nils-herrmann/clean_scirepeval huggingface dataset:
Why reduce ? Phonological neighborhood density and phonetic reduction in spontaneous speech

Frequent or contextually predictable words are often phonetically reduced, i.e. shortened and produced with articulatory undershoot. Explanations for phonetic reduction of predictable forms tend to take one of two approaches: Intelligibility-based accounts hold that talkers maximize intelligibility of words that might otherwise be difficult to recognize; production-based accounts hold that variation reflects the speed of lexical access and retrieval in the language production system. Here we examine phonetic variation as a function of phonological neighborhood density, capitalizing on the fact that words from dense phonological neighborhoods tend to be relatively difficult to recognize, yet easy to produce. We show that words with many phonological neighbors tend to be phonetically reduced (shortened in duration and produced with more centralized vowels) in connected speech, when other predictors of phonetic variation are brought under statistical control. We argue that our findings are consistent with the predictions of production-based accounts of pronunciation variation.
Survey of Face Detection on Low-Quality Images

Face detection is a well-explored problem. Many challenges on face detectors like extreme pose, illumination, low resolution and small scales are studied in the previous work. However, previous proposed models are mostly trained and tested on good-quality images which are not always the case for practical applications like surveillance systems. In this paper, we first review the current state-of-the-art face detectors and their performance on benchmark dataset FDDB, and compare the design protocols of the algorithms. Secondly, we investigate their performance degradation while testing on low-quality images with different levels of blur, noise, and contrast. Our results demonstrate that both hand-crafted and deep-learning based face detectors are not robust enough for low-quality images. It inspires researchers to produce more robust design for face detection in the wild.
Functional cortical connectivity analysis of mental fatigue unmasks hemispheric asymmetry and changes in small-world networks

Changes in functional connectivity across mental states can provide richer information about human cognition than simpler univariate approaches. Here, we applied a graph theoretical approach to analyze such changes in the lower alpha (8-10 Hz) band of EEG data from 26 subjects undergoing a mentally-demanding test of sustained attention: the Psychomotor Vigilance Test. Behavior and connectivity maps were compared between the first and last 5 min of the task. Reaction times were significantly slower in the final minutes of the task, showing a clear time-on-task effect. A significant increase was observed in weighted characteristic path length, a measure of the efficiency of information transfer within the cortical network. This increase was correlated with reaction time change. Functional connectivity patterns were also estimated on the cortical surface via source localization of cortical activities in 26 predefined regions of interest. Increased characteristic path length was revealed, providing further support for the presence of a reshaped global topology in cortical connectivity networks under fatigue state. Additional analysis showed an asymmetrical pattern of connectivity (right>left) in fronto-parietal regions associated with sustained attention, supporting the right-lateralization of this function. Interestingly, in the fatigue state, significance decreases were observed in left, but not right fronto-parietal connectivity. Our results indicate that functional network organization can change over relatively short time scales with mental fatigue, and that decreased connectivity has a meaningful relationship with individual difference in behavior and performance.
Alternative Risk Premia : What Do We Know ? 2 The rationale of alternative risk premia

The concept of alternative risk premia is an extension of the factor investing approach. Factor investing consists in building long-only equity portfolios, which are directly exposed to common risk factors like size, value or momentum. Alternative risk premia designate non-traditional risk premia other than a long exposure to equities and bonds. They may involve equities, rates, credit, currencies or commodities and correspond to long/short portfolios. However, contrary to traditional risk premia, it is more difficult to define alternative risk premia and which risk premia really matter. In fact, the term “alternative risk premia” encompasses two different types of systematic risk factor: skewness risk premia and market anomalies. For example, the most frequent alternative risk premia are carry and momentum, which are respectively a skewness risk premium and a market anomaly. Because the returns of alternative risk premia exhibit heterogeneous patterns in terms of statistical properties, option profile and drawdown, asset allocation is more complex than with traditional risk premia. In this context, risk diversification cannot be reduced to volatility diversification and skewness risk becomes a key component of portfolio optimization. Understanding these different concepts and how they interconnect is essential for improving multi-asset allocation.
Bounds and Algorithms for Fast Exact Searches of Chemical Fingerprints in Linear and Sublinear Time

