Miracle-dz/UniFAR-FTUs huggingface dataset:
Profiling-Based Workload Consolidation and Migration in Virtualized Data Centers

Improving energy efficiency of data centers has become increasingly important nowadays due to the significant amounts of power needed to operate these centers. An important method for achieving energy efficiency is server consolidation supported by virtualization. However, server consolidation may incur significant degradation to workload performance due to virtual machine (VM) co-location and migration. How to reduce such performance degradation becomes a critical issue to address. In this paper, we propose a profiling-based server consolidation framework which minimizes the number of physical machines (PMs) used in data centers while maintaining satisfactory performance of various workloads. Inside this framework, we first profile the performance losses of various workloads under two situations: running in co-location and experiencing migrations. We then design two modules: (1) consolidation planning module which, given a set of workloads, minimizes the number of PMs by an integer programming model, and (2) migration planning module which, given a source VM placement scenario and a target VM placement scenario, minimizes the number of VM migrations by a polynomial time algorithm. Also, based on the workload performance profiles, both modules can guarantee the performance losses of various workloads below configurable thresholds. Our experiments for workload profiling are conducted with real data center workloads and our experiments on our two modules validate the integer programming model and the polynomial time algorithm.
Modeling of blood flow in arterial trees

Advances in computational methods and medical imaging techniques have enabled accurate simulations of subject‐specific blood flows at the level of individual blood cell and in complex arterial networks. While in the past, we were limited to simulations with one arterial bifurcation, the current state‐of‐the‐art is simulations of arterial networks consisting of hundreds of arteries. In this paper, we review the advances in methods for vascular flow simulations in large arterial trees. We discuss alternative approaches and validity of various assumptions often made to simplify the modeling. To highlight the similarities and discrepancies of data computed with different models, computationally intensive three‐dimensional (3D) and inexpensive one‐dimensional (1D) flow simulations in very large arterial networks are employed. Finally, we discuss the possibilities, challenges, and limitations of the computational methods for predicting outcomes of therapeutic interventions for individual patients. Copyright © 2010 John Wiley & Sons, Inc.
Research on Energy Hole Problem for Wireless Sensor Networks Based on Alternation between Dormancy and Work

This paper theoretically proposes a more accurate analytical method for avoiding energy hole in wireless sensor networks with nonuniform node distribution. Based on the typical energy consumption model of wireless sensor networks, we obtain the relationship of the densities of sensor nodes in different regions of network through rigorous mathematical analysis, and prove that if the densities of sensor nodes working at the same time are ρ , the entire work can achieve uniform energy consumption by making the sensor nodes whose density is greater than ρ alternate between dormancy and work. Through theoretical analysis and numerical simulation, we obtain the following conclusions: 1). Under the circumstance that the parameters of network do not change, the network lifetime can increase several times by selecting appropriate transmission radius of sensor node. 2). To achieve uniform energy consumption of network, the densities of sensor nodes whose distances from the base station are different should be different. Even if the sensor nodes were in the same circular ring, the densities of sensor nodes in different places should not the same. 3). Through nonuniform node distribution and alternation between dormancy and work, the efficiency of network can increase several times and the residual energy of network is nearly zero when the network lifetime ends. Therefore, our research can give a significant guidance on the optimization of wireless sensor networks.
p75 neurotrophin receptor: A potential surface marker of tongue squamous cell carcinoma stem cells

