Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Workload Colocation in Modern Data Centers

Despite the high costs of acquisition and maintenance of modern data centers, machine resource utilization is often low. Servers running online interactive services are over-provisioned to support peak load (which only occurs for a fraction of the time), due to business continuity and fault-tolerance plans that dictate these services tolerate major unforeseen events (e.g., the failure of another data center). Underutilized computing resources correlate with reduced data center efficiency and capital losses incurred by operators. Colocating different types of workloads is a promising solution to increase resource utilization. The challenges are protecting interactive services from resource interference and handling limited resource availability: if a best-effort workload directly competes for resources with an interactive service, the latter can fail to meet its service-level objectives (SLOs), impacting user experience and resulting in lost revenue. In this dissertation we focus on how to mitigate CPU interference and limited memory availability when colocating interactive services with batch workloads such as data-parallel applications. Existing work on mitigating CPU interference assumes that progress and performance metrics are available in real-time, and that hardware support for low-level isolation mechanisms is prevalent, neither of which are always true in commercial environments. Furthermore, previous work on improving resource allocation in clusters does not attempt to over-commit memory due to concerns of unpredictable performance degradation and sporadic failures. We first focus on the colocation of open-source in-memory interactive services, and conduct a thorough investigation on how they are impacted by batch jobs. We find that existing isolation techniques do not prevent performance degradation, and explain in detail why they are insufficient. Second, we study the behavior of a large commercial-scale service (i.e., a component of a web search platform), finding that it is highly bursty. We show that, by keeping a headroom of idle cores available at all times, the service’s response tail-latency is maintained within its SLO even under heavy load. Third, we address the impact of limited memory availability on data-parallel applications by identifying an intrinsic property of their tasks: memory elasticity — the ability to handle memory pressure by writing intermediate data to external storage, incurring only moderate performance penalties when memory-constrained. We further show that memory elasticity is prevalent across various data-parallel application frameworks. We implement a scheduler that leverages memory elasticity and achieves reduced job completion times and increased memory utilization. We conclude that these techniques make it possible to colocate even highly-bursty latencysensitive services with batch jobs, significantly increasing resource utilization.
On Semi-Automatic Creation of Dataset for Multi-Document Automatic Summarization of News Articles an Forum Threads

The problem of semi-automatic dataset creation for multi-document summarization and forum threads summarization is analyzed. Aspects specific to Slavic languages are underlined. Dedicated algorithms for this purpose were designed and tested. Due to not smooth nature of the optimization problem genetic algorithms were suggested. Some new and interesting results are received.
Single-photon image sensors in CMOS - picosecond resolution for three-dimensional imaging

v VERSION ABREGEE . vii 1. INTRODUCTION . 1 1.1 GENERAL CLASSIFICATION OF OPTICAL 3D IMAGE SENSORS . 2 1.2 SOLID‐STATE 3D IMAGE SENSORS: STATE‐OF‐THE‐ART . 6 1.2.1 TOF based on Phase of First Harmonic . 10 1.2.2 Direct Time­of­Flight Principle . 15 1.2.3 Indirect Time­of­Flight Principle . 17 1.2.4 Time­of­Flight based on Pseudo­Noise Sequences . 21 1.2.5 Time­of­Flight based on Time­Correlated Single­Photon Counting . 22 1.3 APPLICATIONS . 24 1.3. 1 Automotive Applications . 24 1.3. 2 Human­Computer Interfaces . 26 1.3. 3 Machine Vision . 28 1.3. 4 Other Applications . 30 1.4 THESIS MOTIVATIONS AND CHALLENGES . 32 1.5 ORGANIZATION OF THE THESIS . 35 2. SINGLE­PHOTON AVALANCHE DIODES IN CMOS TECHNOLOGY . 37 2.1 INTRODUCTION . 37 2.2 PERFORMANCE PARAMETERS . 40 2.2.1 Photon Detection Probability . 40 2.2.2 Dark Count Rate . 42 2.2.3 Dead Time . 43 2.2.4 Afterpulsing Probability . 44 2.2.5 Timing Resolution . 45 2.2. 6 Crosstalk Probability . 46 2.3 DEVICE ENGINEERING . 48 2.3. 1 State­of­the­Art . 48 2.3. 2 Deep­submicron CMOS Technology . 58 2.4 FRONT‐END CIRCUITS . 59 2.4. 1 Passive Quenching and Recharge based on Single MOS Transistors. 62 2.4. 2 Dual­Threshold Quenching and Recharge Circuit . 71 2.4. 3 Event­Driven Readout Circuit . 76 2.5 SPAD INTEGRATION IN 0.8‐MICRON CMOS . 80 2.5. 1 Photon Detection Probability . 83 2.5. 2 Dark Count Rate . 85 2.5. 3 Timing Resolution . 87
Understanding and Mitigating Latency Variability of Latency-Critical Applications

