Miracle-dz/UniFAR-FTUs huggingface dataset:
On Guided Installation of Basic Linear Algebra Routines in Nodes with Manycore Components

Computational systems are nowadays composed of basic computational components which share multiprocessors and coprocessors of different types, typically several GPUs or MICs. The software previously developed and optimized for simpler systems needs to be redesigned and re-optimized for these new, more complex systems. The adaptation to hybrid multicore+multiGPU and multicore+multiMIC of auto-tuning techniques for basic linear algebra routines is analyzed. The matrix-matrix multiplication kernel, which is optimized for different computational system components through guided experimentation, is studied. The basic matrix-matrix multiplication is, in turn, used inside higher level routines, which delegate their efficient execution to the optimization of the lower level routine. Experimental results are satisfactory in different multicore+multiGPU and multicore+multiMIC systems. So, the guided search of execution configurations for satisfactory execution times proves to be a useful tool for heterogeneous systems, where the complexity of the system means a correct use of highly efficient routines and libraries is difficult.
An Integrated Contract Strategy in a Three-Echelon Supply Chain with Capacity Limitation under the Forecast Update

In this paper, a model with one supplier, one manufacturer and one retailer is constructed to analyze the situation that a retailer makes orders twice before and after the forecast update from the supply chain coordination perspective. The manufacturerpsilas capacity is insufficient according to the second order of the retailer, so the manufacturer may recover the deficit by capacity outsourcing from the supplier at a certain option price at first and then supplying the very product back to the retailer by exercising the options. There are two decision points in this model. At the beginning of the planning horizon, the retailer decide how much to order for the first time, and the manufacturer knows her limited capacity and decides to purchase options from the supplier to assure the retailerpsilas demand to be completely satisfied. At the second decision point, with forecast update, the retailer makes his second order, and the manufacturer exercises the options to meet the retailerpsilas order. A numerical example is presented to illustrate the efficacy of the developed model in the end.
A survey of big data in social media using data mining techniques

World's largest community Facebook's `Like' button pressed 2.7 billion times every day across the web revealing what people care about, such an impact of social media that internet user average almost spends 2.5 hours daily on liking, chatting, poking, tweeting on social media, which has become vast source of unstructured data. While dealing with big data it's difficult for traditional databases and architecture to modify, grill and then structure this data, it can lead to many consumer insights which can help to create win-win situations. It has become necessary to find out value from large data sets to show relationships, dependencies as well as to perform predictions of outcomes and behaviors. Big Data has been characterized by 5 Vs - Volume, Velocity, Variety, Veracity and Value. This paper deals with all these 5Vs, features, challenges, future of Big Data in social media arena using data mining algorithms, tools and Hadoop framework for overcoming challenges of Big Data.
Improvements to filterbank and delta learning within a deep neural network framework

Many features used in speech recognition tasks are hand-crafted and are not always related to the objective at hand, that is minimizing word error rate. Recently, we showed that replacing a perceptually motivated mel-filter bank with a filter bank layer that is learned jointly with the rest of a deep neural network was promising. In this paper, we extend filter learning to a speaker-adapted, state-of-the-art system. First, we incorporate delta learning into the filter learning framework. Second, we incorporate various speaker adaptation techniques, including VTLN warping and speaker identity features. On a 50-hour English Broadcast News task, we show that we can achieve a 5% relative improvement in word error rate (WER) using the filter and delta learning, compared to having a fixed set of filters and deltas. Furthermore, after speaker adaptation, we find that filter and delta learning allows for a 3% relative improvement in WER compared to a state-of-the-art CNN.
Urokinase receptor antibody can reduce tumor volume and detect the presence of occult tumor metastases in vivo.

