Miracle-dz/UniFAR-FTUs huggingface dataset:
Myeloperoxidase Targets Apolipoprotein A-I, the Major High Density Lipoprotein Protein, for Site-Specific Oxidation in Human Atherosclerotic Lesions*

Background: Oxidation of apolipoprotein A-I by myeloperoxidase has been proposed to deprive HDL of its cardioprotective effects. Results: Tyrosine 192 is the major site of chlorination in apoA-I in both plasma and lesion HDL isolated from humans. Conclusion: Chlorination of apolipoprotein A-I by myeloperoxidase may contribute to generation of a dysfunctional form of HDL in vivo. Significance: Quantifying apolipoprotein A-I chlorination might help diagnose and perhaps treat human cardiovascular disease. Oxidative damage by myeloperoxidase (MPO) has been proposed to deprive HDL of its cardioprotective effects. In vitro studies reveal that MPO chlorinates and nitrates specific tyrosine residues of apoA-I, the major HDL protein. After Tyr-192 is chlorinated, apoA-I is less able to promote cholesterol efflux by the ABCA1 pathway. To investigate the potential role of this pathway in vivo, we used tandem mass spectrometry with selected reaction monitoring to quantify the regiospecific oxidation of apoA-I. This approach demonstrated that Tyr-192 is the major chlorination site in apoA-I in both plasma and lesion HDL of humans. We also found that Tyr-192 is the major nitration site in apoA-I of circulating HDL but that Tyr-18 is the major site in lesion HDL. Levels of 3-nitrotyrosine strongly correlated with levels of 3-chlorotyrosine in lesion HDL, and Tyr-18 of apoA-I was the major nitration site in HDL exposed to MPO in vitro, suggesting that MPO is the major pathway for chlorination and nitration of HDL in human atherosclerotic tissue. These observations may have implications for treating cardiovascular disease, because recombinant apoA-I is under investigation as a therapeutic agent and mutant forms of apoA-I that resist oxidation might be more cardioprotective than the native form.
FireSim: FPGA-Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud

We present FireSim, an open-source simulation platform that enables cycle-exact microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated simulation of silicon-proven RTL designs with a scalable, distributed network simulation. Unlike prior FPGA-accelerated simulation tools, FireSim runs on Amazon EC2 F1, a public cloud FPGA platform, which greatly improves usability, provides elasticity, and lowers the cost of large-scale FPGA-based experiments. We describe the design and implementation of FireSim and show how it can provide sufficient performance to run modern applications at scale, to enable true hardware-software co-design. As an example, we demonstrate automatically generating and deploying a target cluster of 1,024 3.2 GHz quad-core server nodes, each with 16 GB of DRAM, interconnected by a 200 Gbit/s network with 2 microsecond latency, which simulates at a 3.4 MHz processor clock rate (less than 1,000x slowdown over real-time). In aggregate, this FireSim instantiation simulates 4,096 cores and 16 TB of memory, runs ~14 billion instructions per second, and harnesses 12.8 million dollars worth of FPGAs—at a total cost of only ~$100 per simulation hour to the user. We present several examples to show how FireSim can be used to explore various research directions in warehouse-scale machine design, including modeling networks with high-bandwidth and low-latency, integrating arbitrary RTL designs for a variety of commodity and specialized datacenter nodes, and modeling a variety of datacenter organizations, as well as reusing the scale-out FireSim infrastructure to enable fast, massively parallel cycle-exact single-node microarchitectural experimentation.
Computing the force distribution on the surface of complex, deforming geometries using vortex methods and Brinkman penalization

The distribution of forces on the surface of complex, deforming geometries is an invaluable output of flow simulations. One particular example of such geometries involves self‐propelled swimmers. Surface forces can provide significant information about the flow field sensed by the swimmers and are difficult to obtain experimentally. At the same time, simulations of flow around complex, deforming shapes can be computationally prohibitive when body‐fitted grids are used. Alternatively, such simulations may use penalization techniques. Penalization methods rely on simple Cartesian grids to discretize the governing equations, which are enhanced by a penalty term to account for the boundary conditions. They have been shown to provide a robust estimation of mean quantities, such as drag and propulsion velocity, but the computation of surface force distribution remains a challenge. We present a method for determining flow‐induced forces on the surface of both rigid and deforming bodies, in simulations using remeshed vortex methods and Brinkman penalization. The pressure field is recovered from the velocity by solving a Poisson's equation using the Green's function approach, augmented with a fast multipole expansion and a tree‐code algorithm. The viscous forces are determined by evaluating the strain‐rate tensor on the surface of deforming bodies, and on a “lifted” surface in simulations involving rigid objects. We present results for benchmark flows demonstrating that we can obtain an accurate distribution of flow‐induced surface forces. The capabilities of our method are demonstrated using simulations of self‐propelled swimmers, where we obtain the pressure and shear distribution on their deforming surfaces.
SOCIAL MEDIA ANALYSIS BASED ON SEMANTICITY OF STREAMING AND BATCH DATA