Chemical fingerprints are used to represent chemical molecules by recording the presence or absence, or by counting the number of occurrences, of particular features or substructures, such as labeled paths in the 2D graph of bonds, of the corresponding molecule. These fingerprint vectors are used to search large databases of small molecules, currently containing millions of entries, using various similarity measures, such as the Tanimoto or Tversky's measures and their variants. Here, we derive simple bounds on these similarity measures and show how these bounds can be used to considerably reduce the subset of molecules that need to be searched. We consider both the case of single-molecule and multiple-molecule queries, as well as queries based on fixed similarity thresholds or aimed at retrieving the top K hits. We study the speedup as a function of query size and distribution, fingerprint length, similarity threshold, and database size |D| and derive analytical formulas that are in excellent agreement with empirical values. The theoretical considerations and experiments show that this approach can provide linear speedups of one or more orders of magnitude in the case of searches with a fixed threshold, and achieve sublinear speedups in the range of O(|D|0.6) for the top K hits in current large databases. This pruning approach yields subsecond search times across the 5 million compounds in the ChemDB database, without any loss of accuracy.
Efficient load balancing for multi-controller in SDN-based mission-critical networks

This paper proposes an efficient load balancing scheme for distributed controller in Software-Defined Networking-based mission-critical networks. Based on load status and dynamic weight coefficient on each controller, the proposed scheme guarantees load balancing in control plane to achieve better resource usage, reliability and resilience in the network. Furthermore, by using pre-defined load threshold, communication overhead is significantly reduced in comparison with existing approaches. Simulation results demonstrate that the proposed scheme is effective in terms of communication overhead and performance of load balancing in the control plane.
Management of a giant ovarian cyst by keyless abdominal rope-lifting surgery (KARS)

Ovarian cysts over 5 and 15 cm in diameter are described as large and giant, respectively3. In addition, women having large cysts without regression in 6-8 weeks time are candidates for surgery. Although data has been published on laparoscopic or laparoscopy assisted management of large and giant cysts, midline laparotomy is still preferred by many surgeons, particularly in cases of giant cysts.
Robust control for line-of-sight stabilization of a two-axis gimbal system

Line-of-sight stabilization against various disturbances is an essential property of gimbaled imaging systems mounted on mobile platforms. In recent years, the importance of target detection from higher distances has increased. This has raised the need for better stabilization performance. For that reason, stabilization loops are designed such that they have higher gains and larger bandwidths. As these are required for good disturbance attenuation, sufficient loop stability is also needed. However, model uncertainties around structural resonances impose strict restrictions on sufficient loop stability. Therefore, to satisfy high stabilization performance in the presence of model uncertainties, robust control methods are required. In this paper, a robust controller design in LQG/LTR, H∞ , and μ -synthesis framework is described for a two-axis gimbal. First, the performance criteria and weights are determined to minimize the stabilization error with moderate control effort under known platform disturbance profile. Second, model uncertainties are determined by considering locally linearized models at different operating points. Next, robust LQG/LTR, H∞ , and μ controllers are designed. Robust stability and performance of the three designs are investigated and compared. The paper finishes with the experimental performances to validate the designed robust controllers.
ACTION SPACE DEFINED BY NATURAL LANGUAGE

In this paper, we propose the deep reinforcement relevance network (DRRN), a novel deep architecture, to design a model for handling an action space characterized using natural language with applications to text-based games. For a particular class of games, a user must choose among a number of actions described by text, with the goal of maximizing long-term reward. In these games, the best action is typically what fits the current situation best (modeled as a state in the DRRN), also described by text. Because of the exponential complexity of natural language with respect to sentence length, there is typically an unbounded set of unique actions. Even with a constrained vocabulary, the action space is very large and sparse, posing challenges for learning. To address this challenge, the DRRN extracts separate high-level embedding vectors from the texts that describe states and actions, respectively, using a general interaction function, such as inner product, bilinear, and DNN interaction, between these embedding vectors to approximate the Qfunction. We evaluate the DRRN on two popular text games, showing superior performance over other deep Q-learning architectures.
Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework

Faced with unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the full benefits of personal health and healthcare-related data. Imagine visiting your physician’s office with a list of concerns and questions. What if you could walk out the office with a personalized assessment of your health? What if you could have personalized disease management and wellness plan? These are the goals and vision of the work discussed in this paper. The timing is right for such a research direction—given the changes in health care, reimbursement, reform, meaningful use of electronic health care data, and patient-centered outcome mandate. We present the foundations of work that takes a Big Data driven approach towards personalized healthcare, and demonstrate its applicability to patient-centered outcomes, meaningful use, and reducing re-admission rates.
A Parallel and Scalable Processor for JSON Data

