nils-herrmann/clean_scirepeval huggingface dataset:
ARS: Cross-layer adaptive request scheduling to mitigate TCP incast in data center networks

In data center networks, many network-intensive applications typically suffer TCP incast throughput collapse when bursty concurrent TCP flows share a single bottleneck link. To address the TCP incast problem, we first reveal theoretically and empirically that controlling the number of concurrent flows is much more effective in reducing the incast probability than controlling the congestion window. We further propose a novel cross-layer design called Adaptive Request Schedule (ARS), which dynamically adjusts the number of concurrent TCP flows by batching application requests according to the congestion state acquired from transport layer. ARS is deployed only at the aggregator-side, while making no modification on hundreds or thousands of workers. Broad applicability is another advantage of ARS. We integrated ARS transparently (i.e., without modification) with DCTCP and TCP NewReno on NS2 simulation and a physical testbed, respectively. The experimental results show that ARS significantly reduces the incast probability across different TCP protocols and that the network goodput can be increased consistently by on average 6x under severe congestion.
A research agenda for highly effective human-computer interaction: useful, usable, and universal

The ACM SIGCHI (Association for Computing Machinery Special Interest Group in Computer Human Interaction) community conducted a deliberative process involving a high-visibility committee, a day-long workshop at CHI99 (Pittsburgh, PA, May 15, 1999) and a collaborative authoring process. This interim report is offered to produce further discussion and input leading to endorsement by the SIGCHI Executive Committee and then other professional societies. The scope of this research agenda included advanced information and communications technology research that could yield benefits over the next two to five years.
Towards Machine Learning on the Automata Processor

A variety of applications employ ensemble learning models, using a collection of decision trees, to quickly and accurately classify an input based on its vector of features. In this paper, we discuss the implementation of such a method, namely Random Forests, as the first machine learning algorithm to be executed on the Automata Processor (AP). The AP is an upcoming reconfigurable co-processor accelerator which supports the execution of numerous automata in parallel against a single input data-flow. Owing to this execution model, our approach is fundamentally di↵erent, translating Random Forest models from existing memory-bound tree-traversal algorithms to pipelined designs that use multiple automata to check all of the required thresholds independently and in parallel. We also describe techniques to handle floatingpoint feature values which are not supported in the native hardware, pipelining of the execution stages, and compression of automata for the fastest execution times. The net result is a solution which when evaluated using two applications, namely handwritten digit recognition and sentiment analysis, produce up to 63 and 93 times speed-up respectively over single-core state-of-the-art CPU-based solutions. We foresee these algorithmic techniques to be useful not only in the acceleration of other applications employing Random Forests, but also in the implementation of other machine learning methods on this novel architecture.
Kinesthesia in a sustained-attention driving task

This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time-frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain-behavior relations in operating environments.
Global Tactical Asset Allocation

SPRING 2001 THE JOURNAL OF GLOBAL CAPITAL MARKETS 1 T he goal of asset allocation is to achieve the best possible expected return/risk profile. It is useful to distinguish three levels of asset allocation. Benchmark asset allocation is a program that exactly replicates the investment weights of the benchmark index. For example, if the manager is benchmarked to the Morgan Stanley Capital International (MSCI) world portfolio, the benchmark asset allocation assumes the same weights as this index. This type of asset allocation is sometimes referred to as indexing. No information is used other than the usual details of indexing: determining market weights, and managing delistings, new listings, buy-backs, secondary market offerings, dividends, and warrants. If the benchmark is very broad, like the MSCI world, this asset allocation is sometimes referred to as equilibrium asset allocation. This convention arises from the main implication of the capital asset pricing model (CAPM) that investors hold the world market portfolio in equilibrium, levered up or down to match the desired risk aversion (Sharpe [1964] and Black [1972]). Indeed, we often assume that the ex ante variance-covariance structure is represented by the historical one, and deduce the expected returns that are implied by the market investment weights. That is, given a variance-covariance matrix, we can back out of the optimization the expected returns that exactly imply the market weights. These expected returns are often referred to as the equilibrium expected returns. We need to be careful here. There are many known caveats to the CAPM holding. We therefore prefer to refer to this asset allocation as the benchmark allocation.1 The second level of asset allocation is strategic asset allocation. This asset allocation is typically long-term in nature, usually with a five-year horizon. Investment managers place bets or tilts based on long-term views of asset class performance. For example, it may be the manager’s view (whether based on a quantitative model, judgment, or both) that Japanese government bonds will underperform (relative to historical performance) over the next five years. A decision would be made to deviate from the benchmark. While the weights are based on five-year forecasts, it is not usual to update the five-year forecast every year and hence rebalance annually. Deviation from the benchmark introduces tracking error. The tracking error is the standard deviation of the differences between the benchmark return and the portfolio return (“strategic” tracking error). The third level of asset allocation is tactical asset allocation. Here investment managers will take short-term bets, usually one month to one quarter, and deviate from the strategic weights. This also induces tracking error. The difference between the strategic and tactical weights induces “tactical” tracking error. The difference between the benchmark weights and the tactical weights is the total tracking error. Note that the strategic and tactical tracking Global Tactical Asset Allocation
Effect of sodium oxybate on growth hormone secretion in narcolepsy patients and healthy controls.

