Miracle-dz/UniFAR-FTUs huggingface dataset:
Precise Runahead Execution

Runahead execution improves processor performance by accurately prefetching long-latency memory accesses. When a long-latency load causes the instruction window to fill up and halt the pipeline, the processor enters runahead mode and keeps speculatively executing code to trigger accurate prefetches. A recent improvement tracks the chain of instructions that leads to the long-latency load, stores it in a runahead buffer, and executes only this chain during runahead execution, with the purpose of generating more prefetch requests during runahead execution. Unfortunately, all these prior runahead proposals have shortcomings that limit performance and energy efficiency because they discard the full instruction window to enter runahead mode and then flush the pipeline to restart normal operation. This significantly constrains the performance benefits and increases the energy overhead of runahead execution. In addition, runahead buffer limits prefetch coverage by tracking only a single chain of instructions that lead to the same long-latency load. We propose precise runahead execution (PRE) to mitigate the shortcomings of prior work. PRE leverages the renaming unit to track all the dependency chains leading to long-latency loads. PRE uses a novel approach to manage free processor resources to execute the detected instruction chains in runahead mode without flushing the pipeline. Our results show that PRE achieves an additional 21.1 percent performance improvement over the recent runahead proposals while reducing energy consumption by 6.1 percent.
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources

Scarcity of health care resources could result in the unavoidable consequence of rationing. For example, ventilators are often limited in supply, especially during public health emergencies or in resource-constrained health care settings, such as amid the pandemic of COVID-19. Currently, there is no universally accepted standard for health care resource allocation protocols, resulting in different governments prioritizing patients based on various criteria and heuristic-based protocols. In this study, we investigate the use of reinforcement learning for critical care resource allocation policy optimization to fairly and effectively ration resources. We propose a transformer-based deep Q-network to integrate the disease progression of individual patients and the interaction effects among patients during the critical care resource allocation. We aim to improve both fairness of allocation and overall patient outcomes. Our experiments demonstrate that our method significantly reduces excess deaths and achieves a more equitable distribution under different levels of ventilator shortage, when compared to existing severity-based and comorbidity-based methods in use by different governments. Our source code is included in the supplement and will be released on Github upon publication.
A Ground Segmentation Method Based on Point Cloud Map for Unstructured Roads

Ground segmentation, as the basic task of unmanned intelligent perception, provides an important support for the target detection task. Unstructured road scenes represented by open-pit mines have irregular boundary lines and uneven road surfaces, which lead to segmentation errors in current ground segmentation methods. To solve this problem, a ground segmentation method based on point cloud map is proposed, which involves three parts: region of interest extraction, point cloud registration and background subtraction. Firstly, establishing boundary semantic associations to obtain regions of interest in unstructured roads. Secondly, establishing the location association between point cloud map and the real-time point cloud of region of interest by semantics information. Thirdly, establishing a background model based on Gaussian distribution according to location association, and segments the ground in real-time point cloud by the background substraction method. Experimental results show that the correct segmentation rate of ground points is 99.95%, and the running time is 26ms. Compared with state of the art ground segmentation algorithm Patchwork++, the average accuracy of ground point segmentation is increased by 7.43%, and the running time is increased by 17ms. Furthermore, the proposed method is practically applied to unstructured road scenarios represented by open pit mines.
A Subspace Framework for L∞ Model Reduction

We consider the problem of locating a nearest descriptor system of prescribed reduced order to a descriptor system with large order with respect to the ${\mathcal L}_\infty$ norm. Widely employed approaches such as the balanced truncation and best Hankel norm approximation for this ${\mathcal L}_\infty$ model reduction problem are usually expensive and yield solutions that are not optimal, not even locally. We propose approaches based on the minimization of the ${\mathcal L}_\infty$ objective by means of smooth optimization techniques. As we illustrate, direct applications of smooth optimization techniques are not feasible, since the optimization techniques converge at best at a linear rate requiring too many evaluations of the costly ${\mathcal L}_\infty$-norm objective to be practical. We replace the original large-scale system with a system of smaller order that interpolates the original system at points on the imaginary axis, and minimize the ${\mathcal L}_\infty$ objective after this replacement. The smaller system is refined by interpolating at additional imaginary points determined based on the local minimizer of the ${\mathcal L}_\infty$ objective, and the optimization is repeated. We argue the framework converges at a quadratic rate under smoothness and nondegeneracy assumptions, and describe how asymptotic stability constraints on the reduced system sought can be incorporated into our approach. The numerical experiments on benchmark examples illustrate that the approach leads to locally optimal solutions to the ${\mathcal L}_\infty$ model reduction problem, and the convergence occurs quickly for descriptors systems of order a few ten thousands.
Unified Risk Analysis for Weakly Supervised Learning