Increasing interest in JSON data has created a need for its efficient processing. Although JSON is a simple data exchange format, its querying is not always effective, especially in the case of large repositories of data. This work aims to integrate the JSONiq extension to the XQuery language specification into an existing query processor (Apache VXQuery) to enable it to query JSON data in parallel. VXQuery is built on top of Hyracks (a framework that generates parallel jobs) and Algebricks (a language-agnostic query algebra toolbox) and can process data on the fly, in contrast to other well-known systems which need to load data first. Thus, the extra cost of data loading is eliminated. In this paper, we implement three categories of rewrite rules which exploit the features of the above platforms to efficiently handle path expressions along with introducing intra-query parallelism. We evaluate our implementation using a large (803GB) dataset of sensor readings. Our results show that the proposed rewrite rules lead to efficient and scalable parallel processing of JSON data.
Erythropoietin-Coated ZP-Microneedle Transdermal System: Preclinical Formulation, Stability, and Delivery

To evaluate the feasibility of coating formulated recombinant human erythropoietin alfa (EPO) on a titanium microneedle transdermal delivery system, ZP-EPO, and assess preclinical patch delivery performance. Formulation rheology and surface activity were assessed by viscometry and contact angle measurement. EPO liquid formulation was coated onto titanium microneedles by dip-coating and drying. Stability of coated EPO was assessed by SEC-HPLC, CZE and potency assay. Preclinical in vivo delivery and pharmacokinetic studies were conducted in rats with EPO-coated microneedle patches and compared to subcutaneous EPO injection. Studies demonstrated successful EPO formulation development and coating on microneedle arrays. ZP-EPO patch was stable at 25°C for at least 3 months with no significant change in % aggregates, isoforms, or potency. Preclinical studies in rats showed the ZP-EPO microneedle patches, coated with 750 IU to 22,000 IU, delivered with high efficiency (75–90%) with a linear dose response. PK profile was similar to subcutaneous injection of commercial EPO. Results suggest transdermal microneedle patch delivery of EPO is feasible and may offer an efficient, dose-adjustable, patient-friendly alternative to current intravenous or subcutaneous routes of administration.
Study of the extraction process of papain from latex of papaya ( Carica papaya L . ) fruits cv . Maradol

In this work, we studied the extraction process of papain, present in the latex of papaya fruit (Carica papaya L.) cv. Maradol. The variables studied in the extraction of papain were: latex:alcohol ratio (1:2.1 and 1:3) and drying method (vacuum and refractance window). Papain enzyme responses were obtained in terms of enzymatic activity and yield of the extraction process. The best result in terms of enzyme activity and yield was obtained by vacuum drying and a latex:alcohol ratio of 1:3. The enzyme obtained was characterized by physicochemical and microbiological properties and, enzymatic activity when compared with a commercial sample used as standard.
Current mode quadrature oscillator using current differencing transconductance amplifiers ( CDTA )

A CDTA-based quadrature oscillator circuit is proposed. The circuit employs two current-mode allpass sections in a loop, and provides high-frequency sinusoidal oscillations in quadrature at high impedance output terminals of the CDTAs. The circuit has no floating capacitors, which is advantageous from the integrated circuit manufacturing point of view. Moreover, the oscillation frequency of this configuration can be made adjustable by using voltage controlled elements (MOSFETs), since the resistors in the circuit are either grounded or virtually grounded.
Compositional Verification for Autonomous Systems with Deep Learning Components

As autonomy becomes prevalent in many applications, ranging from recommendation systems to fully autonomous vehicles, there is an increased need to provide safety guarantees for such systems. The problem is difficult, as these are large, complex systems which operate in uncertain environments, requiring data-driven machine-learning components. However, learning techniques such as Deep Neural Networks, widely used today, are inherently unpredictable and lack the theoretical foundations to provide strong assurance guarantees. We present a compositional approach for the scalable, formal verification of autonomous systems that contain Deep Neural Network components. The approach uses assumeguarantee reasoning whereby contracts, encoding the input-output behavior of individual components, allow the designer to model and incorporate the behavior of the learning-enabled components working side-by-side with the other components. We illustrate the approach on an example taken from the autonomous vehicles domain.
Nightmares may emerge as an independent Evaluation of the Distress and Effects Caused By Nightmares : A Study of the Psychometric Properties of the Nightmare Distress Questionnaire and the Nightmare Effects Survey

Nightmares are extremely distressing dreams that usually interrupt the sleep of the person, bringing them to a state of total wakefulness in which they vividly remember the dream content. The themes of these dreams normally relate to threats to physical integrity (e.g., attacks) or self-esteem (e.g., personal failure), which may reflect real experiences or fictitious events. Nightmares usually occur in the second half of the night, when the REM (Rapid Eye Movement) phase is most prominent. Nightmares may emerge as an independent Evaluation of the Distress and Effects Caused By Nightmares: A Study of the Psychometric Properties of the Nightmare Distress Questionnaire and the Nightmare Effects Survey
Percutaneous Hallux Valgus Surgery Without Distal Metatarsal Articular Angle Correction.

