Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Fine-Grained Resource Rental Planning in Cloud Computing

Application services based on cloud computing infrastructure are proliferating over the Internet. In this paper, we investigate the problem of how to minimize cloud resource rental cost associated with hosting such cloud-based application services, while meeting the projected service demand. This problem arises when applications generate high volume of data that incurs significant cost on storage and transfer. As a result, an application service provider (ASP) needs to carefully evaluate various resource rental options before finalizing the application deployment. We choose Amazon EC2 marketplace as a case of study, and analyze the economical trade-off for on-demand resource rental strategies. Given fixed resource pricing, we first develop a deterministic model, using a mixed integer linear program, to facilitate resource rental decision making. Evaluation results show that our planning optimization model reduces resource rental cost by as much as 50 percent compared with a baseline strategy. Next, we further investigate planning solutions to resource market featuring time-varying pricing (Amazon Spot Instance Market). We perform time-series analysis over the spot price trace and examine its predictability using auto-regressive integrated moving-average (ARIMA). We also develop a stochastic planning model based on multistage recourse. By comparing these two approaches, we discover that spot price forecasting does not provide our planning model with a crystal ball due to the weak correlation of past and future price, and the stochastic planning model better hedges against resource pricing uncertainty than resource rental planning using forecast prices.
On the Use of Cauchy Prior Distributions for Bayesian Logistic Regression

In logistic regression, separation occurs when a linear combination of the predictors can perfectly classify part or all of the observations in the sample, and as a result, finite maximum likelihood estimates of the regression coefficients do not exist. Gelman et al. (2008) recommended independent Cauchy distributions as default priors for the regression coefficients in logistic regression, even in the case of separation, and reported posterior modes in their analyses. As the mean does not exist for the Cauchy prior, a natural question is whether the posterior means of the regression coefficients exist under separation. We prove theorems that provide necessary and sufficient conditions for the existence of posterior means under independent Cauchy priors for the logit link and a general family of link functions, including the probit link. We also study the existence of posterior means under multivariate Cauchy priors. For full Bayesian inference, we develop a Gibbs sampler based on Polya-Gamma data augmentation to sample from the posterior distribution under independent Student-t priors including Cauchy priors, and provide a companion R package in the supplement. We demonstrate empirically that even when the posterior means of the regression coefficients exist under separation, the magnitude of the posterior samples for Cauchy priors may be unusually large, and the corresponding Gibbs sampler shows extremely slow mixing. While alternative algorithms such as the No-U-Turn Sampler in Stan can greatly improve mixing, in order to resolve the issue of extremely heavy tailed posteriors for Cauchy priors under separation, one would need to consider lighter tailed priors such as normal priors or Student-t priors with degrees of freedom larger than one.
Virtual characterization of Interior Permanent Magnet (IPM) motor for EV traction applications

Interior Permanent Magnet (IPM) motors are strengthening its candidature as traction motor in Electric Vehicles (EV) because of their advantages like high power density, extended field weakening and high efficiency. IPM motor being a complex control problem with inherent rotor saliency characteristic, extensive characterization on regenerative dynamometer is required for traction applications. This paper proposes a virtual characterization methodology for IPM motor using 2D Finite Element Analysis (FEA). The bench and vehicle test results confirmed the dynamic performance of IPM motor drive with acceptable accuracy.
VizMal: A Visualization Tool for Analyzing the Behavior of Android Malware

Malware signature extraction is currently a manual and a time-consuming process. As a matter of fact, security analysts have to manually inspect samples under analysis in order to find the malicious behavior. From research side, current literature is lacking of methods focused on the malicious behavior localization: designed approaches basically mark an entire application as malware or non-malware (i.e., take a binary decision) without knowledge about the malicious behavior localization inside the analysed sample. In this paper, with the twofold aim of assisting the malware analyst in the inspection process and of pushing the research community in malicious behavior localization, we propose VizMal, a tool for visualizing the dynamic trace of an Android application which highlights the portions of the application which look potentially malicious. VizMal performs a detailed analysis of the application activities showing for each second of the execution whether the behavior exhibited is legitimate or malicious. The analyst may hence visualize at a glance when at to which degree an application execution looks malicious.
A nanoscale reorganization of the IL-15 receptor is triggered by NKG2D in a ligand-dependent manner

