Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding Modular Reconfigurable Robots: A Survey on Mechanisms and Design

The intrinsic modularity and reconfigurability of modular reconfigurable robots (MRR) confer advantages such as versatility, fault tolerance, and economic efficacy, thereby showcasing considerable potential across diverse applications. The continuous evolution of the technology landscape and the emergence of diverse conceptual designs have generated multiple MRR categories, each described by its respective morphology or capability characteristics, leading to some ambiguity in the taxonomy. This paper conducts a comprehensive survey encompassing the entirety of MRR hardware and design, spanning from the inception in 1985 to 2023. This paper introduces an innovative, unified conceptual framework for understanding MRR hardware, which encompasses three pivotal elements: connectors, actuators, and homogeneity. Through the utilization of this trilateral framework, this paper provide an intuitive understanding of the diverse spectrum of MRR hardware iterations while systematically deciphering and classifying the entire range, offering a more structured perspective. This survey elucidates the fundamental attributes characterizing MRRs and their compositional aspects, providinig insights into their design, technology, functionality, and categorization. Augmented by the proposed trilateral framework, this paper also elaborates on the trajectory of evolution, prevailing trends, principal challenges, and potential prospects within the field of MRRs.
Solving Max-Min Fair Resource Allocations Quickly on Large Graphs

We consider the max-min fair resource allocation problem. The best-known solutions use either a sequence of optimizations or waterfilling, which only applies to a narrow set of cases. These solutions have become a practical bottleneck in WAN traffic engineering and cluster scheduling, especially at larger problem sizes. We improve both approaches: (1) we show how to convert the optimization sequence into a single fast optimization, and (2) we generalize waterfilling to the multi-path case. We empirically show our new algorithms Pareto-dominate prior techniques: they produce faster, fairer, and more efficient allocations. Some of our allocators also have theoretical guarantees: they trade off a bounded amount of unfairness for faster allocation. We have deployed our allocators in Azure's WAN traffic engineering pipeline, where we preserve solution quality and achieve a roughly $3\times$ speedup.
On the energy method for the global solutions to the three dimensional incompressible non-resistive MHD near equilibrium

We prove the global existence of the smooth solutions near equilibrium to the Cauchy problem of the incompressible non-resistive magnetohydrodynamic equations in the whole three dimensional space under some admissible condition. The result has been obtained by Xu and Zhang (SIAM J. Math. Anal. 47: 26--65, 2015) in anisotropic Besov space framework. In this paper, we provide a new proof based on the temporal weighted energy method.
Prompt Engineering or Fine Tuning: An Empirical Assessment of Large Language Models in Automated Software Engineering Tasks

In this paper, we investigate the effectiveness of state-of-the-art LLM, i.e., GPT-4, with three different prompting engineering techniques (i.e., basic prompting, in-context learning, and task-specific prompting) against 18 fine-tuned LLMs on three typical ASE tasks, i.e., code generation, code summarization, and code translation. Our quantitative analysis of these prompting strategies suggests that prompt engineering GPT-4 cannot necessarily and significantly outperform fine-tuning smaller/older LLMs in all three tasks. For comment generation, GPT-4 with the best prompting strategy (i.e., task-specific prompt) had outperformed the first-ranked fine-tuned model by 8.33% points on average in BLEU. However, for code generation, the first-ranked fine-tuned model outperforms GPT-4 with best prompting by 16.61% and 28.3% points, on average in BLEU. For code translation, GPT-4 and fine-tuned baselines tie as they outperform each other on different translation tasks. To explore the impact of different prompting strategies, we conducted a user study with 27 graduate students and 10 industry practitioners. From our qualitative analysis, we find that the GPT-4 with conversational prompts (i.e., when a human provides feedback and instructions back and forth with a model to achieve best results) showed drastic improvement compared to GPT-4 with automatic prompting strategies. Moreover, we observe that participants tend to request improvements, add more context, or give specific instructions as conversational prompts, which goes beyond typical and generic prompting strategies. Our study suggests that, at its current state, GPT-4 with conversational prompting has great potential for ASE tasks, but fully automated prompt engineering with no human in the loop requires more study and improvement.
Organic weed management in chickpea: a simulation approach

The effect of chickpea (Cicer arietinum L.) canopy on a weed (annual sowthistle) growth and development has been simulated using a virtual model of architectural development. The model simulates the effect of crop canopy architecture, seeding rate, and row spacing on sowthistle. Such a simulation study on crop-weed competition may help to develop appropriate cultural weed management strategies in chickpea. According to the simulations, rapid early leaf production is an important trait to improve the competitive ability of chickpea. Results also revealed that the emergence time of sowthistle in relation to chickpea plays a more crucial role in sowthistle performance and survival in chickpea than the density of crop.
Editorial: Knowledge from information

