nils-herrmann/clean_scirepeval huggingface dataset:
Abnormal Structural Networks Characterize Major Depressive Disorder: A Connectome Analysis

Major depressive disorder (MDD) has been shown to be associated with a disrupted topological organization of functional brain networks. However, little is known regarding whether these changes have a structural basis. Diffusion tensor imaging (DTI) enables comprehensive whole-brain mapping of the white matter tracts that link regions distributed throughout the entire brain, the so-called human connectome.



We examined whole-brain structural networks in a cohort of 95 MDD outpatients and 102 matched control subjects. Structural networks were represented by an 84 × 84 connectivity matrix representing probabilistic white matter connections between 84 parcellated cortical and subcortical regions using DTI tractography. Network-based statistics were used to assess differences in the interregional connectivity matrix between the two groups, and graph theory was used to examine overall topological organization.



Our network-based statistics analysis demonstrates lowered structural connectivity within two distinct brain networks that are present in depression: the first primarily involves the regions of the default mode network and the second comprises the frontal cortex, thalamus, and caudate regions that are central in emotional and cognitive processing. These two altered networks were observed in the context of an overall preservation of topology as reflected as no significant group differences for the graph-theory measures.



This is the first report to use DTI to show the structural connectomic alterations present in MDD. Our findings highlight that altered structural connectivity between nodes of the default mode network and the frontal-thalamo-caudate regions are core neurobiological features associated with MDD.
Artificial Intelligence and Asymmetric Information Theory

When human agents come together to make decisions it is often the case that one human agent has more information than the other and this phenomenon is called information asymmetry and this distorts the market. Often if one human agent intends to manipulate a decision in its favor the human agent can signal wrong or right information. Alternatively, one human agent can screen for information to reduce the impact of asymmetric information on decisions. With the advent of artificial intelligence, signaling and screening have been made easier. This chapter studies the impact of artificial intelligence on the theory of asymmetric information. It is surmised that artificial intelligent agents reduce the degree of information asymmetry and thus the market where these agents are deployed become more efficient. It is also postulated that the more artificial intelligent agents there are deployed in the market the less is the volume of trades in the market. This is because for trade to happen the asymmetry of information on goods and services to be traded should exist.
A triple-band monopole antenna for WLAN and WiMAX applications

In this paper, a triple-band monopole antenna for WLAN and WiMAX wireless communication applications is presented. The antenna has a simple structure designed for 2.4/5.2/5.8 GHz WLAN and 3.5/5.5 GHz WiMAX bands. The radiator is composed of just two branches and a short stub. The antenna is designed on a 40 × 40 × 0.8 mm3 substrate using computer simulation. For verification of simulation results, a prototype is fabricated and measured. Results show that the antenna can provide three impedance bandwidths, 2.35-2.58 GHz, 3.25-4 GHz and 4.95-5.9 GHz, for the WLAN and WiMAX applications. The simulated and measured radiation patterns, efficiencies and gains of the antenna are all presented.
Relational Reinforcement Learning

Relational reinforcement learning is presented , a learning technique that combines reinforcement learning with relational learning or inductive logic programming. Due to the use of a more expressive representation language to represent states, actions and Q-functions, relational reinforcement learning can be potentially applied to a new range of learning tasks. One such task that we investigate is planning in the blocks world, where it is assumed that the eeects of the actions are unknown to the agent and the agent has to learn a policy. Within this simple domain we show that relational reinforcement learning solves some existing problems with reinforcement learning. In particular, relational reinforcement learning allows us to employ structural representations, make abstraction of speciic goals pursued and exploit the results of previous learning phases when addressing new (more complex) situations.
Data Mining : Future Trends and Applications

Knowledge has played a significant role on human activities since his development. Data mining is the process of knowledge discovery where knowledge is gained by analyzing the data store in very large repositories, which are analyzed from various perspectives and the result is summarized it into useful information. Due to the importance of extracting knowledge/information from the large data repositories, data mining has become a very important and guaranteed branch of engineering affecting human life in various spheres directly or indirectly. The purpose of this paper is to survey many of the future trends in the field of data mining, with a focus on those which are thought to have the most promise and applicability to future data mining applications.
Two-electrode biopotential measurements: power line interference analysis