The activating receptor NKG2D clusters with the IL-15 receptor in NK cells upon stimulation with ULBP2. Distinct effects on NK cells Natural killer (NK) cells perform immune surveillance for virally infected cells and tumor cells. The balance between the engagement of activating receptors, such as NKG2D, and inhibitory receptors on the NK cell surface determines whether the cells kill their targets. Through superresolution microscopy, Bálint et al. showed that NKG2D on the surface of unstimulated NK cells was organized into nanoclusters, which became differentially reorganized depending on whether the cells were exposed to MICA or ULBP2, two NKG2D ligands found on tumor cells. Stimulation with ULBP2, but not MICA, caused NKG2D to cluster with the receptor for the cytokine IL-15, which can also be presented on tumor cells, synergizing with NKG2D to enhance NK cell responses. These data suggest that NKG2D ligands have distinct outcomes on NK cell function, which may have relevance in the use of NK cells as an immunotherapy. Natural killer group 2D (NKG2D), an activating receptor on natural killer (NK) cells and a subset of T cells, recognizes stress-inducible proteins, including MICA and ULBP2, which are present on infected or transformed cells. Whether each NKG2D ligand (NKG2DL) has a distinct biological role is not clear. Using superresolution microscopy, we found that NKG2D is constitutively arranged in nanoclusters at the surface of human primary NK cells. Nanoclusters of NKG2D became smaller upon ligation with MICA but became larger upon activation by ULBP2. In addition, ULBP2 induced the reorganization of nanoclusters of the cytokine receptor subunit for both interleukin-2 (IL-2) and IL-15 (IL-2/IL-15Rβ), such that these cytokine receptor subunits coalesced with nanoclusters of NKG2D. Functionally, the response of NK cells activated by ULBP2 was augmented by an interaction between ULBP2-bound NKG2D and IL-15R ligated by IL-15 (trans-presented by IL-15Rα–coated surfaces). These data suggest that NKG2DLs are not equivalent in their capacity to activate NKG2D and establish a previously unknown paradigm in how ligand-induced changes to the nanoscale organization of the cell surface can affect immune responses.
On the streaming complexity of fundamental geometric problems

In this paper, we focus on lower bounds and algorithms for some basic geometric problems in the one-pass (insertion only) streaming model. The problems considered are grouped into three categories: (i) Klee's measure (ii) Convex body approximation, geometric query, and (iii) Discrepancy Klee's measure is the problem of finding the area of the union of hyperrectangles. Under convex body approximation, we consider the problems of convex hull, convex body approximation, linear programming in fixed dimensions. The results for convex body approximation implies a property testing type result to find if a query point lies inside a convex polyhedron. Under discrepancy, we consider both the geometric and combinatorial discrepancy. For all the problems considered, we present (randomized) lower bounds on space. Most of our lower bounds are in terms of approximating the solution with respect to an error parameter $\epsilon$. We provide approximation algorithms that closely match the lower bound on space for most of the problems.
Characterization of IDH1 p.R132H Mutant Clones Using Mutation-specific Antibody in Myeloid Neoplasms

Isocitrate dehydrogenase 1 (IDH1) and IDH2 mutations occur in a variety of myeloid neoplasms. Immunohistochemistry (IHC)-based direct visualization of mutant clones of hematopoietic cells can be useful for rapid diagnostic screening and for monitoring treatment response. In this study, we first evaluated the sensitivity and specificity of the IDH1 p.R132H mutation-specific antibody by IHC. All IDH1 wild type cases (n=11) and IDH1 mutant cases with a non-p.R132H mutation (n=30) were negative by IHC, demonstrating 100% antibody specificity. All the initial diagnostic specimens with IDH1 p.R132H mutation including acute myeloid leukemia (n=30), myelodysplastic syndromes (MDS) (n=10), MDS/myeloproliferative neoplasms (MPN) (n=4), and MPN (n=5) were positive by IHC, demonstrating 100% antibody sensitivity. Both immature and mature myeloid cells showed immunoreactivity. Erythroid precursors, lymphoid cells, endothelial cells, and osteoblasts were consistently negative by IHC. We then evaluated the follow-up specimens with a known IDH1 mutation status including acute myeloid leukemia (n=23), MDS (n=2), MDS/MPN (n=2), and MPN (n=2). Thirty-three IDH1 p.R132H mutant cases were positive by IHC and 12 IDH1 mutation negative cases were negative by IHC. However, IHC reactivity in up to 25% of bone marrow cells was noted in 8 of 20 polymerase chain reaction-negative cases, all from patients with a known history of IDH1 p.R132H mutation indicating sampling error or a sensitivity issue with molecular tests. These data indicate that IHC is a highly specific and sensitive tool to detect IDH1 p.R132H mutation in bone marrow involved by myeloid neoplasms. In addition, IDH1 p.R132H IHC also allows localization and assessment of the maturation stage of the clones carrying the mutation.
Active Braking of Whole-Arm Reaching Movements Provides Single-Trial Neuromuscular Measures of Movement Cancellation

