Miracle-dz/UniFAR-FTUs huggingface dataset:
On-demand, Spot, or Both: Dynamic Resource Allocation for Executing Batch Jobs in the Cloud

Cloud computing provides an attractive computing paradigm in which computational resources are rented on-demand to users with zero capital and maintenance costs. Cloud providers offer different pricing options to meet computing requirements of a wide variety of applications. An attractive option for batch computing is spot-instances, which allows users to place bids for spare computing instances and rent them at a (often) substantially lower price compared to the fixed on-demand price. However, this raises three main challenges for users: how many instances to rent at any time? what type (on-demand, spot, or both)? and what bid value to use for spot instances? In particular, renting on-demand risks high costs while renting spot instances risks job interruption and delayed completion when the spot market price exceeds the bid. This paper introduces an online learning algorithm for resource allocation to address this fundamental tradeoff between computation cost and performance. Our algorithm dynamically adapts resource allocation by learning from its performance on prior job executions while incorporating history of spot prices and workload characteristics. We provide theoretical bounds on its performance and prove that the average regret of our approach (compared to the best policy in hindsight) vanishes to zero with time. Evaluation on traces from a large datacenter cluster shows that our algorithm outperforms greedy allocation heuristics and quickly converges to a small set of best performing policies.
Bartender: an ultrafast and accurate clustering algorithm to count barcode and amplicon reads

Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing – from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs, sgRNAs, or transposon insertion libraries, to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq have not been well developed. Available methods, which use prior information and/or simple brute-force comparisons, are slow and often result in overclustering artifacts that group distinct barcodes together. Here, we developed Bartender: an ultrafast and accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data. To improve speed and reduce unnecessary pairwise comparisons, Bartender employs a divide-and-conquer strategy that intelligently sorts barcode reads into distinct bins before performing comparisons. To improve accuracy and reduce over-clustering artifacts, Bartender employs a modified two-sample proportion test that uses information on both the cluster sequence distances and cluster sizes to make merging decisions. Additionally, Bartender includes a “multiple time point” mode, which matches barcode clusters between different clustering runs for seamless handling of time course data. For both simulated and real data, Bartender clusters millions of unique barcodes in a few minutes at high accuracy (>99.9%), and is ~100-fold faster than previous methods. Bartender is a set of simple-to-use command line tools that can be performed on a laptop. Availability Bartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1.
Precise Information Extraction from Text Based on Two-Level Concept Lattice

Aiming at the problems of high cost of time consumption and low precision of IE (information extraction) in IE systems based on concept lattice, a novel mechanism of two-level concept lattice-based IE is put forward. The structure concept lattice is the logical storage of semantic structure of documents, and the content concept lattice is used to store content information of documents. The paper gives the formal descriptions of structure and content concept lattice and analyses the time complexity of them. The consequence shows our method has obvious advantages concerning the time consumption and the precision of content extraction compared with existing IE methods.
Combined fibrinogen and neutrophil‐lymphocyte ratio as a prognostic marker of advanced esophageal squamous cell carcinoma

Patients with advanced esophageal squamous cell carcinoma (ESCC) is received chemoradiotherapy or chemotherapy for clinical management. However, it is difficult to predict tumor response and prognosis using blood markers before starting treatments. The purpose of this study was to investigate the pre‐treatment plasma fibrinogen and neutrophil–lymphocyte ratio (NLR) in patients with advanced ESCC treated with chemoradiotherapy or chemotherapy, and to assess the clinical utility of a combined score using these blood markers, named as the F‐NLR (fibrinogen and NLR) score, as a predictor of tumor response and prognosis. A total of 98 advanced ESCC patients, treated with chemoradiotherapy or chemotherapy, were classified into three groups: F‐NLR score of 2, having both hyperfibrinogenemia (>400 mg/dL) and high NLR (>3.0), score of 1, one of these hematological abnormalities, and score of 0, having neither hyperfibrinogenemia nor high NLR. Fibrinogen and NLR were significantly higher in the progressive disease (PD) group than the non‐PD group (P = 0.0419, and P = 0.0001, respectively). A significantly higher F‐NLR score was found in the PD group than the non‐PD group (P = 0.0140). Overall survival was significantly lower in patients with an F‐NLR score of 2 than in those with an F‐NLR score of 0 or 1 (P < 0.0001). Multivariate analysis showed that the F‐NLR score was one of the independent prognostic factors (P = 0.0081). Our study demonstrates that the F‐NLR score is promising as a predictive marker for therapeutic effects and prognosis in patients with advanced ESCC.
A Tool for Bottleneck Analysis and Performance Prediction for GPU-Accelerated Applications