A major theme of IT in the past decade has been the shift from on-premise hardware to cloud computing. Running a service in the public cloud is practical, because a large number of resources can be bought on-demand, but this shift comes with its own set of challenges, e.g., customers having less control over their environment. In scale-out deployments of on-line data-intensive services, each client request typically executes queries on many servers in parallel, and its final response time is often lower-bounded by the slowest query execution. There are numerous hard-to-trace reasons why some queries finish later than others. Latency variability appears at different timescales. To understand its cause and impact we need to measure it correctly. It is clear that we need hardware support to measure latencies on the nanosecond-scale, and that software-based tools are good-enough for the millisecondscale, but it is not clear whether software-based tools are also reliable at the μs-scale, which is of growing interest to a large research and industry community. Measuring is a first step towards understanding the problems that cause latency variability, but what if some of them are extremely complex, or fixing them is out of reach given a service provider’s restrictions? Even large companies that own datacenters and build their own software and hardware stack sometimes suffer from hard-to-understand or hard-to-fix performance issues. Medium-sized companies that simply use shared public infrastructure, and do not themselves develop most of the code running on the machines, have limited capabilities and often cannot rule out each and every source of system interference. This reality inspired the line of research on what is known as hedging policies. By using redundant requests we can reduce overall latency at the cost of consuming additional resources. This dissertation characterizes latency variability of interactive services, shows how to measure it and how to mitigate its effect on end-to-end latency without sacrificing system capacity. Concretely, this dissertation makes three contributions: First, it shows how to measure μsscale latency variability with both low cost and high precision, with the help of kernel bypass and hardware timestamps. Second, it empirically derives a lower bound on the tail latency that any implementable hedging policy might achieve for a given workload. Through evaluating our lower bound on a large parameter space, we determine when hedging is beneficial. Lastly, we describe and evaluate a practical policy, LÆDGE, that approximates our theoretical lower bound and achieves as much as half of its hedging potential. We show the applicability of our solution to real applications deployed in the public cloud where LÆDGE outperforms the state-of-the-art scheduling policies by up to 49%, averaged on low to medium load.
Feature Selection via Quantum Annealers for Ranking and Classification Tasks

Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can be itself a computationally expensive process. While for decades confined to theoretical algorithmic papers, quantum computing is now becoming a viable tool to tackle realistic problems, in particular special-purpose solvers based on the Quantum Annealing paradigm. This paper aims to explore the feasibility of using currently available quantum computing architectures to solve some quadratic feature selection algorithms for both ranking and classification. Our experimental analysis shows that the effectiveness obtained with quantum computing hardware is comparable to that of classical solvers, indicating that quantum computers are now reliable enough to tackle interesting problems.
Galaxy evolution: A new version of the Besançon Galaxy Model constrained with Tycho data