Movement inhibition is an aspect of executive control that can be studied using the countermanding paradigm, wherein subjects try to cancel an impending movement following presentation of a stop signal. This paradigm permits estimation of the stop-signal reaction time or the time needed to respond to the stop signal. Numerous countermanding studies have examined fast, ballistic movements, such as saccades, even though many movements in daily life are not ballistic and can be stopped at any point during their trajectory. A benefit of studying the control of nonballistic movements is that antagonist muscle recruitment, which serves to actively brake a movement, presumably arises in response to the stop signal. Here, nine human participants (2 female) performed a center-out whole-arm reaching task with a countermanding component, while we recorded the activity of upper-limb muscles contributing to movement generation and braking. The data show a clear response on antagonist muscles to a stop signal, even for movements that have barely begun. As predicted, the timing of such antagonist recruitment relative to the stop signal covaried with conventional estimates of the stop-signal reaction time, both within and across subjects. The timing of antagonist muscle recruitment also attested to a rapid reprioritization of movement inhibition, with antagonist latencies decreasing across sequences consisting of repeated stop trials; such reprioritization also scaled with error magnitude. We conclude that antagonist muscle recruitment arises as a manifestation of a stopping process, providing a novel, accessible, and within-trial measure of the stop-signal reaction time. SIGNIFICANCE STATEMENT The countermanding or stop-signal paradigm permits estimation of how quickly subjects cancel an impending movement. Traditionally, this paradigm has been studied using simple movements, such as saccadic eye movements or button presses. Here, by measuring upper limb muscle activity while human subjects countermand whole-arm reaching movements, we show that movement cancellation often involves prominent recruitment of antagonist muscles that serves to actively brake the movement, even on movements that have barely begun. The timing of antagonist muscle recruitment correlates with traditional estimates of movement cancellation. Because they can be detected on a single-trial basis, muscle-based measures may provide a new way of characterizing movement cancellation at an unprecedented within-trial resolution.
Role of alkyl silatranes as plant growth regulators: comparative substitution effect on root and shoot development of wheat and maize.

BACKGROUND The present investigation reports the stimulating effects of different substituted alkyl silatranes (3a-3e) on the early seedling growth of wheat (Triticum aestivum) and maize (Zea mays). Seeds of these plants were exposed to six different concentrations (0, 10, 50, 100, 200 and 500 µmol L-1 ). The results revealed that different substitutions (3a-3e) had different effects on root and shoot elongation. Silatranes (3a-3e) were synthesized employing microwave irradiation by a solvent-mediated transesterification reaction, thereby reducing reaction times from several hours under conventional reflux conditions to 15 min under microwave irradiation. RESULTS It was of interest that the effect of these silatranes did not show a dose-dependent relationship but an optimum concentration, which was 100 µmol L-1 for maize and 200 µmol L-1 for wheat. γ-Aminopropyl silatranes (3b and 3e) gave the best results in maize, whereas γ-chloropropyl silatrane (3a) was most efficient for wheat at these optimum concentrations. CONCLUSION All the synthesized silatranes were effective in promoting root and shoot growth of wheat and maize. Furthermore, an efficient green microwave methodology was successful for the synthesis of silatranes. These observations pave the way for silatranes as efficient plant growth regulators for crops. © 2018 Society of Chemical Industry.
Using Application Layer Metrics to Detect Advanced SCADA Attacks

Current state-of-the-art intrusion detection and network monitoring systems have a tendency to focus on the ‘Five-Tuple’ features (protocol, IP src/dst and port src/dest). As a result there is a gap in visibility of security at an application level. We propose a collection of network application layer metrics to provide a greater insight into SCADA communications. These metrics are devised from an analysis of the industrial control system (ICS) threat landscape and the current state-of-the-art detection systems. Our metrics are able to detect a range of adversary capabilities which goes beyond previous literature in the SCADA domain.
Efficient detection of mesial temporal sclerosis using hippocampus and CSF features in MRI images

Mesial temporal sclerosis (MTS) is one of the most common pathological abnormalities associated with temporal lobe epilepsy. Prompt identification of MTS can determine surgical candidacy of a medically refractory epilepsy patient thus reducing morbidity and mortality. Traditionally, MTS is detected by visual inspection or manual quantification using structural brain MRI images based on characteristics such as the volume loss, shape variance and high intensities. However, it is a subjective process with inter-observer variance. In this paper, we propose an automated detection method for MTS based on brain MRI image analysis. It includes brain and hippocampus segmentation followed by extraction of volume, shape and CSF-ratio features from the 3D hippocampal images. Support vector machines are then used for MTS detection based on the extracted features. Experimental results show that the proposed technique provides promising performance in MTS detection.
Structural Analysis of Metal-Binding Peptides Using Molecular Dynamics