High-level tools for analyzing and predicting the performance GPU-accelerated applications are scarce, at best. Although performance modeling approaches for GPUs exist, their complexity makes them virtually impossible to use to quickly analyze the performance of real life applications and obtain easy-to-use, readable feedback. This is why, although GPUs are significant performance boosters in many HPC domains, performance prediction is still based on extensive benchmarking, and performance bottleneck analysis remains a nonsystematic, experience-driven process. In this context, we propose a tool for bottleneck analysis and performance prediction for GPU-accelerated applications. Based on random forest modeling, and using hardware performance counters data, our method can be used to quickly and accurately evaluate application performance on GPU-based systems for different problem characteristics and different hardware generations. We illustrate the benefits of our approach with three detailed use cases: a simple step-by-step example on a parallel reduction kernel, and two classical benchmarks (matrix multiplication and sequence alignment). Our results so far indicate that our statistical modeling is a quick, easy-to-use method to grasp the performance characteristics of applications running on GPUs. Our current work focuses on tackling some of its applicability limitations (more applications, more platforms) and improving its usability (full automation from input to user feedback).
Adaptive object-models (poster session)

The unrelenting pace of change that confronts contemporary software developers compels them to make their applications more configurable, flexible, and adaptable. A possible way to meet such requirements is to use an Adaptive Object-Model (AOM). This poster describes common architectures for adaptive object-models and summarizes the results from our ECOOP 2000 workshop [9].
A quantitative evaluation of surface normal estimation in point clouds

We revisit a well-studied problem in the analysis of range data: surface normal estimation for a set of unorganized points. Surface normal estimation has been well-studied initially due to its theoretical appeal and more recently due to its many practical applications. The latter cover several aspects of range data analysis from plane or surface fitting to segmentation, object detection and scene analysis. Following the vast majority of the literature, we also focus our attention on techniques that operate in small neighborhoods around the point whose normal is to be estimated. We pay close attention to aspects of the implementation, such as the use of weights and normalization, that have not been studied in detail in the past. We perform quantitative evaluation on a diverse set of point clouds derived from 3D meshes, which allows us to obtain accurate ground truth.
Investigation into Interoperability in Cloud Computing: An Architectural Model

This paper presents an architectural model that enables the convergence of the interoperability of Enterprise Architecture (EA), Service Oriented Architecture (SOA) and Cloud Services. As per the reviewed literature by the authors, there is a lack of research in this space of cloud computing. Hence the authors' research developed an innovative solution to provide a ‘plug and play’ architectural model to seamlessly connect the enterprise systems to cloud within the governance standards of enterprise architecture. The rest of the paper discusses the identified research problem and articulates the developed model while validating the model with a few use cases, and concluding that the research community could further validate the model with their respective application use cases.
Contextual Sensitivity and Bird Song: A Basis for Social Life

This chapter presents evidence from songbirds of evolutionary convergence with certain features of the vocal flexibility found in humans, addresses human communication skills as a reference, and analyzes the possible convergences in animal species, highlighting a particular social songbird species. It suggests that animal communication may offer a useful reminder that the evolution of most basic principles of communication rules and contextual sensitivity may not have required higher cognitive processes. The chapter also proposes that regulations of vocal interactions, their contextual sensitivity, and dependency upon social experience indicate further convergence with human communication.
Agents, believability and embodiment in advanced learning environments. Introduction to a panel discussion

On the World Wide Web we see a growing number of general HCI interfaces, interfaces to educational or entertainment systems, interfaces to professional environments, etc., where an animated face, a cartoon character or a human-like virtual agent has the task to assist the user, to engage the user into a conversation or to educate the user. What can be said say about the effects a human-like agent has on a student's performance? We discuss agents, their intelligence, embodiment and interaction modalities. In particular, we introduce viewpoints and questions about roles embodied agents can play in educational environments.
The Distance to the Galactic Center Derived from Infrared Photometry of Bulge Red Clump Stars