The present study detected p75 neurotrophin receptor (p75NTR) expression in tongue squamous cell carcinoma (TSCC) cell lines, in order to define the biological properties of p75NTR+ cells and to confirm the use of p75NTR+ as a surface marker for TSCC stem cells. p75NTR+ cells were separated from Tca-8113 and CAL-27 TSCC cells by fluorescence-activated cell sorting. Colony formation, MTT and scratch assays, and a tumorigenicity analysis were performed to measure self-renewal and proliferation, multidirectional differentiation, and tumorigenicity of p75NTR+ cells. p75NTR+ cells comprised 3.1 and 1.9% of Tca-8113 and CAL-27 cells (mean of three experiments), respectively, and were more able to form colonies compared with non-sorted cells (P<0.01). In addition, the proportion of p75NTR+ cells generated from monoclonal p75NTR+ cells decreased to 14.5 (Tca-8113) and 5.8% (CAL-27) of cells within 2 weeks, thus suggesting that p75NTR+ cells are able to generate p75NTR+ and p75NTR− cells. Furthermore, p75NTR+ cells exhibited increased proliferation, as evidenced by MTT assay (P<0.01) and had greater metastatic ability according to the scratch assay (P<0.01), compared with non-sorted cells. p75NTR+ cells also exhibited a greater tumorigenic capacity compared with non-sorted cells. In conclusion, p75NTR+ cells isolated from TSCC cell lines possess the characteristics of cancer stem cells; therefore, p75NTR may be considered a useful surface marker for the identification of TSCC stem cells.
File Aware Wear Leveling for PCM-based Mobile Consumer Electronics

Phase Change Memory (PCM) is considered as one of the most popular candidates to replace flash memory in mobile consumer systems. PCM has many superior performance characteristics, including non-volatility, byte-addressability, low access latency and power consumption. However, it also suffers from finite program counts like flash memory. Prior researches used PCM as a black box, and implemented the wear leveling schemes in device controller, which failed to utilize file attributes in host side and result in poor efficiency of wear evenness. In this paper, we propose a file aware wear leveling algorithm (called FAWL) for PCM-based storage system in mobile consumer electronics. FAWL is designed in the host side, which combines file attributes and statistical information of PCM. It exploits rich attributes of files to divide files into different categories and distribute them in suitable pages to avoid extra swap overhead. In addition, by utilizing an adjust management in FAWL, the wear imbalance can be greatly mitigated. Experimental results show that FAWL effectively improves the lifetime of PCM compared with existing wear leveling algorithms, including random swapping, start-gap and segment swapping.
Adaptive Fuzzy Output-Constrained Fault-Tolerant Control of Nonlinear Stochastic Large-Scale Systems With Actuator Faults

The problem of adaptive fuzzy output-constrained tracking fault-tolerant control (FTC) is investigated for the large-scale stochastic nonlinear systems of pure-feedback form. The nonlinear systems considered in this paper possess the unstructured uncertainties, unknown interconnected terms and unknown nonaffine nonlinear faults. The fuzzy logic systems are employed to identify the unknown lumped nonlinear functions so that the problems of structured uncertainties can be solved. An adaptive fuzzy state observer is designed to solve the nonmeasurable state problem. By combining the barrier Lyapunov function theory, adaptive decentralized and stochastic control principles, a novel fuzzy adaptive output-constrained FTC approach is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.
Safety and efficacy of dual‐wavelength laser (1064 + 595 nm) for treatment of non‐treated port‐wine stains

Patients with port‐wine stain (PWS) suffer physically and psychologically because of the high incidence (0.3%–0.5%) of the disease. Pulsed‐dye laser (PDL) at 595 nm is the gold standard of the treatment for PWS. Nevertheless, clinicians intend to determine whether the dual‐wavelength laser (DWL; 595‐nm PDL + 1064‐nm Nd:YAG) is an adequate choice in the treatment of non‐treated PWS. This study is the first prospective within‐patient controlled research seeking to investigate the safety and efficacy of DWL for the treatment of non‐treated PWS.
Effects of visual induced motion sickness of stereoscopic 3D interactive video

We study the effects of visual induced motion sickness (VIMS) of Stereoscopic 3D interactive video as compared to the 2D condition using a subjective score and bio-signal index. LF/HF ratio from R-R interval from ECG and respiration is used as the bio-signal index. The results show that 3D video yields significantly higher subjective discomfort scores than 2D video. However, the bio-signal index with 3D is not significantly different. Concerning to the relationship between global motion vector (GMV) and body motion, the cross-correlation between GMV-pan and horizontal body motion in 2D interactive condition is higher than that in 3D.
VoicePlay — An affective sports game operated by speech emotion recognition based on the component process model

