Miracle-dz/UniFAR-FTUs huggingface dataset:
Safe Road-Crossing by Autonomous Wheelchairs: a Novel Dataset and its Experimental Evaluation

Safe road-crossing by self-driving vehicles is a crucial problem to address in smart-cities. In this paper, we introduce a multi-sensor fusion approach to support road-crossing decisions in a system composed by an autonomous wheelchair and a flying drone featuring a robust sensory system made of diverse and redundant components. To that aim, we designed an analytical danger function based on explainable physical conditions evaluated by single sensors, including those using machine learning and artificial vision. As a proof-of-concept, we provide an experimental evaluation in a laboratory environment, showing the advantages of using multiple sensors, which can improve decision accuracy and effectively support safety assessment. We made the dataset available to the scientific community for further experimentation. The work has been developed in the context of an European project named REXASI-PRO, which aims to develop trustworthy artificial intelligence for social navigation of people with reduced mobility.
Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption

Deviating from conventional perspectives that frame artificial intelligence (AI) systems solely as logic emulators, we propose a novel program of heuristic reasoning. We distinguish between the 'instrumental' use of heuristics to match resources with objectives, and 'mimetic absorption,' whereby heuristics manifest randomly and universally. Through a series of innovative experiments, including variations of the classic Linda problem and a novel application of the Beauty Contest game, we uncover trade-offs between maximizing accuracy and reducing effort that shape the conditions under which AIs transition between exhaustive logical processing and the use of cognitive shortcuts (heuristics). We provide evidence that AIs manifest an adaptive balancing of precision and efficiency, consistent with principles of resource-rational human cognition as explicated in classical theories of bounded rationality and dual-process theory. Our findings reveal a nuanced picture of AI cognition, where trade-offs between resources and objectives lead to the emulation of biological systems, especially human cognition, despite AIs being designed without a sense of self and lacking introspective capabilities.
Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging

This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study. We propose an analytical convolutional forward model that transforms reflectivity maps into FMC data. Our findings demonstrate that the convolutional model excels over its matrix-based counterpart in terms of computational efficiency and storage requirements. This accelerated forward modeling approach holds significant potential for various inverse problems, notably enhancing Sparse Signal Recovery (SSR) within the context LASSO regression, which facilitates efficient Convolutional Sparse Coding (CSC) algorithms. Additionally, we explore the integration of Convolutional Neural Networks (CNNs) for the forward model, employing deep unfolding to implement the Learned Block Convolutional ISTA (BC-LISTA).
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization

Medical data often exhibits distribution shifts, which cause test-time performance degradation for deep learning models trained using standard supervised learning pipelines. This challenge is addressed in the field of Domain Generalization (DG) with the sub-field of Single Domain Generalization (SDG) being specifically interesting due to the privacy- or logistics-related issues often associated with medical data. Existing disentanglement-based SDG methods heavily rely on structural information embedded in segmentation masks, however classification labels do not provide such dense information. This work introduces a novel SDG method aimed at medical image classification that leverages channel-wise contrastive disentanglement. It is further enhanced with reconstruction-based style regularization to ensure extraction of distinct style and structure feature representations. We evaluate our method on the complex task of multicenter histopathology image classification, comparing it against state-of-the-art (SOTA) SDG baselines. Results demonstrate that our method surpasses the SOTA by a margin of 1% in average accuracy while also showing more stable performance. This study highlights the importance and challenges of exploring SDG frameworks in the context of the classification task. The code is publicly available at https://github.com/BioMedIA-MBZUAI/ConDiSR
Bandwidth-Effective DRAM Cache for GPUs with Storage-Class Memory

We propose overcoming the memory capacity limitation of GPUs with high-capacity Storage-Class Memory (SCM) and DRAM cache. By significantly increasing the memory capacity with SCM, the GPU can capture a larger fraction of the memory footprint than HBM for workloads that oversubscribe memory, achieving high speedups. However, the DRAM cache needs to be carefully designed to address the latency and BW limitations of the SCM while minimizing cost overhead and considering GPU's characteristics. Because the massive number of GPU threads can thrash the DRAM cache, we first propose an SCM-aware DRAM cache bypass policy for GPUs that considers the multi-dimensional characteristics of memory accesses by GPUs with SCM to bypass DRAM for data with low performance utility. In addition, to reduce DRAM cache probes and increase effective DRAM BW with minimal cost, we propose a Configurable Tag Cache (CTC) that repurposes part of the L2 cache to cache DRAM cacheline tags. The L2 capacity used for the CTC can be adjusted by users for adaptability. Furthermore, to minimize DRAM cache probe traffic from CTC misses, our Aggregated Metadata-In-Last-column (AMIL) DRAM cache organization co-locates all DRAM cacheline tags in a single column within a row. The AMIL also retains the full ECC protection, unlike prior DRAM cache's Tag-And-Data (TAD) organization. Additionally, we propose SCM throttling to curtail power and exploiting SCM's SLC/MLC modes to adapt to workload's memory footprint. While our techniques can be used for different DRAM and SCM devices, we focus on a Heterogeneous Memory Stack (HMS) organization that stacks SCM dies on top of DRAM dies for high performance. Compared to HBM, HMS improves performance by up to 12.5x (2.9x overall) and reduces energy by up to 89.3% (48.1% overall). Compared to prior works, we reduce DRAM cache probe and SCM write traffic by 91-93% and 57-75%, respectively.
Direct observation of nanometer-scale orbital angular momentum accumulation

