nils-herrmann/clean_scirepeval huggingface dataset:
Why musical memory can be preserved in advanced Alzheimer's disease.

Musical memory is considered to be partly independent from other memory systems. In Alzheimer's disease and different types of dementia, musical memory is surprisingly robust, and likewise for brain lesions affecting other kinds of memory. However, the mechanisms and neural substrates of musical memory remain poorly understood. In a group of 32 normal young human subjects (16 male and 16 female, mean age of 28.0 ± 2.2 years), we performed a 7 T functional magnetic resonance imaging study of brain responses to music excerpts that were unknown, recently known (heard an hour before scanning), and long-known. We used multivariate pattern classification to identify brain regions that encode long-term musical memory. The results showed a crucial role for the caudal anterior cingulate and the ventral pre-supplementary motor area in the neural encoding of long-known as compared with recently known and unknown music. In the second part of the study, we analysed data of three essential Alzheimer's disease biomarkers in a region of interest derived from our musical memory findings (caudal anterior cingulate cortex and ventral pre-supplementary motor area) in 20 patients with Alzheimer's disease (10 male and 10 female, mean age of 68.9 ± 9.0 years) and 34 healthy control subjects (14 male and 20 female, mean age of 68.1 ± 7.2 years). Interestingly, the regions identified to encode musical memory corresponded to areas that showed substantially minimal cortical atrophy (as measured with magnetic resonance imaging), and minimal disruption of glucose-metabolism (as measured with (18)F-fluorodeoxyglucose positron emission tomography), as compared to the rest of the brain. However, amyloid-β deposition (as measured with (18)F-flobetapir positron emission tomography) within the currently observed regions of interest was not substantially less than in the rest of the brain, which suggests that the regions of interest were still in a very early stage of the expected course of biomarker development in these regions (amyloid accumulation → hypometabolism → cortical atrophy) and therefore relatively well preserved. Given the observed overlap of musical memory regions with areas that are relatively spared in Alzheimer's disease, the current findings may thus explain the surprising preservation of musical memory in this neurodegenerative disease.
Design and implementation of web based on Laravel framework

With the traditional framework design methods to design web, resulting in large limitations, time-consuming and other issues, for such problems, this paper presents the design and implementation method of a web based on Laravel framework, Laravel make the development process is standardized, processing some non-business logic relationship automatically. This paper designs and implements a simple Laravel model, which achieved automated processing for part of the design. The experimental and simulation proved, web design based on Laravel framework, has scalability and robust scalability, so as to improve the developing efficiency.
Improving read performance by isolating multiple queues in NVMe SSDs

As NVMe SSDs have become viable, recent researches have focused on optimizing the performance of NVMe SSDs, which provide multiple I/O queues to maximize the I/O parallelism of flash-chip, while traditional operating systems are designed ordinarily for single queue storage, such as HDD and SATA SSD. Unfortunately, no prior works have considered the write interference while NVMe SSD still has the possibility of the interference. This interference is crucial in read-intensive environments because write requests negatively affect the latency of read requests. In this paper, we propose a novel queue isolation scheme that efficiently eliminates the write interference and improves the read performance by isolating read and write requests. Our experimental results clearly show that our scheme improves the read performance by up to 33% in heavy read workloads, compared to Baseline.
Influence of surface on muscle damage and soreness induced by consecutive drop jumps.

This study compared the magnitude of muscle damage induced when consecutive drop jumps (DJs) were performed on sand vs. firm (wood) surfaces from a height of 0.6 m. Eight subjects performed DJs on a sand surface at a depth of 0.2 m (S condition), and 8 other subjects performed DJs on a wood surface (F condition). Each set consisted of 20 DJs with an interval of 10 seconds between jumps. Subjects performed 5 sets of DJs with 2 minutes between sets. Maximal isometric force, muscle soreness, and plasma creatine kinase (CK) activity were measured immediately before and immediately after the DJ exercise as well as 1, 24, 48, 72, and 96 hours after the DJ exercise. All measures changed significantly (p < 0.05) after exercise for both conditions; however, significantly (p < 0.05) smaller changes in these measures were evident for the S condition than for the F condition. These results show that DJs on a sand surface induce less muscle damage than on a firm surface. Training on sand may improve aerobic capacity or strength with a low risk of muscle damage.
A model-theoretic coreference scoring scheme

