nils-herrmann/clean_scirepeval huggingface dataset:
Towards Scalable and Reliable In-Memory Storage System: A Case Study with Redis

In recent years, in-memory key-value storage systems have become more and more popular in solving real-time and interactive tasks. Compared with disks, memories have much higher throughput and lower latency which enables them to process data requests with much higher performance. However, since memories have much smaller capacity than disks, how to expand the capacity of in-memory storage system while maintain its high performance become a crucial problem. At the same time, since data in memories are non-persistent, the data may be lost when the system is down. In this paper, we make a case study with Redis, which is one popular in-memory key-value storage system. We find that although the latest release of Redis support clustering so that data can be stored in distributed nodes to support a larger storage capacity, its performance is limited by its decentralized design that clients usually need two connections to get their request served. To make the system more scalable, we propose a Clientside Key-to-Node Caching method that can help direct request to the right service node. Experimental results show that by applying this technique, it can significantly improve the system's performance by near 2 times. We also find that although Redis supports data replication on slave nodes to ensure data safety, it still gets a chance of losing a part of the data due to a weak consistency between master and slave nodes that its defective order of data replication and request reply may lead to losing data without notifying the client. To make it more reliable, we propose a Master-slave Semi Synchronization method which utilizes TCP protocol to ensure the order of data replication and request reply so that when a client receives an "OK" message, the corresponding data must have been replicated. With a significant improvement in data reliability, its performance overhead is limited within 5%.
Review of the secondary injury theory of acute spinal cord trauma with emphasis on vascular mechanisms.

In patients with spinal cord injury, the primary or mechanical trauma seldom causes total transection, even though the functional loss may be complete. In addition, biochemical and pathological changes in the cord may worsen after injury. To explain these phenomena, the concept of the secondary injury has evolved for which numerous pathophysiological mechanisms have been postulated. This paper reviews the concept of secondary injury with special emphasis on vascular mechanisms. Evidence is presented to support the theory of secondary injury and the hypothesis that a key mechanism is posttraumatic ischemia with resultant infarction of the spinal cord. Evidence for the role of vascular mechanisms has been obtained from a variety of models of acute spinal cord injury in several species. Many different angiographic methods have been used for assessing microcirculation of the cord and for measuring spinal cord blood flow after trauma. With these techniques, the major systemic and local vascular effects of acute spinal cord injury have been identified and implicated in the etiology of secondary injury. The systemic effects of acute spinal cord injury include hypotension and reduced cardiac output. The local effects include loss of autoregulation in the injured segment of the spinal cord and a marked reduction of the microcirculation in both gray and white matter, especially in hemorrhagic regions and in adjacent zones. The microcirculatory loss extends for a considerable distance proximal and distal to the site of injury. Many studies have shown a dose-dependent reduction of spinal cord blood flow varying with the severity of injury, and a reduction of spinal cord blood flow which worsens with time after injury. The functional deficits due to acute spinal cord injury have been measured electrophysiologically with techniques such as motor and somatosensory evoked potentials and have been found proportional to the degree of posttraumatic ischemia. The histological effects include early hemorrhagic necrosis leading to major infarction at the injury site. These posttraumatic vascular effects can be treated. Systemic normotension can be restored with volume expansion or vasopressors, and spinal cord blood flow can be improved with dopamine, steroids, nimodipine, or volume expansion. The combination of nimodipine and volume expansion improves posttraumatic spinal cord blood flow and spinal cord function measured by evoked potentials. These results provide strong evidence that posttraumatic ischemia is an important secondary mechanism of injury, and that it can be counteracted.
The Nurse Scheduling Problem in Real-Life

The aim of this paper is to study the scheduling process for two types of nursing teams, regular teams from care units and the float team that covers for shortages in the hospital. When managers address this problem, they either use a manual approach or have to invest in expensive commercial tool. We propose a simple heuristic approach, flexible and easy enough to be implemented on spreadsheets, and requiring almost no investment. The approach leads to streamlined process and higher-quality schedules for nurses. The multi-objective model and heuristics are presented, and additional analysis is performed to compare the performance of the approach. We show that our approach compares very well with an optimization software (CPLEX solver) and may be implemented at no cost. It addresses the lack of choice between either manual solution method or a commercial package at a high cost.
A Panel Data Analysis of the Brain Gain

