nils-herrmann/clean_scirepeval huggingface dataset:
Why Public Schools Lose Teachers

Many school districts experience difficulties attracting and retaining teachers, and schools in urban areas serving economically disadvantaged and minority students appear particularly vulnerable to these problems. This paper investigates factors that affect the probabilities that teachers will switch schools or exit the public schools entirely. Researchers used matched student/teacher panel data on Texas public elementary schools to gain a better understanding of the effects of salary and other school factors on teacher transitions. These data permitted a detailed description of student demographic and school characteristics and preand post-move comparisons for teachers who switched schools within Texas or left the Texas public schools. Teachers were divided by experience, school community type, ethnicity and other factors so researchers could examine differences in the responsiveness to salary and student characteristics on the basis of teacher experience, race, and ethnicity. Results indicate that teacher mobility is much more strongly related to characteristics of students, particularly race and achievement, than to salary, although salary exerts a modest impact once compensating differentials are taken into account. Non-black, non Hispanic teachers systematically prefer non-black, non-Hispanic students, while the opposite is true for black and Hispanic teachers.
Answering Why-not Questions on Reverse Top-k Queries

Why-not questions, which aim to seek clarifications on the missing tuples for query results, have recently received considerable attention from the database community. In this paper, we systematically explore why-not questions on reverse top-k queries, owing to its importance in multi-criteria decision making. Given an initial reverse top-k query and a missing/why-not weighting vector set Wm that is absent from the query result, why-not questions on reverse top-k queries explain why Wm does not appear in the query result and provide suggestions on how to refine the initial query with minimum penalty to include Wm in the refined query result. We first formalize why-not questions on reverse top-k queries and reveal their semantics, and then propose a unified framework called WQRTQ to answer why-not questions on both monochromatic and bichromatic reverse top-k queries. Our framework offers three solutions, namely, (i) modifying a query point q, (ii) modifying a why-not weighting vector set Wm and a parameter k, and (iii) modifying q, Wm, and k simultaneously, to cater for different application scenarios. Extensive experimental evaluation using both real and synthetic data sets verifies the effectiveness and efficiency of the presented algorithms.
Access to special care dentistry, part 5. Safety

This article considers the delivery of efficient and effective dental services for patients whose disability and/or medical condition may not be obvious and which consequently can present a hidden challenge in the dental setting. Knowing that the patient has a particular condition, what its features are and how it impacts on dental treatment and oral health, and modifying treatment accordingly can minimise the risk of complications. The taking of a careful medical history that asks the right questions in a manner that encourages disclosure is key to highlighting hidden hazards and this article offers guidance for treating those patients who have epilepsy, latex sensitivity, acquired or inherited bleeding disorders and patients taking oral or intravenous bisphosphonates.
Laws of order: expensive synchronization in concurrent algorithms cannot be eliminated

Building correct and efficient concurrent algorithms is known to be a difficult problem of fundamental importance. To achieve efficiency, designers try to remove unnecessary and costly synchronization. However, not only is this manual trial-and-error process ad-hoc, time consuming and error-prone, but it often leaves designers pondering the question of: is it inherently impossible to eliminate certain synchronization, or is it that I was unable to eliminate it on this attempt and I should keep trying?
 In this paper we respond to this question. We prove that it is impossible to build concurrent implementations of classic and ubiquitous specifications such as sets, queues, stacks, mutual exclusion and read-modify-write operations, that completely eliminate the use of expensive synchronization.
 We prove that one cannot avoid the use of either: i) read-after-write (RAW), where a write to shared variable A is followed by a read to a different shared variable B without a write to B in between, or ii) atomic write-after-read (AWAR), where an atomic operation reads and then writes to shared locations. Unfortunately, enforcing RAW or AWAR is expensive on all current mainstream processors. To enforce RAW, memory ordering--also called fence or barrier--instructions must be used. To enforce AWAR, atomic instructions such as compare-and-swap are required. However, these instructions are typically substantially slower than regular instructions.
 Although algorithm designers frequently struggle to avoid RAW and AWAR, their attempts are often futile. Our result characterizes the cases where avoiding RAW and AWAR is impossible. On the flip side, our result can be used to guide designers towards new algorithms where RAW and AWAR can be eliminated.
Bilateral recruitment of prefrontal cortex in working memory is associated with task demand but not with age.

