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In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit regularization and label embedding. Unlike the sparse feature selection methods that use a penalized estimator with explicit regularization terms such as $l_{2,1}$-norm, MCP or SCAD, we propose a simple alternative method via Hadamard product parameterization. In order to guide the feature selection process, a latent semantic of multi-label information method is adopted, as a label embedding. Experimental results on some known benchmark datasets suggest that the proposed estimator suffers much less from extra bias, and may lead to benign overfitting., Comment: 11 pages, 7 figures, My paper is currently under review at TPAMI journal

The polarization of light is critical in various applications, including quantum communication, where the photon polarization encoding a qubit can undergo uncontrolled changes when transmitted through optical fibers. Bends in the fiber, internal and external stresses, and environmental factors cause these polarization changes, which lead to errors and therein limit the range of quantum communication. To prevent this, we present a fast and automated method for polarization compensation using liquid crystals. This approach combines polarimetry based on a rotating quarter-waveplate with high-speed control of the liquid-crystal cell, offering high-fidelity compensation suitable for diverse applications. Our method directly solves for compensation parameters, avoiding reliance on stochastic approaches or cryptographic metrics. Experimental results demonstrate that our method achieves over 99% fidelity within an average of fewer than six iterations, with further fine-tuning to reach above 99.5% fidelity, providing a robust solution for maintaining precise polarization states in optical systems.

The evolution of wireless communication systems will be fundamentally impacted by an open radio access network (O-RAN), a new concept defining an intelligent architecture with enhanced flexibility, openness, and the ability to slice services more efficiently. For all its promises, and like any technological advancement, O-RAN is not without risks that need to be carefully assessed and properly addressed to accelerate its wide adoption in future mobile networks. In this paper, we present an in-depth security analysis of the O-RAN architecture, discussing the potential threats that may arise in the different O-RAN architecture layers and their impact on the Confidentiality, Integrity, and Availability (CIA) triad. We also promote the potential of zero trust, Moving Target Defense (MTD), blockchain, and large language models(LLM) technologies in fortifying O-RAN's security posture. Furthermore, we numerically demonstrate the effectiveness of MTD in empowering robust deep reinforcement learning methods for dynamic network slice admission control in the O-RAN architecture. Moreover, we examine the effect of explainable AI (XAI) based on LLMs in securing the system., Comment: 10 pages

Blockchains are being positioned as the "technology of trust" that can be used to mediate transactions between non-trusting parties without the need for a central authority. They support transaction types that are native to the blockchain platform or user-defined via user programs called smart contracts. Despite the significant flexibility in transaction programmability that smart contracts offer, they pose several usability, robustness, and performance challenges. This paper proposes an alternative transaction framework that incorporates more primitives into the native set of transaction types (reducing the likelihood of requiring user-defined transaction programs often). The framework is based on the concept of declarative blockchain transactions whose strength lies in the fact that it addresses several of the limitations of smart contracts simultaneously. A formal and implementation framework is presented, and a subset of commonly occurring transaction behaviors are modeled and implemented as use cases, using an open-source blockchain database, BigchchainDB, as the implementation context. A performance study comparing the declarative transaction approach to equivalent smart contract transaction models reveals several advantages of the proposed approach.

Raw nanopore signal analysis is a common approach in genomics to provide fast and resource-efficient analysis without translating the signals to bases (i.e., without basecalling). However, existing solutions cannot interpret raw signals directly if a reference genome is unknown due to a lack of accurate mechanisms to handle increased noise in pairwise raw signal comparison. Our goal is to enable the direct analysis of raw signals without a reference genome. To this end, we propose Rawsamble, the first mechanism that can 1) identify regions of similarity between all raw signal pairs, known as all-vs-all overlapping, using a hash-based search mechanism and 2) use these to construct genomes from scratch, called de novo assembly. Our extensive evaluations across multiple genomes of varying sizes show that Rawsamble provides a significant speedup (on average by 16.36x and up to 41.59x) and reduces peak memory usage (on average by 11.73x and up to by 41.99x) compared to a conventional genome assembly pipeline using the state-of-the-art tools for basecalling (Dorado's fastest mode) and overlapping (minimap2) on a CPU. We find that 36.57% of overlapping pairs generated by Rawsamble are identical to those generated by minimap2. Using the overlaps from Rawsamble, we construct the first de novo assemblies directly from raw signals without basecalling. We show that we can construct contiguous assembly segments (unitigs) up to 2.7 million bases in length (half the genome length of E. coli). We identify previously unexplored directions that can be enabled by finding overlaps and constructing de novo assemblies. Rawsamble is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.