Hypocretin deficiency causes narcolepsy and may affect neuroendocrine systems and body composition. Additionally, growth hormone (GH) alterations my influence weight in narcolepsy. Symptoms can be treated effectively with sodium oxybate (SXB; γ-hydroxybutyrate) in many patients. This study compared growth hormone secretion in patients and matched controls and established the effect of SXB administration on GH and sleep in both groups. Eight male hypocretin-deficient patients with narcolepsy and cataplexy and eight controls matched for sex, age, BMI, waist-to-hip ratio, and fat percentage were enrolled. Blood was sampled before and on the 5th day of SXB administration. SXB was taken two times 3 g/night for 5 consecutive nights. Both groups underwent 24-h blood sampling at 10-min intervals for measurement of GH concentrations. The GH concentration time series were analyzed with AutoDecon and approximate entropy (ApEn). Basal and pulsatile GH secretion, pulse regularity, and frequency, as well as ApEn values, were similar in patients and controls. Administration of SXB caused a significant increase in total 24-h GH secretion rate in narcolepsy patients, but not in controls. After SXB, slow-wave sleep (SWS) and, importantly, the cross-correlation between GH levels and SWS more than doubled in both groups. In conclusion, SXB leads to a consistent increase in nocturnal GH secretion and strengthens the temporal relation between GH secretion and SWS. These data suggest that SXB may alter somatotropic tone in addition to its consolidating effect on nighttime sleep in narcolepsy. This could explain the suggested nonsleep effects of SXB, including body weight reduction.
Patients' awareness of stroke signs, symptoms, and risk factors.

We sought to determine knowledge at the time of symptom onset regarding the signs, symptoms, and risk factors of stroke in patients presenting to the emergency department with potential stroke.



Patients admitted from the emergency department with possible stroke were identified prospectively. A standardized, structured interview with open-ended questions was performed within 48 hours of symptom onset to assess patients' knowledge base concerning stroke signs, symptoms, and risk factors.



Of the 174 eligible patients, 163 patients were able to respond to the interview questions. Of these 163 patients, 39% (63) did not know a single sign or symptom of stroke. Unilateral weakness (26%) and numbness (22%) were the most frequently noted symptoms. Patients aged > or = 65 years were less likely to know a sign or symptom of stroke than those aged < 65 years (percentage not knowing a single sign or symptom, 47% versus 28%, P = .016). Similarly, 43% of patients did not know a single risk factor for stroke. The elderly were less likely to know a risk factor than their younger counterparts.



Almost 40% of patients admitted with a possible stroke did not know the signs, symptoms, or risk factor of a stroke. Further public education is needed to increase awareness of the warning signs and risk factors of stroke.
Internal Capital Allocation and Firm Performance

Do conglomerate firms have the ability to allocate resources efficiently across business segments? We address this question by comparing the performance of firms that follow passive benchmark strategies in their capital allocation process to those that actively deviate from those benchmarks. Using three measures of capital allocation style to capture various aspects of activeness, we show that active firms have a lower average industry-adjusted profitability than passive firms. This result is robust to controlling for potential endogeneity using matching analysis and regression analysis with firm fixed effects. Moreover, active firms obtain lower valuation and lower excess stock returns in subsequent periods. Our findings suggest that, on average, conglomerate firms that actively allocate resources across their business segments do not do so efficiently and that the stock market does not fully incorporate information revealed in the internal capital allocation process. Guedj and Huang are from the McCombs School of Business, University of Texas at Austin. Guedj: guedj@mail.utexas.edu and (512) 471-5781. Huang: jennifer.huang@mccombs.utexas.edu and (512) 232-9375. Sulaeman is from the Cox School of Business, Southern Methodist University, sulaeman@smu.edu and (214) 768-8284. The authors thank Alexander Butler, Amar Gande, Mark Leary, Darius Miller, Maureen O’Hara, Owen Lamont, Gordon Phillips, Mike Roberts, Oleg Rytchkov, Gideon Saar, Zacharias Sautner, Clemens Sialm, Rex Thompson, Sheridan Titman, Yuhai Xuan, participants at the Financial Research Association meeting and seminars at Cornell University, Southern Methodist University, the University of Texas at Austin, and the University of Texas at Dallas for their helpful comments.
Combining named entities and tags for novel sentence detection