Among the flourishing research of weakly supervised learning (WSL), we recognize the lack of a unified interpretation of the mechanism behind the weakly supervised scenarios, let alone a systematic treatment of the risk rewrite problem, a crucial step in the empirical risk minimization approach. In this paper, we introduce a framework providing a comprehensive understanding and a unified methodology for WSL. The formulation component of the framework, leveraging a contamination perspective, provides a unified interpretation of how weak supervision is formed and subsumes fifteen existing WSL settings. The induced reduction graphs offer comprehensive connections over WSLs. The analysis component of the framework, viewed as a decontamination process, provides a systematic method of conducting risk rewrite. In addition to the conventional inverse matrix approach, we devise a novel strategy called marginal chain aiming to decontaminate distributions. We justify the feasibility of the proposed framework by recovering existing rewrites reported in the literature.
Connecting the Dots in News Analysis: A Cross-Disciplinary Survey of Media Bias and Framing

The manifestation and effect of bias in news reporting have been central topics in the social sciences for decades, and have received increasing attention in the NLP community recently. While NLP can help to scale up analyses or contribute automatic procedures to investigate the impact of biased news in society, we argue that methodologies that are currently dominant fall short of addressing the complex questions and effects addressed in theoretical media studies. In this survey paper, we review social science approaches and draw a comparison with typical task formulations, methods, and evaluation metrics used in the analysis of media bias in NLP. We discuss open questions and suggest possible directions to close identified gaps between theory and predictive models, and their evaluation. These include model transparency, considering document-external information, and cross-document reasoning rather than single-label assignment.
Treatable traits for long COVID

Long COVID, or post‐acute COVID‐19 sequelae, is experienced by an estimated one in eight adults following acute COVID‐19. Long COVID is a new and complex chronic health condition that typically includes multiple symptoms that cross organ systems and fluctuate over time; a one‐size‐fits‐all approach is, therefore, not likely to be appropriate nor relevant for long COVID treatment. ‘Treatable Traits’ is a personalized medicine approach, purpose‐built to address the complexity and heterogeneity of complex chronic conditions. This comprehensive review aimed to understand how a treatable traits approach could be applied to long COVID, by first identifying the most prevalent long COVID treatable traits and then the available evidence for strategies to target these traits. An umbrella review of 22 systematic reviews identified 34 symptoms and complications common with long COVID, grouped into eight long COVID treatable trait clusters: neurological, chest, psychological, pain, fatigue, sleep impairment, functional impairment and other. A systematic review of randomized control trials identified 18 studies that explored different intervention approaches for long COVID prevention (k = 4) or management (k = 14). While a single study reported metformin as effective for long COVID prevention, the findings need to be replicated and consensus is required around how to define long COVID as a clinical trial endpoint. For long COVID management, current evidence supports exercise training or respiratory muscle training for long COVID treatable traits in the chest and functional limitation clusters. While there are studies exploring interventions targeting other long COVID treatable traits, further high‐quality RCTs are needed, particularly targeting treatable traits in the clusters of fatigue, psychological, pain and sleep impairment.
Bit Representation Can Improve SDP Relaxations of Mixed-Integer Quadratic Programs

A standard trick in integer programming is to replace bounded integer variables with binary variables, using a bit representation. In a previous paper, we showed that this process can be used to improve linear programming relaxations of mixed-integer quadratic programs. In this paper, we show that it can also be used to improve {\em semidefinite}\/ programming relaxations.
3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images

Automated segmentation of the blood vessels in 3D volumes is an essential step for the quantitative diagnosis and treatment of many vascular diseases. 3D vessel segmentation is being actively investigated in existing works, mostly in deep learning approaches. However, training 3D deep networks requires large amounts of manual 3D annotations from experts, which are laborious to obtain. This is especially the case for 3D vessel segmentation, as vessels are sparse yet spread out over many slices and disconnected when visualized in 2D slices. In this work, we propose a novel method to segment the 3D peripancreatic arteries solely from one annotated 2D projection per training image with depth supervision. We perform extensive experiments on the segmentation of peripancreatic arteries on 3D contrast-enhanced CT images and demonstrate how well we capture the rich depth information from 2D projections. We demonstrate that by annotating a single, randomly chosen projection for each training sample, we obtain comparable performance to annotating multiple 2D projections, thereby reducing the annotation effort. Furthermore, by mapping the 2D labels to the 3D space using depth information and incorporating this into training, we almost close the performance gap between 3D supervision and 2D supervision. Our code is available at: https://github.com/alinafdima/3Dseg-mip-depth.
Form factors for the charm-baryon semileptonic decay $\Xi_c\to \Xi \ell \nu$ from domain-wall lattice QCD