In the present work we outline the first computer game which is operated by real-time emotion recognition from speech. Urgency is detected from the voice of a player and conveyed to control parameters like speed during a race or accuracy while shooting on a target for a biathlon sports simulation. Moreover, the game showcases the world's first automatic speech emotion recognition which is based on the Component Process Model, a fundamental theory of cognitive psychology.
Online Discriminative Tracking With Active Example Selection

Most existing discriminative tracking algorithms use a sampling-and-labeling strategy to collect examples and treat the training example collection as a task that is independent of classifier learning. However, the examples collected directly by sampling are neither necessarily informative nor intended to be useful for classifier learning. Updating the classifier with these examples might introduce ambiguity to the tracker. In this paper, we present a novel online discriminative tracking framework that explicitly couples the objectives of example collection and classifier learning. Our method uses Laplacian regularized least squares (LapRLS) to learn a robust classifier that can sufficiently exploit unlabeled data and preserve the local geometrical structure of the feature space. To ensure the high classification confidence of the classifier, we propose an active example selection approach to automatically select the most informative examples for LapRLS. Part of the selected examples that satisfy strict constraints are labeled to enhance the adaptivity of our tracker, which actually provides robust supervisory information to guide semisupervised learning. With active example selection, we are able to avoid the ambiguity introduced by an independent example collection strategy and to alleviate the drift problem caused by misaligned examples. Comparison with the state-of-the-art trackers on the comprehensive benchmark demonstrates that our tracking algorithm is more effective and accurate.
What's new in Nanotoxicology? Brief review of the 2007 literature

The use and commercial potential of engineered nanomaterials is increasing, but questions of occupational and public health safety remain. Here, we review research published in 2007 concerning toxicology of nanomaterials. Articles were selected from the Medline Pubmed database, published or pre-published during 2007, using keywords (nanomaterials or nanoparticles or nanostructures) and (toxicity or health). From the 238 articles, we chose to concentrate mainly on research into carbonaceous (carbon nanotubes [CNTs] and fullerenes) and metallic materials (pure metal, oxides), because of their relevance. The induction of oxidative stress was repeatedly reported, and new information on the movement of nanomaterials through membranes was publicized. Concerning CNTs, information was revealed on DNA damage in vitro and pulmonary and systemic in vivo effects. Several of the reports failed to follow recent expert recommendations concerning good practice for nanotoxicologic research, complicating the integration of the new data into the larger picture of safety of nanomaterials.
Assessment of Gaps in Care and the Development of a Care Pathway for Anemia in Patients with Inflammatory Bowel Diseases

Background: Anemia is a common complication among patients with inflammatory bowel diseases (IBD) and is associated with high rates of IBD-related complications, resource utilization, and impaired quality of life. Despite practice guidelines for anemia in patients with IBD, gaps remain in the perceptions of anemia among health care providers. The aims of this study were to identify gaps in care and to develop a care pathway for anemia in patients with IBD. Methods: The Crohn's & Colitis Foundation of America anemia care pathway was developed by a committee using principles of cognitive task analysis. Focus groups of providers of patients with IBD were performed to identify domains of perceptions and management decisions for anemia and IBD. Knowledge elicitation from subject experts in anemia was conducted using case-based scenarios of patients with IBD and anemia to determine decision-making branch points. The care pathway was modified in an iterative fashion to encompass clinical presentations of anemia in IBD and potential barriers to the recognition, management, and follow-up of anemia. Results: Variations were observed in how providers define iron deficiency, thresholds for treatment of anemia, and route of iron therapy. A care pathway for anemia incorporating the World Health Organization definition of anemia, universal hemoglobin and ferritin screening, evaluation of iron stores using ferritin and transferrin saturation, management of anemia based on adequacy of iron stores, and follow-up was developed. Conclusions: The authors identified domains of how providers perceive and manage patients with IBD and anemia, and developed a care pathway to align clinical practices with guideline recommendations.
Identification of genetic variants predictive of early onset pancreatic cancer through a population science analysis of functional genomic datasets