This paper explores a variation of the Traveling Salesperson Problem, where the agent places a circular obstacle next to each node once it visits it. Referred to as the Traveling Salesperson Problem with Circle Placement (TSP-CP), the aim is to maximize the obstacle radius for which a valid closed tour exists and then minimize the tour cost. The TSP-CP finds relevance in various real-world applications, such as harvesting, quarrying, and open-pit mining. We propose several novel solvers to address the TSP-CP, its variant tailored for Dubins vehicles, and a crucial subproblem known as the Traveling Salesperson Problem on self-deleting graphs (TSP-SD). Our extensive experimental results show that the proposed solvers outperform the current state-of-the-art on related problems in solution quality., Comment: 8 pages, 7 figures, accepted to IEEE Robotics and Automation Letters in August 2024

Our team developed a riding ballbot (called PURE) that is dynamically stable, omnidirectional, and driven by lean-to-steer control. A hands-free admittance control scheme (HACS) was previously integrated to allow riders with different torso functions to control the robot's movements via torso leaning and twisting. Such an interface requires motor coordination skills and could result in collisions with obstacles due to low proficiency. Hence, a shared controller (SC) that limits the speed of PURE could be helpful to ensure the safety of riders. However, the self-balancing dynamics of PURE could result in a weak control authority of its motion, in which the torso motion of the rider could easily result in poor tracking of the command speed dictated by the shared controller. Thus, we proposed an interactive hands-free admittance control scheme (iHACS), which added two modules to HACS to improve the speed-tracking performance of PURE: control gain personalization module and interaction compensation module. Human riding tests of simple tasks, idle-keeping and speed-limiting, were conducted to compare the performance of HACS and iHACS. Two manual wheelchair users and two able-bodied individuals participated in this study. They were instructed to use "adversarial" torso motions that would tax the SC's ability to keep the ballbot idling or below a set speed. In the idle-keeping tasks, iHACS demonstrated minimal translational motion and low command speed tracking RMSE, even with significant torso lean angles. During the speed-limiting task with command speed saturated at 0.5 m/s, the system achieved an average maximum speed of 1.1 m/s with iHACS, compared with that of over 1.9 m/s with HACS. These results suggest that iHACS can enhance PURE's control authority over the rider, which enables PURE to provide physical interactions back to the rider and results in a collaborative rider-robot synergy.

Fog computing is of particular interest to Internet of Things (IoT), where inexpensive simple devices can offload their computation tasks to nearby Fog Nodes. Online scheduling in such fog networks is challenging due to stochastic network states such as task arrivals, wireless channels and location of nodes. In this paper, we focus on the problem of optimizing computation offloading management, arrival data admission control and resource scheduling, in order to improve the overall system performance, in terms of throughput fairness, power efficiency, and average mean of queue backlogs. We investigate this problem for a fog network with homogeneous mobile Fog Nodes, serving multiple wireless devices, controlled by a Fog Control Node. By formulating the problem as a stochastic optimization problem, maximizing utility-power efficiency, defined as achievable utility per-unit power consumption, subject to queue backlog stability, we modify Lyapunov optimization techniques to deal with the fractional form of utility-power efficiency function. Then we propose an online utility-power efficient task scheduling algorithm, which is asymptotically optimal. Our online task scheduling algorithm can achieve the theoretical [O(1/V), O(V)] trade-off between utility-power efficiency and average mean of queue backlogs, Comment: 11 pages, 6 figures