Novel sentence detection aims at identifying novel information from an incoming stream of sentences. Our research applies named entity recognition (NER) and part-of-speech (POS) tagging on sentence-level novelty detection and proposes a mixed method to utilize these two techniques. Furthermore, we discuss the performance when setting different history sentence sets. Experimental results of different approaches on TREC'04 Novelty Track show that our new combined method outperforms some other novelty detection methods in terms of precision and recall. The experimental observations of each approach are also discussed.
Quantitative models of Pavlovian conditioning

Over the last few years, research on learning and memory has become increasingly interdisciplinary. In the past, theories of learning, as a prerogative of psychologists, were generally formulated in purely verbal terms and evaluated exclusively at the behavioral level. At present, scientists are trying to build theories with a quantitative and biological flavor, seeking to embrace more complex behavioral phenomena. Pavlovian conditioning, one of the simplest and ubiquitous forms of learning, is especially suited for this multiple level analysis (i.e., quantitative, neurobiological, and behavioral), in part because of recent discoveries showing a correspondence between behavioral phenomena and associative properties at the cellular and systems levels, and in part because of its well established quantitative theoretical tradition. The present review, examines the mayor quantitative theories of Pavlovian conditioning and the phenomena to which they have been designed to account. In order to provide researchers from different disciplines with a simple guideline about the rationale of the different theoretical choices, all the models are described through a single formalism based on the neural network connectionist perspective.
Declarative Support for Sensor Data Cleaning

Pervasive applications rely on data captured from the physical world through sensor devices. Data provided by these devices, however, tend to be unreliable. The data must, therefore, be cleaned before an application can make use of them, leading to additional complexity for application development and deployment. Here we present Extensible Sensor stream Processing (ESP), a framework for building sensor data cleaning infrastructures for use in pervasive applications. ESP is designed as a pipeline using declarative cleaning mechanisms based on spatial and temporal characteristics of sensor data. We demonstrate ESP’s effectiveness and ease of use through three real-world scenarios.
Feature-driven Time Series Generation

Time series data are an ubiquitous and important data source in many domains. Most companies and organizations rely on this data for critical tasks like decision-making, planning, and analytics in general. Usually, all these tasks focus on actual data representing organization and business processes. In order to assess the robustness of current systems and methods, it is also desirable to focus on time-series scenarios which represent specific time-series features. This work presents a generally applicable and easy-to-use method for the feature-driven generation of time series data. Our approach extracts descriptive features of a data set and allows the construction of a specific version by means of the modification of these features.
The Neurobiology and Genetics of Suicide and Attempted Suicide: A Focus on the Serotonergic System

Numerous abnormalities have been found in the serotonergic system in suicide attempters and completers. There is considerable evidence that the serotonergic system is partly under genetic control and that as yet unknown genetic factors mediate the risk for suicidal behavior independently of the genetic factors responsible for the heritability of major psychiatric conditions associated with suicide. An argument is made that there is a relationship of genetic variants to intermediate phenotypes, such as impulsivity, psychomotor change, pathological aggression and biological abnormalities including specific gene products. A variety of biological indices have been generated by new approaches using postmortem tissue and in vivo imaging that will provide a rich substrate for further genetic studies.
Attacking smart card systems: Theory and practice

Smart card technology has evolved over the last few years following notable improvements in the underlying hardware and software platforms. Advanced smart card microprocessors, along with robust smart card operating systems and platforms, contribute towards a broader acceptance of the technology. These improvements have eliminated some of the traditional smart card security concerns. However, researchers and hackers are constantly looking for new issues and vulnerabilities. In this article we provide a brief overview of the main smart card attack categories and their corresponding countermeasures. We also provide examples of well-documented attacks on systems that use smart card technology (e.g. satellite TV, EMV, proximity identification) in an attempt to highlight the importance of the security of the overall system rather than just the smart card. a 2009
New Techniques for Automated Architectural Reconstruction from Photographs