The construction of a dynamical model of the Milky Way from the upcoming Gaia data will require a complex comparison between models and data in the space of the observables. To be ready for this challenge, in this PhD thesis, we have optimized the Besancon stellar population synthesis model. The new version of the model has been constructed and ingredients as critical as the IMF, the SFR, the binary fraction, the age-metallicity relation and the age-kinematic relation can now be fitted to the observed data. The optimization includes also the use of most updated evolutionary tracks and model atmospheres. Various scenarios for those parameters have been checked against the Tycho-2 data.
A Machine Learning Pipeline for Extracting Decision-Support Features from Traffic Scenes

Traffic systems play a key role in modern society. However, these systems are increasingly suffering from problems, such as congestions. A well-known way to efficiently reduce this kind of problem is to perform traffic light control intelligently through reinforcement learning (RL) algorithms. In this context, extracting relevant features from the traffic environment to support decision-making becomes a central concern. Examples of such features include vehicle counting on each queue, identification of vehicles’ origins and destinations, among others. Recently, the advent of deep learning has paved to way to efficient methods for extracting some of the aforementioned features. However, the problem of identifying vehicles and their origins and destinations within an intersection has not been fully addressed in the literature, even though such information has shown to play a role in RL-based traffic signal control. Building against this background, in this work we propose a deep learning pipeline for extracting relevant features from intersections based on traffic scenes. Our pipeline comprises three main steps: (i) a YOLO-based object detector fine-tuned using the UAVDT dataset, (ii) a tracking algorithm to keep track of vehicles along their trajectories, and (iii) an origin-destination identification algorithm. Using this pipeline, it is possible to identify vehicles as well as their origins and destinations within a given intersection. In order to assess our pipeline, we evaluated each of its modules separately as well as the pipeline as a whole. The object detector model obtained 98.2% recall and 79.5% precision, on average. The tracking algorithm obtained a MOTA of 72.6% and a MOTP of 74.4%. Finally, the complete pipeline obtained an average error rate of 3.065% in terms of origin and destination counts.
Social Minder: a Tool for Social Media Monitoring and its Use for Detecting COVID-19 Misinformation

In this work, we introduce Social Minder, a Big Data platform for Social Media monitoring that allows massive extraction of textual information, and stands on a modular and scalable architecture for efficient real-time and batch processing. This demo is oriented to present a use case that provides users with estimates of credibility for webpages linked in Social Media. Social Minder can serve multiple research and commercial purposes but we use it here for identifying COVID-19 related misinformation posted on Twitter. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
S1759078721001173jra 926..931

This article presents the design of an ultra-compact cavity-backed self-diplexing antenna with high isolation employing quarter-mode substrate integrated waveguide (QMSIW). The proposed antenna is constructed by using QMSIW, slot, and two 50Ω feed lines. Two eighthmode cavity resonators are designed by inserting a slot on the top side of the rectangular substrate integrated waveguide to operate at 2.6 and 4.9 GHz for long-term evolution and public safety band applications, respectively. The proposed design allows to tune any frequency band independently by keeping other bands unaltered. The size of antenna is ultra-compact, due to the use of QMSIW cavity. The isolation between two ports is >35 dB. The antenna achieves 5.34 and 5.68 dBi peak gains at 2.6 and 4.9 GHz, respectively. The efficiency of the antenna is >85% at both frequency bands. The antenna provides more than 20.9 dB front-to-backratio and better than 21 dB separation between co-to-cross polarization levels. The designed antenna is validated through fabrication and measurement.
Building community capacity to stimulate physical activity and dietary behavior in Dutch secondary schools: Evaluation of the FLASH intervention using the REAIM framework