This study introduces the development of hands-free control schemes for a riding ballbot, designed to allow riders including manual wheelchair users to control its movement through torso leaning and twisting. The hardware platform, Personal Unique Rolling Experience (PURE), utilizes a ballbot drivetrain, a dynamically stable mobile robot that uses a ball as its wheel to provide omnidirectional maneuverability. To accommodate users with varying torso motion functions, the hanads-free control scheme should be adjustable based on the rider's torso function and personal preferences. Therefore, concepts of (a) impedance control and (b) admittance control were integrated into the control scheme. A duo-agent optimization framework was utilized to assess the efficiency of this rider-ballbot system for a safety-critical task: braking from 1.4 m/s. The candidate control schemes were further implemented in the physical robot hardware and validated with two experienced users, demonstrating the efficiency and robustness of the hands-free admittance control scheme (HACS). This interface, which utilized physical human-robot interaction (pHRI) as the input, resulted in lower braking effort and shorter braking distance and time. Subsequently, 12 novice participants (six able-bodied users and six manual wheelchair users) with different levels of torso motion capability were then recruited to benchmark the braking performance with HACS. The indoor navigation capability of PURE was further demonstrated with these participants in courses simulating narrow hallways, tight turns, and navigation through static and dynamic obstacles. By exploiting pHRI, the proposed admittance-style control scheme provided effective control of the ballbot via torso motions. This interface enables PURE to provide a personal unique rolling experience to manual wheelchair users for safe and agile indoor navigation.

This study introduces a shared-control approach for collision avoidance in a self-balancing riding ballbot, called PURE, marked by its dynamic stability, omnidirectional movement, and hands-free interface. Integrated with a sensor array and a novel Passive Artificial Potential Field (PAPF) method, PURE provides intuitive navigation with deceleration assistance and haptic/audio feedback, effectively mitigating collision risks. This approach addresses the limitations of traditional APF methods, such as control oscillations and unnecessary speed reduction in challenging scenarios. A human-robot interaction experiment, with 20 manual wheelchair users and able-bodied individuals, was conducted to evaluate the performance of indoor navigation and obstacle avoidance with the proposed shared-control algorithm. Results indicated that shared-control significantly reduced collisions and cognitive load without affecting travel speed, offering intuitive and safe operation. These findings highlight the shared-control system's suitability for enhancing collision avoidance in self-balancing mobility devices, a relatively unexplored area in assistive mobility research., Comment: 7 pages and 7 figures, IEEE ICRA format

Mathematical information is essential for technical work, but its creation, interpretation, and search are challenging. To help address these challenges, researchers have developed multimodal search engines and mathematical question answering systems. This book begins with a simple framework characterizing the information tasks that people and systems perform as we work to answer math-related questions. The framework is used to organize and relate the other core topics of the book, including interactions between people and systems, representing math formulas in sources, and evaluation. We close by addressing some key questions and presenting directions for future work. This book is intended for students, instructors, and researchers interested in systems that help us find and use mathematical information., Comment: [DRAFT] Revised (2nd) draft

Metagenomics has led to significant advances in many fields. Metagenomic analysis commonly involves the key tasks of determining the species present in a sample and their relative abundances. These tasks require searching large metagenomic databases. Metagenomic analysis suffers from significant data movement overhead due to moving large amounts of low-reuse data from the storage system. In-storage processing can be a fundamental solution for reducing this overhead. However, designing an in-storage processing system for metagenomics is challenging because existing approaches to metagenomic analysis cannot be directly implemented in storage effectively due to the hardware limitations of modern SSDs. We propose MegIS, the first in-storage processing system designed to significantly reduce the data movement overhead of the end-to-end metagenomic analysis pipeline. MegIS is enabled by our lightweight design that effectively leverages and orchestrates processing inside and outside the storage system. We address in-storage processing challenges for metagenomics via specialized and efficient 1) task partitioning, 2) data/computation flow coordination, 3) storage technology-aware algorithmic optimizations, 4) data mapping, and 5) lightweight in-storage accelerators. MegIS's design is flexible, capable of supporting different types of metagenomic input datasets, and can be integrated into various metagenomic analysis pipelines. Our evaluation shows that MegIS outperforms the state-of-the-art performance- and accuracy-optimized software metagenomic tools by 2.7$\times$-37.2$\times$ and 6.9$\times$-100.2$\times$, respectively, while matching the accuracy of the accuracy-optimized tool. MegIS achieves 1.5$\times$-5.1$\times$ speedup compared to the state-of-the-art metagenomic hardware-accelerated (using processing-in-memory) tool, while achieving significantly higher accuracy., Comment: To appear in ISCA 2024. arXiv admin note: substantial text overlap with arXiv:2311.12527

