Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing G-EDF scheduling tardiness with uniform instances on multiprocessors

Soft real-time multiprocessor systems frequently adopt the G-EDF (Global-Earliest Deadline First) scheduling policy as it is lightweight and it guarantees bounded tardiness. Much effort has been spent in literature to provide efficiently computable tardiness bounds for periodic task systems scheduled on multiprocessors, but still, no exact bound is known and the best-known approximation takes non-polynomial time to compute. In this paper, we consider synchronous and periodic task systems in which tasks have the same period length and the same job length, named uniform scheduling instances. We analytically derive a tight bound on the maximum tardiness and provide the exact values of the tardiness of each processor, showing that the latter can be computed in time linear in the number of processors. This result provides a lower bound to tardiness for the more general class of instances and is intended to close the gap with the upper bound from below. We also show that the results can be applied in practice to a wider set of instances than those considered in the theoretical study.
An Evaluation of Time-triggered Scheduling in the Linux Kernel

The GNU/Linux operating system (OS) is becoming more commonly used in real-time systems and has been modified with specific updates to provide faster and bounded response times as well as with fixed- and dynamic-priority real-time scheduling mechanisms. While the EDF-based scheduler ensures deadlines and temporal isolation for real-time tasks, it may not provide the level of determinism needed for modern applications that also have to consider complex jitter and multi-rate task dependencies. In this paper, we propose, implement, and evaluate an open-source, kernel-level time-triggered scheduling approach for Linux, examining the level of determinism achievable in terms of task execution and end-to-end latencies. We show that time-triggered scheduling in the Linux kernel achieves reduced latency and jitter for real-time applications when compared to the existing scheduling policies and user-space time-triggered implementations. Additionally, in terms of end-to-end communication latencies for distributed real-time applications, we compare a software-based IEEE 802.1Qvb time-aware gating implementation for time-sensitive networking (TSN) in which the time-triggered application schedule can be aligned to the network schedule to the standard Linux networking subsystem.
Enabling multi-link data transmission for collaborative sensing in open road scenarios

Fully autonomous driving applications rely on a complete knowledge of their operational environment to ensure safety while maintaining efficient driving. However, in scenarios with close visual restrictions, such as urban traffic, the lack of sensor data leads to a safety-related degradation of the autonomous service. Collaborative sensing, describing the timely wireless transmission of additional large external sensor data, can fill this information gap. However, real-time protocols for error-protected transmission of sensor data that also consider load dynamics as well as link prioritization are not available. To make the channel usable for such collaborative sensing requirements, even in challenging load scenarios, we introduce an adaptive minimum distance shaping mechanism of fragments that exploits the round-trip time information from the protocol’s error protection mechanism. We place that mechanism in the context of the IEEE 802.11p WLAN-based V2X software stack and evaluate them with an OMNeT++ simulation. The results show that link robustness is achieved under varying load conditions while channel access and load injection follow the expected prioritization.
Progresses in Link Prediction: A Survey

Link prediction is a technique to forecast future new or missing relationships between entities based on the current dynamic network information. After a brief introduction of the standard problem and evaluation metrics of link prediction, this review will summarize representative progresses about matrix factorization, probabilistic models, network embedding, deep learning, and some others, mainly extracted from related publications in the last decade. Finally, this review will outline some long-standing challenges for future studies.
Reducing Overall Path Latency in Automotive Logical Execution Time Scheduling via Reinforcement Learning

The Logical Execution Time paradigm is a promising approach for achieving time-deterministic communication on multi-core CPUs. Task scheduling under this paradigm is a variant of the Multi-Row Facility Layout Problem, which is known to be NP-hard. In this paper, we propose using reinforcement learning to reduce the overall path latency among all scheduled runnables while adhering to other constraints, such as schedulability, load balance, and data contention control. The neural networks, also known as agents, are trained on a real-world automotive powertrain project. We compare two schedules generated by the agents to the current one and one produced by a genetic algorithm. The agent trained with the Proximal Policy Optimization algorithm demonstrated the best performance. Additionally, we investigate the generalization ability of the agents against software updates, and the results show that our agents are well-generalized.
Heart Rate Detection Using Motion Compensation with Multiple ROIs