Background Building community capacity in secondary schools is a promising strategy for the sustainable implementation of school-based health promotion. The Fit Lifestyle at School and at Home (FLASH) intervention explored how building community capacity works for the prevention of overweight following four strategies: leadership, participatory school culture, tailored health-promotion activities, and local networks. This study evaluates the intervention's impact on community capacity and capacity-building processes over a period of 3 years, as well as its effects on adolescents' BMI and waist circumference. Methods A mixed-methods design guided by the RE-AIM framework was used. Impact on community capacity was evaluated with semi-structured interviews at the start and end of the intervention and analyzed using an anchored coding scale. Capacity-building processes were evaluated using interviews, journals, questionnaires, and the minutes of meetings. The effects on BMI z-scores and waist circumference were evaluated using a quasi-experimental design comparing an intervention (IG) and reference group (RG), based on multi-level analyses. Results Community capacity improved across all intervention schools but varied between capacity-building strategies. Leadership recorded the greatest improvements, aided by the appointment of Healthy School Coordinators, who increasingly focused on coordinating processes and fostering collaborations. Participatory school culture also improved through the adoption and implementation of participatory methods and a general increase in awareness concerning the importance of the Healthy School approach. Although additional health-promotion activities were implemented, stakeholders struggled with tailoring these to the specific dynamics of their schools. Limited improvements were observed in setting-up local networks that could help schools encourage healthy behavior among pupils. Differences in BMI z-scores between IG and RG over the total sample were negligible whereas waist circumference increased slightly more in IG (0.99 cm, 95% CI [.04; 1.93]). However, differences were inconsistent over time and between cohorts. Conclusions This study highlights the potential of building community capacity. It emphasizes that this is a process in which stakeholders must become acquainted with new leadership roles and responsibilities. To navigate this process, schools need support in improving communication, establishing local networks, and sustaining capacity-building efforts in school policy. Trial registration ISRCTN67201841; date registered: 09/05/2019, retrospectively registered.
Anti-Migratory Effect of Dipotassium Glycyrrhizinate on Glioblastoma Cell Lines: Microarray Data for the Identification of Key MicroRNA Signatures

The nuclear factor kappa B (NF-κB) pathway has been reported to be responsible for the aggressive disease phenomenon observed in glioblastoma (GBM). Dipotassium glycyrrhizinate (DPG), a dipotassium salt of glycyrrhizic acid isolated from licorice, has recently demonstrated an anti-tumoral effect on GBM cell lines U87MG and T98G through NF-κB suppression by IRAK2- and TRAF6-mediating microRNA (miR)-16 and miR-146a, respectively. Thus, the present study aimed to evaluate the expression profiles of miRNAs related to NF-κB suppression in T98G GBM cell line after DPG exposure using miRNA microarray (Affymetrix Human miRNA 4.0A), considering only predicted miRNAs as NF-κB regulator genes. Additional assays using U251 and U138MG cells were performed to validate the array results. DPG cytotoxicity was determined by (4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay, and cellular apoptosis was quantified by DNA fragmentation and terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay. The anti-proliferative effect was observed by cell proliferation and wound-healing assays, and the sphere formation assay examined whether DPG reduced stem cell subpopulation formation. The most over-expressed miRNAs were miR-4443 and miR-3620. The cytotoxic effect of DPG in U251 and U138MG was observed with an IC50 of 32 and 20 mM for 48 h, respectively. The IC50 of each cell line was used in all further assays. DPG treatment-induced apoptosis is observed by DNA fragmentation and increased TUNEL-positive cells. Cell proliferation and wound-healing assays showed an anti-proliferative and anti-migratory effect by DPG on the evaluated cell lines. In addition, DPG treatment led to a 100% reduction in sphere formation. The qPCR results in U251 and U138MG cells showed that DPG increased miR-4443 (2.44 vs. 1.11, p-value = 0.11; 8.27 vs. 1.25, p-value = 0.04) and miR-3620 expression (1.66 vs. 1.00, p-value = 0.03; 8.47 vs. 1.01, p-value = 0.03) and decreased CD209 (0.44 vs. 1.10, p-value = 0.03; 0.49 vs. 1.07, p-value = 0.04) and TNC (0.20 vs. 1.03, p-value = 0.001; 0.39 vs. 1.06, p-value = 0.01) mRNA levels compared to controls. Our results suggest that DPG inhibits cell viability by activating apoptosis and inhibiting cell proliferation and stem cell subpopulation formation through miR-4443 and miR-3620 upregulation. Both miRNAs are responsible for the post-transcriptional inhibition of NF-κB by CD209 and TNC modulation.
NF-κB in neurodegenerative diseases: Recent evidence from human genetics

