Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Server Efficiency in the Face of Killer Microseconds

—We are entering an era of “killer microseconds” in data center applications. Killer microseconds refer to µ s-scale “holes” in CPU schedules caused by stalls to access fast I/O devices or brief idle times between requests in high throughput microservices. Whereas modern computing platforms can efﬁciently hide ns-scale and ms-scale stalls through micro-architectural techniques and OS context switching, they lack efﬁcient support to hide the latency of µ s-scale stalls. Simultaneous Multithreading (SMT) is an efﬁcient way to improve core utilization and increase server performance density. Unfortunately, scaling SMT to provision enough threads to hide frequent µ s-scale stalls is prohibitive and SMT co-location can often drastically increase the tail latency of cloud microservices. In this paper, we propose Duplexity , a heterogeneous server architecture that employs aggressive multithreading to hide the latency of killer microseconds, without sacriﬁcing the Quality-of-Service (QoS) of latency-sensitive microservices. Duplexity provisions dyads (pairs) of two kinds of cores: master-cores , which each primarily executes a single latency-critical master-thread , and lender-cores , which multiplex latency-insensitive throughput threads. When the master-thread stalls, the master-core borrows ﬁller-threads from the lender-core, ﬁlling µ s-scale utilization holes of the microservice. We propose critical mechanisms, including separate memory paths for the master-thread and ﬁller-threads, to enable master-cores to borrow ﬁller-threads while protecting master-threads’ state from disruption. Duplexity facilitates fast master-thread restart when stalls resolve and minimizes the microservice’s QoS violation. Our evaluation demonstrates that Duplexity is able to achieve 1 . 9 × higher core utilization and 2 . 7 × lower iso-throughput 99th-percentile
μDPM: Dynamic Power Management for the Microsecond Era

—The complex, distributed nature of data centers have spawned the adoption of distributed, multi-tiered software architectures, consisting of many inter-connected microservices. These microservices exhibit extremely short request service times, often less than 250 µ s. We show that these “killer microsecond” service times can cause state-of-the-art dynamic power management techniques to break down, due to short idle period length and low power state transition overheads. In this paper, we propose µ DPM , a dynamic power management scheme for the microsecond era that coordinates request delaying, per-core sleep states, and voltage frequency scaling. The idea is to postpone the wake up of a CPU as long as possible and then adjust the frequency so that the tail latency constraint of requests are satisﬁed just-in-time. µ DPM reduces processor energy consumption by up to 32% and consistently outperforms state-of-the-art techniques by 2x.
PIM-VR: Erasing Motion Anomalies In Highly-Interactive Virtual Reality World with Customized Memory Cube

With the revolutionary innovations emerging in the computer graphics domain, virtual reality (VR) has become increasingly popular and shown great potential for entertainment, medical simulation and education. In the highly interactive VR world, the motion-to-photon delay (MPD) which represents the delay from users’ head motion to the responded image displayed on their head devices, is the most critical factor for a successful VR experience. Long MPD may cause users to experience significant motion anomalies: judder, lagging and sickness. In order to achieve the short MPD and alleviate the motion anomalies, asynchronous time warp (ATW) which is known as an image re-projection technique, has been proposed by VR vendors to map the previously rendered frame to the correct position using the latest headmotion information. However, after a careful investigation on the efficiency of the current GPU-accelerated ATW through executing real VR applications on modern VR hardware, we observe that the state-of-the-art ATW technique cannot deliver the ideal MPD and often misses the refresh deadline, resulting in reduced frame rate and motion anomalies. This is caused by two major challenges: inefficient VR execution model and intensive off-chip memory accesses. To tackle these, we propose a preemption-free Processing-In-Memory based ATW design which asynchronously executes ATW within a 3D-stacked memory, without interrupting the rendering tasks on the host GPU. We also identify a redundancy reduction mechanism to further simplify and accelerate the ATW operation. A comprehensive evaluation of our proposed design demonstrates that our PIM-based ATW can achieve the ideal MPD and provide superior user experience. Finally, we provide a design space exploration to showcase different design choices for the PIM-based ATW design, and the results show that our design scales well in future VR scenarios with higher frame resolution and even lower ideal MPD.
A Three-Stage Operational Transconductance Amplifier for Delta Sigma Modulator