In this paper, an analysis of power line interference in two-electrode biopotential measurement amplifiers is presented. A model of the amplifier that includes its input stage and takes into account the effects of the common mode input impedance Z/sub C/ is proposed. This approach is valid for high Z/sub C/ values, and also for some recently proposed low-Z/sub C/ strategies. It is shown that power line interference rejection becomes minimal for extreme Z/sub C/ values (null or infinite), depending on the electrode-skin impedance's unbalance /spl Delta/Z/sub E/. For low /spl Delta/Z/sub E/ values, minimal interference is achieved by a low Z/sub C/ strategy (Z/sub ), while for high /spl Delta/Z/sub E/ values a very high Z/sub C/ is required. A critical /spl Delta/Z/sub E/ is defined to select the best choice, as a function of the amplifier's Common Mode Rejection Ratio (CMRR) and stray coupling capacitances. Conclusions are verified experimentally using a biopotential amplifier specially designed for this test.
Wide-Bandwidth Power-Combining and Inverse Class-F GaN Power Amplifier at X-Band

In this paper, we present a wide bandwidth power amplifier (PA) utilizing a Marchand balun to achieve power combining and inverse class-F (class-F-1) matching. We have used a 4-to-1 balun and its out-of-band characteristics to achieve matching at the second and third harmonic for differential class-F-1 operation. Thus, the balun simultaneously acts as a combiner and harmonic matching network. The experimental results demonstrate that the prototype PA achieves an output power of 41.5 dBm at the 1-dB compression point (P1 dB), power-added efficiency of 55%, and gain of 10 dB over a fractional bandwidth of 40% from 8 to 12 GHz. Due to the differential operation, the class-F-1 PA has also achieved a measured second harmonic distortion of -50 dBc.
Positive Clinical Psychology: a new vision and strategy for integrated research and practice.

This review argues for the development of a Positive Clinical Psychology, which has an integrated and equally weighted focus on both positive and negative functioning in all areas of research and practice. Positive characteristics (such as gratitude, flexibility, and positive emotions) can uniquely predict disorder beyond the predictive power of the presence of negative characteristics, and buffer the impact of negative life events, potentially preventing the development of disorder. Increased study of these characteristics can rapidly expand the knowledge base of clinical psychology and utilize the promising new interventions to treat disorder through promoting the positive. Further, positive and negative characteristics cannot logically be studied or changed in isolation as (a) they interact to predict clinical outcomes, (b) characteristics are neither "positive" or "negative", with outcomes depending on specific situation and concomitant goals and motivations, and (c) positive and negative well-being often exist on the same continuum. Responding to criticisms of the Positive Psychology movement, we do not suggest the study of positive functioning as a separate field of clinical psychology, but rather that clinical psychology itself changes to become a more integrative discipline. An agenda for research and practice is proposed including reconceptualizing well-being, forming stronger collaborations with allied disciplines, rigorously evaluating the new positive interventions, and considering a role for clinical psychologists in promoting well-being as well as treating distress.
Direct Electricity Trading in Smart Grid: A Coalitional Game Analysis

Integration of distributed generation based on renewable energy sources into the power system has gained popularity in recent years. Many small-scale electricity suppliers (SESs) have recently entered the electricity market, which has been traditionally dominated by a few large-scale electricity suppliers. The emergence of SESs enables direct trading (DT) of electricity between SESs and end-users (EUs), without going through retailers, and promotes the possibility of improving the benefits to both parties. In this paper, the cooperation between SESs and EUs in DT is analyzed based on coalitional game theory. In particular, an electricity pricing scheme that achieves a fair division of revenue between SESs and EUs is analytically derived by using the asymptotic Shapley value. The asymptotic Shapley value is shown to be in the core of the coalitional game such that no group of SESs and EUs has an incentive to abandon the coalition, which implies the stable operation of DT for the proposed pricing scheme. Unlike the existing pricing schemes that typically require multiple stages of calculations and real time information about each participant, the electricity price for the proposed scheme can be determined instantaneously based on the number of participants in DT and statistical information about electricity supply and demand. Therefore, the proposed pricing scheme is suitable for practical implementation. Using computer simulations, the price of electricity for the proposed DT scheme is examined in various environments, and the numerical results validate the asymptotic analysis. Moreover, the revenues of the SESs and EUs are evaluated for various types of SESs and different numbers of participants in DT. The optimal ratio of different types of SESs is also investigated.
Urethral prolapse in a paediatric patient with urogenital bleeding: diagnosis by imaging

A girl of 4 years and 5 months of age was admitted as an outpatient to perform ultrasound because of painless vaginal bleeding for 2–3 days. First, on transabdominal US, the kidneys, bladder, and uterus appeared normal. A translabial perineal approach revealed a hyperaemic mass . One day after, MRI was performed to exclude a possible aggressive lesion. MRI revealed the mass to be in apparent continuity with the urethra . Inspection under anaesthesia revealed a prolapsed urethra  a doughnut of red and purple tissue surrounded the urethra, obscuring the hymeneal orifice. The patient was treated by undergoing a resection of the prolapsed mucosa and reocclusion of the mucous membrane around a Foley catheter. Three months later we saw: normal external genitalia, normal external urethral meatus, and no mucosal ectropion. We did not see any pathological secretions.
Emotional conception: how embodied emotion concepts guide perception and facial action.