Elderly adults may master challenging cognitive demands by additionally recruiting the cross-hemispheric counterparts of otherwise unilaterally engaged brain regions, a strategy that seems to be at odds with the notion of lateralized functions in cerebral cortex. We wondered whether bilateral activation might be a general coping strategy that is independent of age, task content and brain region. While using functional magnetic resonance imaging (fMRI), we pushed young and old subjects to their working memory (WM) capacity limits in verbal, spatial, and object domains. Then, we compared the fMRI signal reflecting WM maintenance between hemispheric counterparts of various task-relevant cerebral regions that are known to exhibit lateralization. Whereas language-related areas kept their lateralized activation pattern independent of age in difficult tasks, we observed bilaterality in dorsolateral and anterior prefrontal cortex across WM domains and age groups. In summary, the additional recruitment of cross-hemispheric counterparts seems to be an age-independent domain-general strategy to master cognitive challenges. This phenomenon is largely confined to prefrontal cortex, which is arguably less specialized and more flexible than other parts of the brain.
Freepal: A Large Collection of Deep Lexico-Syntactic Patterns for Relation Extraction

The increasing availability and maturity of both scalable computing architectures and deep syntactic parsers is opening up new possibilities for Relation Extraction (RE) on large corpora of natural language text. In this paper, we present FREEPAL, a resource designed to assist with the creation of relation extractors for more than 5,000 relations defined in the FREEBASE knowledge base (KB). The resource consists of over 10 million distinct lexico-syntactic patterns extracted from dependency trees, each of which is assigned to one or more FREEBASE relations with different confidence strengths. We generate the resource by executing a large-scale distant supervision approach on the CLUEWEB09 corpus to extract and parse over 260 million sentences labeled with FREEBASE entities and relations. We make FREEPAL freely available to the research community, and present a web demonstrator to the dataset, accessible from free-pal.appspot.com.
Design of wall-climbing robot using electrically activated rotational-flow adsorption unit

Traditional climbing robots that use vacuum suckers have some technical problems, e.g., inability to climb coarse walls, frictional resistance and abrasion of suckers, and poor obstacle-surmounting ability. In this study, a new negative pressure adsorption mechanism is applied to the design of a climbing robot. This mechanism generates and maintains negative pressure and adsorption force by using the air's rotational inertia effect; therefore, the structure incorporating this mechanism is called the electrically activated rotational-flow adsorption unit. The most important characteristic of the adsorption unit is that it can function without being in contact with the wall, which fundamentally solves these technical problems associated with traditional climbing robots. In this study, we designed a square-shaped rotational-flow adsorption unit to improve the robot's load ability (18% increase in the adsorption force) and designed a soft skirt structure to improve the robot's obstacle-surmounting ability. Finally, we fabricated a prototype of the climbing robot and tested it on a actual wall (extremely coarse wall, wall containing a large groove). The test results show that our prototype robot can move stably on extremely coarse walls and can pass over large grooves easily.
Non-Profiled Deep Learning-based Side-Channel attacks with Sensitivity Analysis

Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis . Until now, studies have been focused on applying Deep Learning techniques to perform Profiled SideChannel attacks where an attacker has a full control of a profiling device and is able to collect a large amount of traces for different key values in order to characterize the device leakage prior to the attack. In this paper we introduce a new method to apply Deep Learning techniques in a Non-Profiled context, where an attacker can only collect a limited number of side-channel traces for a fixed unknown key value from a closed device. We show that by combining key guesses with observations of Deep Learning metrics, it is possible to recover information about the secret key. The main interest of this method, is that it is possible to use the power of Deep Learning and Neural Networks in a Non-Profiled scenario. We show that it is possible to exploit the translation-invariance property of Convolutional Neural Networks [2] against de-synchronized traces and use Data Augmentation techniques also during Non-Profiled side-channel attacks. Additionally, the present work shows that in some conditions, this method can outperform classic Non-Profiled attacks as Correlation Power Analysis. We also highlight that it is possible to target masked implementations without leakages combination pre-preprocessing and with less assumptions than classic high-order attacks. To illustrate these properties, we present a series of experiments performed on simulated data and real traces collected from the ChipWhisperer board and from the ASCAD database . The results of our experiments demonstrate the interests of this new method and show that this attack can be performed in practice.
An Introduction to Functional Verification of I2C Protocol using UVM

The fabrication technology advancements lead to place more logic on a silicon die which makes verification more challenging task than ever. The large number of resources is required because more than 70% of the design cycle is used for verification. Universal Verification Methodology was developed to provide a well structured and reusable verification environment which does not interfere with the device under test (DUT). This paper contrasts the reusability of I2C using UVM and introduces how the verification environment is constructed and test cases are implemented for this protocol.
Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and susceptibility to UVB-induced DNA damage in the epidermis.