The design and simulation of a low power, high gain three stage operational transconductance amplifier (OTA) is presented. This OTA has a DC gain of 73.5 dB, a unity gain bandwidth (UGB) of 39.8 MHz and a phase margin of 59°. The total power consumed by OTA is 332 μW. The DC gain and the power dissipation parameters of the OTA are found to be better than the previously published results of [1]–[4]. Further, a first order Delta Sigma Modulator (DSM) is designed as a vehicle to test the OTA by integrating it with a comparator and a DAC for a signal bandwidth of 2 kHz with an oversampling ratio (OSR) of 250 for low frequency biomedical applications. All the blocks are designed using UMC 180nm CMOS 1P9M technology, with 1.8 V supply voltage. The simulation results show that the in-band signal to noise and distortion ratio (SNDR) of the DSM is 53 dB, which is equivalent to 8.5 effective number of bits (ENOB).
Static-Time Extraction and Analysis of the ROS Computation Graph

—The Robot Operating System (ROS) is one of the most popular open source robotic frameworks, and has contributed signiﬁcantly to the fast development of robotics. Even though ROS provides many ready-made components, a robotic system is inherently complex, in particular regarding the architecture and orchestration of such components. Availability and analysis of a system’s architecture at compile time is fundamental to ease comprehension and development of higher-quality software. However, ROS developers have to overcome this complexity relying mostly on testing and runtime visualisers. This work aims to enhance static-time support by proposing, ﬁrstly, a metamodel to describe the software architecture of ROS systems (the ROS Computation Graph ) and, secondly, model extraction and visualisation tools for such architectural models. The provided tools allow users to specify custom-made queries over these models, enabling the static veriﬁcation of relevant properties that had to be (manually) checked at runtime before.
Extracellular electron uptake by two Methanosarcina species

Direct electron uptake by prokaryotes is a recently described mechanism with a potential application for energy and CO2 storage into value added chemicals. Members of Methanosarcinales, an environmentally and biotechnologically relevant group of methanogens, were previously shown to retrieve electrons from an extracellular electrogenic partner performing Direct Interspecies Electron Transfer (DIET) and were therefore proposed to be electroactive. However, their intrinsic electroactivity has never been examined. In this study, we tested two methanogens belonging to Methanosarcina, M. barkeri and M. horonobensis, regarding their ability to accept electrons directly from insoluble electron donors like other cells, conductive particles and electrodes. Both methanogens were able to retrieve electrons from Geobacter metallireducens via DIET. Furthermore, DIET was also stimulated upon addition of electrically conductive granular activated carbon (GAC) when each was co-cultured with G. metallireducens. However, when provided with a cathode poised at – 400 mV (vs SHE), only M. barkeri could perform electromethanogenesis. In contrast, the strict hydrogenotrophic methanogen, Methanobacterium formicicum, did not produce methane regardless of the type of insoluble electron donor provided (Geobacter cells, GAC or electrodes). A comparison of functional gene categories between Methanobacterium and the two Methanosarcinas revealed a higher abundance of genes associated with extracellular electron transfer in Methanosarcina species. Between the two Methanosarcina we observed differences regarding energy metabolism, which could explain dissimilarities concerning electromethanogenesis at fixed potentials. We suggest that these dissimilarities are minimized in the presence of an electrogenic DIET partner (i.e. Geobacter), which can modulate its surface redox potentials by adjusting the expression of electroactive surface proteins.
Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors

Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors.
Cognitive Profiles and Functional Connectivity in First-Episode Schizophrenia Spectrum Disorders – Linking Behavioral and Neuronal Data

The character of cognitive deficit in schizophrenia is not clear due to the heterogeneity in research results. In heterogeneous conditions, the cluster solution allows the classification of individuals based on profiles. Our aim was to examine the cognitive profiles of first-episode schizophrenia spectrum disorder (FES) subjects based on cluster analysis, and to correlate these profiles with clinical variables and resting state brain connectivity, as measured with magnetic resonance imaging. A total of 67 FES subjects were assessed with a neuropsychological test battery and on clinical variables. The results of the cognitive domains were cluster analyzed. In addition, functional connectivity was calculated using ROI-to-ROI analysis with four groups: Three groups were defined based on the cluster analysis of cognitive performance and a control group with a normal cognitive performance. The connectivity was compared between the patient clusters and controls. We found different cognitive profiles based on three clusters: Cluster 1: decline in the attention, working memory/flexibility, and verbal memory domains. Cluster 2: decline in the verbal memory domain and above average performance in the attention domain. Cluster 3: generalized and severe deficit in all of the cognitive domains. FES diagnoses were distributed among all of the clusters. Cluster comparisons in neural connectivity also showed differences between the groups. Cluster 1 showed both hyperconnectivity between the cerebellum and precentral gyrus, the salience network (SN) (insula cortex), and fronto-parietal network (FPN) as well as between the PreCG and SN (insula cortex) and hypoconnectivity between the default mode network (DMN) and seeds of SN [insula and supramarginal gyrus (SMG)]; Cluster 2 showed hyperconnectivity between the DMN and cerebellum, SN (insula) and precentral gyrus, and FPN and IFG; Cluster 3 showed hypoconnectivity between the DMN and SN (insula) and SN (SMG) and pallidum. The cluster solution confirms the prevalence of a cognitive decline with different patterns of cognitive performance, and different levels of severity in FES. Moreover, separate behavioral cognitive subsets can be linked to patterns of brain functional connectivity.
Genome-Wide and Phenotypic Evaluation of Stem Cell Progenitors Derived From Gprc5a-Deficient Murine Lung Adenocarcinoma With Somatic Kras Mutations