This note describes a scoring scheme for the coreference task in MUC6 . It improves o n the original approach l by: (1) grounding the scoring scheme in terms of a model ; (2) producing more intuitive recall and precision scores ; and (3) not requiring explici t computation of the transitive closure of coreference . The principal conceptual differenc e is that we have moved from a syntactic scoring model based on following coreferenc e links to an approach defined by the model theory of those links .
Finding Bernie Madoff : Detecting Fraud by Investment Managers

Using a panel of mandatory SEC disclosure filings we test the predictability of investment fraud. We find that past regulatory and legal violations, conflicts of interest, and monitoring, are significantly associated with future fraud. Avoiding the 5% of firms with the highest fraud risk allows investors to avoid 29.7% of investment frauds, and over half of the total dollar losses from fraud. Even after excluding small frauds and fraud by rogue employees, we are able to predict at least 24.1% of frauds at a false positive rate of 5%. There is no evidence that investors are compensated for fraud risk through superior performance or lower fees. We also find that investors react strongly to the discovery of fraud, resulting in significantly higher rates of firm death and investor outflows. Our results provide investors and regulators with tools for predicting investment fraud.
Concepts extraction for medical documents using ontology

In the biomedical domain large amount of text documents are unstructured information is available in digital text form. Text Mining is the method or technique to find for interesting and useful information from unstructured text. Text Mining is also an important task in medical domain. The technique uses for Information retrieval, Information extraction and natural language processing (NLP). Traditional approaches for information retrieval are based on key based similarity. These approaches are used to overcome these problems; Semantic text mining is to discover the hidden information from unstructured text and making relationships of the terms occurring in them. In the biomedical text, the text should be in the form of text which can be present in the books, articles, literature abstracts, and so forth. Most of information is stored in the text format, so in this paper we will focus on the role of ontology for semantic text mining by using WordNet. Specifically, we have presented a model for extracting concepts from text documents using linguistic ontology in the domain of medical.
Training and analyzing deep recurrent neural networks

Time series often have a temporal hierarchy, with information that is spread out over multiple time scales. Common recurrent neural networks, however, do not explicitly accommodate such a hierarchy, and most research on them has been focusing on training algorithms rather than on their basic architecture. In this paper we study the effect of a hierarchy of recurrent neural networks on processing time series. Here, each layer is a recurrent network which receives the hidden state of the previous layer as input. This architecture allows us to perform hierarchical processing on difficult temporal tasks, and more naturally capture the structure of time series. We show that they reach state-of-the-art performance for recurrent networks in character-level language modeling when trained with simple stochastic gradient descent. We also offer an analysis of the different emergent time scales.
Business-to-business interactions: issues and enabling technologies

Business-to-Business (B2B) technologies pre-date the Web. They have existed for at least as long as the Internet. B2B applications were among the first to take advantage of advances in computer networking. The Electronic Data Interchange (EDI) business standard is an illustration of such an early adoption of the advances in computer networking. The ubiquity and the affordability of the Web has made it possible for the masses of businesses to automate their B2B interactions. However, several issues related to scale, content exchange, autonomy, heterogeneity, and other issues still need to be addressed. In this paper, we survey the main techniques, systems, products, and standards for B2B interactions. We propose a set of criteria for assessing the different B2B interaction techniques, standards, and products.
Agile Software Development at Scale

Since 2001 agile software development app roaches are being adopted across a wide range of organizations and are now be ing applied at scale. There are eight factors to consider – team size, geograph ic l distribution, entrenched culture, system complexity, legacy systems, regulat ory compliance, organizational distribution, governance and enterpr ise focus – when scaling agile. Luckily a collection of techniques and stra tegies exist which scale agile approaches, including considering the full developm ent lifecycle, Agile Model Driven Development (AMDD), continuous independent t esting, adopting proven strategies, agile database techniques, and l e development governance. It is possible to scale agile approaches, but you w ill need to look beyond the advice provided by the “agile in the small” literat ure.
Age-related change in executive function: Developmental trends and a latent variable analysis