Languages shared by people differ in different regions based on their accents, pronunciation and word usages. In this era sharing of language takes place mainly through social media and blogs. Every second swing of such a micro posts exist which induces the need of processing those micro posts, in-order to extract knowledge out of it. Knowledge extraction differs with respect to the application in which the research on cognitive science fed the necessities for the same. This work further moves forward such a research by extracting semantic information of streaming and batch data in applications like Named Entity Recognition and Author Profiling. In the case of Named Entity Recognition context of a single micro post has been utilized and context that lies in the pool of micro posts were utilized to identify the sociolect aspects of the author of those micro posts. In this work Conditional Random Field has been utilized to do the entity recognition and a novel approach has been proposed to find the sociolect aspects of the author (Gender, Age group).
Linear-time Constituency Parsing with RNNs and Dynamic Programming

Recently, span-based constituency parsing has achieved competitive accuracies with extremely simple models by using bidirectional RNNs to model “spans”. However, the minimal span parser of Stern et al. (2017a) which holds the current state of the art accuracy is a chart parser running in cubic time, O(n^3), which is too slow for longer sentences and for applications beyond sentence boundaries such as end-to-end discourse parsing and joint sentence boundary detection and parsing. We propose a linear-time constituency parser with RNNs and dynamic programming using graph-structured stack and beam search, which runs in time O(n b^2) where b is the beam size. We further speed this up to O(n b log b) by integrating cube pruning. Compared with chart parsing baselines, this linear-time parser is substantially faster for long sentences on the Penn Treebank and orders of magnitude faster for discourse parsing, and achieves the highest F1 accuracy on the Penn Treebank among single model end-to-end systems.
A Survey of Optimizing the Performance of iSCSI

: Mobile devices are playing important role in everybody’s life. Day to day mobile network, wireless network and mobile devices are becoming more dominant. So there is a requirement in building wireless storage system which can access information efficiently and correctly. Small Computer System Interface (SCSI) is a set of parallel interface standards used to connect scanners, printers, disk drives and other peripheral devices to a computer. iSCSI (internet SCSI) is an important protocol used for linking data storage facilities. iSCSI carries SCSI commands over IP network, and is also used to transmit data over internet and other networks and also enables location-independent storage and retrieval. Optimization of iSCSI parameters is an interesting research topic. Many researches have been made to analyse and implement iSCSI. This paper presents a survey on performance measurement and optimization of iSCSI parameters for mobile networks. The iSCSI parameters are the most suitable way of optimizing the performance, since changing kernel level TCP/IP parameters might negatively affect other applications’ performance.
TGFβ signaling limits lineage plasticity in prostate cancer

Although treatment options for localized prostate cancer (CaP) are initially effective, the five-year survival for metastatic CaP is below 30%. Mutation or deletion of the PTEN tumor suppressor is a frequent event in metastatic CaP, and inactivation of the transforming growth factor (TGF) ß signaling pathway is associated with more advanced disease. We previously demonstrated that mouse models of CaP based on inactivation of Pten and the TGFß type II receptor (Tgfbr2) rapidly become invasive and metastatic. Here we show that mouse prostate tumors lacking Pten and Tgfbr2 have higher expression of stem cell markers and genes indicative of basal epithelial cells, and that basal cell proliferation is increased compared to Pten mutants. To better model the primarily luminal phenotype of human CaP we mutated Pten and Tgfbr2 specifically in luminal cells, and found that these tumors also progress to invasive and metastatic cancer. Accompanying the transition to invasive cancer we observed de-differentiation of luminal tumor cells to an intermediate cell type with both basal and luminal markers, as well as differentiation to basal cells. Proliferation rates in these de-differentiated cells were lower than in either basal or luminal cells. However, de-differentiated cells account for the majority of cells in micro-metastases consistent with a preferential contribution to metastasis. We suggest that active TGFß signaling limits lineage plasticity in prostate luminal cells, and that de-differentiation of luminal tumor cells can drive progression to metastatic disease.
Multiple Access for Massive Machine Type Communications