Many factors are considered predictors of recurrence after hallux valgus (HV) surgery, including preoperative distal metatarsal articular angle (DMAA). The restoration of the bone and joint alignment would be more important than realigning the cartilaginous surface of the metatarsal head. Therefore, is DMAA correction essential for a good clinical and radiological results after HV surgery? This study aims to illustrate the results of percutaneous forefoot surgery (PFS) for correction of HV deformity without DMAA correction.



A prospective single-center study of 74 patients (89 feet), with mild-to-moderate hallux valgus deformity, who underwent PFS. The mean latest follow-up was 57.3 months.



Preoperative median visual analog scale was 7 points and AOFAS scores were 52 points. At the mean latest follow up both scores improved to 0 points and 90 points, respectively. Median HV angle and intermetatarsal angle changed from 30° and 12° preoperatively, to 21° and 11° at mean latest follow-up. Overall, 80% of the patients were satisfied or very satisfied. Recurrence of medial first metatarsal head pain occurred in 12 cases (13.5%).



PFS, without DMAA correction, is a valid procedure for surgical correction in patients with HV, despite the slightly worse radiographic results in our study.


LEVELS OF EVIDENCE
Comment on "Why does deep and cheap learning work so well?" [arXiv: 1608.08225]

In a recent paper, “Why does deep and cheap learning work so well?”, Lin and Tegmark claim to show that the mapping between deep belief networks and the variational renormalization group derived in  is invalid, and present a “counterexample” that claims to show that this mapping does not hold. In this comment, we show that these claims are incorrect and stem from a misunderstanding of the variational RG procedure proposed by Kadanoff. We also explain why the “counterexample” of Lin and Tegmark is compatible with the mapping proposed in .
Efficient Path Planning Algorithm for Additive Manufacturing Systems

Recent additive manufacturing technologies, such as 3-D printing and printed electronics, require constant speed motion for consistent material deposition. In this paper, a new path planning algorithm is developed for an \(XY\) -motion stage with an emphasis on aerosol printing. The continuous aerosol stream provided by the printing nozzle requires constant velocity in relative motion of a substrate to evenly deposit inks. During transitioning between print segments, a shutter prevents the aerosol from reaching the substrate, therefore wasting material. The proposed path planning algorithm can control motion of an \(XY\) stage for an arbitrary printing path and desired velocity while minimizing material waste. Linear segments with parabolic blends (LSPBs) trajectory planning is used during printing, and minimum time trajectory (MTT) planning is used during printer transition. Simulation results show that combining LSPB with MTT can minimize the printing time while following the desired path.
An Exploratory Study of the Relationships between Narcissism , Self-Esteem and Instagram Use

The aim of this mixed-methods exploratory study was to examine the relationship between narcissism, self-esteem and Instagram usage and was motivated by unsubstantiated media claims of increasing narcissism due to excessive use of social networks. A sample of 200 participants responded to an online survey which consisted of the Five Factor Narcissism Inventory (FFNI), the Rosenberg Self-Esteem scale, and the Instagram Usage, Behaviours, and Affective Responses Questionnaire (IUBARQ) constructed specifically for the purposes of this study. There was only weak evidence for any relationship between narcissism and Instagram usage, suggesting that media concerns are somewhat exaggerated. However the negative correlation between vulnerable narcissism and self-esteem warrants further examination.
Depressive Symptoms and Their Correlates with Locus of Control and Satisfaction with Life among Jordanian College Students

to establish estimates of the prevalence of depressive symptoms, and their correlates with locus of control and satisfaction with life among undergraduate students in Hashemite University (HU) Jordan.  A randomized sample of college students (N=492), 67 (33.9 %) were male, completed the Multidimensional Health Locus of Control Scale (MHLC), the Satisfaction with Life Scale (SLS), and the Center for Epidemiologic Studies Depression Scale (CES-D).  Study outcomes showed a great ratio of depressive symptoms among HU students, almost half of college-aged individuals had a major depression, and statistical analyses showed no relationship between externality of locus of control (Powerful others) and depression, while Externality of locus of control (Chance) was found to be significantly positively related to depression, and in line to previous studies a significant negative relationship was found between internality of locus of control and depression. Additionally, significant negative relationship was found between satisfaction with life (SLS) and depression. However, Satisfaction with life was found to be the first best predictor of depressive symptoms and Chance was found to be the second best predictor of depressive symptoms.  Findings of this study hold implications for depressive symptoms interventions, such as expanding psychoeducation courses to include strategies for enhancing and maintaining a sense of personal control and selfactualization.