This study examined the developmental trajectories of three frequently postulated executive function (EF) components, Working Memory, Shifting, and Inhibition of responses, and their relation to performance on standard, but complex, neuropsychological EF tasks, the Wisconsin Card Sorting Task (WCST), and the Tower of London (ToL). Participants in four age groups (7-, 11-, 15-, and 21-year olds) carried out nine basic experimental tasks (three tasks for each EF), the WCST, and the ToL. Analyses were done in two steps: (1) analyses of (co)variance to examine developmental trends in individual EF tasks while correcting for basic processing speed, (2) confirmatory factor analysis to extract latent variables from the nine basic EF tasks, and to explain variance in the performance on WCST and ToL, using these latent variables. Analyses of (co)variance revealed a continuation of EF development into adolescence. Confirmatory factor analysis yielded two common factors: Working Memory and Shifting. However, the variables assumed to tap Inhibition proved unrelated. At a latent level, again correcting for basic processing speed, the development of Shifting was seen to continue into adolescence, while Working Memory continued to develop into young-adulthood. Regression analyses revealed that Working Memory contributed most strongly to WCST performance in all age groups. These results suggest that EF component processes develop at different rates, and that it is important to recognize both the unity and diversity of EF component processes in studying the development of EF.
Replication and Analysis of Ebbinghaus’ Forgetting Curve

We present a successful replication of Ebbinghaus' classic forgetting curve from 1880 based on the method of savings. One subject spent 70 hours learning lists and relearning them after 20 min, 1 hour, 9 hours, 1 day, 2 days, or 31 days. The results are similar to Ebbinghaus' original data. We analyze the effects of serial position on forgetting and investigate what mathematical equations present a good fit to the Ebbinghaus forgetting curve and its replications. We conclude that the Ebbinghaus forgetting curve has indeed been replicated and that it is not completely smooth but most probably shows a jump upwards starting at the 24 hour data point.
Matching Node Embeddings for Graph Similarity

Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph kernels focus on local properties of graphs and ignore global structure. In this paper, we compare graphs based on their global properties as these are captured by the eigenvectors of their adjacency matrices. We present two algorithms for both labeled and unlabeled graph comparison. These algorithms represent each graph as a set of vectors corresponding to the embeddings of its vertices. The similarity between two graphs is then determined using the Earth Mover’s Distance metric. These similarities do not yield a positive semidefinite matrix. To address for this, we employ an algorithm for SVM classification using indefinite kernels. We also present a graph kernel based on the Pyramid Match kernel that finds an approximate correspondence between the sets of vectors of the two graphs. We further improve the proposed kernel using the Weisfeiler-Lehman framework. We evaluate the proposed methods on several benchmark datasets for graph classification and compare their performance to state-of-the-art graph kernels. In most cases, the proposed algorithms outperform the competing methods, while their time complexity remains very attractive.
A patient-specific in vivo tumor model.

A significant body of research, spanning approximately the last 25 years, has focused upon the task of developing a better understanding of tumor growth through the use of in vitro mathematical models. Although such models are useful for simulation, in vivo growth differs in significant ways due to the variety of competing biological, biochemical, and mechanical factors present in a living biological system. An in vivo, macroscopic, primary brain tumor growth model is developed, incorporating previous in vitro growth pattern research as well as scientific investigations into the biological and biochemical factors that affect in vivo neoplastic growth. The tumor growth potential model presents an integrated, universal framework that can be employed to predict the direction and extent of spread of a primary brain tumor with respect to time for a specific patient. This framework may be extended as necessary to include the results of current and future research into parameters affecting neoplastic proliferation. The patient-specific primary brain tumor growth model is expected to have multiple clinical uses, including: predictive modeling, tumor boundary delineation, growth pattern research, improved radiation surgery planning, and expert diagnostic assistance.
Person re-identification by distance metric learning to discrete hashing

Most of the existing works on person re-identification have focused on improving matching rate at top ranks. Few efforts are devoted to address the problem of efficient storage and fast search for person re-identification. In this paper, we investigate the prevailing hashing method, originally designed for large scale image retrieval, for fast person re-identification with efficient storage. We propose a novel hashing approach, namely Distance Metric Learning to Discrete Hashing (DMLDH), which jointly learns a discriminative projection via metric learning to alleviate cross-view variations, and a hashing function for discriminative binary coding by minimizing inner-class Hamming distances and maximizing inter-class Hamming distances. To deal with the formulated non-convex optimization problem, we develop an alternative iteration algorithm by solving several subproblems with analytical solutions. Experimental results on benchmarks demonstrate that the proposed method outperforms the state-of-the-art hashing approaches.
Interacting with sexist men triggers social identity threat among female engineers.

