nils-herrmann/clean_scirepeval huggingface dataset:
Pneumogastric (Vagus) Nerve Activity Indexed by Heart Rate Variability in Chronic Pain Patients Compared to Healthy Controls: A Systematic Review and Meta-Analysis.

A large body of scientific literature derived from experimental studies emphasizes the vital role of vagal-nociceptive networks in acute pain processing. However, research on vagal activity, indexed by vagally-mediated heart rate variability (vmHRV) in chronic pain patients (CPPs), has not yet been summarized.



To systematically investigate differences in vagus nerve activity indexed by time- and frequency-domain measures of vmHRV in CPPs compared to healthy controls (HCs).



A systematic review and meta-analysis, including meta-regression on a variety of populations (i.e., clinical etiology) and study-level (i.e., length of HRV recording) covariates.



Not applicable (variety of studies included in the meta-analysis).



Eight computerized databases (PubMed via MEDLINE, PsycNET, PsycINFO, Embase, CINAHL, Web of Science, PSYNDEX, and the Cochrane Library) in addition to a hand search were systematically screened for eligible studies based on pre-defined inclusion criteria. A meta-analysis on all empirical investigations reporting short- and long-term recordings of continuous time- (root-mean-square of successive R-R-interval differences [RMSSD]) and frequency-domain measures (high-frequency [HF] HRV) of vmHRV in CPPs and HCs was performed. True effect estimates as adjusted standardized mean differences (SMD; Hedges g) combined with inverse variance weights using a random effects model were computed.



CPPs show lower vmHRV than HCs indexed by RMSSD (Z = 5.47, P < .0001; g = -0.24;95% CI [-0.33, -0.16]; k = 25) and HF (Z = 4.54, P < .0001; g = -0.30; 95% CI [-0.44, -0.17]; k = 61).Meta-regression on covariates revealed significant differences by clinical etiology, age, gender, and length of HRV recording.



We did not control for other potential covariates (i.e., duration of chronic pain, medication intake) which may carry potential risk of bias.


CONCLUSION(S)
The present meta-analysis is the most extensive review of the current evidence on vagal activity indexed by vmHRV in CPPs. CPPs were shown to have lower vagal activity, indexed by vmHRV, compared to HCs. Several covariates in this relationship have been identified. Further research is needed to investigate vagal activity in CPPs, in particular prospective and longitudinal follow-up studies are encouraged.
Cross-modal Hallucination for Few-shot Fine-grained Recognition

State-of-the-art deep learning algorithms generally require large amounts of data for model training. Lack thereof can severely deteriorate the performance, particularly in scenarios with fine-grained boundaries between categories. To this end, we propose a multimodal approach that facilitates bridging the information gap by means of meaningful joint embeddings. Specifically, we present a benchmark that is multimodal during training (i.e. images and texts) and single-modal in testing time (i.e. images), with the associated task to utilize multimodal data in base classes (with many samples), to learn explicit visual classifiers for novel classes (with few samples). Next, we propose a framework built upon the idea of cross-modal data hallucination. In this regard, we introduce a discriminative text-conditional GAN for sample generation with a simple self-paced strategy for sample selection. We show the results of our proposed discriminative hallucinated method for 1-, 2-, and 5shot learning on the CUB dataset, where the accuracy is improved by employing multimodal data.
Formation mechanism and optimization of highly luminescent N-doped graphene quantum dots

Photoluminescent graphene quantum dots (GQDs) have received enormous attention because of their unique chemical, electronic and optical properties. Here a series of GQDs were synthesized under hydrothermal processes in order to investigate the formation process and optical properties of N-doped GQDs. Citric acid (CA) was used as a carbon precursor and self-assembled into sheet structure in a basic condition and formed N-free GQD graphite framework through intermolecular dehydrolysis reaction. N-doped GQDs were prepared using a series of N-containing bases such as urea. Detailed structural and property studies demonstrated the formation mechanism of N-doped GQDs for tunable optical emissions. Hydrothermal conditions promote formation of amide between -NH₂ and -COOH with the presence of amine in the reaction. The intramoleculur dehydrolysis between neighbour amide and COOH groups led to formation of pyrrolic N in the graphene framework. Further, the pyrrolic N transformed to graphite N under hydrothermal conditions. N-doping results in a great improvement of PL quantum yield (QY) of GQDs. By optimized reaction conditions, the highest PL QY (94%) of N-doped GQDs was obtained using CA as a carbon source and ethylene diamine as a N source. The obtained N-doped GQDs exhibit an excitation-independent blue emission with single exponential lifetime decay.
Algorithms for speeding up distance-based outlier detection