On the basis of the near-infrared observations of bulge red clump stars near the Galactic center, we have determined the galactocentric distance to be R0 = 7.52 ± 0.10 (stat) ±0.35 (sys) kpc. We observed the red clump stars at ∣ l ∣ ≲ 1. °0 and 0. °7 ≲ ∣ b ∣ ≲ 1. °0 with the IRSF 1.4 m telescope and the SIRIUS camera in the H and KS bands. After extinction and population corrections, we obtained (m - M)0 = 14.38 ± 0.03 (stat) ± 0.10 (sys). The statistical error is dominated by the uncertainty of the intrinsic local red clump stars' luminosity. The systematic error is estimated to be ±0.10, including uncertainties in extinction and population correction, zero point of photometry, and the fitting of the luminosity function of the red clump stars. Our result, R0 = 7.52 kpc, is in excellent agreement with the distance determined geometrically with the star orbiting the massive black hole in the Galactic center. The recent result based on the spatial distribution of globular clusters is also consistent with our result. In addition, our study exhibits that the distance determination to the Galactic center with the red clump stars, even if the error of the population correction is taken into account, can achieve an uncertainty of about 5%, which is almost the same level as that in recent geometrical determinations.
Computationally efficient navigation system for Unmanned Ground Vehicles

This paper proposes to enhance the existing methods of Self-Supervised Learning (SSL) with application to autonomous navigation systems through efficient computational approaches that are the principal requirements in a practical system. First, confidence-based auto labeling for self-supervised learning is introduced which identifies and eliminates the input samples with low confidence level that are susceptible to be mislabeled. Then, a biologically inspired saliency detection approach for feature biasing is presented which is able to detect the salient features through top-down task specific guidance. The proposed methods are general and can be applied to a variety of applications. Finally, experimental results on real datasets from the DARPA-LAGR program are given to illustrate the effectiveness of the proposed approaches.
The use of heuristics in persuasion : deriving cues on source expertise from argument quality

Dual-process models of persuasion contrast the expertise heuristic "experts' statements can be trusted" with systematic processing of message content. Studies in which source expertise and argument quality were simultaneously manipulated revealed that the expertise manipulation affects attitudes when receivers are not highly motivated to scrutinize the provided message. In contrast, when receivers are highly motivated and are able to scrutinize a message their attitude is usually affected by argument quality but is independent of the expertise cue. We argue that this does not rule out that receivers still make use of the expertise heuristic. Rather, they may consider argument quality to infer the expertise of the source. We show that a classic study (Petty, Cacioppo, & Goldman, 1981) may be interpreted by this alternative explanation and present a study, in which the effect of argument quality on receivers' attitudes was partially mediated by perceived source expertise. This mediation tended to be stronger among receivers reporting low self-expertise than among receivers reporting high self-expertise. Current Research in Social Psychology (Vol. 10, No. 6) (Reimer, Mata, & Stoecklin)
ROLE OF FACEBOOK USAGE IN PREDICTING ACADEMIC ACHIEVEMENT OF ADOLESCENT STUDENTS

This paper deals with the increasing use of facebook among adolescents and facebook usage as a predictor of academic achievement in adolescent students. A sample of 400 students of Haryana state was considered. Facebook Usage Scale developed by Hooda and Tyagi (2016) was used to collect the data. Results shows that there is a significant positive correlation between the facebook usage and academic achievement and approximately 3 % of academic achievement can be predicted by facebook usage.
Basic Principles of Fluorescence Spectroscopy

As fluorophores play the central role in fluorescence spectroscopy and imaging we will start with an investigation of their manifold interactions with light. A fluorophore is a component that causes a molecule to absorb energy of a specific wavelength and then re-remit energy at a different but equally specific wavelength. The amount and wavelength of the emitted energy depend on both the fluorophore and the chemical environment of the fluorophore. Fluorophores are also denoted as chromophores, historically speaking the part or moiety of a molecule responsible for its color. In addition, the denotation chromophore implies that the molecule absorbs light while fluorophore means that the molecule, likewise,emits light. The umbrella term used in light emission is luminescence, whereas fluorescence denotes allowed transitions with a lifetime in the nanosecond range from higher to lower excited singlet states of molecules. In the following we will try to understand why some compounds are colored and others are not. Therefore, we will take a closer look at the relationship of conjugation to color with fluorescence emission, and investigate the absorption of light at different wavelengths in and near the visible part of the spectrum of various compounds. For example, organic compounds (i.e., hydrocarbons and derivatives) without double or triple bonds absorb light at wavelengths below 160 nm, corresponding to a photon energy of >180 kcal mol À1 (1 cal ¼ 4.184 J), or >7.8 eV (Figure 1.1), that is, significantly higher than the dissociation energy of common carbon-to-carbon single bonds. Below a wavelength of 200 nm the energy of a single photon is sufficient to ionize molecules. Therefore, photochemical decomposition is most likely to occur when unsaturated compounds, where all bonds are formed by s-electrons, are irradiated with photon energies >6.2 eV. Double and triple bonds also use p-electrons in addition to a s-bond for bonding. In contrast to s-electrons, which are characterized by the rotational symmetry of their wavefunction with respect to the bond direction, p-electrons are characterized by a wavefunction having a node at the nucleus and rotational symmetry along a line through the nucleus. p-bonds Handbook of Fluorescence Spectroscopy and Imaging.
Estimation of Microcirculatory Blood Flow Velocity Profiles