Biomarkers are critically needed for the early detection of pancreatic cancer (PC) are urgently needed. Our purpose was to identify a panel of genetic variants that, combined, can predict increased risk for early-onset PC and thereby identify individuals who should begin screening at an early age. Previously, we identified genes using a functional genomic approach that were aberrantly expressed in early pathways to PC tumorigenesis. We now report the discovery of single nucleotide polymorphisms (SNPs) in these genes associated with early age at diagnosis of PC using a two-phase study design. In silico and bioinformatics tools were used to examine functional relevance of the identified SNPs. Eight SNPs were consistently associated with age at diagnosis in the discovery phase, validation phase and pooled analysis. Further analysis of the joint effects of these 8 SNPs showed that, compared to participants carrying none of these unfavorable genotypes (median age at PC diagnosis 70 years), those carrying 1–2, 3–4, or 5 or more unfavorable genotypes had median ages at diagnosis of 64, 63, and 62 years, respectively (P = 3.0E–04). A gene-dosage effect was observed, with age at diagnosis inversely related to number of unfavorable genotypes (Ptrend = 1.0E–04). Using bioinformatics tools, we found that all of the 8 SNPs were predicted to play functional roles in the disruption of transcription factor and/or enhancer binding sites and most of them were expression quantitative trait loci (eQTL) of the target genes. The panel of genetic markers identified may serve as susceptibility markers for earlier PC diagnosis.
Time-domain A-EFIE solution based on Nystrom scheme with MOD approach

A time domain augmented electric field integral equation (TDAEFIE) solution is proposed for transient electromagnetic analysis for conducting bodies of arbitrary shape. Compared with TDEFIE, the TDAEFIE is very effective in eliminating low frequency instabilities. Unlike conventional method, Nystrom method is adopted instead of method of moments (MoM) which usually requires conforming meshes with a well-designed basis function. While, the Nyström method does not require any basis or testing functions, and can be used to deal with nonconforming meshes. In the time domain, in order to avoid the late-time instability problem of marching-on-in-time (MOT) approach, Laguerre basis and testing functions are used to form a marching-on-in-degree manner. Derivation of formulas will be presented and numerical examples will be included in our future work.
A Large Dataset for Improving Patch Matching

We propose a new dataset for learning local image descriptors which can be used for significantly improved patch matching. Our proposed dataset consists of an order of magnitude more number of scenes, images, and positive and negative correspondences compared to the currently available Multi-View Stereo (MVS) dataset from Brown et al. The new dataset also has better coverage of the overall viewpoint, scale, and lighting changes in comparison to the MVS dataset. Our dataset also provides supplementary information like RGB patches with scale and rotations values, and intrinsic and extrinsic camera parameters which as shown later can be used to customize training data as per application. We train an existing state-of-the-art model on our dataset and evaluate on publicly available benchmarks such as HPatches dataset and Strecha et al.\cite{strecha} to quantify the image descriptor performance. Experimental evaluations show that the descriptors trained using our proposed dataset outperform the current state-of-the-art descriptors trained on MVS by 8%, 4% and 10% on matching, verification and retrieval tasks respectively on the HPatches dataset. Similarly on the Strecha dataset, we see an improvement of 3-5% for the matching task in non-planar scenes.
Power Beacon-Assisted Millimeter Wave Ad Hoc Networks

