Miracle-dz/UniFAR-FTUs huggingface dataset:
English2Gbe: A multilingual machine translation model for {Fon/Ewe}Gbe

Language is an essential factor of emancipation. Unfortunately, most of the more than 2,000 African languages are low-resourced. The community has recently used machine translation to revive and strengthen several African languages. However, the trained models are often bilingual, resulting in a potentially exponential number of models to train and maintain to cover all possible translation directions. Additionally, bilingual models do not leverage the similarity between some of the languages. Consequently, multilingual neural machine translation (NMT) is gaining considerable interest, especially for low-resourced languages. Nevertheless, its adoption by the community is still limited. This paper introduces English2Gbe, a multilingual NMT model capable of translating from English to Ewe or Fon. Using the BLEU, CHRF, and TER scores computed with the Sacrebleu (Post, 2018) package for reproducibility, we show that English2Gbe outperforms bilingual models (English to Ewe and English to Fon) and gives state-of-the-art results on the JW300 benchmark for Fon established by Nekoto et al. (2020). We hope this work will contribute to the massive adoption of Multilingual models inside the community. Our code is made accessible from Github.
An Evaluation of the Inhibition Effects of Cell Migration of Aspirin Soaking 360°Square-Edge Intraocular Lens in an in Vitro Lens Capsule Model

ABSTRACT Purpose This study performs to evaluate the Hydrophobic and Hydrosmart 360°square-edge intraocular lens drug delivery of Aspirin using an in vitro lens capsular model. Methods Cell counting kit-8 assay was used to calculate 50% inhibiting concentration values in both SRA01/04 and HLE-B3 cells. Hoechst staining and transwell assay were used to detect cell proliferation and cell migration. The in vitro lens capsule model was established mainly with a special transwell-col and cell climbing sheet, in which an intraocular lens and the TGF-β2 were added. The ultraviolet spectrophotometer was used to measure the drug concentrations released in vitro. Cell-exclusion zone assay was used to detect the cell migration in the in vitro capsular model. Results It shows that cell morphology and distribution of SRA01/04 in the in vitro lens capsular model were closer to those in vivo. The results revealed that there could be significant inhibiting effects on cell migration of the hydrosmart intraocular lens with a sustained drug release in vitro in 7 days, while the hydrophobic intraocular lens drug delivery of Aspirin was mainly performed only from day 1 to day 3. Conclusions Results showed the developed hydrosmart intraocular lens could release Aspirin continuously in vitro to inhibit the cell migration of lens epithelial cells.
Nonlinear transverse current on $\mathcal{C}_{3v}$-symmetric honeycomb lattice

At nonlinear orders in the electric field, the vanishing of the Hall conductivity does not prevent the nonlinear component of the current from being transverse for selected field directions. We study electrons on C3v-symmetric honeycomb lattice for which the Hall conductivity vanishes at first and second order. Nevertheless, the second-order current component is transverse for fields perpendicular to the three mirror lines. The C3v symmetry constrains the first-order and secondorder conductivity tensors to have only one independent component each, which we calculate using the quantum kinetic equation. In linearly-polarized oscillating field, the current has a zero-frequency component switching sign upon π/2-rotation of the polarization angle.
An Evaluation of Software Fault Tolerance in a Practical System

An experimental project to assess the effectiveness of software fault tolerance techniques is described. Techniques were developed for, and applied to, a realistic implementation of a practical real-time system, namely a naval command and control system. Reliability data was collected by running this system with a simulated tactical environment for a variety of action scenarios. Analysis of the data confirms that software fault tolerance techniques can significantly enhance system reliability.
Interference and Coverage Analysis of mmWave Inter-Vehicle Broadcast with Directional Antennas

Thanks to the availability of large bandwidth and high-gain directional antennas at the millimeter-wave (mmWave) bands, mmWave communications have been considered as one of the primary solutions to meet the high data rates needs in vehicular networks. Unicast in mmWave vehicle-to-vehicle (V2V) communications has been well-studied, but much less attention has been paid to V2V broadcast which is required by many V2V applications such as active safety. To fill the gap, this paper systematically investigates mmWave V2V broadcast by considering the unique properties of mmWave signal propagation in V2V environments as well as the impacts of directional antennas and interference, aiming to provide unique insight into mmWave V2V broadcast and to shed light on designing effective V2V broadcast protocols. Based on widely-accepted, high-fidelity system models, we mathematically analyze the receiver-side signal-to-interference-plus-noise-ratio (SINR) and broadcast coverage, and we study the impacts of blockage, inter-vehicle distance, vehicle density and beam pattern. Through comprehensive numerical analysis, we find out that, instead of a single unique optimal beamwidth, there exists an optimal range of beamwidth, in which the beamwidths have similar performance and can maximize the coverage. We also find out that the selection of carrier sensing range plays an important role as it highly influences the performance of the whole vehicular networks.
The Impact of Neutron Transfer Reactions on the Heating and Cooling of Accreted Neutron Star Crusts