An approach to estimate blood flow cross-sectional velocity profiles in intravital microscopy videos of post-capillary venules is proposed. Given an image sequence, the cross-section upon which the velocity profile is to be estimated is manually chosen. The velocity of each streamline, assumed perpendicular to the chosen cross-section, is estimated separately from other streamlines, except for a smoothness constraint imposed on the velocity profile. Video instability and undesirable structures transparently superimposed on the flow stream are accounted for in order to diminish their adverse effect on the estimation. The proposed approach is tested on synthetic, in vitro and in vivo videos.
Pose estimation of a human arm using kinematic constraints

Thispaperis concernedwith visualmotioncaptureof ahumanarm. Theinput is a colour imageandtheoutputis the 3D poseof thearmin thecurrentimage.To handletheobvious ambiguitiesinvolved in 3D estimationbasedon 2D dataa model-basedapproachis adaptedwhereall configurationsof thearmarematchedwith the imagedata.These configurationsareefficiently foundthrougha compactrepresentationof themodelof thearmtogetherwith kinematic constraints.Therepresentationis obtainedby exploiting the geometryof thearmtogetherwith imagemeasurements of the positionof the hand. It turnsout that two parameters aresufficient to representhe3D poseof thearm.Theseparametersareprunedusingkinematicconstraints. Theconstraintsaredefinedfrom thelengthof theupperandlower arms,togetherwith thelimits on thefour joint anglesof the arm. Altogetherthe compactrepresentationand the constraintsreducethe numberof configurationssignificantly andthereforeallows for anexhausti vematchingprocedure. Themethodis testedin amodel-basedsilhouetteframework andtestsshow promisingresults.
Vehicle detection combining gradient analysis and AdaBoost classification

This paper presents a real-time vision-based vehicle's rear detection system using gradient based methods and Adaboost classification, for ACC applications. Our detection algorithm consists of two main steps: gradient driven hypothesis generation and appearance based hypothesis verification. In the hypothesis generation step, possible target locations are hypothesized. This step uses an adaptive range-dependant threshold and symmetry for gradient maxima localization. Appearance-based hypothesis validation verifies those hypothesis using AdaBoost for classification with illumination independent classifiers. The monocular system was tested under different traffic scenarios (e.g., simply structured highway, complex urban environments, varying lightening conditions), illustrating good performance.
Stress exposure using small mobile robot both in immature and mature period induces mental disorder in rat

The number of patients with mental disorders is increasing in advanced countries, hence more effective psychotropic drugs are recently desired. In process of development of psychotropic drugs, animal experiments have been playing a very important role. Mental disorder model animals which exhibit behavior disorder like patients with mental disorders are used in these experiments. These animals are normally developed by genetic manipulation, surgical operation in their brain or drug administration. A candidate for a new drug is administrated in these animals to evaluate its effect. However, we have some doubts about conventional mental disorder model animals because they are induced these disorders by using methods which are quite different from causes of mental disorder of human beings. Therefore, the purpose of this study is to develop an novel methodology to create mental disorder model animals. We then developed a small mobile robot and a control system for it. Using them, we have performed some experiments to create a mental disorder model rat. We had then succeeded in developing a mental disorder model rat by exposing stress using the robot during immature period. This rat exhibits low activity in some behavior tests during mature period. For better understanding of how stress exposure induces mental disorder in a rat, we conducted another experiment based on stress vulnerability hypothesis. In this experiment, stress was exposed during both immature and mature period while that had been exposed only during immature period. We prepared several conditions of stress exposure by changing robot behavior pattern to find the one to induce much stress in a rat. From a result of experiment, we found that a rat which received gentle chase by the robot during immature period was induced much stress when it received robot attack during mature period. Thus, we consider that this rat is more appropriate to the mental disorder model than that was developed in our past experiment.