Social identity threat is the notion that one of a person's many social identities may be at risk of being devalued in a particular context (C. M. Steele, S. J. Spencer, & J. Aronson, 2002). The authors suggest that in domains in which women are already negatively stereotyped, interacting with a sexist man can trigger social identity threat, undermining women's performance. In Study 1, male engineering students who scored highly on a subtle measure of sexism behaved in a dominant and sexually interested way toward an ostensible female classmate. In Studies 2 and 3, female engineering students who interacted with such sexist men, or with confederates trained to behave in the same way, performed worse on an engineering test than did women who interacted with nonsexist men. Study 4 replicated this finding and showed that women's underperformance did not extend to an English test, an area in which women are not negatively stereotyped. Study 5 showed that interacting with sexist men leads women to suppress concerns about gender stereotypes, an established mechanism of stereotype threat. Discussion addresses implications for social identity threat and for women's performance in school and at work.
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction

In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, however, text information can only disclose the "tip of the iceberg»» about users» true opinions, of which the most are unobserved but implied by other sources of information such as social relation and users» profile. To address this problem, in this paper we investigate how to predict possibly existing sentiment links in the presence of heterogeneous information. First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users» sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method. Then we propose a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users» latent representations from heterogeneous networks and predict the sign of unobserved sentiment links. SHINE utilizes multiple deep autoencoders to map each user into a low-dimension feature space while preserving the network structure. We demonstrate the superiority of SHINE over state-of-the-art baselines on link prediction and node recommendation in two real-world datasets. The experimental results also prove the efficacy of SHINE in cold start scenario.
Progressive resistance strength training for improving physical function in older adults.

Muscle weakness in old age is associated with physical function decline. Progressive resistance strength training (PRT) exercises are designed to increase strength.



To assess the effects of PRT on older people and identify adverse events.


SEARCH STRATEGY
We searched the Cochrane Bone, Joint and Muscle Trauma Group Specialized Register (to March 2007), the Cochrane Central Register of Controlled Trials (The Cochrane Library 2007, Issue 2), MEDLINE (1966 to May 01, 2008), EMBASE (1980 to February 06 2007), CINAHL (1982 to July 01 2007) and two other electronic databases. We also searched reference lists of articles, reviewed conference abstracts and contacted authors.



Randomised controlled trials reporting physical outcomes of PRT for older people were included.



Two review authors independently selected trials, assessed trial quality and extracted data. Data were pooled where appropriate.



One hundred and twenty one trials with 6700 participants were included. In most trials, PRT was performed two to three times per week and at a high intensity. PRT resulted in a small but significant improvement in physical ability (33 trials, 2172 participants; SMD 0.14, 95% CI 0.05 to 0.22). Functional limitation measures also showed improvements: e.g. there was a modest improvement in gait speed (24 trials, 1179 participants, MD 0.08 m/s, 95% CI 0.04 to 0.12); and a moderate to large effect for getting out of a chair (11 trials, 384 participants, SMD -0.94, 95% CI -1.49 to -0.38). PRT had a large positive effect on muscle strength (73 trials, 3059 participants, SMD 0.84, 95% CI 0.67 to 1.00). Participants with osteoarthritis reported a reduction in pain following PRT(6 trials, 503 participants, SMD -0.30, 95% CI -0.48 to -0.13). There was no evidence from 10 other trials (587 participants) that PRT had an effect on bodily pain. Adverse events were poorly recorded but adverse events related to musculoskeletal complaints, such as joint pain and muscle soreness, were reported in many of the studies that prospectively defined and monitored these events. Serious adverse events were rare, and no serious events were reported to be directly related to the exercise programme.



This review provides evidence that PRT is an effective intervention for improving physical functioning in older people, including improving strength and the performance of some simple and complex activities. However, some caution is needed with transferring these exercises for use with clinical populations because adverse events are not adequately reported.
The rise of deep learning in drug discovery.

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis.
Neural Syntactic Generative Models with Exact Marginalization

We present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shiftreduce parsing and minimal RNN-based feature sets. Our algorithms complement previous approaches by supporting batched training and enabling online computation of next word probabilities. For supervised dependency parsing, our model achieves a stateof-the-art result among generative approaches. We also report empirical results on unsupervised syntactic models and their role in language modeling. We find that our model formulation of latent dependencies with exact marginalization do not lead to better intrinsic language modeling performance than vanilla RNNs, and that parsing accuracy is not correlated with language modeling perplexity in stack-based models.