nils-herrmann/clean_scirepeval huggingface dataset:
Circadian rhythmicity in emerging mood disorders: state or trait marker?

Circadian rhythm disturbances overlap with the symptoms of mood episodes and may trigger or prolong mood symptoms. There is limited research on the role of circadian disturbances in mood disorders in young people and/or first episode cases of unipolar and bipolar disorders.



Actigraphy was undertaken for about 14 days in 63 post-pubertal individuals aged 13-25 years with a recent onset of a mood disorder meeting recognised diagnostic criteria. We examined associations between three easily interpretable markers of circadian rhythm activity (amplitude, acrophase and rhythmicity index) and demography and clinical characteristics. Then, circadian markers were compared between diagnostic groups, controlling for potential confounders.



Longer duration of illness was correlated with reduced circadian rhythmicity and lower levels of activity over 24 h. A delay in the timing of maximum activity was associated with the level of manic but not depressive symptoms. The circadian rhythmicity index differentiated unipolar from bipolar cases, and in bipolar but not unipolar disorder, the rhythmicity was less robust in those with more severe manic or depressive symptoms.



Less robust circadian rhythmicity, especially associated with increasing symptom severity, may represent a more specific or a trait marker of young people with mood disorders who are at higher risk of a bipolar course of illness.
Regularized Deep Learning for Face Recognition With Weight Variations

Body weight variations are an integral part of a person's aging process. However, the lack of association between the age and the weight of an individual makes it challenging to model these variations for automatic face recognition. In this paper, we propose a regularizer-based approach to learn weight invariant facial representations using two different deep learning architectures, namely, sparse-stacked denoising autoencoders and deep Boltzmann machines. We incorporate a body-weight aware regularization parameter in the loss function of these architectures to help learn weight-aware features. The experiments performed on the extended WIT database show that the introduction of weight aware regularization improves the identification accuracy of the architectures both with and without dropout.
Interaction Design in a Mobile Food Recommender System

One of the most important steps in building a recommender system is the interaction design process, which defines how the recommender system interacts with a user. It also shapes the experience the user gets, from the point she registers and provides her preferences to the system, to the point she receives recommendations generated by the system. A proper interaction design may improve user experience and hence may result in higher usability of the system, as well as, in higher satisfaction. In this paper, we focus on the interaction design of a mobile food recommender system that, through a novel interaction process, elicits users’ long-term and short-term preferences for recipes. User’s long-term preferences are captured by asking the user to rate and tag familiar recipes, while for collecting the short-term preferences, the user is asked to select the ingredients she would like to include in the recipe to be prepared. Based on the combined exploitation of both types of preferences, a set of personalized recommendations is generated. We conducted a user study measuring the usability of the proposed interaction. The results of the study show that the majority of users rates the quality of the recommendations high and the system achieves usability scores above the standard benchmark.
Exploring the vectorization of python constructs using pythran and boost SIMD

The Python language is highly dynamic, most notably due to late binding. As a consequence, programs using Python typically run an order of magnitude slower than their C counterpart. It is also a high level language whose semantic can be made more static without much change from a user point of view in the case of mathematical applications. In that case, the language provides several vectorization opportunities that are studied in this paper, and evaluated in the context of Pythran, an ahead-of-time compiler that turns Python module into C++ meta-programs.
Hepatic porphyrias: diagnosis and management.

Porphyrias are a group of metabolic disorders in which there are defects in the normal pathway for the biosynthesis of heme, the critical prosthetic group for numerous hemoproteins. The clinical manifestations of the porphyrias can be highly varied, and patients may present to general physicians and be referred to a wide variety of subspecialists because of these manifestations. However, two major clinical forms are represented by the so-called "acute" porphyrias, in which patients suffer recurrent bouts of pain, especially pain in the abdomen, and the "cutaneous" porphyrias, in which patients have painful skin lesions. Knowledge of the factors chiefly responsible for regulating the rate of synthesis of heme has helped to explain how drugs and other factors may cause porphyria. Knowledge of the physical and chemical properties of porphyrins also forms an important part of the foundation for understanding the clinical manifestations of these diseases. Thus, the porphyrias can best be understood after reviewing the chemical properties of porphyrins and heme and the control of their biosynthesis.
DIY Human Action Dataset Generation

The recent successes in applying deep learning techniques to solve standard computer vision problems has aspired researchers to propose new computer vision problems in different domains. As previously established in the field, training data itself plays a significant role in the machine learning process, especially deep learning approaches which are data hungry. In order to solve each new problem and get a decent performance, a large amount of data needs to be captured which may in many cases pose logistical difficulties. Therefore, the ability to generate de novo data or expand an existing dataset, however small, in order to satisfy data requirement of current networks may be invaluable. Herein, we introduce a novel way to partition an action video clip into action, subject and context. Each part is manipulated separately and reassembled with our proposed video generation technique. Furthermore, our novel human skeleton trajectory generation along with our proposed video generation technique, enables us to generate unlimited action recognition training data. These techniques enables us to generate video action clips from an small set without costly and time-consuming data acquisition. Lastly, we prove through extensive set of experiments on two small human action recognition datasets, that this new data generation technique can improve the performance of current action recognition neural nets.
The evolutionary history of termites as inferred from 66 mitochondrial genomes.