In this paper, we are interested in solvable complete Lie algebras, over the field $\K=\R$ or $\mathbb{C}$, which admit a symplectic structure. Specifically, important classes are studied, and a description of complete Lie Algebra with the dimension of nilradical less or equal than six, which supported symplectic structure is given., Comment: 14 pages

Members of the Human-Robot Interaction (HRI) and Artificial Intelligence (AI) communities have proposed Large Language Models (LLMs) as a promising resource for robotics tasks such as natural language interactions, doing household and workplace tasks, approximating `common sense reasoning', and modeling humans. However, recent research has raised concerns about the potential for LLMs to produce discriminatory outcomes and unsafe behaviors in real-world robot experiments and applications. To address these concerns, we conduct an HRI-based evaluation of discrimination and safety criteria on several highly-rated LLMs. Our evaluation reveals that LLMs currently lack robustness when encountering people across a diverse range of protected identity characteristics (e.g., race, gender, disability status, nationality, religion, and their intersections), producing biased outputs consistent with directly discriminatory outcomes -- e.g. `gypsy' and `mute' people are labeled untrustworthy, but not `european' or `able-bodied' people. Furthermore, we test models in settings with unconstrained natural language (open vocabulary) inputs, and find they fail to act safely, generating responses that accept dangerous, violent, or unlawful instructions -- such as incident-causing misstatements, taking people's mobility aids, and sexual predation. Our results underscore the urgent need for systematic, routine, and comprehensive risk assessments and assurances to improve outcomes and ensure LLMs only operate on robots when it is safe, effective, and just to do so. Data and code will be made available., Comment: 40 pages (52 with references), 21 Figures, 6 Tables

Utilizing an emergent metric developed from deep learning techniques, we determine the complex potential associated with static quarkonium. This study explores the disintegration process of quarkonium by analyzing the real component of this potential, which is crucial for understanding its stability in various conditions. We show that the dissociation length, the critical distance at which a quark and antiquark pair disintegrate, decreases as the temperature increases. Furthermore, our assessment of the imaginary component of the potential indicates an increase in the magnitude of the imaginary potential for quarkonium as temperatures rise. This enhancement contributes to the quarkonium's suppression within the quark-gluon plasma, mirroring the anticipated outcomes from QCD. Our findings not only confirm the theoretical predictions but also demonstrate the efficacy of deep learning methods in advancing our understanding of high-energy particle physics., Comment: 7 pages, 6 figures, 1 table

Radiation shielding plays a crucial role in various industries, including nuclear and space exploration. Among the most abundant elements and isotopes found in nature, 10B has one of the highest neutron absorption cross-sections, closely followed by 6Li. It is worth noting that hydrogen, with its light nucleus, serves as an excellent neutron reflector. Surprisingly, the potential of the lithium borohydride molecule (LiBH4), which consists exclusively of these elements, as a shield against neutron radiation has not yet been explored. This study investigates various materials that can potentially be used as shields. First, we assessed traditional shields and previous optimizations for shielding. The findings showed that concrete containing 10% B4C yielded the best results. High-performance concrete (HPC) replaced regular concrete. By gradually incorporating lithium borohydride into the shield, along with the appropriate level of boron carbide, further optimization was achieved. Calculations were performed using the MCNPX 2.7E code. The introduction of the new shield resulted in a significant 40% reduction in volume compared with the previous sample. The study findings showed that a 30 cm thick shield effectively blocked 95% of the total neutrons and 92% of the total gamma radiation. Additionally, it was noted that the shielding effects of lithium borohydride against fast neutrons are greater than those of boron carbide. Various parameters and data of the designed shield were calculated and compared with those of the previous sample.