In this paper, we revisit the impact of skilled emigration on human capital accumulation using new panel data covering 147 countries on the period 1975-2000. We derive testable predictions from a stylized theoretical model and test them in dynamic regression models. Our empirical analysis predicts conditional convergence of human capital indicators. Our ndings also reveal that skilled migration prospects foster human capital accumulation in low-income countries. In these countries, a net brain gain can be obtained if the skilled emigration rate is not too large (i.e. does not exceed 20 to 30 percent depending on other country characteristics). On the contrary, we nd no evidence of a signicant incentive mechanism in middle-income and, unsuprisingly, in high-income countries.  human capital, convergence, brain drain We thank anonymous referees for their helpful comments. Suggestions from Barry Chiswick, Hubert Jayet, Joel Hellier and Fatemeh Shadman-Mehta were also appreciated. This article beneted from comments received at the SIUTE seminar (Lille, January 2006), the CReAM conference on Immigration: Impacts, Integration and Intergenerational Issues (London, March 2006), the Spring Meeting of Young Economists (Sevilla, May 2006), the XIV Villa Mondragone International Economic Seminar (Rome, July 2006) and the ESPE meeting (Chicago, 2007). The third author is grateful for the nancial support from the Belgian French-speaking Communitys programme Action de recherches concertées (ARC 03/08 -302) and from the Belgian Federal Government (PAI grant P6/07 Economic Policy and Finance in the Global Equilibrium Analysis and Social Evaluation). The usual disclaimers apply.  Michel Beine (michel.beine@uni.lu), University of Luxembourg, 162a av. de la Faiencerie, L-1511 Luxembourg.
Research through design as a method for interaction design research in HCI

For years the HCI community has struggled to integrate design in research and practice. While design has gained a strong foothold in practice, it has had much less impact on the HCI research community. In this paper we propose a new model for interaction design research within HCI. Following a research through design approach, designers produce novel integrations of HCI research in an attempt to make the right thing: a product that transforms the world from its current state to a preferred state. This model allows interaction designers to make research contributions based on their strength in addressing under-constrained problems. To formalize this model, we provide a set of four lenses for evaluating the research contribution and a set of three examples to illustrate the benefits of this type of research.
Capillary Hemangioma in Maxillary Anterior Region: A Case Report

Hemangiomas are relatively common benign proliferative lesion of vascular tissue origin. They are often present at birth and may become more apparent throughout life. They are seen on facial skin, tongue, lips, buccal mucosa and palate as well as muscles. Hemangiomas occur more common in females than males. This case report presents a case of capillary hemangioma in maxillary anterior region in a 10-year-old boy.
Study on Security Problems and Key Technologies of the Internet of Things

The IOT is a huge and widely distributed the Internet that things connect things.It connects all the articles to the internet through information sensing devices. It is the second information wave after Computer, Internet and mobile communication network. With the rapid development of the Internet of Things, its security problems have become more concentrated. This paper addresses the security issues and key technologies in IOT. It elaborated the basic concepts and the principle of the IOT and combined the relevant characteristics of the IOT as well as the International main research results to analysis the security issues and key technologies of the IOT which in order to plays a positive role in the construction and the development of the IOT through the research.
Reusing trace buffers to enhance cache performance

With the increasing complexity of modern Systems-on-Chip, the possibility of functional errors escaping design verification is growing. Post-silicon validation targets the discovery of these errors in early hardware prototypes. Due to limited visibility and observability, dedicated design-for-debug (DFD) hardware such as trace buffers are inserted to aid post-silicon validation. In spite of its benefit, such hardware incurs area overheads, which impose size limitations. However, the overhead could be overcome if the area dedicated to DFD could be reused in-field. In this work, we present a novel method for reusing an existing trace buffer as a victim cache of a processor to enhance performance. The trace buffer storage space is reused for the victim cache, with a small additional controller logic. Experimental results on several benchmarks and trace buffer sizes show that the proposed approach can enhance the average performance by up to 8.3% over a baseline architecture. We also propose a strategy for dynamic power management of the structure, to enable saving energy with negligible impact on performance.
Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges.

Smartphones now offer the promise of collecting behavioral data unobtrusively, in situ, as it unfolds in the course of daily life. Data can be collected from the onboard sensors and other phone logs embedded in today's off-the-shelf smartphone devices. These data permit fine-grained, continuous collection of people's social interactions (e.g., speaking rates in conversation, size of social groups, calls, and text messages), daily activities (e.g., physical activity and sleep), and mobility patterns (e.g., frequency and duration of time spent at various locations). In this article, we have drawn on the lessons from the first wave of smartphone-sensing research to highlight areas of opportunity for psychological research, present practical considerations for designing smartphone studies, and discuss the ongoing methodological and ethical challenges associated with research in this domain. It is our hope that these practical guidelines will facilitate the use of smartphones as a behavioral observation tool in psychological science.
A new, evidence-based estimate of patient harms associated with hospital care.