The serine protease urinary plasminogen activator or urokinase (uPA), produced in abundance by many malignancies, plays a key role in tumor cell invasion and metastasis. uPA is localized within the malignant cell milieu via its cell surface receptor [uPA receptor (uPAR)], which is expressed by tumor and tumor-associated cells. In the present study, we have used a syngeneic model of rat breast cancer to directly evaluate the role of uPAR as a diagnostic and therapeutic target in metastatic breast cancer. A polyclonal antibody against the ligand-binding NH(2)-terminal domain of rat uPAR (ruPAR) was developed. This antibody recognizes ruPAR by both immunofluorescence and Western blot analysis. Recombinant ruPAR and ruPAR IgG displaced the binding of (125)I-labeled ruPAR IgG to rat prostate cancer cells (Dunning R3227 Mat Ly Lu) and breast cancer cells (Mat B-III) overexpressing ruPAR (Mat B-III-uPAR). ruPAR IgG also blocked the invasive capacity of these tumor cells in a dose-dependent manner. Mat B-III-uPAR cells were inoculated s.c. into the mammary fat pad of syngeneic female Fischer rats. On day 10 after tumor cell inoculation, animals were injected with (125)I-labeled preimmune or ruPAR IgG and then sacrificed at timed intervals. Maximum (125)I uptake was observed in primary tumors and in tissues commonly affected by tumor metastases (liver, spleen, lungs, and lymph nodes) at 12 h. Injection of (125)I-labeled preimmune or ruPAR IgG into normal non-tumor-bearing animals resulted in minimal basal levels of uPAR expression and established the specificity of the ruPAR IgG. Similar results were obtained by Northern blot and PCR analysis of mRNA isolated from tissues of normal and tumor-bearing animals. To evaluate the effectiveness of this antibody in tumor progression, ruPAR IgG (50-100 microg/day) was injected s.c. for 7 days (day 1-7) at the site of tumor cell inoculation (mammary fat pad), and animals were sacrificed at various time points for evaluation of tumor growth and metastases. Animals receiving ruPAR IgG showed a marked decrease in tumor growth and metastases as compared with control tumor-bearing animals receiving the same dose of preimmune rabbit IgG. Histological analysis of experimental primary tumors showed marked tumor necrosis that was due to increased tumor cell apoptosis as determined by terminal deoxynucleotidyl transferase-mediated nick end labeling assay. Together, these studies demonstrate the ability of anti-uPAR antibody to decrease tumor volume and detect the presence of microscopic occult tumor metastases in malignancies where uPA/uPAR play a key role in tumor progression.
Neutrino-driven wind and wind termination shock in supernova cores

The neutrino-driven wind from a nascent neutron star at the center of a supernova expands into the earlier ejecta of the explosion. Upon collision with this slower matter the wind material is decelerated in a wind termination shock. By means of hydrodynamic simulations in spherical symmetry we demonstrate that this can lead to a large increase of the wind entropy, density, and temperature, and to a strong deceleration of the wind expansion. The consequences of this phenomenon for the possible r-process nucleosynthesis in the late wind still need to be explored in detail. Two-dimensional models show that the wind-ejecta collision is highly anisotropic and could lead to a directional dependence of the nucleosynthesis even if the neutrino-driven wind itself is spherically symmetric.
Delay minimization of multilayer graphene nanoribbon based interconnect using wire sizing method

The current article represents a delay optimization model of multi layered graphene nanoribbon (MLGNR) and Cu based nanointerconnects using wire sizing method. Using this model, the delay analysis for different interconnects width (8nm, 11nm, 16nm ITRS technology node) and length is performed. The novelty in our work is that this is the first time a delay optimization model for MLGNR and Cu interconnects is proposed. Our analysis shows that MLGNR based interconnects can achieve ~ 35-76% repeater reduction and ~ 10-25× reduction in propagation delay as compared with Cu based interconnects for three different interconnect widths i.e. 8nm, 11nm and 16nm and for local (10μm), intermediate (50μm) and global (100μm) interconnect lengths.
Analysis of Business Intelligence and Its Derivative - Financial Intelligence

Business intelligence is that the banking industry has to resolve the majority of the existing bank of the current problems faced by (for example: how to improve customer service, how to control financial risks, and how to improve the bank's operating performance, how to ensure that the sustained growth in profits, etc.) strong one of the weapons; Based on the data analysis tools ETL processing, data warehousing, online analytical processing, data mining, data analysis and display tools integrated applications, business intelligence agencies can provide large-scale enterprises or on-line data processing, data mining, data analysis, financial statements show, rational decision-making Business Intelligent services to achieve specific business objectives of the overall performance of enterprises or provide quantitative decision support integrated solutions.
What Does(n't) K-theory Classify?

We review various K-theory classification conjectures in string theory. Sen conjecture based proposals classify D-brane trajectories in backgrounds with no H flux, while Freed-Witten anomaly based proposals classify conserved RR charges and magnetic RR fluxes in topologically time-independent backgrounds. In exactly solvable CFTs a classification of well-defined boundary states implies that there are branes representing every twisted K-theory class. Some of these proposals fail to respect the self-duality of the RR fields in the democratic formulation of type II supergravity and none respect S-duality in type IIB string theory. We discuss two applications. The twisted K-theory classification has led to a conjecture for the topology of the T-dual of any configuration. In the Klebanov-Strassler geometry twisted K-theory classifies universality classes of baryonic vacua.
An energy management system for a battery ultracapacitor Hybrid Electric Vehicle

In this paper, a simple and efficient power split rule based energy management algorithm is designed for optimal sharing of energy between different energy sources in parallel HEV. This algorithm is simulated in MATLAB for Indian driving cycle (urban drive cycle) and Indian express highway drive cycle. The results indicate that the split of power can allow the engine to operate at its best efficiency and the life of battery can also be improved significantly with the introduction of ultracapacitor by maintaining the battery state of charge (SOC) within desired limits.
Is fluoride a risk factor for bone cancer? Small area analysis of osteosarcoma and Ewing sarcoma diagnosed among 0–49-year-olds in Great Britain, 1980–2005