Emergent magnetic phenomena at interfaces represent a frontier in materials science, pivotal for advancing technologies in spintronics and magnetic storage. In this letter, we utilize a suite of advanced X-ray spectroscopic and scattering techniques to investigate emergent interfacial ferromagnetism in oxide superlattices comprised of antiferromagnetic CaMnO3 and paramagnetic CaRuO3. Our findings challenge prior theoretical models by demonstrating that the ferromagnetism extends beyond the interfacial layer into multiple unit cells of CaMnO3 and exhibits an asymmetric profile. Complementary density functional calculations reveal that the interfacial ferromagnetism is driven by the double exchange mechanism, facilitated by charge transfer from Ru to Mn ions. Additionally, defect chemistry, particularly the presence of oxygen vacancies, likely plays a crucial role in modifying the magnetic moments at the interface, leading to the observed asymmetry between the top and bottom CaMnO3 interfacial magnetic layers. Our findings underscore the potential of manipulating interfacial ferromagnetism through point defect engineering.

We introduce the notion of cosymplectic structure on Jacobi-Jordan algebras, and we state that they are related to symplectic Jacobi-Jordan algebras. We show, in particular, that they support a right-skew-symmetric product. We also study the double extension constructions of cosymplectic Jacobi-Jordan algebras and give a complete classification in dimension five., Comment: 15 pages

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

The adoption of processing-in-memory (PiM) architectures has been gaining momentum because they provide high performance and low energy consumption by alleviating the data movement bottleneck. Yet, the security of such architectures has not been thoroughly explored. The adoption of PiM solutions provides a new way to directly access main memory, which malicious user applications can exploit. We show that this new way to access main memory opens opportunities for high-throughput timing attacks that are hard-to-mitigate without significant performance overhead. We introduce IMPACT, a set of high-throughput main memory-based timing attacks that leverage characteristics of PiM architectures to establish covert and side channels. IMPACT enables high-throughput communication and private information leakage by exploiting the shared DRAM row buffer. To achieve high throughput, IMPACT (i) eliminates cache bypassing steps required by processor-centric main memory and cache-based timing attacks and (ii) leverages the intrinsic parallelism of PiM operations. We showcase two applications of IMPACT. First, we build two covert-channel attacks that run on the host CPU and leverage different PiM approaches to gain direct and fast access to main memory and establish high-throughput communication covert channels. Second, we showcase a side-channel attack that leaks private information of concurrently running victim applications that are accelerated with PiM. Our results demonstrate that (i) our covert channels achieve 12.87 Mb/s and 14.16 Mb/s communication throughput, respectively, which is up to 4.91x and 5.41x faster than the state-of-the-art main memory-based covert channels, and (ii) our side-channel attack allows the attacker to leak secrets with a low error rate. To avoid such covert and side channels in emerging PiM systems, we propose and evaluate three defenses.

The proliferation of hate speech and offensive comments on social media has become increasingly prevalent due to user activities. Such comments can have detrimental effects on individuals' psychological well-being and social behavior. While numerous datasets in the English language exist in this domain, few equivalent resources are available for Persian language. To address this gap, this paper introduces two offensive datasets. The first dataset comprises annotations provided by domain experts, while the second consists of a large collection of unlabeled data obtained through web crawling for unsupervised learning purposes. To ensure the quality of the former dataset, a meticulous three-stage labeling process was conducted, and kappa measures were computed to assess inter-annotator agreement. Furthermore, experiments were performed on the dataset using state-of-the-art language models, both with and without employing masked language modeling techniques, as well as machine learning algorithms, in order to establish the baselines for the dataset using contemporary cutting-edge approaches. The obtained F1-scores for the three-class and two-class versions of the dataset were 76.9% and 89.9% for XLM-RoBERTa, respectively., Comment: 16 pages, 5 figures, 8 tables