The internet we have known thus far has been an internet of people, as it has connected people with one another. However, these connections are forecasted to occupy only a minuscule of future communications. The internet of tomorrow is indeed: the internet of things. The Internet of Things (IoT) promises to improve all aspects of life by connecting everything to everything. An enormous amount of effort is being exerted to turn these visions into a reality. Sensors and actuators will communicate and operate in an automated fashion with no or minimal human intervention. In the current literature, these sensors and actuators are referred to as machines, and the communication amongst these machines is referred to as Machine to Machine (M2M) communication or Machine-Type Communication (MTC). As IoT requires a seamless mode of communication that is available anywhere and anytime, wireless communications will be one of the key enabling technologies for IoT. In existing wireless cellular networks, users with data to transmit first need to request channel access. All access requests are processed by a central unit that in return either grants or denies the access request. Once granted access, users’ data transmissions are non-overlapping and interference free. However, as the number of IoT devices is forecasted to be in the order of hundreds of millions, if not billions, in the near future, the access channels of existing cellular networks are predicted to suffer from severe congestion and, thus, incur unpredictable latencies in the system. On the other hand, in random access, users with data to transmit will access the channel in an uncoordinated and probabilistic fashion, thus, requiring little or no signalling overhead. However, this reduction in overhead is at the expense of reliability and efficiency due to the interference caused by contending users. In most existing random access schemes, packets are lost when they experience interference from other packets transmitted over the same resources. Moreover, most existing random access schemes are best-effort schemes with almost no Quality of Service (QoS) guarantees. In this thesis, we investigate the performance of different random access schemes in different settings to resolve the problem of the massive access of IoT devices with diverse QoS guarantees. First, we take a step towards re-designing existing random access protocols such that they are more practical and more efficient. For many years, researchers have ii
Massive MIMO With Spatially Correlated Rician Fading Channels

This paper considers multi-cell massive multiple-input multiple-output systems, where the channels are spatially correlated Rician fading. The channel model is composed of a deterministic line-of-sight path and a stochastic non-line-of-sight component describing a practical spatially correlated multipath environment. We derive the statistical properties of the minimum mean squared error (MMSE), element-wise MMSE, and least-square channel estimates for this model. Using these estimates for maximum ratio combining and precoding, rigorous closed-form uplink (UL) and downlink (DL) achievable spectral efficiency (SE) expressions are derived and analyzed. The asymptotic SE behavior, when using the different channel estimators, are also analyzed. The numerical results show that the SE is higher when using the MMSE estimator than that of the other estimators, and the performance gap increases with the number of antennas.
Duration : TimeSpan Name : string Module 1 * Modules SUCCESS , FAILURE , SKIPPED BuildResult « enum » Result Goal Vendor : string Plugin : string Goal : string GoalName Name Content : string LogLine

Continuous integration is an agile software development practice. Instead of integrating features right before a release, they are constantly being integrated in an automated build process. This shortens the release cycle, improves software quality, and reduces time to market. However, the whole process will come to a halt when a commit breaks the build, which can happen for several reasons, e.g., compilation errors or test failures, and fixing the build suddenly becomes a top priority. Developers not only have to find the cause of the build break and fix it, but they have to be quick in all of it to avoid a delay for others. Unfortunately, these steps require deep knowledge and are often time consuming. To support developers in fixing a build break, we propose Bart, a tool that summarizes the reasons of the build failure and suggests possible solutions found on the Internet. We will show in a case study with eight participants that developers find Bart useful to understand build breaks and that using Bart substantially reduces the time to fix a build break, on average by 41%.
Role of Nanotechnology in Cosmeceuticals: A Review of Recent Advances

Nanotechnology manifests the progression in the arena of research and development, by increasing the efficacy of the product through delivery of innovative solutions. To overcome certain drawbacks associated with the traditional products, application of nanotechnology is escalating in the area of cosmeceuticals. Cosmeceuticals are regarded as the fastest growing segment of the personal care industry and the use has risen drastically over the years. Nanocosmeceuticals used for skin, hair, nail, and lip care, for conditions like wrinkles, photoaging, hyperpigmentation, dandruff, and hair damage, have come into widespread use. Novel nanocarriers like liposomes, niosomes, nanoemulsions, microemulsion, solid lipid nanoparticles, nanostructured lipid carrier, and nanospheres have replaced the usage of conventional delivery system. These novel nanocarriers have advantages of enhanced skin penetration, controlled and sustained drug release, higher stability, site specific targeting, and high entrapment efficiency. However, nanotoxicological researches have indicated concern regarding the impact of increased use of nanoparticles in cosmeceuticals as there are possibilities of nanoparticles to penetrate through skin and cause health hazards. This review on nanotechnology used in cosmeceuticals highlights the various novel carriers used for the delivery of cosmeceuticals, their positive and negative aspects, marketed formulations, toxicity, and regulations of nanocosmeceuticals.
Representations of Context in Recognizing the Figurative and Literal Usages of Idioms

Many idiomatic expressions can be interpreted literally or figuratively, depending on the context in which they occur. Developing an appropriate computational model of the context is crucial for automatic idiom usage recognition. While many existing methods incorporate some elements of context, they have not sufficiently captured the interactions between the linguistic properties of idiomatic expressions and the representations of the context. In this paper we perform an in-depth exploration of the role of representations of the context for idiom usage recognition; we highlight the advantages and limitations of different representation choices in existing methods in terms of known linguistic properties of idioms; we then propose a supervised ensemble method that selects representations adaptively for different idioms. Experimental result suggests that the proposed method performs better for a wider range of idioms than previous methods.
Competency Mapping to Identify High Performers: in Context to IT Professionals