The problem of distance-based outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address this problem and develop sequential and distributed algorithms that are significantly more efficient than state-of-the-art methods while still guaranteeing the same outliers. By combining simple but effective indexing and disk block accessing techniques, we have developed a sequential algorithm iOrca that is up to an order-of-magnitude faster than the state-of-the-art. The indexing scheme is based on sorting the data points in order of increasing distance from a fixed reference point and then accessing those points based on this sorted order. To speed up the basic outlier detection technique, we develop two distributed algorithms (DOoR and iDOoR) for modern distributed multi-core clusters of machines, connected on a ring topology. The first algorithm passes data blocks from each machine around the ring, incrementally updating the nearest neighbors of the points passed. By maintaining a cutoff threshold, it is able to prune a large number of points in a distributed fashion. The second distributed algorithm extends this basic idea with the indexing scheme discussed earlier. In our experiments, both distributed algorithms exhibit significant improvements compared to the state-of-the-art distributed method .
Care to Share?: Learning to Rank Personal Photos for Public Sharing

With mobile devices, users are taking ever-growing numbers of photos every day. These photos are uploaded to social sites such as Facebook and Flickr, often automatically. Yet, the portion of these uploaded photos being publicly shared is low, and on a constant decline. Deciding which photo to share takes considerable time and attention, and many users would rather forfeit the social interaction and engagement than sift through their piles of uploaded photos. In this paper, we introduce a novel task of recommending socially-engaging photos to their creators for public sharing. This will turn a tedious manual chore into a quick, software-assisted process. We provide extensive analysis over a large-scale dataset from the Flickr photo sharing website, which reveals some of the traits of photo sharing in such sites. Additionally, we present a ranking algorithm for the task that comprises three steps:(a) grouping of near-duplicate photos;(b) ranking the photos in each group by their "shareability"; and(c) ranking the groups by their likelihood to contain a shareable photo. A large-scale experiment allows us to evaluate our algorithm and show its benefits compared to competitive baselines and algorithmic alternatives.
Trends in Wind Turbine Generator Systems

This paper reviews the trends in wind turbine generator systems. After discussing some important requirements and basic relations, it describes the currently used systems: the constant speed system with squirrel-cage induction generator, and the three variable speed systems with doubly fed induction generator (DFIG), with gearbox and fully rated converter, and direct drive (DD). Then, possible future generator systems are reviewed. Hydraulic transmissions are significantly lighter than gearboxes and enable continuously variable transmission, but their efficiency is lower. A brushless DFIG is a medium speed generator without brushes and with improved low-voltage ride-through characteristics compared with the DFIG. Magnetic pseudo DDs are smaller and lighter than DD generators, but need a sufficiently low and stable magnet price to be successful. In addition, superconducting generators can be smaller and lighter than normal DD generators, but both cost and reliability need experimental demonstration. In power electronics, there is a trend toward reliable modular multilevel topologies.
First molecular detection and identification of a potyvirus associated with severe mosaic disease of amaryllis (Hippeastrum hybridum Hort.) in India

A potyvirus associated with mosaic disease of amaryllis was detected and identified by reverse-transcription and polymerase chain reaction using degenerate primers specific to the core region of coat protein gene of potyviruses followed by cloning and sequencing. The sequence shared highest (98–95%) amino acid identities with Amaryllis potyvirus that is not yet recognised by the International Committee on Taxonomy of Viruses, Amazon lily mosaic virus (tentative potyvirus) and Hippeastrum mosaic virus (recognised potyvirus). Screening bulbs of elite amaryllis cultivars for potyvirus infection would be useful to establish virus-free propagating material for the floriculture industry in India.
Chemotherapy-induced Spontaneous Pneumothorax: Case Series.

We present 2 patients who developed spontaneous pneumothorax (SP) following rapid regression of lymphoma and rhabdomyosarcoma with lung metastases. Case 1, a 43-year old man was admitted to our hospital with dyspnea 10 days before admission. He denied any recent trauma or previous treatment for lung tuberculosis. Three weeks prior to admission, he received first cycle of CHOP for non-Hodgkin's lymphoma stage II BE. Chest X-ray consistent with right pneumothorax. After treatment with chest tube drainage for about 1 month, the patient recovered and chemotherapy could be continued without further complications. Case 2, a 35- year old man was admitted to other hospital with dyspnea and chest pain on day 4 after second cycle of systemic combined chemotherapy for rhabdomyosarcoma stage IV (lung metastases) with doxorubicin, ifosfamide, mesna, and dacarbazine. Chest X-ray showed hydropneumothorax on right and left lung. After treatment with chest tube drainage about 2 weeks, the patient recovered and chemotherapy could be continued without further complications. The mechanism of pneumothorax following chemotherapy is not clearly understood yet, however, several hypotheses have been considered: 1) the rupture of a subpleural bulla after chemotherapy; 2) the rupture of an emphysematous bulla in an over expanded portion of the lung which is partially obstructed by a neoplasm; 3) tumor lyses or necrosis due to cytotoxic chemotherapy directly induces the formation of fistula. Dyspnea and chest pain suddenly appear during successful chemotherapy for metastatic chemosensitive tumors should alert the physician to the possibility of SP. The treatment is directed toward lung re-expansion. Chemotherapy induced pneumothorax should be considered as oncologic emergency.
Numerical treatment for solving one-dimensional Bratu problem using neural networks