Proteins that specifically bind metals have been the target of the research for developing new organic-inorganic hybrid materials. Some amino acid sequences that bind metal have been reported, and the structures of proteins and peptides are considered responsible for binding to metal. The purpose of this study is to identify molecular structures responsible for binding metals. We performed molecular dynamics simulations and structural analyses of metal-binding peptides. The most frequently appearing structure of each peptide was identified. Combined with the previous experimental results, peptides with a stable, specific bent structure were suggested to have strong binding abilities. Peptides with a different bent structure have been suggested to be responsible for weak binding ability.
Ultrasound guidance in regional anesthesia: state of the art review through challenging clinical scenarios

Ultrasound guided regional anesthesia (UGRA) for peripheral nerve blockade is becoming increasingly popular. The advantage of ultrasound technology is that it affords the anesthesiologist the real time ability to visualize neural structures, needle advancement, and local anesthetic spread. Recent data suggest that UGRA generates improved success rates and reductions in performance times in comparison to traditional approaches. Further, the use of ultrasound technology in peripheral nerve blocks has provided insight into needle–nerve interactions, revealing distinct limitations of nerve stimulator techniques. Given that UGRA requires a unique set of skills, formal standards and guidelines are currently being developed by leadership societies in order to foster education and training. This review article, in a case vignette format, highlights important techniques, concepts, and limitations regarding the use of ultrasound to facilitate regional anesthesia. Clinically relevant aspects of ultrasound physics are also discussed.
Accuracy of Electronic Health Record Data for Identifying Stroke Cases in Large-Scale Epidemiological Studies: A Systematic Review from the UK Biobank Stroke Outcomes Group

Objective Long-term follow-up of population-based prospective studies is often achieved through linkages to coded regional or national health care data. Our knowledge of the accuracy of such data is incomplete. To inform methods for identifying stroke cases in UK Biobank (a prospective study of 503,000 UK adults recruited in middle-age), we systematically evaluated the accuracy of these data for stroke and its main pathological types (ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage), determining the optimum codes for case identification. Methods We sought studies published from 1990-November 2013, which compared coded data from death certificates, hospital admissions or primary care with a reference standard for stroke or its pathological types. We extracted information on a range of study characteristics and assessed study quality with the Quality Assessment of Diagnostic Studies tool (QUADAS-2). To assess accuracy, we extracted data on positive predictive values (PPV) and—where available—on sensitivity, specificity, and negative predictive values (NPV). Results 37 of 39 eligible studies assessed accuracy of International Classification of Diseases (ICD)-coded hospital or death certificate data. They varied widely in their settings, methods, reporting, quality, and in the choice and accuracy of codes. Although PPVs for stroke and its pathological types ranged from 6–97%, appropriately selected, stroke-specific codes (rather than broad cerebrovascular codes) consistently produced PPVs >70%, and in several studies >90%. The few studies with data on sensitivity, specificity and NPV showed higher sensitivity of hospital versus death certificate data for stroke, with specificity and NPV consistently >96%. Few studies assessed either primary care data or combinations of data sources. Conclusions Particular stroke-specific codes can yield high PPVs (>90%) for stroke/stroke types. Inclusion of primary care data and combining data sources should improve accuracy in large epidemiological studies, but there is limited published information about these strategies.
Hierarchical Electricity Demand Forecasting by Exploring the Electricity Consumption Patterns

Accurate electricity demand forecasting is necessary to develop an efficient and sustainable power system. Total demand of the whole region can be disaggregated at different levels, thus producing a hierarchical structure. In the hierarchical demand forecasting, the prediction accuracy and aggregate consistency between levels are two important issues, however in the previous works the prediction accuracy is often affected by conducting the aggregate consistency. In this work, we propose a novel pattern-based hierarchical time series forecasting (PHF) method which consists of two aggregation stages. In the first aggregation stage, by exploring the electricity consuming patterns with clustering method, the bottom level electricity demand forecasting is improved, and in the second stage the region level aggregation is conducted to achieve the whole level forecasting. The experiments are conducted on the Energy Demand Research Project (EDRP) datasets, and the experimental results show that compared with the previous state-of-the-art methods, our method improves the prediction accuracy in all hierarchical levels with keeping aggregation consistency.