Information, absent careful analysis, produced confusion rather than useful knowledge. In biomedicine, we are amidst an information explosion. The twin biotechnology and information technology revolutions have produced novel measures of molecules, cells, individuals and populations. We now routinely measure hundreds of thousands of DNA base pairs (singlenucleotide polymorphisms), RNA copy numbers for tens of thousands of genes and expression for thousands of proteins on large numbers of individuals under different experimental and observational conditions. Never before has there been so much information about the basic mechanisms of health and disease. The potential for major discoveries is palpable, but it is yet to be fully realized. So an important question is where can statisticians best contribute to scientific progress in specialties like biomedicine as the explosion of information continues. This editorial suggests that we can usefully
How Do Outer Spiral Rainbands Affect Tropical Cyclone Structure and Intensity

Abstract A long-standing issue on how outer spiral rainbands affect the structure and intensity of tropical cyclones is studied through a series of numerical experiments using the cloud-resolving tropical cyclone model TCM4. Because diabatic heating due to phase changes is the main driving force of outer spiral rainbands, their effect on the tropical cyclone structure and intensity is evaluated by artificially modifying the heating and cooling rate due to cloud microphysical processes in the model. The view proposed here is that the effect of diabatic heating in outer spiral rainbands on the storm structure and intensity results mainly from hydrostatic adjustment; that is, heating (cooling) of an atmospheric column decreases (increases) the surface pressure underneath the column. The change in surface pressure due to heating in the outer spiral rainbands is significant on the inward side of the rainbands where the inertial stability is generally high. Outside the rainbands in the far field, where the iner.
Quadratic Inverse Function Tsallis Entropy Multi-modulus Blind Equalization Algorithm

In underwater acoustic communication systems, inter-symbol interference (ISI) caused by communication channel distortion is the main factor affecting the quality of communication. Aiming at the shortcomings of computational complexity, slow convergence rate, and poor stability of Multi-Modulus Algorithm (MMA), a quadratic inverse function Tsallis entropy of Cascade Multi-Modulus blind equalization Algorithm (TCMMA) was proposed. In the proposed algorithm, quadratic inverse function Tsallis entropy is fused with Parzen window probability density estimation method, and directly used as the cost function in cascade blind equalizer to reduce the residual mean square error, and the normalized wavelet transform is used to speed up the convergence speed. The simulation results with underwater acoustic channel show the superiority of the proposed algorithm. DOI: http://dx.doi. org/10.11591/telkomnika.v11i4. 2371
Temporal dynamics of the internally generated radiations in a two-photon excited four-level potassium atom

A numerical study, stimulated by existing experimental results concerning the temporal dynamics of internally generated radiations, is presented for a four-level system of the potassium atom. An intense nanosecond duration laser pulse excites the two-photon transition and initiates the generation of internal radiations from quantum noise. It is shown that the temporal profiles of the generated radiations along the atomic path-1, ( ) and path-2, ( ), evolve differently as a function of certain system parameters. It is also shown that the excitation laser intensity affects the parametric emissions along path-1 and the population of the states. The path-2 emissions, which also depend on the excitation laser intensity, are most probably due to amplified spontaneous emission (ASE), without population inversion, in the case of strong excitation. We finally examine the radiation dynamics under the effect of the elastic dephasing collisions between the potassium atoms with the buffer gas atoms.
ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models

Alignment is crucial for training large language models. The predominant strategy is Reinforcement Learning from Human Feedback (RLHF), with Proximal Policy Optimization (PPO) as the de-facto algorithm. Yet, PPO is known to struggle with computational inefficiency, a challenge that this paper aims to address. We identify three important properties of RLHF tasks: fast simulation, deterministic transitions, and trajectory-level rewards, which are not leveraged in PPO. Based on these properties, we develop ReMax, a new algorithm tailored for RLHF. The design of ReMax builds on the celebrated algorithm REINFORCE but is enhanced with a new variance-reduction technique. ReMax offers threefold advantages over PPO: first, it is simple to implement with just 6 lines of code. It further eliminates more than 4 hyper-parameters in PPO, which are laborious to tune. Second, ReMax reduces memory usage by about 50%. To illustrate, PPO runs out of memory when fine-tuning a Llama2-7B model on A100-80GB GPUs, whereas ReMax can support the training. Even though memory-efficient techniques (e.g., ZeRO and offload) are employed for PPO to afford training, ReMax can utilize a larger batch size to increase throughput. Third, in terms of wall-clock time, PPO is about twice as slow as ReMax per iteration. Importantly, these improvements do not sacrifice task performance. We hypothesize that these advantages can be maintained in larger-scale models.
Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning

Automatic coding of International Classification of Diseases (ICD) is a multi-label text categorization task that involves extracting disease or procedure codes from clinical notes. Despite the application of state-of-the-art natural language processing (NLP) techniques, there are still challenges including limited availability of data due to privacy constraints and the high variability of clinical notes caused by different writing habits of medical professionals and various pathological features of patients. In this work, we investigate the semi-structured nature of clinical notes and propose an automatic algorithm to segment them into sections. To address the variability issues in existing ICD coding models with limited data, we introduce a contrastive pre-training approach on sections using a soft multi-label similarity metric based on tree edit distance. Additionally, we design a masked section training strategy to enable ICD coding models to locate sections related to ICD codes. Extensive experimental results demonstrate that our proposed training strategies effectively enhance the performance of existing ICD coding methods.
Magnetic field-induced reverse transformation in B2-type NiCoMnAl shape memory alloys

Effects of the addition of Co to the NiMnAl alloy on the martensitic and magnetic properties were investigated. The magnetic properties of the parent (P) phase drastically changed from paramagnetic to ferromagnetic, while the magnetization in the martensite (M) phase slightly decreased with increasing Co composition. Consequently, in the Ni40Co10Mn33Al17 alloy with the B2 ordered structure, the martensitic transformation from a ferromagnetic P to a weak magnetic M phase was obtained. The martensitic transformation temperatures in this alloy decreased by about 30K with the application of a magnetic field of 7T and a metamagnetic phase transformation was confirmed.
Correlation between information diffusion and opinion evolution on social media

Information diffusion and opinion evolution are often treated as two independent processes. Opinion models assume the topic reaches each agent and agents initially have their own ideas. In fact, the processes of information diffusion and opinion evolution often intertwine with each other. Whether the influence between these two processes plays a role in the system state is unclear. In this paper, we collected more than one million real data from a well-known social platform, and analysed large-scale user diffusion behaviour and opinion formation. We found that user inter-event time follows a two-scaling power-law distribution with two different power exponents. Public opinion stabilizes quickly and evolves toward the direction of convergence, but the consensus state is prevented by a few opponents. We propose a three-state opinion model accompanied by information diffusion. Agents form and exchange their opinions during information diffusion. Conversely, agents' opinions also influence their diffusion actions. Simulations show that the model with a correlation of the two processes produces similar statistical characteristics as empirical results. A fast epidemic process drives individual opinions to converge more obviously. Unlike previous epidemic models, the number of infected agents does not always increase with the update rate, but has a peak with an intermediate value of the rate.
Universal Bounds on Information-Processing Capabilities of Markov Processes

We consider a finite-state, continuous-time Markov process, represented in the"linear framework"by a directed graph with labelled edges which specifies the infinitesimal generator of the process. If the graph is strongly connected, the process has a unique steady-state probability distribution, $p$, which may not be one of thermodynamic equilibrium. If the label (rate) of any edge (transition) is perturbed, to reach the new steady-state probability distribution $p'$, we find that the Kullback-Leibler (KL) divergence between these distributions is bounded by the change in the thermodynamic affinity, $\Delta A(C)$, of any cycle, $C$, that includes the altered transition, D$_{KL}$$(p'||p) \leq |\Delta A(C)|$, irrespective of the structure of the graph. It follows that, if an equilibrium distribution is shifted away from equilibrium by perturbing a single rate, then the free energy difference between these distributions is similarly bounded $F^{neq}-F^{eq}\leq |\Delta A(C)|$. Our analysis reveals universal, energy-induced bounds on the information-processing capabilities of Markov systems operating arbitrarily far from thermodynamic equilibrium.
The CPHD and R-RANSAC trackers applied to the VIVID dataset

In this work, two multitarget trackers - the Cardinalized Probability Hypothesis Density (CPHD) filter and the Recursive Random Sample Consensus (R-RANSAC) algorithm - were applied to three scenarios of the Video Verification of IDentity (VIVID) dataset provided by DARPA. The dataset consists of real video data of multiple cars observed from an unmanned aerial vehicle (UAV) and includes challenging situations such as dense traffic and occlusions. The same detector output was given to each tracker and the same metrics of performance were computed in order to ensure fair comparison of the two tracking approaches. The results show the CPHD did better overall, which was to be expected given that it is the more mature approach.
Characterization of plant growth promoting rhizobacteria isolated from soils of Senegalese Semi-arid Sahelian Zone