Remote photoplethysmography (rPPG) has the ability to make use of image frame sequences including human faces collected by cameras for measuring heart rate (HR) without any contact. This method generates a time series signal based on the RGB spatial average of the selected region of interest (ROI) to estimate physiological signals such as HR. It is worth to note that the motion artifact produced by the subject’s face shaking is equivalent to adding considerable noise to the signal which will greatly affect the accuracy of the measurement. In this paper, a novel anti-interference multi-ROI analysis (AMA) approach is proposed which effectively utilizes the local information with multiple ROIs, the Euler angle information of the subject’s head, and the interpolation resampling technique of the video for suppressing the influence of face shaking on the accuracy of non-contact heart rate measurement. The proposed method is evaluated on the UBFC-RPPG and PURE datasets, and the experimental results demonstrate that the proposed methods are superior to many state-of-the-art methods.
Enabling timing predictability in the presence of store buffers

We study the effect of store buffers on the timing predictability of processor pipelines. We show that the concurrency between the load unit and the store buffer to access the memory bus is an obstacle to timing predictability, even in simple scalar in-order pipeline designs. We then propose a gating mechanism that removes these shortcomings. We model subsets of pipelines that implement our mechanism using an established logic framework. Using the Coq proof assistant, we prove that it enables timing predictability. Finally, we show that the performance cost of our mechanism measured on an FPGA-synthesized RISC-V core is around 2% only on average, with a minimal increase in resource usage.
Quantum Learning and Quantum Perceptrons

Ce document constitue le rapport de stage de ﬁn d’´etudes de l’ ´Ecole Centrale Marseille eﬀectu´e `a UCSD . La th´ematique du stage dont ce rapport fait l’objet, s’arti-cule autour de l’ information quantique . Le stage en lui-mˆeme s’intitule (cid:28) Quantum Learning and Quantum Perceptron (cid:29) , la traduction fran¸caise pouvant ˆetre : (cid:28) Apprentissage-Machine Quantique et Perceptron Quantique (cid:29) . Le document est divis´e en cinq parties. La premi`ere d’entre elles pose les bases de l’informatique classique et quantique. On y apprend les diﬀ´erences notables entre les deux paradigmes et on y trouve les propri´et´es importantes. Quelques brefs exemples d’algorithmes quantiques sont rappel´es. Dans la deuxi`eme partie, les aspects classiques de l’apprentissage machine et des perceptrons sont introduits. Leurs limitations classiques sont pr´esent´ees. Enﬁn, dans une troisi`eme partie, nous introduisons la version quantique du perceptron et de l’apprentissage machine mise au point du-rant ce stage. Dans une quatri`eme partie nous pr´esentons des limitations concernant un apprentissage en une seule requˆete aupr`es de l’oracle. Finalement, dans une cin-qui`eme partie nous pr´esentons les ´evolutions possibles du projet pour obtenir des am´eliorations. document This document is divided in ﬁve parts. First of all we present some basis of classical and quantum information. Diﬀerences and properties between the two paradigms are listed. Some examples of quantum algorithms are presented. In the second part the classical machine learning model is described and the perceptron is deﬁned. The computational limitations of this classical model are detailed in the case of discrete perceptrons. Then, the quantum version developped during this internship is introduced. In the fourth part we give some limitation results from the area of Oracle Identiﬁcation. Finally, we expose the ideas that could help to bypass these limitations and help to ﬁnd an optimal algorithm.
Smash: Flexible, Fast, and Resource-efficient Placement and Lookup of Distributed Storage