Background: Artificial fluoridation of drinking water to improve dental health has long been a topic of controversy. Opponents of this public health measure have cited the possibility of bone cancer induction. The study objective was to examine whether increased risk of primary bone cancer was associated with living in areas with higher concentrations of fluoride in drinking water. Methods: Case data on osteosarcoma and Ewing sarcoma, diagnosed at ages 0–49 years in Great Britain (GB) (defined here as England, Scotland and Wales) during the period 1980–2005, were obtained from population-based cancer registries. Data on fluoride levels in drinking water in England and Wales were accessed through regional water companies and the Drinking Water Inspectorate. Scottish Water provided data for Scotland. Negative binomial regression was used to examine the relationship between incidence rates and level of fluoride in drinking water at small area level. Results: The study analysed 2566 osteosarcoma and 1650 Ewing sarcoma cases. There was no evidence of an association between osteosarcoma risk and fluoride in drinking water [relative risk (RR) per one part per million increase in the level of fluoride = 1·001; 90% confidence interval (CI) 0·871, 1·151] and similarly there was no association for Ewing sarcoma (RR = 0·929; 90% CI 0·773, 1·115). Conclusions: The findings from this study provide no evidence that higher levels of fluoride (whether natural or artificial) in drinking water in GB lead to greater risk of either osteosarcoma or Ewing sarcoma.
The Impact of Conceptual Modeling on Dataset Completeness: A Field Experiment

This paper investigates the impact of conceptual modeling on the information completeness dimension of information quality in the context of user-generated content. We propose a theoretical relationship between conceptual modeling approaches and information completeness and hypothesize that traditional classbased conceptual modeling negatively affects information completeness. We conducted a field experiment in the context of citizen science in biology. The empirical evidence demonstrates that users assigned to an instantiation that is based on class-based conceptual modeling provide fewer observations than users assigned to an instance-based condition. Users in the instance-based condition also provided a greater number of new classes of organisms. The findings support the proposed hypotheses and establish that conceptual modeling is an important factor in evaluating and increasing information completeness in user-generated content.
Learning what to look in chest X-rays with a recurrent visual attention model

X-rays are commonly performed imaging tests that use small amounts of radiation to produce pictures of the organs, tissues, and bones of the body. X-rays of the chest are used to detect abnormalities or diseases of the airways, blood vessels, bones, heart, and lungs. In this work we present a stochastic attention-based model that is capable of learning what regions within a chest X-ray scan should be visually explored in order to conclude that the scan contains a specific radiological abnormality. The proposed model is a recurrent neural network (RNN) that learns to sequentially sample the entire X-ray and focus only on informative areas that are likely to contain the relevant information. We report on experiments carried out with more than $100,000$ X-rays containing enlarged hearts or medical devices. The model has been trained using reinforcement learning methods to learn task-specific policies.
Zhejiang University at TRECVID 2006

We participated in the high-level feature extraction and interactive-search task for TRECVID 2006. Interaction and integration of multi-modality media types such as visual, audio and textual data in video are the essence of video content analysis. Although any uni-modality type partially expresses limited semantics less or more, video semantics are fully manifested only by interaction and integration of any unimodal. For the high-level feature extraction and interactive-search tasks, taking the temporal-sequenced associated cooccurence characteristic of multimodal media data in video into consideration, we develop a new approach to represent the relations between seperate shots, which mainly uses SimFusion and Locality Preserving Projections (LPP). SimFusion is an effective algorithm to reinforce or propagate the similarity relations between multi-modalities. LPP is an optimal combination of linear and nonlinear dimensionality reduction method. For high-level feature extraction task, e.g.semantic concept detection which actually is a pattern recognition problem, we use SVM as the powerful classifier to perform detection. For interactive search, we make use of relevance feedback to revise search results. We submitted one fun for each task.
Exploiting Full Parsing Information to Label Semantic Roles Using an Ensemble of ME and SVM via Integer Linear Programming

In this paper, we propose a method that exploits full parsing information by representing it as features of argument classification models and as constraints in integer linear learning programs. In addition, to take advantage of SVM-based and Maximum Entropy-based argument classification models, we incorporate their scoring matrices, and use the combined matrix in the above-mentioned integer linear programs. The experimental results show that full parsing information not only increases the F-score of argument classification models by 0.7%, but also effectively removes all labeling inconsistencies, which increases the F-score by 0.64%. The ensemble of SVM and ME also boosts the F-score by 0.77%. Our system achieves an F-score of 76.53% in the development set and 76.38% in Test WSJ.
WORKING PAPER No . 35 / 2013 A Capital Adequacy Buffer Model