Based on 1984 data developed from reviews of medical records of patients treated in New York hospitals, the Institute of Medicine estimated that up to 98,000 Americans die each year from medical errors. The basis of this estimate is nearly 3 decades old; herein, an updated estimate is developed from modern studies published from 2008 to 2011.



A literature review identified 4 limited studies that used primarily the Global Trigger Tool to flag specific evidence in medical records, such as medication stop orders or abnormal laboratory results, which point to an adverse event that may have harmed a patient. Ultimately, a physician must concur on the findings of an adverse event and then classify the severity of patient harm.



Using a weighted average of the 4 studies, a lower limit of 210,000 deaths per year was associated with preventable harm in hospitals. Given limitations in the search capability of the Global Trigger Tool and the incompleteness of medical records on which the Tool depends, the true number of premature deaths associated with preventable harm to patients was estimated at more than 400,000 per year. Serious harm seems to be 10- to 20-fold more common than lethal harm.



The epidemic of patient harm in hospitals must be taken more seriously if it is to be curtailed. Fully engaging patients and their advocates during hospital care, systematically seeking the patients' voice in identifying harms, transparent accountability for harm, and intentional correction of root causes of harm will be necessary to accomplish this goal.
Social Recommendation with Tag Side Information

Social recommendation is the recommendation process by using social information. Being created to annotate and categorize items, tags are shared by users to find, organize and understand online entities, and provide the inherent flexibility to predict users' preference. In this paper, tag information and social relations are combined into recommendation, and a new tag information aided social recommendation method is proposed. By means of matrix factorization and regularization technology, we calculate the feature vectors and predict the rating of users. Also a new similarity measuring method is put forward when exploiting tag information through link prediction. Experiments show that our method generates better performance than state-of-the-art methods.
Group Consistent Similarity Learning via Deep CRF for Person Re-identification

Person re-identification benefits greatly from deep neural networks (DNN) to learn accurate similarity metrics and robust feature embeddings. However, most of the current methods impose only local constraints for similarity learning. In this paper, we incorporate constraints on large image groups by combining the CRF with deep neural networks. The proposed method aims to learn the "local similarity" metrics for image pairs while taking into account the dependencies from all the images in a group, forming "group similarities". Our method involves multiple images to model the relationships among the local and global similarities in a unified CRF during training, while combines multi-scale local similarities as the predicted similarity in testing. We adopt an approximate inference scheme for estimating the group similarity, enabling end-to-end training. Extensive experiments demonstrate the effectiveness of our model that combines DNN and CRF for learning robust multi-scale local similarities. The overall results outperform those by state-of-the-arts with considerable margins on three widely-used benchmarks.
A Tutorial on Fingerprint Recognition

This tutorial introduces fingerprint recognition systems and their main components: sensing, feature extraction and matching. The basic technologies are surveyed and some state-of-the-art algorithms are discussed. Due to the extent of this topic it is not possible to provide here all the details and to cover a number of interesting issues such as classification, indexing and multimodal systems. Interested readers can find in [21] a complete and comprehensive guide to fingerprint recognition.
Movie review mining and summarization

With the flourish of the Web, online review is becoming a more and more useful and important information resource for people. As a result, automatic review mining and summarization has become a hot research topic recently. Different from traditional text summarization, review mining and summarization aims at extracting the features on which the reviewers express their opinions and determining whether the opinions are positive or negative. In this paper, we focus on a specific domain - movie review. A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis and movie knowledge. The experimental results show the effectiveness of the proposed approach in movie review mining and summarization.
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem

This paper proposes a new method for the Karmed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a sharp finite-time regret bound of order O(K log T ) on a very general class of dueling bandit problems that matches a lower bound proven in (Yue et al., 2012). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art.
Association of Physical Function with Clinical and Subclinical Brain Disease: The Framingham Offspring Study.

Handgrip strength and gait speed are simple measures of physical capability and have been associated with current and future health outcomes. However, studies on their associations with brain structure and function in middle-aged adults are lacking.



To assess the relationship of fast-paced walking speed and handgrip strength with risk of dementia, Alzheimer's disease (AD), and stroke, as well as the cross-sectional associations with cognitive and brain magnetic resonance imaging (MRI) measures in a middle-aged community sample.