Nuclear reactions heat and cool the crust of accreting neutron stars and need to be understood to interpret observations of X-ray bursts and long-term cooling in transiently accreting systems. It was recently suggested that previously ignored neutron transfer reactions may play a significant role in the nuclear processes. We present results from full nuclear network calculations that now include these reactions and determine their impact on crust composition, crust impurity, heating, and cooling. We find that a large number of neutron transfer reactions indeed occur and impact crust models. In particular, we identify a new type of reaction cycle that brings a pair of nuclei across the nuclear chart into equilibrium via alternating neutron capture and neutron release, interspersed with a neutron transfer. While neutron transfer reactions lead to changes in crust model predictions and need to be considered in future studies, previous conclusions concerning heating, cooling, and compositional evolution are remarkably robust.
ON FREE BOUNDARY MINIMAL ANNULI IN A HALF-BALL

We consider a uniqueness problem of embedded free boundary minimal annuli in the three-dimensional unit half-ball and show that the half of the critical catenoid is the only such surfaces. Using this result and a symmetry principle, we obtain several characterizations of the critical catenoid among embedded free boundary minimal annuli in the three-dimensional unit ball. For example, we show that if a boundary component of such surfaces is invariant under the reflections through two coordinate planes, then the critical catenoid is the only such surfaces.
Effectiveness of multi-physics numerical model in simulating accelerated corrosion with spatial and temporal non-uniformity

This study is motivated by the need to develop an efficient numerical model to simulate non-uniform interfacial degradation of reinforcing steel in concrete in an accelerated corrosion setup considering the influence of differential aeration, moisture and conductivity. In this study, a multi-physics finite element (FE) model is presented to simulate spatially and temporally non-uniform surface topography of corroding steel with rust layer, and eliminates the assumption of uniform mass loss and its linear variation with time as per available literature that uses classical approach of Faraday’s law. The model is validated experimentally with accelerated corrosion setup. Unlike previous studies, pore saturation (PS) is continuously monitored and its existing experimental correlations with conductivity and oxygen diffusivity are adopted so that the model can be extended to simulate natural corrosion to enhance the accuracy of service life predictions. These evaluations can be made completely nondestructive and in real-time eliminating the challenges in capturing the influence of environment by the use of alternative parameters such as relative humidity from real climate change predictions. The key findings from the investigation reveal that incorporating local environmental parameters measured in situ allows for the model to naturally evolve with anodic and cathodic locations on steel surface avoiding the need to assume predefined locations. It is envisioned that the developed multi-physics FE model can be used as a reliable substitute to accelerated corrosion experiment which is frequently used for durability studies of corroding reinforced concrete (RC) members. It is shown that the daily and cumulative mass loss evaluated from the degraded surface topography of the corroded rebar using the simulated model are in good agreement with those evaluated experimentally. Further, the maximum pit depth evaluated from the simulated surface is validated that has valuable applications in estimating degraded strength of corroding steel and service life of RC members.
Evaluating the Robustness of Deep Reinforcement Learning for Autonomous and Adversarial Policies in a Multi-agent Urban Driving Environment