Lung adenocarcinomas (LUADs) with somatic mutations in the KRAS oncogene comprise the most common molecular subtype of lung cancer in smokers and present with overall dismal prognosis and resistance to most therapies. Our group recently demonstrated that tobacco carcinogen-exposed mice with knockout of the airway lineage G-protein coupled receptor, Gprc5a, develop LUADs with somatic mutations in Kras. Earlier work has suggested that cancer stem cells (CSCs) play crucial roles in clonal evolution of tumors and in therapy resistance. To date, our understanding of CSCs in LUADs with somatic Kras mutations remains lagging. Here we derived CSCs (as spheres in 3D cultures) with self-renewal properties from a murine Kras-mutant LUAD cell line we previously established from a tobacco carcinogen-exposed Gprc5a−/− mouse. Using syngeneic Gprc5a−/− models, we found that these CSCs, compared to their parental isoforms, exhibited increased tumorigenic potential in vivo, particularly in female animals. Using whole-transcriptome sequencing coupled with pathways analysis and confirmatory PCR, we identified gene features (n = 2,600) differentially expressed in the CSCs compared to parental cells and that were enriched with functional modules associated with an augmented malignant phenotype including stemness, tumor-promoting inflammation and anti-oxidant responses. Further, based on in silico predicted activation of GSK3β in CSCs, we found that tideglusib, an irreversible inhibitor of the kinase, exhibited marked anti-growth effects in the cultured CSCs. Our study underscores molecular cues in the pathogenesis of Kras-mutant LUAD and presents new models to study the evolution, and thus high-potential targets, of this aggressive malignancy.
Activity of a Synthetic Peptide Targeting MgtC on Pseudomonas aeruginosa Intramacrophage Survival and Biofilm Formation

Antivirulence strategies aim to target pathogenicity factors while bypassing the pressure on the bacterium to develop resistance. The MgtC membrane protein has been proposed as an attractive target that is involved in the ability of several major bacterial pathogens, including Pseudomonas aeruginosa, to survive inside macrophages. In liquid culture, P. aeruginosa MgtC acts negatively on biofilm formation. However, a putative link between these two functions of MgtC in P. aeruginosa has not been experimentally addressed. In the present study, we first investigated the contribution of exopolysaccharides (EPS) in the intramacrophage survival defect and biofilm increase of mgtC mutant. Within infected macrophages, expression of EPS genes psl and alg was increased in a P. aeruginosa mgtC mutant strain comparatively to wild-type strain. However, the intramacrophage survival defect of mgtC mutant was not rescued upon introduction of psl or alg mutation, suggesting that MgtC intramacrophage role is unrelated to EPS production, whereas the increased biofilm formation of mgtC mutant was partially suppressed by introduction of psl mutation. We aimed to develop an antivirulence strategy targeting MgtC, by taking advantage of a natural antagonistic peptide, MgtR. Heterologous expression of mgtR in P. aeruginosa PAO1 was shown to reduce its ability to survive within macrophages. We investigated for the first time the biological effect of a synthetic MgtR peptide on P. aeruginosa. Exogenously added synthetic MgtR peptide lowered the intramacrophage survival of wild-type P. aeruginosa PAO1, thus mimicking the phenotype of an mgtC mutant as well as the effect of endogenously produced MgtR peptide. In correlation with this finding, addition of MgtR peptide to bacterial culture strongly reduced MgtC protein level, without reducing bacterial growth or viability, thus differing from classical antimicrobial peptides. On the other hand, the addition of exogenous MgtR peptide did not affect significantly biofilm formation, indicating an action toward EPS-independent phenotype rather than EPS-related phenotype. Cumulatively, our results show an antivirulence action of synthetic MgtR peptide, which may be more potent against acute infection, and provide a proof of concept for further exploitation of anti-Pseudomonas strategies.
A Comparative Study on Characteristics and Properties of Adaptive Algorithms applied to Noise Cancellation Techniques