: Competency mapping is one of the leading HRM initiatives of identifying the skills necessary for performing in a job. In this study we have focused on identifying the skills for IT professionals and analyzing the effect of those skills on their performance at workplace. This is an exploratory study which utilizes both qualitative and quantitative approach of problem solving. The qualitative research through literature review had helped us in building the foundation for the quantitative assessment. It is a sample survey research where the objectives and hypotheses were designed only after performing thorough review of literature. The statistical assessment of the recorded responses had been done in order to prove our hypotheses which were framed in the beginning. The results and their discussion had helped us in believing more strongly over the application of competency mapping within the industry. This study also laid the path for future researchers and scope of the same in the Indian IT industry.
Application of image guidance in pituitary surgery

Background: Surgical treatment of pituitary pathologies has evolved along the years, adding safety and decreasing morbidity related to the procedure. Advances in the field of radiology, coupled with stereotactic technology and computer modeling, have culminated in the contemporary and widespread use of image guidance systems, as we know them today. Image guidance navigation has become a frequently used technology that provides continuous three-dimensional information for the accurate performance of neurosurgical procedures. We present a discussion about the application of image guidance in pituitary surgeries. Methods: Major indications for image guidance neuronavigation application in pituitary surgery are presented and demonstrated with illustrative cases. Limitations of this technology are also presented. Results: Patients presenting a history of previous transsphenoidal surgeries, anatomical variances of the sphenoid sinus, tumors with a close relation to the internal carotid arteries, and extrasellar tumors are the most important indications for image guidance in pituitary surgeries. The high cost of the equipment, increased time of surgery due to setup time, and registration and the need of specific training for the operating room personnel could be pointed as limitations of this technology. Conclusion: Intraoperative image guidance systems provide real-time images, increasing surgical accuracy and enabling safe, minimally invasive interventions. However, the use of intraoperative navigation is not a replacement for surgical experience and a systematic knowledge of regional anatomy. It must be recognized as a tool by which the neurosurgeon can reduce the risk associated with surgical approach and treatment of pituitary pathologies.
Learning to Optimize Tensor Programs

We introduce a learning-based framework to optimize tensor programs for deep learning workloads. Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems. However, existing systems rely on manually optimized libraries such as cuDNN where only a narrow range of server class GPUs are well-supported. The reliance on hardware-specific operator libraries limits the applicability of high-level graph optimizations and incurs significant engineering costs when deploying to new hardware targets. We use learning to remove this engineering burden. We learn domain-specific statistical cost models to guide the search of tensor operator implementations over billions of possible program variants. We further accelerate the search by effective model transfer across workloads. Experimental results show that our framework delivers performance competitive with state-of-the-art hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPU.
Predicting Early Warning Signals of Financial Distress: Theory and Empirical Evidence

This study proposes a simple accounting-based framework that allows to assess financial distress up to five years in advance for US listed firms over the period of 1980-2010. We jointly model financial distress by two of its key driving forces, (1) a declining profitability, modeled by independent stochastic processes, and (2) insufficient liquid assets, viewed as a minimum level of operating liquidity. The financial distress model incorporates important underlying (i) time-series (ii) industry-sectors (iii) firm-level growth, and (iv) volatility developments. Cross-sectional and longitudinal analyses show improvements in the discriminatory power and demonstrate incremental information content beyond state of the art accounting-based models. For example, we find a 11 percent (4 percent) higher ranking performance documented by the area under the receiver operating characteristic curve (AUROC) relative to the standard Altman’s z-score (Ohlson’s o-score) models. Consequently, this study might provide important ex ante warning signals for investors, regulators and practitioners.
A Fast-Transient Output Capacitor-Less Low-Dropout Regulator Using Active-Feedback and Current-Reuse Feedforward Compensation

In this paper, output capacitor-less low-dropout (LDO) regulator using active-feedback and current-reuse feedforward compensation (AFCFC) is presented. The open-loop transfer function was obtained using small-signal modeling. The stability of the proposed LDO was analyzed using pole-zero plots, and it was confirmed by simulations that the stability was ensured under the load current of 50 mA. The proposed compensation method increases gain-bandwidth product (GBW) and reduces the on-chip compensation capacitor. The proposed AFCFC technique was applied to a three-stage output capacitor-less LDO. The LDO has a GBW of 5.6 MHz with a small on-chip capacitor of 2.6 pF. Fast-transient time of 450 ns with low quiescent current of 65.8 μA was achieved. The LDO was fabricated in 130 nm CMOS process consuming 180 × 140 μm2 of the silicon area.