Recent experimental progress measuring the branching fractions of the heavy-baryon semileptonic decays $\Xi_c\to \Xi \ell \nu$ has stimulated theoretical interest and motivates precise lattice calculations of the form factors. Here we present such a calculation using domain-wall fermions for the up, down, and strange quarks, and an anisotropic clover action for the charm quark. We use four ensembles generated by the RBC and UKQCD collaborations, with lattice spacings between 0.111 and 0.073 fm and pion masses ranging from 420 to 230 MeV. Our preliminary results for the form factors are larger in magnitude than previous lattice results.
HIVE: Scalable Hardware-Firmware Co-Verification using Scenario-based Decomposition and Automated Hint Extraction

Hardware-firmware co-verification is critical to design trustworthy systems. While formal methods can provide verification guarantees, due to the complexity of firmware and hardware, it can lead to state space explosion. There are promising avenues to reduce the state space during firmware verification through manual abstraction of hardware or manual generation of hints. Manual development of abstraction or hints requires domain expertise and can be time-consuming and error-prone, leading to incorrect proofs or inaccurate results. In this paper, we effectively combine the scalability of simulation-based validation and the completeness of formal verification. Our proposed approach is applicable to actual firmware and hardware implementations without requiring any manual intervention during formal model generation or hint extraction. To reduce the state space complexity, we utilize both static module-level analysis and dynamic execution of verification scenarios to automatically generate system-level hints. These hints guide the underlying solver to perform scalable equivalence checking using proofs. The extracted hints are validated against the implementation before using them in the proofs. Experimental evaluation on RISC-V based systems demonstrates that our proposed framework is scalable due to scenario-based decomposition and automated hint extraction. Moreover, our fully automated framework can identify complex bugs in actual firmware-hardware implementations.
An Efficient Wide-Range Pseudo-3D Vehicle Detection Using A Single Camera

Wide-range and fine-grained vehicle detection plays a critical role in enabling active safety features in intelligent driving systems. However, existing vehicle detection methods based on rectangular bounding boxes (BBox) often struggle with perceiving wide-range objects, especially small objects at long distances. And BBox expression cannot provide detailed geometric shape and pose information of vehicles. This paper proposes a novel wide-range Pseudo-3D Vehicle Detection method based on images from a single camera and incorporates efficient learning methods. This model takes a spliced image as input, which is obtained by combining two sub-window images from a high-resolution image. This image format maximizes the utilization of limited image resolution to retain essential information about wide-range vehicle objects. To detect pseudo-3D objects, our model adopts specifically designed detection heads. These heads simultaneously output extended BBox and Side Projection Line (SPL) representations, which capture vehicle shapes and poses, enabling high-precision detection. To further enhance the performance of detection, a joint constraint loss combining both the object box and SPL is designed during model training, improving the efficiency, stability, and prediction accuracy of the model. Experimental results on our self-built dataset demonstrate that our model achieves favorable performance in wide-range pseudo-3D vehicle detection across multiple evaluation metrics. Our demo video has been placed at https://www.youtube. com/watch? v=1gk1PmsQ5Q8.
Human sphingomyelin synthase 1 generates diacylglycerol in the presence and absence of ceramide via multiple enzymatic activities

Sphingomyelin (SM) synthase 1 (SMS1), which is involved in lipodystrophy, deafness, and thrombasthenia, generates diacylglycerol (DG) and SM using phosphatidylcholine (PC) and ceramide as substrates. Here, we found that SMS1 possesses DG‐generating activities via hydrolysis of PC and phosphatidylethanolamine (PE) in the absence of ceramide and ceramide phosphoethanolamine synthase (CPES) activity. In the presence of the same concentration (4.7 mol%) of PC and ceramide, the amounts of DG produced by SMS and PC‐phospholipase C (PLC) activities of SMS1 were approximately 65% and 35% of total DG production, respectively. PC‐PLC activity showed substrate selectivity for saturated and/or monounsaturated fatty acid‐containing PC species. A PC‐PLC/SMS inhibitor, D609, inhibited only SMS activity. Mn2+ inhibited only PC‐PLC activity. Intriguingly, DG attenuated SMS/CPES activities. Our study indicates that SMS1 is a unique enzyme with PC‐PLC/PE‐PLC/SMS/CPES activities.
A comparative ab-initio investigation of the physical properties of cubic Laves phase compounds XBi$_2$ (X = K, Rb)

In this study, we looked into a number of physical properties of alkali-bismuth compounds KBi$_2$ and RbBi$_2$ in the cubic Laves phase using the density functional theory (DFT). The structural, elastic, anisotropy indices, hardness, thermo-physical parameters, electronic band structure, and optoelectronic properties have been explored. Most of the results presented in this work are novel in nature.
Accelerated neurodevelopment of reward anticipation processing in adolescent girls with depression