Adaptive algorithms offer a wide area of research in the field of digital signal processing. If the received signal is changing with time so as the noise the need for predictive algorithms to be adaptive with the changes becomes predominant further noise being a random signal strongly needs an adaptive approach for analysis and thus presents a wide scope for research. This paper presents a basic comparative study of various adaptive algorithms such as least mean square (LMS) algorithm, recursive least square (RLS) algorithm and other adaptive algorithms with their application in active noise cancellation. The purpose of presenting this paper is to provide an insight of the initial building blocks of an adaptive algorithm for use in development of further complex algorithms.
Analysis and Optimization of the Memory Hierarchy for Graph Processing Workloads

—Graph processing is an important analysis tech- nique for a wide range of big data applications. The ability to explicitly represent relationships between entities gives graph analytics a signiﬁcant performance advantage over traditional relational databases. However, at the microarchitecture level, performance is bounded by the inefﬁciencies in the memory subsystem for single-machine in-memory graph analytics. This paper consists of two contributions in which we analyze and optimize the memory hierarchy for graph processing workloads. First,we perform an in-depth data-type-aware characteriza- tion of graph processing workloads on a simulated multi-core architecture. We analyze 1) the memory-level parallelism in an out-of-order core and 2) the request reuse distance in the cache hierarchy. We ﬁnd that the load-load dependency chains involving different application data types form the primary bottleneck in achieving a high memory-level parallelism. We also observe that different graph data types exhibit heterogeneous reuse distances. As a result, the private L2 cache has negligible contribution to performance, whereas the shared L3 cache shows higher performance sensitivity. Second, based on our proﬁling observations, we propose DROPLET, a Data-awaRe decOuPLed prEfeTcher for graph applications. DROPLET prefetches different graph data types differently according to their inherent reuse distances. In addition, DROPLET is physically decoupled to overcome the serialization due to the dependency chains between different data types. DROPLET achieves 19%-102% performance im- provement over a no-prefetch baseline, 9%-74% performance improvement over a conventional stream prefetcher, 14%-74% performance improvement
Etiology, Risk Factors and Outcome of 1003 Cases of Infective Endocarditis from a 33-year National Survey in the Slovak Republic: An increasing proportion of elderly patients.

BACKGROUND Infective endocarditis (IE) is still a significant cause of mortality in European hospitals, despite of the fact, that large nationwide studies were performed in last twenty years and pathogens are well known. The aim of the study was to assess risk factors, mortality, etiology and proportion of elderly patients within a longitudinal nation wide survey of infectious endocarditis in Slovakia. PATIENTS AND METHODS Etiology, risk factors and outcome of 1003 cases of infective endocarditis (IE) in Slovakia over the last 33 years have been assessed. RESULTS The majority of IE were caused by Staphylococci (28.3%), 15.6% were due to Viridans streptococci, 10% due to Enterococci, 8.2% by gram-negative bacteria, Acinetobacter baumannii and Pseudomonas aeruginosa, 3.7% by other organisms and 31.0% of all cases were culture negative. The following risk factors were recorded: age > 65 (36.8%), rheumatic fever (15.3%), dental surgery (8.7%), previous non-cardiological surgery (8.2 %), neoplasia (8.1%), diabetes (7.8%), any endoscopy (8.5%) and dialysis (4.6%). All patients were treated with antimicrobials, 507 (51%) also with surgery. Survival rate at day 60 after diagnosis was 88.1% (n=883). Only age >65 (34.3% vs. 49.5%, p=0.045) and persistent bacteremia (with three or more positive blood cultures 15.7% vs. 34.5%, p=0,001) were significantly associated with higher attributable mortality. Concerning risk factors, etiology and therapeutic strategies, rheumatic fever and neoplasia showed decrease in tendency. Dental surgery and tonsillitis were less frequent as well (26.7% vs. 2%, p<0,001 and 16% vs. 1%, p<0.001). There was a significant shift in etiology after 1997: culture-negative endocarditis was surprisingly more frequently observed in the 2007-2017 period than before and represented 10.7% of all cases in 1984-1990 in comparison to 25.1-25.6% in 2007-2010 and 2011-2017. Staphylococci decreased from 48% to 29.6% (2007-2017), but are still major pathogens. Persistent bacteremia (3 or more positive blood cultures 5.3% vs. 24.7%, p<0,001) was less commonly observed within the 1st period (1984-1990) in comparison to 2007-2010. More patients in the 1st period (1984-1990) had embolization complications of IE than in the fifth and sixth period (2007-2017) (76 vs. 16.3% p<0.001). CNS embolization decreased from 14% to less than 5% (p<0.003). Attributable mortality was lower too (26.7% vs. 9.5%, p<0.001) because of increased proportion of cardiac surgery in the treatment of IE in 2007-2017 in comparison to 1984-1990. CONCLUSIONS Study has showed significant shifts in etiology, risk factors and complications over the observed time periods in Slovakia.