We investigate a strategy for reconstructing of buildings from multiple (uncalibrated) images. In a similar manner to the Facade approach we first generate a coarse piecewise planar model of the principal scene planes and their delineations, and then use these facets to guide the search for indentations and protrusions such as windows and doors. However, unlike the Facade approach which involves manual selection and alignment of the geometric primitives, the strategy here is fully automatic. There are several points of novelty: first we demonstrate that the use of quite generic models together with particular scene constraints (the availability of several principal directions) is sufficiently powerful to enable successful reconstruction of the targeted scenes. Second, we develop and refine a technique for piecewise planar model fitting involving sweeping polygonal primitives, and assess the performance of this technique. Third, lines at infinity are constructed from image correspondences and used to sweep planes in the principal directions. The strategy is illustrated on several image triplets of College buildings. It is demonstrated that convincing texture mapped models are generated which include the main walls and roofs, together with inset windows and also protruding (dormer) roof windows.
Dynamic Integrated Scheduling of Hard Real-Time, Soft Real-Time and Non-Real-Time Processes

Real-time systems are growing in complexity and realtime and soft real-time applications are becoming common in general-purpose computing environments. Thus, there is a growing need for scheduling solutions that simultaneously support processes with a variety of different timeliness constraints. Toward this goal we have developed the Resource Allocation/Dispatching (RAD) integrated scheduling model and the Rate-Based Earliest Deadline (RBED) integrated multi-class real-time scheduler based on this model. We present RAD and the RBED scheduler and formally prove the correctness of the operations that RBED employs. We then describe our implementation of RBED and present results demonstrating how RBED simultaneously and seamlessly supports hard real-time, soft real-time, and best-effort processes.
A REVIEW ON A NEW TETRASPANIN, TSPAN-11

The tetraspanins are a superfamily of four-span membrane proteins that are widely expressed in mammalian cells. Although they have been implicated in important cell processes and some diseases, it has been difficult to characterise their exact functions. Tspan-11 is a recently identified member of the tetraspanin family. Little is known about Tspan-11; however sequence evidence indicates that Tspan-11 most closely resembles tetraspanin CD151. Recently, Tspan-11 GFP transfected CHO cells were used to immunize mice for generation of monoclonal antibodies (mAbs) and found that these mAbs bound to the surface of Tspan-11 GFP transfected CHO cells, indicating that they are able to recognize native forms of the tetraspanin. Surprisingly, one of cancer cell lines tested, the A549 lung adenocarcinoma cell line, was strongly positive for Tspan-11 mAbs, showing both intracellular and plasma membrane staining. Some preliminary investigations have been carried out on normal cells/tissues; these indicate that Tspan-11 is only expressed weakly, if at all on white blood cells, and may be present on epithelial cells in the colon. Taken together, these findings suggest that Tspan-11 has low, or restricted expression, or that the mAbs characterised so far recognize only some forms of the protein.
An FPGA implementation of elliptic curve cryptography for future secure web transaction

Elliptic curve cryptography (ECC) is an alternative to traditional techniques for public key cryptography. It offers smaller key size without sacrificing security level. In a typical elliptic curve cryptosystem, elliptic curve point multiplication is the most computationally expensive component. So it would be more attractive to implement this unit using hardware than using software. In this paper, we propose an efficient FPGA implementation of the elliptic curve point multiplication in GF(2). We have designed and synthesized the elliptic curve point multiplication with Xilinx’s FPGA. Experimental results demonstrate that the FPGA implementation can speedup the point multiplication by 31.6 times compared to a software based implementation.
Real Power Loss Minimization using Big Bang Big Crunch Algorithm

In power system operation, minimizing the power loss in transmission lines and/or minimizing the voltage deviation at the load buses by controlling the reactive power is referred to as optimal reactive power dispatch (ORPD). ORPD is necessary for secured operation of power systems with regard to voltage stability. In this paper, the nature inspired Big Bang – Big Crunch (BB-BC) algorithm is introduced to solve multi constrained optimal reactive power flow problem in power systems. Generator bus voltages, transformer tap positions and switchable shunt capacitor banks are used as variables to control the reactive power flow. Big Bang – Big Crunch algorithm was tested on standard IEEE 30 bus system and the results are compared with other methods to prove the effectiveness of the new algorithm. The results are quite encouraging and the algorithm is found to be simple and easy to implement.