The semi-arid Sahelian region of Africa, characterized by its deleterious agro-climatic conditions, is also subject to agricultural overexploitation to support human and livestock food. This situation has strongly accentuated the continuous degradation of arable land. However, green biotechnologies are often proposed to promote more sustainable agricultural systems. Thus, the soil microbiome is explored for the production of biofertilizers that are able to ensure the growth and biocontrol of cultivated plants. For this purpose, plant growth promoting rhizobacteria (PGPR) were widely used. In order to increase the production of fodder for livestock, the production of biofertilizers is undertaken using indigenous PGPR strains able to sustainably improve agricultural productivity. From soil samples collected in the investigated zone, rhizobacteria are isolated and characterized in vitro for a selection of elite strains. Thus, a collection of 71 isolates presenting a great phenotypic and physiological diversity was established. The most efficient strains were selected for their ability to promote plant growth by the production of catalase and siderophores, auxin synthesis and soil P bioavailability. At the end of this study, candidate strains for the formulation of a suitable biofertilizer were highlighted on the basis of the correlations between the highest PGP activities of bacterial strains.
BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning

Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and the real world. However, DR often requires careful tuning of randomization parameters. Methods like Bayesian Domain Randomization (Bayesian DR) and Active Domain Randomization (Adaptive DR) address this issue by automating parameter range selection using real-world experience. While effective, these algorithms often require long computation time, as a new policy is trained from scratch every iteration. In this work, we propose Adaptive Bayesian Domain Randomization via Strategic Fine-tuning (BayRnTune), which inherits the spirit of BayRn but aims to significantly accelerate the learning processes by fine-tuning from previously learned policy. This idea leads to a critical question: which previous policy should we use as a prior during fine-tuning? We investigated four different fine-tuning strategies and compared them against baseline algorithms in five simulated environments, ranging from simple benchmark tasks to more complex legged robot environments. Our analysis demonstrates that our method yields better rewards in the same amount of timesteps compared to vanilla domain randomization or Bayesian DR.
Morty: Scaling Concurrency Control with Re-Execution

Serializable systems often perform poorly under high contention. In this work, we analyze this performance limitation through a novel take on conflict windows. Through the lens of these windows, we develop a new concurrency control technique that leverages transaction re-execution to improve throughput scalability under high contention. Our system, Morty, achieves up to 1.7x-96x the throughput of state-of-the-art systems, with similar or better latency.
Deep Reinforcement Learning for UAV Routing in the Presence of Multiple Charging Stations

Deploying Unmanned Aerial Vehicles (UAVs) for traffic monitoring has been a hotspot given their flexibility and broader view. However, a UAV is usually constrained by battery capacity due to limited payload. On the other hand, the development of wireless charging technology has allowed UAVs to replenish energy from charging stations.In this paper, we study a UAV routing problem in the presence of multiple charging stations (URPMCS) with the objective of minimizing the total distance traveled by the UAV during traffic monitoring. We present a deep reinforcement learning based method, where a multi-head heterogeneous attention mechanism is designed to facilitate learning a policy that automatically and sequentially constructs the route, while taking the energy consumption into account. In our method, two types of attentions are leveraged to learn the relations between monitoring targets and charging station nodes, adopting an encoder-decoder-like policy network. Moreover, we also employ a curriculum learning strategy to enhance generalization to different numbers of charging stations. Computational results show that our method outperforms conventional algorithms with higher solution quality (except for exact methods such as Gurobi) and shorter runtime in general, and also exhibits strong generalized performance on problem instances with different distributions and sizes.
Hyperfinite von Neumann algebras and Poisson boundaries of time dependent random walks

On considere la caracterisation des limites de Poisson de marches aleatoires de Markov dependante du temps invariantes par un groupe sur des groupes localement compacts G. On montre que de telles limites de Poisson sont moyennables et approximativement transitives. On etablit une relation entre des algebres de Von Neumann et des limites de Poisson quand G=R ou Z
Interface Physics in Complex Oxide Heterostructures

Complex transition metal oxides span a wide range of crystalline structures and play host to an incredible variety of physical phenomena. High dielectric permittivities, piezo-, pyro-, and ferroelectricity are just a few of the functionalities offered by this class of materials, while the potential for applications of the more exotic properties like high temperature superconductivity and colossal magnetoresistance is still waiting to be fully exploited. With recent advances in deposition techniques, the structural quality of oxide heterostructures now rivals that of the best conventional semiconductors, taking oxide electronics to a new level. Such heterostructures have enabled the fabrication of artificial multifunctional materials. At the same time they have exposed a wealth of phenomena at the boundaries where compounds with different structural instabilities and electronic properties meet, giving unprecedented access to new physics emerging at oxide interfaces. Here we highlight some of these exciting.