Deployment of low-cost power beacons (PBs) is a promising solution for dedicated wireless power transfer (WPT) in future wireless networks. In this paper, we present a tractable model for PB-assisted millimeter wave (mmWave) wireless ad hoc networks, where each transmitter (TX) harvests energy from all PBs and then uses the harvested energy to transmit information to its desired receiver. Our model accounts for realistic aspects of WPT and mmWave transmissions, such as power circuit activation threshold, allowed maximum harvested power, maximum transmit power, beamforming, and blockage. Using stochastic geometry, we obtain the Laplace transform of the aggregate received power at the TX to calculate the power coverage probability. We approximate and discretize the transmit power of each TX into a finite number of discrete power levels in log scale to compute the channel and total coverage probability. We compare our analytical predictions to simulations and observe good accuracy. The proposed model allows insights into effect of system parameters, such as transmit power of PBs, PB density, main lobe beamwidth, and power circuit activation threshold on the overall coverage probability. The results confirm that it is feasible and safe to power TXs in an mmWave ad hoc network using PBs.
Impact of Inhalational Anesthetics on Liver Regeneration After Living Donor Hepatectomy: A Propensity Score-Matched Analysis

BACKGROUND: Although desflurane and sevoflurane, the most commonly used inhalational anesthetics, have been linked to postoperative liver injury, their impact on liver regeneration remains unclear. We compared the influence of these anesthetics on the postoperative liver regeneration index (LRI) after living donor hepatectomy (LDH). METHODS: We conducted a retrospective chart review of 1629 living donors who underwent right hepatectomy for LDH between January 2008 and August 2016. The patients were divided into sevoflurane (n = 1206) and desflurane (n = 423) groups. Factors associated with LRI were investigated using multivariable logistic regression analysis. Propensity score matching analysis compared early (1 postoperative week) and late (within 1–2 months) LRIs and delayed recovery of hepatic function between the 2 groups. RESULTS: The mean early and late LRIs in the 1629 patients were 63.3% ± 41.5% and 93.7% ± 48.1%, respectively. After propensity score matching (n = 403 pairs), there were no significant differences in early and late LRIs between the sevoflurane and desflurane groups (early LRI: 61.2% ± 41.5% vs 58.9% ± 42.4%, P = .438; late LRI: 88.3% ± 44.3% vs 94.6% ± 52.4%, P = .168). Male sex (regression coefficient [&bgr;], 4.6; confidence interval, 1.6–7.6; P = .003) and remnant liver volume (&bgr;, –4.92; confidence interval, –5.2 to –4.7; P < .001) were associated with LRI. The incidence of delayed recovery of hepatic function was 3.6% (n = 29) with no significant difference between the 2 groups (3.0% vs 4.2%, P = .375) after LDH. CONCLUSIONS: Both sevoflurane and desflurane can be safely used without affecting liver regeneration and delaying liver function recovery after LDH.
Self-ensembling for visual domain adaptation

This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant (Tarvainen et al., 2017) of temporal ensembling (Laine et al;, 2017), a technique that achieved state of the art results in the area of semi-supervised learning. We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness. Our approach achieves state of the art results in a variety of benchmarks, including our winning entry in the VISDA-2017 visual domain adaptation challenge. In small image benchmarks, our algorithm not only outperforms prior art, but can also achieve accuracy that is close to that of a classifier trained in a supervised fashion.
Deep CNN based pseudo-concept selection and modeling for generation of semantic multinomial representation of scene images

Though recent convolutional neural network (CNN) based method for scene classification task show impressive results but lacks in capturing the complex semantic content of the scene images. To reduce the semantic gap a semantic multinomial (SMN) representation is introduced. SMN representation corresponds to a vector of posterior probabilities of concepts. The core part of SMN generation is building the concept model. For building the concept model, it is necessary to have ground truth (true) concept labels for every image in the database. In this research work, we propose novel deep CNN based SMN representation which exploits convolutional layer filters response as pseudo concepts to build the concept model in the absence of true concept labels. The effectiveness of the proposed approach is studied for scene classification tasks on standard datasets like MIT67 and SUN397.