In this paper, numerical treatment is presented for the solution of boundary value problems of one-dimensional Bratu-type equations using artificial neural networks. Three types of transfer functions including Log-sigmoid, radial basis, and tan-sigmoid are used in the neural networks’ modeling. The optimum weights for all the three networks are searched with the interior point method. Various test cases of Bratu-type equations have been simulated using the developed models. The accuracy, convergence, and effectiveness of the methods are substantiated by a large number of simulation data for each model by taking enough independent runs.
Communication dynamics in hospice teams: understanding the role of the chaplain in interdisciplinary team collaboration.

Hospice chaplains provide a specific expertise to patient and family care, however, individual roles and responsibilities that facilitate the interdisciplinary team environment are less well known.



The primary aim of this study was to investigate how hospice chaplains perceive their role in interdisciplinary team meetings and to what extent hospice chaplains share common experiences within the interdisciplinary team approach in hospice.



Hospice chaplains within a 10-state region participated in a 39-item phone survey about professional roles, group roles, and structural characteristics that influence their ability to participate in interdisciplinary collaboration.



Findings revealed that professional role conflict is experienced, primarily with social workers. Informal group task and maintenance roles included team spiritual care advisor and conflict manager, and structural characteristics consisted of extracurricular communication outside of the organization.



Although chaplains foster interdisciplinary collaboration within the hospice team, future research needs to address improvements to the chaplain's role within the interdisciplinary team process.
A Multi-Objective Deep Reinforcement Learning Framework

This paper presents a new multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We propose the use of linear and non-linear methods to develop the MODRL framework that includes both single-policy and multi-policy strategies. The experimental results on two benchmark problems including the two-objective deep sea treasure environment and the three-objective mountain car problem indicate that the proposed framework is able to converge to the optimal Pareto solutions effectively. The proposed framework is generic, which allows implementation of different deep reinforcement learning algorithms in different complex environments. This therefore overcomes many difficulties involved with standard multi-objective reinforcement learning (MORL) methods existing in the current literature. The framework creates a platform as a testbed environment to develop methods for solving various problems associated with the current MORL. Details of the framework implementation can be referred to http://www.deakin.edu.au/~thanhthi/drl.htm.
Leading a multigenerational nursing workforce: issues, challenges and strategies.

Today's nursing workforce is made up of staff and nursing leaders from four different generational cohorts. Generational diversity, including workforce differences in attitudes, beliefs, work habits, and expectations, has proven challenging for nursing leaders. The purpose of this article is to assist nursing leaders to reframe perceptions about generational differences and to view these differences in attitudes and behaviors as potential strengths. Developing the skill to view generational differences through a different lens will allow the leader to flex their leadership style, enhance quality and productivity, reduce conflict, and maximize the contributions of all staff. This article provides an overview of the generational cohorts and presents strategies which nursing leaders can use to coach and motivate, communication with, and reduce conflict for each generational cohort of nurses.
Beyond stereotypes of adolescent risk taking: Placing the adolescent brain in developmental context

Recent neuroscience models of adolescent brain development attribute the morbidity and mortality of this period to structural and functional imbalances between more fully developed limbic regions that subserve reward and emotion as opposed to those that enable cognitive control. We challenge this interpretation of adolescent development by distinguishing risk-taking that peaks during adolescence (sensation seeking and impulsive action) from risk taking that declines monotonically from childhood to adulthood (impulsive choice and other decisions under known risk). Sensation seeking is primarily motivated by exploration of the environment under ambiguous risk contexts, while impulsive action, which is likely to be maladaptive, is more characteristic of a subset of youth with weak control over limbic motivation. Risk taking that declines monotonically from childhood to adulthood occurs primarily under conditions of known risks and reflects increases in executive function as well as aversion to risk based on increases in gist-based reasoning. We propose an alternative Life-span Wisdom Model that highlights the importance of experience gained through exploration during adolescence. We propose, therefore, that brain models that recognize the adaptive roles that cognition and experience play during adolescence provide a more complete and helpful picture of this period of development.
Dynamic Tolerance Region Computing for Multimedia