The transcription factor NF-κB is commonly known to drive inflammation and cancer progression, but is also a crucial regulator of a broad range of cellular processes within the mammalian nervous system. In the present review, we provide an overview on the role of NF-κB in the nervous system particularly including its constitutive activity within cortical and hippocampal regions, neuroprotection as well as learning and memory. Our discussion further emphasizes the increasing role of human genetics in neurodegenerative disorders, namely, germline mutations leading to defects in NF-κB-signaling. In particular, we propose that loss of function mutations upstream of NF-κB such as ADAM17, SHARPIN, HOIL, or OTULIN affect NF-κB-activity in Alzheimer’s disease (AD) patients, in turn driving anatomical defects such as shrinkage of entorhinal cortex and the limbic system in early AD. Similarly, E3 type ubiquitin ligase PARKIN is positively involved in NF-κB signaling. PARKIN loss of function mutations are most frequently observed in Parkinson’s disease patients. In contrast to AD, relying on germline mutations of week alleles and a disease development over decades, somatic mutations affecting NF-κB activation are commonly observed in cells derived from glioblastoma multiforme (GBM), the most common malignant primary brain tumor. Here, our present review particularly sheds light on the mutual exclusion of either the deletion of NFKBIA or amplification of epidermal growth factor receptor (EGFR) in GBM, both resulting in constitutive NF-κB-activity driving tumorigenesis. We also discuss emerging roles of long non-coding RNAs such as HOTAIR in suppressing phosphorylation of IκBα in the context of GBM. In summary, the recent progress in the genetic analysis of patients, particularly those suffering from AD, harbors the potential to open up new vistas for research and therapy based on TNFα/NF-κB pathway and neuroprotection.
Insomnia in patients treated with checkpoint inhibitors for cancer: A meta-analysis

Purpose Insomnia in cancer patients is a common symptom contributing to poor quality of life and poor functioning. Sleep disturbances have been associated with inflammatory activity, and systemic cancer therapies chemotherapy, hormonal therapy, and immunotherapy may cause insomnia. We have carried out a meta-analysis to estimate the occurrence of insomnia in patients with solid cancer treated with immunotherapy using checkpoint inhibitors (CPI). Methods PubMed and ClinicalTrials.gov were searched for phase 3 studies in solid tumours where treatment included a checkpoint inhibitor in the experimental arm. Data on the incidence of insomnia were acquired from the adverse events tables available from clinicaltrials.gov and/or from the full texts. Random effect logistic model was used to compare pooled data. Heterogeneity between studies was assessed using Cochrane Q statistics and I2 statistics. Results A total of 54 studies (including six three-arm studies) involving 37,352 patients were included in the analysis. Insomnia was reported in 8.3% of subjects (95% confidence interval [CI] 8.0%-8.7%) treated with immunotherapy. Insomnia was significantly more common in patients receiving immunotherapy compared to those enrolled in study arms with inactive treatment (odds ratio [OR] 1.49, 95% CI 1.13-1.96). The odds for insomnia were similar between the arms for studies comparing CPI versus chemotherapy and CPI versus non-immunologic targeted therapies (OR 1.07, 95% CI 0.94-1.22 and OR 1.40, 95% CI 0.90-2.18, respectively). The OR for insomnia was higher for cytotoxic T-lymphocyte antigen 4 (CTLA-4) receptor inhibitors compared to the inhibitors of programmed death-1 (PD-1) receptor (OR 1.36, 95% CI 1.06 – 1.74). Conclusion Cancer immunotherapy using CPI is associated with insomnia but the odds of developing the symptom are not greater with immunotherapy than with other systemic modalities including chemotherapy and non-immunologic targeted therapies.