Large-scale distributed storage systems, such as object stores, usually apply hashing-based placement and lookup methods to achieve scalability and resource efficiency. However, when object locations are determined by hash values, placement becomes inflexible, failing to optimize or satisfy application requirements such as load balance, failure tolerance, parallelism, and network/system performance. This work presents a novel solution to achieve the best of two worlds: flexibility while maintaining cost-effectiveness and scalability. The proposed method Smash is an object placement and lookup method that achieves full placement flexibility, balanced load, low resource cost, and short latency. Smash utilizes a recent space-efficient data structure and applies it to object-location lookups. We implement Smash as a prototype system and evaluate it in a public cloud. The analysis and experimental results show that Smash achieves full placement flexibility, fast storage operations, fast recovery from node dynamics, and lower DRAM cost (<60%) compared to existing hash-based solutions such as Ceph and MapX.
Cache-Aware Allocation of Parallel Jobs on Multi-cores based on Learned Recency

Scheduling of tasks on multi- and many-cores benefits significantly from the efficient use of caches. Most previous approaches use the static analysis of software in the context of the processing hardware to derive fixed allocations of software to the cache. However, there are many issues with this approach in terms of pessimism, scalability, analysis complexity, maintenance cost, etc. Furthermore, with ever more complex functionalities being implemented in the system, it becomes nearly impracticable to use static analysis for deriving cache-aware scheduling methods. This paper focuses on a dynamic approach to maximise the throughput of multi-core systems by benefiting from the cache based on empirical assessments. The principal contribution is a novel cache-aware allocation for parallel jobs that are organised as directed acyclic graphs (DAGs). Instead of allocating instruction and data blocks to caches, the proposed allocation operates at a higher abstraction level that allocates jobs to cores, based on the guidance of a predictive model that approximates the execution time of jobs with caching effects taken into account. An implementation of the predictive model is constructed to demonstrate that the execution time approximations can be effectively obtained. The experimental results, including a real-world case study, prove the concept of the proposed cache-aware allocation approach and demonstrate its effectiveness over the state-of-the-art.
CubeGAN: Omnidirectional Image Synthesis Using Generative Adversarial Networks

We propose a framework to create projectively‐correct and seam‐free cube‐map images using generative adversarial learning. Deep generation of cube‐maps that contain the correct projection of the environment onto its faces is not straightforward as has been recognized in prior work. Our approach extends an existing framework, StyleGAN3, to produce cube‐maps instead of planar images. In addition to reshaping the output, we include a cube‐specific volumetric initialization component, a projective resampling component, and a modification of augmentation operations to the spherical domain. Our results demonstrate the network's generation capabilities trained on imagery from various 3D environments. Additionally, we show the power and quality of our GAN design in an inversion task, combined with navigation capabilities, to perform novel view synthesis.
Non‐linear Rough 2D Animation using Transient Embeddings

Traditional 2D animation requires time and dedication since tens of thousands of frames need to be drawn by hand for a typical production. Many computer‐assisted methods have been proposed to automatize the generation of inbetween frames from a set of clean line drawings, but they are all limited by a rigid workflow and a lack of artistic controls, which is in the most part due to the one‐to‐one stroke matching and interpolation problems they attempt to solve. In this work, we take a novel view on those problems by focusing on an earlier phase of the animation process that uses rough drawings (i.e., sketches). Our key idea is to recast the matching and interpolation problems so that they apply to transient embeddings, which are groups of strokes that only exist for a few keyframes. A transient embedding carries strokes between keyframes both forward and backward in time through a sequence of transformed lattices. Forward and backward strokes are then cross‐faded using their thickness to yield rough inbetweens. With our approach, complex topological changes may be introduced while preserving visual motion continuity. As demonstrated on state‐of‐the‐art 2D animation exercises, our system provides unprecedented artistic control through the non‐linear exploration of movements and dynamics in real‐time.
Cytomegalovirus-induced inactivation of TSC2 disrupts the coupling of fatty acid biosynthesis to glucose availability resulting in a vulnerability to glucose limitation