In this paper, we develop a new capital adequacy buffer model (CABM) which is sensitive to dynamic economic circumstances. The model, which measures additional bank capital required to compensate for fluctuating credit risk, is a novel combination of the Merton structural model, which measures distance to default, and the timeless capital asset pricing model (CAPM) which measures additional returns to compensate for additional share price risk.
A 10 Gbit/s switch matrix MMIC using InP HEMTs with a logic-level-independent interface

An InP HEMT with a low on-resistance /spl times/ off-capacitance (Ron /spl times/ Coff) product enables us to configure a dc-to-over-10 GHz switch without using a shunt FET. The series FET configuration makes possible control-voltage-polarity independence, and offers a logic-level-independent interface. A 2/spl times/2 switch matrix MMIC yields an insertion loss of less than 1.16 dB and an isolation of more than 21.2 dB below 10 GHz. The MMIC also achieves error-free switch matrix operation up to 12.5 Gbit/s, using either a source coupled FET logic SCFL (1 V/sub p-p/, dc offset: -0.5 V) or low voltage differential signalling LVDS (0.3 V/sub p-p/, dc offset: +1.2 V) level.
Lock-free garbage collection for multiprocessors

Garbage collection algorithms for shared-memory multiprocessors typically rely on some form of global synchro- nization to preserve consistency. Such global synchronization may lead to problems on asynchronous architectures: if one process is halted or delayed, other, nonfaulty processes will be unable to progress. By contrast, a storage management algorithm is lock- free if (in the absence of resource exhaustion) a process that is allocating or collecting memory can be delayed at any point without forcing other processes to block. This paper presents the first algorithm for lock-free garbage collection in a realistic model. The algorithm assumes that processes synchronize by applying read, write, and cornpre&swap operations to shared memory. This algorithm uses no locks, busy-waiting, or barrier synchronization, it does not assume that processes can observe or modify one another's local variables or registers, and it does not use inter-process interrupts. Index Terms-Garbage collection, lock-free algorithms, mem- ory management, multiprocessors, shared memory, wait-free al- gorithms.
Distributed Games: From Mechanisms to Protocols

The theory of mechanism design in economics/game theory deals with a center who wishes to maximize an objective function which depends on a vector of information variables. The value of each variable is known only to a selfish agent, which is not controlled by the center. In order to obtain its objective the center constructs a game, in which every agent participates and reveals its information, because these actions maximize its utility. However, several crucial new issues arise when one tries to transform existing economic mechanisms into protocols to be used in computational environments. In this paper we deal with two such issues: 1. The communication structure, and 2. the representation (syntax) of the agents' information. The existing literature on mechanism design implicitly assumes that these two features are not relevant. In particular, it assumes a communication structure in which every agent is directly connected to the center. We present new protocols that can be implemented in a large variety of communication structures, and discuss the sensitivity of these protocols to the way in which information is presented.
Performances analysis of SEC based transmit diversity systems with MRC receivers

Switch and Examine combining (SEC) diversity scheme is one of the simplest yet efficient diversity schemes. Earlier in [1] and [2], we proposed sequential power loading algorithm to reduce the average power used at the transmitter in transmit diversity systems. In this paper, we propose the usage of a maximum ratio combining (MRC) receiver on top of sequential power loading, and provide a complete performance analysis of this proposed scheme in terms of average power used, average number of used antennas, average feedback load, and outage probability. Numerical results show that the proposed MRC enhanced scheme provides lower average number of used antennas and average feedback load with better outage performance than post selection and early selection schemes that were proposed earlier. This improved performance comes at the expense of a slightly higher average transmit power used.
Confidence in the Familiar: An International Perspective

One striking feature of international portfolio investment is the extent to which equity portfolios are concentrated in the domestic equity market of the investor—the home bias puzzle. I examine the role of investors' perception of foreign investment risk on their portfolio choices. The expected returns and risk of foreign investment are specified through an asset pricing model with the home portfolio being the benchmark asset—Pastor's (2000) domestic CAPM. The model serves as a reference point around which investors can center their prior beliefs. I focus on investors' prior beliefs that are consistent with the literature on confidence in the familiar—foreign equities, in terms of both expected returns and risk, being viewed less favorably than domestic equities. These prior beliefs are then combined with the data on G7 equities, and the revised beliefs are used to obtain the global optimal asset allocation. To hold predominantly domestic equities, each G7 investor has to believe that the risk of foreign investment is several times higher than the actual risk. The home bias is more of a puzzle for a U.S. investor during the 1970s. Specifying investors' prior beliefs around the world CAPM does not help resolve the puzzle.