The role of the circadian clock in skin and the identity of genes participating in its chronobiology remain largely unknown, leading us to define the circadian transcriptome of mouse skin at two different stages of the hair cycle, telogen and anagen. The circadian transcriptomes of telogen and anagen skin are largely distinct, with the former dominated by genes involved in cell proliferation and metabolism. The expression of many metabolic genes is antiphasic to cell cycle-related genes, the former peaking during the day and the latter at night. Consistently, accumulation of reactive oxygen species, a byproduct of oxidative phosphorylation, and S-phase are antiphasic to each other in telogen skin. Furthermore, the circadian variation in S-phase is controlled by BMAL1 intrinsic to keratinocytes, because keratinocyte-specific deletion of Bmal1 obliterates time-of-day-dependent synchronicity of cell division in the epidermis leading to a constitutively elevated cell proliferation. In agreement with higher cellular susceptibility to UV-induced DNA damage during S-phase, we found that mice are most sensitive to UVB-induced DNA damage in the epidermis at night. Because in the human epidermis maximum numbers of keratinocytes go through S-phase in the late afternoon, we speculate that in humans the circadian clock imposes regulation of epidermal cell proliferation so that skin is at a particularly vulnerable stage during times of maximum UV exposure, thus contributing to the high incidence of human skin cancers.
Estimating Time Models for News Article Excerpts

It is often difficult to ground text to precise time intervals due to the inherent uncertainty arising from either missing or multiple expressions at year, month, and day time granularities. We address the problem of estimating an excerpt-time model capturing the temporal scope of a given news article excerpt as a probability distribution over chronons. For this, we propose a semi-supervised distribution propagation framework that leverages redundancy in the data to improve the quality of estimated time models. Our method generates an event graph with excerpts as nodes and models various inter-excerpt relations as edges. It then propagates empirical excerpt-time models estimated for temporally annotated excerpts, to those that are strongly related but miss annotations. In our experiments, we first generate a test query set by randomly sampling 100 Wikipedia events as queries. For each query, making use of a standard text retrieval model, we then obtain top-10 documents with an average of 150 excerpts. From these, each temporally annotated excerpt is considered as gold standard. The evaluation measures are first computed for each gold standard excerpt for a single query, by comparing the estimated model with our method to the empirical model from the original expressions. Final scores are reported by averaging over all the test queries. Experiments on the English Gigaword corpus show that our method estimates significantly better time models than several baselines taken from the literature.
A new fuzzy support vector machine to evaluate credit risk

Due to recent financial crises and regulatory concerns, financial intermediaries' credit risk assessment is an area of renewed interest in both the academic world and the business community. In this paper, we propose a new fuzzy support vector machine to discriminate good creditors from bad ones. Because in credit scoring areas we usually cannot label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly, our new fuzzy support vector machine treats every sample as both positive and negative classes, but with different memberships. By this way we expect the new fuzzy support vector machine to have more generalization ability, while preserving the merit of insensitive to outliers, as the fuzzy support vector machine (SVM) proposed in previous papers. We reformulate this kind of two-group classification problem into a quadratic programming problem. Empirical tests on three public datasets show that it can have better discriminatory power than the standard support vector machine and the fuzzy support vector machine if appropriate kernel and membership generation method are chosen.
Twitter-driven YouTube Views: Beyond Individual Influencers

This paper proposes a novel method to predict increases in YouTube viewcount driven from the Twitter social network. Specifically, we aim to predict two types of viewcount increases: a sudden increase in viewcount (named as Jump), and the viewcount shortly after the upload of a new video (named as Early). Experiments on hundreds of thousands of videos and millions of tweets show that Twitter-derived features alone can predict whether a video will be in the top 5% for Early popularity with 0.7 Precision@100. Furthermore, our results reveal that while individual influence is indeed important for predicting how Twitter drives YouTube views, it is a diversity of interest from the most active to the least active Twitter users mentioning a video (measured by the variation in their total activity) that is most informative for both Jump and Early prediction. In summary, by going beyond features that quantify individual influence and additionally leveraging collective features of activity variation, we are able to obtain an effective cross-network predictor of Twitter-driven YouTube views.
Examining the effectiveness of an outpatient clinic-based social skills group for high-functioning children with autism.