Human cytomegalovirus (HCMV) modulates cellular metabolism to support productive infection, and the HCMV UL38 protein drives many aspects of this HCMV-induced metabolic program. However, it remains to be determined whether virally-induced metabolic alterations might induce novel therapeutic vulnerabilities in virally infected cells. Here, we explore how HCMV infection and the UL38 protein modulate cellular metabolism and how these changes alter the response to nutrient limitation. We find that expression of UL38, either in the context of HCMV infection or in isolation, sensitizes cells to glucose limitation resulting in cell death. This sensitivity is mediated through UL38’s inactivation of the TSC complex subunit 2 (TSC2) protein, a central metabolic regulator that possesses tumor-suppressive properties. Further, expression of UL38 or the inactivation of TSC2 results in anabolic rigidity in that the resulting increased levels of fatty acid biosynthesis are insensitive to glucose limitation. This failure to regulate fatty acid biosynthesis in response to glucose availability sensitizes cells to glucose limitation, resulting in cell death unless fatty acid biosynthesis is inhibited. These experiments identify a regulatory circuit between glycolysis and fatty acid biosynthesis that is critical for cell survival upon glucose limitation and highlight a metabolic vulnerability associated with viral infection and the inactivation of normal metabolic regulatory controls. Importance Viruses modulate host cell metabolism to support the mass production of viral progeny. For Human Cytomegalovirus, we find that the viral UL38 protein is critical for driving these pro-viral metabolic changes. However, our results indicate that these changes come at a cost, as UL38 induces an anabolic rigidity that leads to a metabolic vulnerability. We find that UL38 decouples the link between glucose availability and fatty acid biosynthetic activity. Normal cells respond to glucose limitation by down-regulating fatty acid biosynthesis. Expression of UL38 results in the inability to modulate fatty acid biosynthesis in response to glucose limitation, which results in cell death. We find this vulnerability in the context of viral infection, but this linkage between fatty acid biosynthesis, glucose availability, and cell death could have broader implications in other contexts or pathologies that rely on glycolytic remodeling, for example, oncogenesis.
Molecular archaeology: individual human DNA and infection profiles from 100 year old mosquitoes

Natural history collections represent a vast biological and genetic resource, which has remained largely untapped. We revisit mosquito collections of varying ages; some made by early tropical medicine pioneers over 100 years ago. By applying species-specific primers, a panel of forensic short tandem repeat (STR) markers, and Plasmodium diagnostics, we were able to obtain the unique genetic profile and Plasmodium infection status of the individual from whom each mosquito obtained their final bloodmeal. We show evidence of long-dead mosquitoes feeding on multiple individuals in the same gonotrophic cycle, and how the human hosts are likely to have been infected with malaria at the time of blood-feeding. This approach may be used to track the host-fidelity of vectors over an evolutionary time scale.
Extraction of plane target image features based on homography matrix in visual measurement

In this paper,a method of extracting plane target image features based on homography matrix is proposed.With four groups of corresponding coordinates of features,the homography matrix of target plane and image plane can be established.For known spatial features,their corresponding rough image coordinates can be computed using the homography matrix.With least square ellipse fitting or accurate corner pick-up algorithm,we can get the exact image coordinates of features further.Experimental results show that this method can achieve accurate features extraction and exact corresponding relationship between features' image coordinates and their space coordinates.
Global diversity of enterococci and description of 18 novel species