Objective To test the hypothesis that depression is associated with differential neurodevelopment of reward circuitry in adolescence. Methods Adolescent girls (N=183, 58 with MDD in early or late adolescence) underwent MRI scans from ages 16-20 (1-4 scans/participant, 477 scans total) and completed a card-guessing fMRI task with monetary rewards. Mixed-effect models tested the effect of age and the moderating effect of MDD on whole-brain regional activation during reward anticipation. Results Eighty of 414 regions showed age effects (pFDR<0.05), consisting primarily of increasing activation with increasing age. Most significant regions were in dorsal attention, salience, and somatomotor networks, and also included the bilateral putamen, pallidum, and right nucleus accumbens. MDD moderated age effects in 40 regions (pFDR<0.05), including the right putamen, medial orbitofrontal cortex, and amygdala, and regions in control and dorsal attention networks. MDD x linear and quadratic age effects were negative, suggesting that MDD was associated with accelerated neurodevelopment. Conclusions Theories of reward processing's contribution to adolescent risk for depression focus primarily on core reward regions, yet a host of regions beyond these continue to develop during late adolescence. Findings demonstrate differing regional patterns of age-related changes in relation to MDD in girls, suggesting that depression involves disruption of a wide range of regions during reward anticipation processing across adolescence. Childhood and adolescent MDD is associated with accelerated neurodevelopment of attention and cognitive control regions during reward anticipation processing, which may have consequences both for cognitive function and the emergence of reward-system-specific disruptions.
Using Large Language Models for Knowledge Engineering (LLMKE): A Case Study on Wikidata

In this work, we explore the use of Large Language Models (LLMs) for knowledge engineering tasks in the context of the ISWC 2023 LM-KBC Challenge. For this task, given subject and relation pairs sourced from Wikidata, we utilize pre-trained LLMs to produce the relevant objects in string format and link them to their respective Wikidata QIDs. We developed a pipeline using LLMs for Knowledge Engineering (LLMKE), combining knowledge probing and Wikidata entity mapping. The method achieved a macro-averaged F1-score of 0.701 across the properties, with the scores varying from 1.00 to 0.328. These results demonstrate that the knowledge of LLMs varies significantly depending on the domain and that further experimentation is required to determine the circumstances under which LLMs can be used for automatic Knowledge Base (e.g., Wikidata) completion and correction. The investigation of the results also suggests the promising contribution of LLMs in collaborative knowledge engineering. LLMKE won Track 2 of the challenge. The implementation is available at https://github.com/bohuizhang/LLMKE.
A systematic investigation of electric field nonlinearity and field reversal in low pressure capacitive discharges driven by sawtooth-like waveforms

Understanding electron and ion heating phenomenon in capacitively coupled radio-frequency plasma discharges is vital for many plasma processing applications. In this article, using particle-in-cell simulation technique we investigate the collisionless argon discharge excited by temporally asymmetric sawtooth-like waveform. In particular, a systematic study of the electric field nonlinearity and field reversal phenomenon by varying the number of harmonics and its effect on electron and ion heating is performed. The simulation results predict higher harmonics generation and multiple field reversal regions formation with an increasing number of harmonics along with the local charge separation and significant displacement current outside sheath region. The field reversal strength is greater during the expanding phase of the sheath edge in comparison to its collapsing phase causing significant ion cooling. The observed behavior is associated with the electron fluid compression/rarefaction and electron inertia during expanding and collapsing phase respectively.
Exact Foldy-Wouthuysen transformation for a Dirac equation describing the interaction of spin-1/2 relativistic particles with an external electromagnetic field

The Thomas-Bargmann-Michel-Telegdi (T-BMT) equation is derived using the Exact Foldy-Wouthuysen transformation. Extra new terms were found, and we discuss their possible physical applications. The main point of this work is to detail the procedure to get the general result. We explicitly present the choice of parametrization we used on the initial Hamiltonian and the motivations to take it. We emphasize that the final equations can depend on this choice, and it is possible to prevent the manipulations of the quadratic Hamiltonian become extremely cumbersome. More importantly, it is done in such a way that the transformed equations allow the direct separation into mass, kinetic, and interaction correction terms to the original T-BMT equation.
A modern approach to transition analysis and process mining with Markov models: A tutorial with R

This chapter presents an introduction to Markovian modeling for the analysis of sequence data. Contrary to the deterministic approach seen in the previous sequence analysis chapters, Markovian models are probabilistic models, focusing on the transitions between states instead of studying sequences as a whole. The chapter provides an introduction to this method and differentiates between its most common variations: first-order Markov models, hidden Markov models, mixture Markov models, and mixture hidden Markov models. In addition to a thorough explanation and contextualization within the existing literature, the chapter provides a step-by-step tutorial on how to implement each type of Markovian model using the R package seqHMM. The chaper also provides a complete guide to performing stochastic process mining with Markovian models as well as plotting, comparing and clustering different process models.