This study assessed embodied simulation via electromyography (EMG) as participants first encoded emotionally ambiguous faces with emotion concepts (i.e., "angry,""happy") and later passively viewed the faces without the concepts. Memory for the faces was also measured. At initial encoding, participants displayed more smiling-related EMG activity in response to faces paired with "happy" than in response to faces paired with "angry." Later, in the absence of concepts, participants remembered happiness-encoded faces as happier than anger-encoded faces. Further, during passive reexposure to the ambiguous faces, participants' EMG indicated spontaneous emotion-specific mimicry, which in turn predicted memory bias. No specific EMG activity was observed when participants encoded or viewed faces with non-emotion-related valenced concepts, or when participants encoded or viewed Chinese ideographs. From an embodiment perspective, emotion simulation is a measure of what is currently perceived. Thus, these findings provide evidence of genuine concept-driven changes in emotion perception. More generally, the findings highlight embodiment's role in the representation and processing of emotional information.
VISION 2020: The Right to Sight: a global initiative to eliminate avoidable blindness.

A n estimated 45 million people around the world are blind. 1 Most of them have lost their sight to diseases that are treatable or preventable. Eighty percent of them live in the lesser-developed world in countries where chronic economic deprivation is exacerbated by the added challenge of failing vision. Without intervention, the number of individuals with blindness might reach 76 million by 2020 because of a number of factors, primarily the rapid aging of populations in most countries. Since eye disease is seen largely in older people, the projected doubling of the world’s population older than 50 years to 2 billion by 2020 has profound effects on the number of those with blindness and low vision.
How Deep is Your Bag of Words?

Many existing deep learning models for natural language processing tasks focus on learning the compositionality of their inputs, which requires expensive computations and long training times. We present a simple deep neural network that competes with and, in some cases, outperforms such models on sentiment analysis and factoid question answering tasks while taking only a fraction of the training time. While our model is syntactically-ignorant, we show significant improvements over previous bag-of-words models by deepening our network, applying a novel variant of dropout, and initializing with pretrained word embeddings. Moreover, our model performs better than syntactic models on datasets with high syntactic variance. Our results indicate that for the tasks we consider, nonlinearly transforming the input is more important than tailoring a network to model word order and syntax.
Unified Blind Quality Assessment of Compressed Natural, Graphic, and Screen Content Images

Digital images in the real world are created by a variety of means and have diverse properties. A photographical natural scene image (NSI) may exhibit substantially different characteristics from a computer graphic image (CGI) or a screen content image (SCI). This casts major challenges to objective image quality assessment, for which existing approaches lack effective mechanisms to capture such content type variations, and thus are difficult to generalize from one type to another. To tackle this problem, we first construct a cross-content-type (CCT) database, which contains 1,320 distorted NSIs, CGIs, and SCIs, compressed using the high efficiency video coding (HEVC) intra coding method and the screen content compression (SCC) extension of HEVC. We then carry out a subjective experiment on the database in a well-controlled laboratory environment. Moreover, we propose a unified content-type adaptive (UCA) blind image quality assessment model that is applicable across content types. A key step in UCA is to incorporate the variations of human perceptual characteristics in viewing different content types through a multi-scale weighting framework. This leads to superior performance on the constructed CCT database. UCA is training-free, implying strong generalizability. To verify this, we test UCA on other databases containing JPEG, MPEG-2, H.264, and HEVC compressed images/videos, and observe that it consistently achieves competitive performance.
Association between substandard classroom ventilation rates and students' academic achievement.

This study focuses on the relationship between classroom ventilation rates and academic achievement. One hundred elementary schools of two school districts in the southwest United States were included in the study. Ventilation rates were estimated from fifth-grade classrooms (one per school) using CO(2) concentrations measured during occupied school days. In addition, standardized test scores and background data related to students in the classrooms studied were obtained from the districts. Of 100 classrooms, 87 had ventilation rates below recommended guidelines based on ASHRAE Standard 62 as of 2004. There is a linear association between classroom ventilation rates and students' academic achievement within the range of 0.9-7.1 l/s per person. For every unit (1 l/s per person) increase in the ventilation rate within that range, the proportion of students passing standardized test (i.e., scoring satisfactory or above) is expected to increase by 2.9% (95%CI 0.9-4.8%) for math and 2.7% (0.5-4.9%) for reading. The linear relationship observed may level off or change direction with higher ventilation rates, but given the limited number of observations, we were unable to test this hypothesis. A larger sample size is needed for estimating the effect of classroom ventilation rates higher than 7.1 l/s per person on academic achievement.



