first train a TN using conventional backpropagation, thus placing no restrictions on the system's ability to encode the new task. We then incorporate the newly learned skill into the latent space by first recalling previously learned skills using our EN and then retraining it on both the new and recalled skills. Our approach can efficiently store an arbitrary number of skills without compromising previously learned information because each skill is stored as a separate latent vector. Whenever a particular skill is needed, we recall the necessary weights using our EN and then load them into the corresponding TN. Experiments on the MNIST and CIFAR datasets show that we can continually learn new skills without compromising the performance of existing skills. To the best of our knowledge, we are the first to demonstrate the feasibility of encoding entire networks in order to facilitate efficient continual learning.
Numerical modeling of the 1840s major eruption of η Carinae as an explosion

In this paper, new two-dimensional hydrodynamical simulations of η Car’s nebulae are performed. In the 1840s, the massive star η Car suffered a major eruption that resulted in the formation of a bipolar structure, which is commonly known as the large Homunculus. During this event, η Car expelled into the circumstellar material a total mass of ~10 M ⊙ and released a total energy of E k ~ 10 50 erg over a very short time (≤5 yr). These kinds of explosive events are frequently called supernova impostors due to their resemblance to a type II supernova, but the stars survive the explosion. In the case of η Car, a brief explosion scenario provides a potential explanation for the behavior of the historical light curve of η Car a few years (~10 yr) after the nineteenth century outburst. Here, such an alternative scenario of an explosion is assumed (instead of a super-Eddington wind) in order to investigate whether an explosive event is also able to explain the shape and kinematics of the large Homunculus. I show that the numerical simulations presented here indeed resemble some of the observed features of the nebula, such as the present-day double-shell structure of the Homunculus, with a thin outer dense shell and a thicker inner layer, as well as thermal instabilities (Rayleigh-Taylor and Kelvin-Helmholtz) along the dense shell that may lead to the current mottled appearance of the large Homunculus. Nonetheless, the explosion model for the 1840s major eruption of η Car is not able to account for the estimated age of the large Homunculus.
Towards OS Heterogeneity Aware Cluster Management for HPC

To achieve extremely high performance in HPC, many researchers have proposed customized operating systems that are tailored to HPC workload characteristics and emerging hardware. Hence, we argue that the HPC cluster will move away from the single OS environment to a cluster with numerous heterogeneous OSes. However, existing HPC cluster management still assumes that all nodes are equipped with the same OS and fails to consider OS heterogeneity during job scheduling. As a result, such unawareness loses most performance benefits provided by specialized OSes. This paper quantitatively investigates the problem of ignoring OS heterogeneity in the current HPC cluster management and analyzes performance trade-offs inside heterogeneous OSes. Preliminary results on a variety of HPC OSes and applications confirm the performance penalty of the existing cluster scheduler. We then propose a cluster scheduler prototype that incorporates OS heterogeneity into cluster configuration, resource monitoring, and job placement. We also present open challenges for future research on OS heterogeneity aware HPC clusters.
Swarm Robotics: A Survey from a Multi-Tasking Perspective

The behaviour of social insects such as bees and ants has influenced the development of swarm robots. To enable robots to cooperate together, swarm robotics employs principles such as communication, coordination, and collaboration. Collaboration among multiple robots can lead to a faster task completion time compared to the utilisation of a single, complex robot. One of the key aspects of swarm robotics is that control is distributed uniformly across the robots in the swarm, which boosts the system’s resilience and fault tolerance. Through the use of the robots’ embodied sensors and actuators, this distributed control often facilitates the emergence of collective behaviours through the interaction of the robots with one another and with the environment. The purpose of this survey is to examine the reasons behind the lack of utilisation of swarm robots in multi-tasking applications, which will be accomplished by studying previous research works in the field. We examine the literature from the perspective of multi-tasking: we pay particular attention to concepts that contribute to the progress of swarm robotics for multi-tasking applications. To do this, we first examine the different studies in multi-tasking swarm robotics, covering platforms, multi-tasking scenarios, sub-task allocation methodologies, and performance metrics. We then highlight several swarm robotics related disciplines that have significant effect on the development of swarm robotics for multi-tasking problems. We propose two taxonomies: the first categorises works based on the characteristics of the scenarios being handled, whereas the second taxonomy categorises works based on the swarming strategies utilised to achieve multi-tasking capabilities. We finish with a discussion of swarm robots’ existing limitations for real-world multi-tasking applications, as well as recommendations for future research directions.
Sentiment Analysis for Hotel Reviews: A Systematic Literature Review

Sentiment Analysis (SA) helps to automatically and meaningfully discover hotel customers’ satisfaction from their shared experiences and feelings on social media. Several studies have been conducted to improve the precision of SA in the hospitality industry, which vary in data preprocessing techniques, feature representation, sentiment classification levels, and models, and they use different datasets. Such variations are worthy of attention and monitoring. Despite the importance of SA in hospitality and tourism, review studies identifying gaps and suggesting future research directions are limited. This article introduces a systematic literature review to label and discuss state-of-the-art studies that deal with SA for hotel reviews.
Quantifying the Security Profile of Linux Applications

There is an increasing interest to quantify and improve the isolation provided by containers to competing applications on multitenant hosts. As a first step to address this need, we introduce several metrics that quantify the exposure of the applications to the source code of the kernel subsystems. Based on existing tracing tools, we develop a common framework and build two toolchains that automate the extraction of the metrics. We experimentally compare the tracing accuracy of the toolchains by calculating the metrics across different workloads and demonstrate the importance of separating the application execution from unrelated system activity.