Background : Deep reinforcement learning is actively used for training autonomous and adversarial car policies in a simulated driving environment. Due to the large availability of various re-inforcement learning algorithms and the lack of their systematic comparison across different driving scenarios, we are unsure of which ones are more effective for training and testing autonomous car software in single-agent as well as multi-agent driving environments. Aims : A benchmarking framework for the comparison of deep reinforcement learning in a vision-based autonomous driving will open up the possibilities for training better autonomous car driving policies. Furthermore, autonomous cars trained on deep reinforcement learning-based algorithms are known for being vulnerable to adversarial attacks. To guard against adversarial attacks, we can train autonomous cars on adversarial driving policies. However, we lack the knowledge of which deep reinforcement learning algorithms would act as good adversarial agents able to effectively test autonomous cars. Method : To address these challenges, we provide an open and reusable benchmarking framework for systematic evaluation and comparative analysis of deep reinforcement learning algorithms for autonomous and adversarial driving in a single-and multi-agent environment. Using the framework, we perform a comparative study of five discrete and two continuous action space deep reinforcement learning algorithms. We run the experiments in a vision-only high fidelity urban driving simulated environments. Results : The results indicate that only some of the deep reinforcement learning algorithms perform consistently better across single and multi-agent scenarios when trained in a multi-agent-only setting. For example, A3C-and TD3-based autonomous cars perform comparatively better in terms of more robust actions and minimal driving errors in both single and multi-agent scenarios. PPO IMPALA from a discrete action space and DDPG TD3 from a continuous action space show to be effective algorithms for training adversarial agents and exposing failure scenarios in autonomous car software. Conclusions : We conclude that different DRL algorithms exhibit different driving and testing performance in different scenarios, which underlines the need for their systematic comparative analysis. The benchmarking framework proposed in this paper facilitates such a comparison.
GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. However, it can be notoriously challenging to inference GCNs over large graph datasets, limiting their application to large real-world graphs and hindering the exploration of deeper and more sophisticated GCN graphs. This is because real-world graphs can be extremely large and sparse. Furthermore, the node degree of GCNs tends to follow the power-law distribution and therefore have highly irregular adjacency matrices, resulting in prohibitive inefficiencies in both data processing and movement and thus substantially limiting the achievable GCN acceleration efficiency. To this end, this paper proposes a GCN algorithm and accelerator Co-Design framework dubbed GCoD which can largely alleviate the aforementioned GCN irregularity and boost GCNs’ inference efficiency. Specifically, on the algorithm level, GCoD integrates a split and conquer GCN training strategy that polarizes the graphs to be either denser or sparser in local neighborhoods without compromising the model accuracy, resulting in graph adjacency matrices that (mostly) have merely two levels of workload and enjoys largely enhanced regularity and thus ease of acceleration. On the hardware level, we further develop a dedicated two-pronged accelerator with a separated engine to process each of the aforementioned denser and sparser workloads, further boosting the overall utilization and acceleration efficiency. Extensive experiments and ablation studies validate that our GCoD consistently reduces the number of off-chip accesses, leading to speedups 15286×, 294×, 7.8×, and 2.5× as compared to CPUs, GPUs, and prior-art GCN accelerators including HyGCN and AWB-GCN, respectively, while maintaining or even improving the task accuracy. Additionally, we visualize GCoD trained graph adjacency matrices for a better understanding of its advantages.
Towards a methodical approach to implement biomimetic paradigms in the design of robotic systems for space applications

Biomimetic design is considered a promising source of novel solutions to problems in space engineering and robotics in particular. With the maturing of this discipline, however, a need is identified: a more systematic approach to its application to reduce the element of chance in the design of biomimetic systems. A methodology is proposed to address this concern and provide a basis for further development of biomimetic design procedures. The application of this process is illustrated through case studies of ongoing biomimetics research with relevance to space robotics in the form of climbing robots utilising synthetic dry adhesives.
Conceptual framework for the design and management of value loops – application to a wheelchair allocation context

This article provides a conceptual framework for the design and management of value loops. A value loop is a logistics network that integrates reverse logistics activities related to the recovery of unused products, their retransformation and the redistribution of the reusable materials. The conceptual framework is elaborated based on the business process re-engineering experience for the wheelchair allocation, maintenance, recovery, retransformation and redistribution context in the Province of Quebec, Canada, governed and managed by a government agency. This framework addresses all the significant generic decisional levels, from the identification of the customers’ expectations or needs, to the design and management of the logistics networks, processes and products from a life cycle perspective, to activity planning and scheduling. The decisional levels are mainly characterised according to the reverse logistics activities. Their implementation is illustrated within the wheelchair context in the Province of Quebec. A brief discussion on the roles of the involved network parties is made from this context.
Accelerating the theoretical study of Li‐polysulfide adsorption on single‐atom catalysts via machine learning approaches

Abstract Li–S batteries are a promising alternative to Li‐ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li‐polysulfide (LiPS) shuttling. Computational screening of LiPS adsorption on single‐atom catalyst (SAC) substrates is of great aid to the design of Li–S batteries which are robust against the LiPS shuttling from the cathode to the anode and the electrolyte. To facilitate this process, we develop a machine learning (ML) protocol to accelerate the systematic mapping of dominant local energy minima found with calculations based on the density functional theory (DFT), and, in turn, fast screening of LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for LiPS adsorbed on graphene decorated with a Fe–N4–C SAC. We identify minima whose binding energies are better or on par with the one previously reported in the literature. We then move to analyze the adsorption trends on Zn–N4–C SAC and observe similar adsorption strength and behavior with the Fe–N4–C SAC, highlighting the good predictive power of our protocol. Our approach offers a comprehensive and computationally efficient alternative to conventional approaches studying LiPS adsorption.
Using Machine Learning Predictions to Speed-up Dijkstra's Shortest Path Algorithm