Conversion of charge to orbital angular momentum through the orbital Hall effect (OHE) holds transformative potential for the development of orbital-based electronics, however, it is challenging to directly observe the electrically generated orbital accumulation. Here, we detect the OHE by directly quantifying the orbital accumulation along the edges of a titanium thin film using a scanning transmission electron microscope. We measure the Ti L-edge using electron energy-loss spectroscopy with nanometer resolution and find a sizable orbital accumulation at the sample's outer perimeters, consistent with all signatures expected for the OHE, and determine an orbital diffusion length $\ell_o \approx 7.3$ nm. Our data points to a surprising dependence of the orbital diffusion length on the nano-structural morphology.
Synchronisation-Oriented Design Approach for Adaptive Control

This study presents a synchronisation-oriented perspective towards adaptive control which views model-referenced adaptation as synchronisation between actual and virtual dynamic systems. In the context of adaptation, model reference adaptive control methods make the state response of the actual plant follow a reference model. In the context of synchronisation, consensus methods involving diffusive coupling induce a collective behaviour across multiple agents. We draw from the understanding about the two time-scale nature of synchronisation motivated by the study of blended dynamics. The synchronisation-oriented approach consists in the design of a coupling input to achieve desired closed-loop error dynamics followed by the input allocation process to shape the collective behaviour. We suggest that synchronisation can be a reasonable design principle allowing a more holistic and systematic approach to the design of adaptive control systems for improved transient characteristics. Most notably, the proposed approach enables not only constructive derivation but also substantial generalisation of the previously developed closed-loop reference model adaptive control method. Practical significance of the proposed generalisation lies at the capability to improve the transient response characteristics and mitigate the unwanted peaking phenomenon at the same time.
Extremal graphical modeling with latent variables

Extremal graphical models encode the conditional independence structure of multivariate extremes and provide a powerful tool for quantifying the risk of rare events. Prior work on learning these graphs from data has focused on the setting where all relevant variables are observed. For the popular class of H\"usler-Reiss models, we propose the \texttt{eglatent} method, a tractable convex program for learning extremal graphical models in the presence of latent variables. Our approach decomposes the H\"usler-Reiss precision matrix into a sparse component encoding the graphical structure among the observed variables after conditioning on the latent variables, and a low-rank component encoding the effect of a few latent variables on the observed variables. We provide finite-sample guarantees of \texttt{eglatent} and show that it consistently recovers the conditional graph as well as the number of latent variables. We highlight the improved performances of our approach on synthetic and real data.
Detecting the N\'{e}el vector of altermagnet by attaching a topological insulator and crystalline valley-edge insulator

In order to detect the N\'{e}el vector of an altermagnet, we investigate topological phases in a bilayer system composed of an altermagnet and a two-dimensional topological insulator described by the Bernevig-Hughes-Zhang model. A topological phase transition occurs from a first-order topological insulator to a trivial insulator at a certain critical altermagnetization if the N\'{e}el vector of altermagnet is along the $x$ axis or the $y$ axis. It is intriguing that valley-protected edge states emerge along the N\'{e}el vector in this trivial insulator, which are as stable as the topological edge states. We name it a crystalline valley-edge insulator. On the other hand, the system turns out to be a second-order topological insulator when the N\'{e}el vector is along the $z$ axis. The tunneling conductance has a strong dependence on the N\'{e}el vector. In addition, the band gap depends on the N\'{e}el vector, which is measurable by optical absorption. Hence, it is possible experimentally to detect the N\'{e}el vector by measuring tunneling conductance and optical absorption.
Constrained Reinforcement Learning for Adaptive Controller Synchronization in Distributed SDN