Framingham Offspring (n = 2,176; mean age 62, 54% female) had physical function, brain MRI, and cognitive evaluations between 1999 and 2005 and were followed-up for incident dementia AD and stroke until 11 years later. We related walking speed and handgrip strength to incident dementia, AD, and stroke using Cox models, and to brain and cognitive measures using multivariable linear and logistic regression. Models were adjusted for age, sex, education, and vascular risk factors.



Slow walking and weak handgrip were associated with more than 2.5-fold increase in risk of AD. Weaker handgrip was associated with an increased risk of incident stroke (HR 1.74, 95% CI: 1.12-2.70/SDU, p = 0.01) in persons ≥65 years. Both measures were associated with lower total brain volume and poorer performance on tests of visual memory, language, executive function, and visuoperceptual function. Slower gait was also related to poorer verbal memory, and weaker handgrip to poorer abstraction.



Tests of walking speed and handgrip strength may serve as clinical markers of brain structure and function and may improve dementia risk prediction.
Loss Functions for Top-k Error: Analysis and Insights

In order to push the performance on realistic computer vision tasks, the number of classes in modern benchmark datasets has significantly increased in recent years. This increase in the number of classes comes along with increased ambiguity between the class labels, raising the question if top-1 error is the right performance measure. In this paper, we provide an extensive comparison and evaluation of established multiclass methods comparing their top-k performance both from a practical as well as from a theoretical perspective. Moreover, we introduce novel top-k loss functions as modifications of the softmax and the multiclass SVM losses and provide efficient optimization schemes for them. In the experiments, we compare on various datasets all of the proposed and established methods for top-k error optimization. An interesting insight of this paper is that the softmax loss yields competitive top-k performance for all k simultaneously. For a specific top-k error, our new top-k losses lead typically to further improvements while being faster to train than the softmax.
Opening the Chrysalis: On the Real Repair Performance of MSR Codes

Emerging fast byte-addressable non-volatile memory (eNVM) technologies such as ReRAM and 3D Xpoint are projected to offer two orders of magnitude higher performance than flash. However, the existing solid-state drive (SSD) architecture optimizes for flash characteristics and is not adequate to exploit the full potential of eNVMs due to architectural and I/O interface (e.g., PCIe, SATA) limitations. To improve the storage performance and reduce the host main memory requirement for KVS, we propose a novel SSD architecture that extends the semantic of SSD with the KVS features and implements indexing capability inside SSD. It has in-storage processing engine that implements key-value operations such as get, put and delete to efficiently operate on KV datasets. The proposed system introduces a compute channel interface to offload key-value operations down to the SSD that significantly reduces the operating system, file system and other software overhead. This SSD achieves 4.96 Mops/sec get and 3.44 Mops/sec put operations and shows better scalability with increasing number of keyvalue pairs as compared to flash-based NVMe (flash-NVMe) and DRAMbased NVMe (DRAM-NVMe) devices. With decreasing DRAM size by 75%, its performance decreases gradually, achieving speedup of 3.23x as compared to DRAM-NVMe. This SSD significantly improves performance and reduces memory by exploiting the fine grain parallelism within a controller and keeping data movement local to effectively utilize eNVM bandwidth and eliminating the superfluous data movement between the host and the SSD.
Innovation trajectories for information visualizations: Comparing treemaps, cone trees, and hyperbolic trees

This paper reviews the trajectory of three information visualization innovations: treemaps, cone trees, and hyperbolic trees. These three ideas were first published around the same time in the early 1990s, so we are able to track academic publications, patents, and trade press articles over almost two decades. We describe the early history of each approach, problems with data collection from differing sources, appropriate metrics, and strategies for visualizing these longitudinal data sets. This paper makes two contributions: (1) it offers the information visualization community a history of how certain ideas evolved, influenced others, and were adopted for widespread use and (2) it provides an example of how such scientometric trajectories of innovations can be gathered and visualized. Guidance for designers is offered, but these conjectures may also be useful to researchers, research managers, science policy analysts, and venture capitalists.
Pivot wear of a centrifugal blood pump developed for circulatory assist

We are developing a monopivot centrifugal pump for circulatory assist for a period of more than 2 weeks. The impeller is supported by a pivot bearing at one end and by a passive magnetic bearing at the other. The pivot undergoes concentrated exposure to the phenomena of wear, hemolysis, and thrombus formation. The pivot durability, especially regarding the combination of male/female pivot radii, was examined through rotating wear tests and animal tests. As a result, combinations of similar radii for the male/female pivots were found to provide improved pump durability. In the extreme case, the no-gap combination would result in no thrombus formation.