The results of this study suggest that increasing the ventilation rates toward recommended guideline ventilation rates in classrooms should translate into improved academic achievement of students. More studies are needed to fully understand the relationships between ventilation rate, other indoor environmental quality parameters, and their effects on students' health and achievement. Achieving the recommended guidelines and pursuing better understanding of the underlying relationships would ultimately support both sustainable and productive school environments for students and personnel.
CAVE: Configuration Assessment, Visualization and Evaluation

To achieve peak performance of an algorithm (in particular for problems in AI), algorithm configuration is often necessary to determine a well-performing parameter configuration. So far, most studies in algorithm configuration focused on proposing better algorithm configuration procedures or on improving a particular algorithm’s performance. In contrast, we use all the collected empirical performance data gathered during algorithm configuration runs to generate extensive insights into an algorithm, given problem instances and the used configurator. To this end, we provide a tool, called CAVE , that automatically generates comprehensive reports and insightful figures from all available empirical data. CAVE aims to help algorithm and configurator developers to better understand their experimental setup in an automated fashion. We showcase its use by thoroughly analyzing the well studied SAT solver spear on a benchmark of software verification instances and by empirically verifying two long-standing assumptions in algorithm configuration and parameter importance: (i) Parameter importance changes depending on the instance set at hand and (ii) Local and global parameter importance analysis do not necessarily agree with each other.
Obesity and the skin: skin physiology and skin manifestations of obesity.

Obesity is widely recognized as an epidemic in the Western world; however, the impact of obesity on the skin has received minimal attention. The purpose of this article is to highlight the association between obesity and dermatologic conditions. We review the impact of obesity on the skin, including skin physiology, skin manifestations of obesity, and dermatologic diseases aggravated by obesity. Obesity is responsible for changes in skin barrier function, sebaceous glands and sebum production, sweat glands, lymphatics, collagen structure and function, wound healing, microcirculation and macrocirculation, and subcutaneous fat. Moreover, obesity is implicated in a wide spectrum of dermatologic diseases, including acanthosis nigricans, acrochordons, keratosis pilaris, hyperandrogenism and hirsutism, striae distensae, adiposis dolorosa, and fat redistribution, lymphedema, chronic venous insufficiency, plantar hyperkeratosis, cellulitis, skin infections, hidradenitis suppurativa, psoriasis, insulin resistance syndrome, and tophaceous gout. We review the clinical features, evidence for association with obesity, and management of these various dermatoses and highlight the profound impact of obesity in clinical dermatology.


LEARNING 
After completing this learning activity, participants should be aware of obesity-associated changes in skin physiology, skin manifestations of obesity, and dermatologic diseases aggravated by obesity, and be able to formulate a pathophysiology-based treatment strategy for obesity-associated dermatoses.
Data Augmentation for Recognition of Handwritten Words and Lines Using a CNN-LSTM Network

We introduce two data augmentation and normalization techniques, which, used with a CNN-LSTM, significantly reduce Word Error Rate (WER) and Character Error Rate (CER) beyond best-reported results on handwriting recognition tasks. (1) We apply a novel profile normalization technique to both word and line images. (2) We augment existing text images using random perturbations on a regular grid. We apply our normalization and augmentation to both training and test images. Our approach achieves low WER and CER over hundreds of authors, multiple languages and a variety of collections written centuries apart. Image augmentation in this manner achieves state-of-the-art recognition accuracy on several popular handwritten word benchmarks.
Arc Mat , a Matlab toolbox for using ArcView Shape files for spatial econometrics and statistics

The design and implementation of software for extracting information from GIS files to a format appropriate for use in a spatial modeling software environment is described. This has resulted in publicly available c/c++ language programs for extracting polygons as well as database information from ArcView shape files into the Matlab software environment. In addition, a set of publicly available mapping functions that employ a graphical user interface (GUI) within Matlab are described. Particular attention is given to the interplay between spatial econometric/statistical modeling and the use of GIS information as well as mapping functions. In a recent survey of the interplay between GIS and regional modeling, Goodchild and Haining (2003) indicate the need for a convergence of these two dimensions of spatial modeling in regional science. Many of the design considerations discussed here would also apply to implementing similar functionality in other software environments for spatial statistical modeling such as R/Splus or Gauss. Toolboxes are the name given by the MathWorks to related sets of Matlab functions aimed at solving a particular class of problems. Toolboxes of functions useful in signal processing, optimization, statistics, finance and a host of other areas are available from the MathWorks as add-ons to the standard Matlab software distribution. We label the set of functions described here for extracting GIS file information as well as the GUI mapping functions the Arc Mat Toolbox.