Enterococci are commensal gut microbes of most land animals. They diversified over hundreds of millions of years adapting to evolving hosts and host diets. Of over 60 known enterococcal species, Enterococcus faecalis and E. faecium uniquely emerged in the antibiotic era among leading causes of multidrug resistant hospital-associated infection. The basis for the association of particular enterococcal species with a host is largely unknown. To begin deciphering enterococcal species traits that drive host association, and to assess the pool of Enterococcus-adapted genes from which known facile gene exchangers such as E. faecalis and E. faecium may draw, we collected 886 enterococcal strains from nearly 1,000 specimens representing widely diverse hosts, ecologies and geographies. This provided data on the global occurrence and host associations of known species, identifying 18 new species in the process expanding genus diversity by >25%. The novel species harbor diverse genes associated with toxins, detoxification, and resource acquisition. E. faecalis and E. faecium were isolated from a wide diversity of hosts highlighting their generalist properties, whereas most other species exhibited more restricted distributions indicative of specialized host associations. The expanded species diversity permitted the Enterococcus genus phylogeny to be viewed with unprecedented resolution, allowing features to be identified that distinguish its four deeply rooted clades as well as genes associated with range expansion, such as B-vitamin biosynthesis and flagellar motility. Collectively, this work provides an unprecedentedly broad and deep view of the genus Enterococcus, potential threats to human health, and new insights into its evolution. SIGNIFICANCE Enterococci, host-associated microbes that are now leading drug-resistant hospital pathogens, arose as animals colonized land over 400 million years ago. To globally assess the diversity of enterococci now associated with land animals, we collected 886 enterococcal specimens from a wide range of geographies and ecologies, ranging from urban environments to remote areas generally inaccessible to humans. Species determination and genome analysis revealed host associations from generalists to specialists, and identified 18 new species, increasing the genus by over 25%. This added diversity provided greater resolution of the genus clade structure, identifying new features associated with species radiations. Moreover, the high rate of new species discovery shows that tremendous genetic diversity in Enterococcus remains to be discovered.
Living cell mTORC1 inhibition reporter mTIR reveals nutrient-sensing targets of histone deacetylase inhibitor

Mammalian or mechanistic target of rapamycin complex 1 (mTORC1) is a clinically effective therapeutic target for diseases such as cancer, diabetes, aging, and neurodegeneration, yet an efficient tool to monitor mTORC1 inhibition in living cells or tissues is still lacking. Here we devised a genetically encoded mTORC1 inhibition reporter termed mTIR that exhibits a highly contrasted fluorescence puncta pattern in response to mTORC1 inhibition. mTIR specifically senses physiological, pharmacological and genetic inhibition of mTORC1 signaling in living cells and tissues. Importantly, mTIR can be applied as an powerful tool for imaging-based visual screening of mTORC1 inhibitors. By this method, we identified histone deacetylase inhibitors (HDACi) that selectively inhibit mTORC1 by inducing nutrient-sensing gene expression. Thus, mTIR is a unique living cell reporter efficiently detecting the inhibition of mTORC1 activity, and the HDACi Panobinostat transcriptionally target mTORC1 signaling via amino acids sensing.
The role of Smarcad1 in retroviral repression in mouse embryonic stem cells

Background Moloney murine leukemia virus (MLV) replication is suppressed in mouse embryonic stem cells (ESCs) by the Trim28-SETDB1 complex. The chromatin remodeler Smarcad1 interacts with Trim28 and was suggested to allow the deposition of the histone variant H3.3. However, the role of Trim28, H3.3, and Smarcad1 in MLV repression in ESCs still needs to be fully understood. Results In this study, we used MLV to explore the role of Smarcad1 in retroviral silencing in ESCs. We show that Smarcad1 is immediately recruited to the MLV provirus. Based on the repression dynamics of a GFP-reporter MLV, our findings suggest that Smarcad1 plays a critical role in the establishment and maintenance of MLV repression, as well as other Trim28-targeted genomic loci. Furthermore, Smarcad1 is important for stabilizing and strengthening Trim28 binding to the provirus over time, and its presence around the provirus is needed for proper deposition of H3.3 on the provirus. Surprisingly, the combined depletion of Smarcad1 and Trim28 results in enhanced MLV derepression, suggesting that these two proteins may also function independently to maintain repressive chromatin states. Conclusions Overall, the results of this study provide evidence for the crucial role of Smarcad1 in the silencing of retroviral elements in embryonic stem cells. Further research is needed to fully understand how Smarcad1 and Trim28 cooperate and their implications for gene expression and genomic stability. Take home Depletion of Smarcad1 impairs retroviral repression. Smarcad1 is necessary for proper recruitment of Trim28 and H3.3 deposition. Depleting Smarcad1 and Trim28 results in enhanced derepression of the MLV provirus.