We study the use of machine learning techniques to solve a fundamental shortest path problem, which is also known as the single-source many targets shortest path problem (SSMTSP) . Given a directed graph with non-negative edge weights, our goal is to compute a shortest path from a given source node to any of several designated target nodes. Basically, our idea is to combine a machine learning approach with an adapted version of Dijkstra’s algorithm to solve this problem: Based on the trace of Dijkstra’s algorithm, we design a neural network that predicts the shortest path distance after only a few iterations. The prediction is then used to prune the search space explored by Dijkstra’s algorithm, which allows us to save a signiﬁcant fraction of operations on the underlying priority queue. Crucially, our approach always computes the exact shortest path distances, even if the prediction is inaccurate, and never uses more queue operations than the standard algorithm. In fact, we are able to prove a lower bound on the number of queue operations saved by our new algorithm, which depends on the accuracy of the prediction. Our bound applies to arbitrary graphs as long as (some of) the edge weights are drawn at random. Our experimental ﬁndings on random graphs conﬁrm these bounds and show that the actual savings are oftentimes signiﬁcantly higher.
A stochastic approach to rainfall-induced slope failure

This paper considers the influence of spatial variability of soil properties on the stability of an unsaturated soil slope during and antecedent to a rainfall event. With water tending to follow a rather tortuous flow path during the infiltration process, slope failures may occur locally due to loss in matric suction with increasing degree of saturation. An elasto-viscoplastic finite element program com bined with random field theory is used to analyse the influence of the heterogeneity of the subsoil, as characterised by the point and spatial statistics of the property values. Using a Monte Carlo framework, the results of multiple realisations have been evaluated in terms of reliability as a function of both global factor of safety and time.
Spectral line shape analysis and systematic frequency shifts of laser frequency standards

A practical realization of the unit of length often relies on values of the frequency of optical frequency standards in the form of frequency stabilized lasers. A technique of frequency stabilization commonly used for He-Ne lasers at 633 nm stabilized in frequency to hyperfine spectral components in the saturated absorption spectrum of molecular iodine is that based on phase-sensitive detection at the third harmonic of a modulation frequency. A systematic study is made of how frequency shifts are related to observations of offset voltages in the feedback loop which vary across the iodine spectrum. A simple model based on spectral line shape analysis is found to apply to a variety of lasers possessing moderate offsets. >
GUX-Analyzer: A Deep Multi-modal Analyzer Via Motivational Flow For Game User Experience

Quantitative analysis of Game User eXperience (GUX) is important to the game industry. Different from the typical questionnaire analysis, this paper focuses on the computational analysis of GUX. We aim to analyze the relationship between game and players using the multi-modal data including physiological data and game process data. We theoretically extend the Flow model from the classic skill-and-challenge plane by expanding new dimension on motivation, which is the result of the multi-modal data analysis on affect, and physiological data. We call this 3D Flow as Motivational Flow, MovFlow. Meanwhile, we implement a quantitative GUX Analysis System (GUXAS), which can predict the player's in-game experience state by only using game process data. It analyzes the correlation among not only in-game state, but the player's psychological-and-physiological reaction in the entire interactive game-play process. The experiments demonstrated our MovFlow model efficiently distinguished the users' in-game experience states from the perspective of GUX.
Toward Educator-focused Automated Scoring Systems for Reading and Writing

This paper presents methods for improving automated essay scoring with techniques that address the computational trade-offs of self-attention and document length. To make Automated Essay Scoring (AES) more useful to practitioners, researchers must overcome the challenges of data and label availability, authentic and extended writing, domain scoring, prompt and source variety, and transfer learning. This paper addresses these challenges using neural network models by employing techniques that preserve essay length as an important feature without increasing model training costs. It introduces techniques for minimizing classification loss on ordinal labels using multi-objective learning, capturing semantic information across the entire essay using sentence embeddings to use transformer architecture across arbitrarily long documents, the use of such models for transfer learning, automated hyperparameter generation based on prompt-corpus metadata, and, most importantly, the use of semantic information to provide meaningful insights into student reading through analysis of passage-dependent writing resulting in state-of-the-art results for various essay tasks.