Although social skills group interventions for children with autism are common in outpatient clinic settings, little research has been conducted to determine the efficacy of such treatments. This study examined the effectiveness of an outpatient clinic-based social skills group intervention with four high-functioning elementary-aged children with autism. The group was designed to teach specific social skills, including greeting, conversation, and play skills in a brief therapy format (eight sessions total). At the end of each skills-training session, children with autism were observed in play sessions with typical peers. Typical peers received peer education about ways to interact with children with autism. Results indicate that a social skills group implemented in an outpatient clinic setting was effective in improving greeting and play skills, with less clear improvements noted in conversation skills. In addition, children with autism reported increased feelings of social support from classmates at school following participation in the group. However, parent report data of greeting, conversation, and play skills outside of the clinic setting indicated significant improvements in only greeting skills. Thus, although the clinic-based intervention led to improvements in social skills, fewer changes were noted in the generalization to nonclinic settings.
The synthesis of planar parallel manipulators with prismatic joints for an optimal, singularity-free workspace

The synthesis of three-degree-of-freedom planar parallel manipulators is performed using a genetic algorithm. The architecture of a manipulator and its position and orientation with respect to a prescribed workspace are determined. The architectural parameters are optimized so that the manipulator’s constantorientation workspace is as close as possible to a prescribed workspace. The manipulator’s workspace is discretized and its dexterity is computed as a global property of the manipulator. An analytical expression of the singularity loci (local null dexterity) can be obtained from the Jacobian matrix determinant, and its intersection with the manipulator’s workspace may be verified and avoided. Results are shown for different conditions. First, the manipulators’ workspaces are optimized for a prescribed workspace, without considering whether the singularity loci intersect it or not. Then the same type of optimization is performed, taking intersections with the singularity loci into account. In the following results, the optimization of the manipulator’s dexterity is also included in an objective function, along with the workspace optimization and the avoidance of singularity loci. Results show that the end-effector’s location has a significant effect on the manipulator’s dexterity.
Deep Learning versus Gist Descriptors for Image-based Malware Classification

Image features known as “gist descriptors” have recently been applied to the malware classification problem. In this research, we implement, test, and analyze a malware score based on gist descriptors, and verify that the resulting score yields very strong classification results. We also analyze the robustness of this gist-based scoring technique when applied to obfuscated malware, and we perform feature reduction to determine a minimal set of gist features. Then we compare the effectiveness of a deep learning technique to this gist-based approach. While scoring based on gist descriptors is effective, we show that our deep learning technique performs equally well. A potential advantage of the deep learning approach is that there is no need to extract the gist features when training or scoring.
A Probabilistic Room Location Service for Wireless Networked Environments

The popularity of wireless networks has increased in recent years and is becoming a common addition to LANs. In this paper we investigate a novel use for a wireless network based on the IEEE 802.11 standard: inferring the location of a wireless client from signal quality measures. Similar work has been limited to prototype systems that rely on nearest-neighbor techniques to infer location. In this paper, we describe Nibble, a Wi-Fi location service that uses Bayesian networks to infer the location of a device. We explain the general theory behind the system and how to use the system, along with describing our experiences at a university campus building and at a research lab. We also discuss how probabilistic modeling can be applied to a diverse range of applications that use sensor data.
Channel Estimation for Millimeter-Wave Massive MIMO With Hybrid Precoding Over Frequency-Selective Fading Channels

This letter proposes a multi-user uplink channel estimation scheme for mmWave massive MIMO over frequency selective fading (FSF) channels. Specifically, by exploiting the angle-domain structured sparsity of mmWave FSF channels, a distributed compressive sensing-based channel estimation scheme is proposed. Moreover, by using the grid matching pursuit strategy with adaptive measurement matrix, the proposed algorithm can solve the power leakage problem caused by the continuous angles of arrival or departure. Simulation results verify the good performance of the proposed solution.
Concurrent filtering and smoothing: A parallel architecture for real-time navigation and full smoothing

We present a parallelized navigation architecture that is capable of running in real-time and incorporating long-term loop closure constraints while producing the optimal Bayesian solution. This architecture splits the inference problem into a low-latency update that incorporates new measurements using just the most recent states (filter), and a high-latency update that is capable of closing long loops and smooths using all past states (smoother). This architecture employs the probabilistic graphical models of Factor Graphs, which allows the low-latency inference and highlatency inference to be viewed as sub-operations of a single optimization performed within a single graphical model. A specific factorization of the full joint density is employed that allows the different inference operations to be performed asynchronously while still recovering the optimal solution produced by a full batch optimization. Due to the real-time, asynchronous nature of this algorithm, updates to the state estimates from the highlatency smoother will naturally be delayed until the smoother calculations have completed. This architecture has been tested within a simulated aerial environment and on real data collected from an autonomous ground vehicle. In all cases, the concurrent architecture is shown to recover the full batch solution, even while updated state estimates are produced in real-time.