In software-defined networking (SDN), the implementation of distributed SDN controllers, with each controller responsible for managing a specific sub-network or domain, plays a critical role in achieving a balance between centralized control, scalability, reliability, and network efficiency. These controllers must be synchronized to maintain a logically centralized view of the entire network. While there are various approaches for synchronizing distributed SDN controllers, most tend to prioritize goals such as optimization of communication latency or load balancing, often neglecting to address both the aspects simultaneously. This limitation becomes particularly significant when considering applications like Augmented and Virtual Reality (AR/VR), which demand constrained network latencies and substantial computational resources. Additionally, many existing studies in this field predominantly rely on value-based reinforcement learning (RL) methods, overlooking the potential advantages offered by state-of-the-art policy-based RL algorithms. To bridge this gap, our work focuses on examining deep reinforcement learning (DRL) techniques, encompassing both value-based and policy-based methods, to guarantee an upper latency threshold for AR/VR task offloading within SDN environments, while selecting the most cost-effective servers for AR/VR task offloading. Our evaluation results indicate that while value-based methods excel in optimizing individual network metrics such as latency or load balancing, policy-based approaches exhibit greater robustness in adapting to sudden network changes or reconfiguration.
ARtVista: Gateway To Empower Anyone Into Artist

Drawing is an art that enables people to express their imagination and emotions. However, individuals usually face challenges in drawing, especially when translating conceptual ideas into visually coherent representations and bridging the gap between mental visualization and practical execution. In response, we propose ARtVista - a novel system integrating AR and generative AI technologies. ARtVista not only recommends reference images aligned with users' abstract ideas and generates sketches for users to draw but also goes beyond, crafting vibrant paintings in various painting styles. ARtVista also offers users an alternative approach to create striking paintings by simulating the paint-by-number concept on reference images, empowering users to create visually stunning artwork devoid of the necessity for advanced drawing skills. We perform a pilot study and reveal positive feedback on its usability, emphasizing its effectiveness in visualizing user ideas and aiding the painting process to achieve stunning pictures without requiring advanced drawing skills. The source code will be available at https://github.com/htrvu/ARtVista.
OutlineSpark: Igniting AI-powered Presentation Slides Creation from Computational Notebooks through Outlines

Computational notebooks are widely utilized for exploration and analysis. However, creating slides to communicate analysis results from these notebooks is quite tedious and time-consuming. Researchers have proposed automatic systems for generating slides from notebooks, which, however, often do not consider the process of users conceiving and organizing their messages from massive code cells. Those systems ask users to go directly into the slide creation process, which causes potentially ill-structured slides and burdens in further refinement. Inspired by the common and widely recommended slide creation practice: drafting outlines first and then adding concrete content, we introduce OutlineSpark, an AI-powered slide creation tool that generates slides from a slide outline written by the user. The tool automatically retrieves relevant notebook cells based on the outlines and converts them into slide content. We evaluated OutlineSpark with 12 users. Both the quantitative and qualitative feedback from the participants verify its effectiveness and usability.
Procedural terrain generation with style transfer

In this study we introduce a new technique for the generation of terrain maps, exploiting a combination of procedural generation and Neural Style Transfer. We consider our approach to be a viable alternative to competing generative models, with our technique achieving greater versatility, lower hardware requirements and greater integration in the creative process of designers and developers. Our method involves generating procedural noise maps using either multi-layered smoothed Gaussian noise or the Perlin algorithm. We then employ an enhanced Neural Style transfer technique, drawing style from real-world height maps. This fusion of algorithmic generation and neural processing holds the potential to produce terrains that are not only diverse but also closely aligned with the morphological characteristics of real-world landscapes, with our process yielding consistent terrain structures with low computational cost and offering the capability to create customized maps. Numerical evaluations further validate our model's enhanced ability to accurately replicate terrain morphology, surpassing traditional procedural methods.
PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models

We present PAPERCLIP (Proposal Abstracts Provide an Effective Representation for Contrastive Language-Image Pre-training), a method which associates astronomical observations imaged by telescopes with natural language using a neural network model. The model is fine-tuned from a pre-trained Contrastive Language-Image Pre-training (CLIP) model using successful observing proposal abstracts and corresponding downstream observations, with the abstracts optionally summarized via guided generation using large language models (LLMs). Using observations from the Hubble Space Telescope (HST) as an example, we show that the fine-tuned model embodies a meaningful joint representation between observations and natural language through tests targeting image retrieval (i.e., finding the most relevant observations using natural language queries) and description retrieval (i.e., querying for astrophysical object classes and use cases most relevant to a given observation). Our study demonstrates the potential for using generalist foundation models rather than task-specific models for interacting with astronomical data by leveraging text as an interface.
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis

Reinforcement Learning (RL) has shown exceptional performance across various applications, enabling autonomous agents to learn optimal policies through interaction with their environments. However, traditional RL frameworks often face challenges in terms of iteration complexity and robustness. Risk-sensitive RL, which balances expected return and risk, has been explored for its potential to yield probabilistically robust policies, yet its iteration complexity analysis remains underexplored. In this study, we conduct a thorough iteration complexity analysis for the risk-sensitive policy gradient method, focusing on the REINFORCE algorithm and employing the exponential utility function. We obtain an iteration complexity of $\mathcal{O}(\epsilon^{-2})$ to reach an $\epsilon$-approximate first-order stationary point (FOSP). We investigate whether risk-sensitive algorithms can achieve better iteration complexity compared to their risk-neutral counterparts. Our theoretical analysis demonstrates that risk-sensitive REINFORCE can have a reduced number of iterations required for convergence. This leads to improved iteration complexity, as employing the exponential utility does not entail additional computation per iteration. We characterize the conditions under which risk-sensitive algorithms can achieve better iteration complexity. Our simulation results also validate that risk-averse cases can converge and stabilize more quickly after approximately half of the episodes compared to their risk-neutral counterparts.
Gender-Specific Health-Adjusted Life Expectancy of Type 2 Diabetes Mellitus Among the Rural Elderly Population

Objectives: This study aimed to estimate the life expectancy (LE) and health-adjusted life expectancy (HALE) of type 2 diabetes mellitus (T2DM) among the rural elderly population. Methods: A total of 10,318 participants aged 65 to 79 were derived from the Henan Rural Cohort. The LE and HALE were calculated via the Sullivan method and multistate life table. Results: Among 10,318 subjects, 1,325 suffered from T2DM at the baseline, and 394 participants had newly-developed T2DM. The results from the Sullivan method showed that the LE, HALE, and HALE/LE were 17.98, 16.18 years, and 89.95% for men aged 65 to 69, and the corresponding estimates for women were 21.81, 18.73 years, and 85.86%, respectively. The LE, HALE and HALE/LE calculated via multistate life table were 19.86, 17.53 years, and 88.29% for men at aged 65, and the corresponding values for women were 25.01, 20.87 years, and 83.44%, respectively. Conclusion: Rural elderly women have a longer LE and HALE of T2DM, but they have lower quality of life than men. More attention should be paid to T2DM among rural elderly people, especially in women. Clinical Trial Registration: The Henan Rural Cohort Study has been registered at Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). Date of registration: 06 July 2015. http://www.chictr. org.cn/showproj.aspx? proj=11375.
Community structure of mangrove in Jeflio Island, Sorong Regency, West Papua, Indonesia

Mangrove forests in Jeflio Island provide livelihoods for the local community with ecological, economical, and social benefits. This study aimed to determine the structure of mangrove community and its condition in Jeflio Bay. It measured the mangrove's density, mangrove's frequency, mangrove's dominance, importance value index, diversity index, evenness index, and dominance index. Water quality parameters include pH, water temperature, and salinity. The Line-transect Plot method was used to determine the bioecological conditions of mangroves. There were four species of mangroves, namely Avicennia alba, Rhizophora mucronata, Bruguiera gymnorrhiza , and Xylocarpus granatum . The total density value of mangroves at station 1 was 1,600 ind ha -1 , at station 2 was 1,966 ind ha -1 , at station 3 was 2,000 ind ha -1 , and at station 4 was 1,966 ind ha -1 . The density levels of mangrove vegetation in Jeflio Island at station 1, station 2, station 3, and station 4 was considered high as they were ≥ 1,500 ind ha -1 . B. gymnorrhiza had the highest density of 1,000 ind ha -1 at station 4. The highest frequencies of mangrove vegetation were found at stations 3 and 4 for R. mucronata relatively at 42.86% and B. gymnorrhiza at 42.86%. R. mucronata was the most dominant at station 3 relatively at 72.76%. The highest importance value index was found at station 3 with 160.6% of R. mucronata . The diversity indexes at stations 1, 2, 3, and 4 were each classified moderate because they were in the range of 0 < H' < 3. The evenness index at station 1 was 0.94, station 2 was 0.95, station 3 was 0.91, and station 4 was 0.91. This showed that the evenness indexed were high. The average dominance index of mangrove vegetation in Jeflio Island was 0.4 indicating low dominance. Water quality parameters obtained the highest pH of 7.38 at station 3, the highest salinity of 28.33‰ at station 2, and the highest water temperature of 30.0°C at station 3. In total, there were 2 clusters where stations 3 and 4 were in one cluster, and stations 1 and 2 were in another cluster based on the density and frequency of mangroves as well as water quality parameters such as water temperature, pH, and salinity. Variations in mangrove density and frequency, as well as water quality parameters such as water temperature, pH, salinity at stations 1 and 2 were higher than those at stations 3 and 4.
Management of Unilateral Hearing Loss in the School-Aged Child

Despite the increasing evidence that school-aged children with unilateral hearing loss (UHL) are at risk for functional difficulties, no gold standard guidelines for management of these children currently exists. Management of school-aged children with UHL is further complicated by the fact the types and severity of functional difficulties vary from case-to-case. For this reason, it is essential to screen and monitor school-aged children with UHL for a variety of functional problems. This paper details a child centered assessment plan that provides clinicians with a tool to help make informed decisions regarding management options for their school-aged patients with UHL