Multimedia computation has clearly become a primary and demanding application segment for new architectures targeted at portable devices. The main challenge for such architectures is to keep pace with the computational requirements of ever evolving media standards and applications while satisfying the power and energy consumption required to leverage smaller form-factors and longer battery lifetimes. One technique aimed at reducing both the energy consumption and the execution time of an application is Reuse. This technique memorizes the outcome of an instruction or set of instructions so that we can reuse it the next time we perform the same operation with the same inputs. In this paper, we analyze a region reuse schema specially focused on multimedia applications. While the technique appears to be, in theory, a promising vehicle to improve both timing and energy for low-end media applications, we will show that the extra hardware cost required becomes a severe shortcoming as we find the undesirable situation where we have to consume more energy in order to reduce the execution time (hence becoming a poor power-oriented solution). To mitigate the overhead of the reuse hardware, we advocate for exploiting a third variable in the power-time trade-off and we evaluate tolerant region reuse, a technique that relies in the tolerance in the output precision of media algorithms to improve reuse. With this technique, we afford to use less consuming hardware structures that drives benefits in both energy and timing. As a trade-off, tolerant region reuse introduces non-noticeable errors in the output data. The main drawback of tolerant region reuse is the strong reliance on application profiling, the need for careful tuning from the application developer, and the inability of the technique to adapt to the variability of the media contents being used as inputs. To address that inflexibility, we introduce dynamic tolerant region reuse. This novel technique overcomes the drawbacks of tolerant region reuse by allowing the hardware to study the precision quality of the region reuse output. Our mechanism allows the programmer to grant a minimum threshold on signal-to-noise ratio (SNR) while letting the technique adapt to the characteristics of the specific application and workload to minimize time and energy consumption. This leads to greater energy-delay savings while keeps output error below noticeable levels, avoiding at the same time the need of profiling. We studied our mechanism applied to a set of three different processors, from low to high end. As we will show our technique leads to consistent performance improvements in all of our benchmark programs while reducing energy consumption. We can report savings up to 30 percent in the energy*delay factor for all three processors.
A Lazy Concurrent List-Based Set Algorithm

List-based implementations of sets are a fundamental building block of many concurrent algorithms. A skiplist based on the lock-free list-based set algorithm of Michael will be included in the Java Concurrency Package of JDK 1.6.0. However, Michael’s lock-free algorithm has several drawbacks, most notably that it requires all list traversal operations, including membership tests, to perform cleanup operations of logically removed nodes, and that it uses the equivalent of an atomically markable reference, a pointer that can be atomically “marked,” which is expensive in some languages and unavailable in others. We present a novel “lazy” list-based implementation of a concurrent set object. It is based on an optimistic locking scheme for inserts and removes, eliminating the need to use the equivalent of an atomically markable reference. It also has a novel wait-free membership test operation (as opposed to Michael’s lock-free one) that does not need to perform cleanup operations and is more efficient than that of all previous algorithms. Empirical testing shows that the new lazy-list algorithm consistently outperforms all known algorithms, including Michael’s lock-free algorithm, throughout the concurrency range. At high load, with 90% membership tests, the lazy algorithm is more than twice as fast as Michael’s. This is encouraging given that typical search structure usage patterns include around 90% membership tests. By replacing the lock-free membership test of Michael’s algorithm with our new wait-free one, we achieve an algorithm that slightly outperforms our new lazy-list (though it may not be as efficient in other contexts as it uses Java’s RTTI mechanism to create pointers that can be atomically marked).
A review of brain oscillations in perception of faces and emotional pictures

The differentiation of faces, facial expressions and affective pictures involves processes of higher mental activity that have considerable applications in the psychology of moods and emotions. At present, the search for functional correlates of brain oscillations is an important trend in neuroscience. Furthermore, analyses of oscillatory responses provide key knowledge on the physiology of brain dynamics. Studies analysing oscillatory dynamics in face perception and emotional pictures have increased in recent years; however, the literature lacks a review of the current state of the art. This study provides a comprehensive review of the delta, theta, alpha, beta and gamma oscillatory responses on presentation of faces, facial expressions and affective pictures (International Affective Picture System, IAPS). The reviewed literature revealed that the brain is more sensitive to emotional stimuli than neutral stimuli. A common and reliable finding from all reviewed studies was the increased brain responsiveness towards negative emotional pictures (face expression or IAPS).