Termites have colonized many habitats and are among the most abundant animals in tropical ecosystems, which they modify considerably through their actions. The timing of their rise in abundance and of the dispersal events that gave rise to modern termite lineages is not well understood. To shed light on termite origins and diversification, we sequenced the mitochondrial genome of 48 termite species and combined them with 18 previously sequenced termite mitochondrial genomes for phylogenetic and molecular clock analyses using multiple fossil calibrations. The 66 genomes represent most major clades of termites. Unlike previous phylogenetic studies based on fewer molecular data, our phylogenetic tree is fully resolved for the lower termites. The phylogenetic positions of Macrotermitinae and Apicotermitinae are also resolved as the basal groups in the higher termites, but in the crown termitid groups, including Termitinae + Syntermitinae + Nasutitermitinae + Cubitermitinae, the position of some nodes remains uncertain. Our molecular clock tree indicates that the lineages leading to termites and Cryptocercus roaches diverged 170 Ma (153-196 Ma 95% confidence interval [CI]), that modern Termitidae arose 54 Ma (46-66 Ma 95% CI), and that the crown termitid group arose 40 Ma (35-49 Ma 95% CI). This indicates that the distribution of basal termite clades was influenced by the final stages of the breakup of Pangaea. Our inference of ancestral geographic ranges shows that the Termitidae, which includes more than 75% of extant termite species, most likely originated in Africa or Asia, and acquired their pantropical distribution after a series of dispersal and subsequent diversification events.
Sentiment Analysis in Social Media Platforms: The Contribution of Social Relationships

The massive amount of data in social media platforms is a key source for companies to analyze customer sentiment and opinions. Many existing sentiment analysis approaches solely rely on textual contents of a sentence (e.g. words) for sentiment identification. Consequently, current sentiment analysis systems are ineffective for analyzing contents in social media because people may use non-standard language (e.g., abbreviations, misspellings, emoticons or multiple languages) in online platforms. Inspired by the attribution theory that is grounded in social psychology, we propose a sentiment analysis framework that considers the social relationships among users and contents. We conduct experiments to compare the proposed approach against the existing approaches on a dataset collected from Facebook. The results indicate that we can more accurately classify sentiment of sentences by utilizing social relationships. The results have important implications for companies to analyze customer opinions.
A simple method for detecting salient regions

A simple method for detecting salient regions in images is proposed. It requires only edge detection, threshold decomposition, the distance transform, and thresholding. Moreover, it avoids the need for setting any parameter values. Experiments show that the resulting regions are relatively coarse, but overall the method is surprisingly effective, and has the benefit of easy implementation. Quantitative tests were carried out on Liu et al.’s dataset of 5000 images. Although the ratings of our simple method were not as good as their approach which involved an extensive training stage, they were comparable to several other popular methods from the literature. Further tests on Kootstra and Schomaker’s dataset of 99 images also showed promising results.
Lean, simulation and optimization: A win-win combination

Lean and simulation analysis are driven by the same objective, how to better design and improve processes making the companies more competitive. The adoption of lean has been widely spread in companies from public to private sectors and simulation is nowadays becoming more and more popular. Several authors have pointed out the benefits of combining simulation and lean, however, they are still rarely used together in practice. Optimization as an additional technique to this combination is even a more powerful approach especially when designing and improving complex processes with multiple conflicting objectives. This paper presents the mutual benefits that are gained when combining lean, simulation and optimization and how they overcome each other's limitations. A framework including the three concepts, some of the barriers for its implementation and a real-world industrial example are also described.
Half Field Offense : An Environment for Multiagent Learning and Ad Hoc Teamwork

The RoboCup 2D simulation domain has served as a platform for research in AI, machine learning, and multiagent systems for more than two decades. However, for the researcher looking to quickly prototype and evaluate different algorithms, the full RoboCup task presents a cumbersome prospect, as it can take several weeks to set up the desired testing environment. The complexity owes in part to the coordination of several agents, each with a multi-layered control hierarchy, and which must balance offensive and defensive goals. This paper introduces a new open source benchmark, based on the Half Field Offense (HFO) subtask of soccer, as an easy-to-use platform for experimentation. While retaining the inherent challenges of soccer, the HFO environment constrains the agent’s attention to decision-making, providing standardized interfaces for interacting with the environment and with other agents, and standardized tools for evaluating performance. The resulting testbed makes it convenient to test algorithms for single and multiagent learning, ad hoc teamwork, and imitation learning. Along with a detailed description of the HFO environment, we present benchmark results for reinforcement learning agents on a diverse set of HFO tasks. We also highlight several other challenges that the HFO environment opens up for future research.
GraSS: Graph Structure Summarization

Large graph databases are commonly collected and analyzed in numerous domains. For reasons related to either space efficiency or for privacy protection (e.g., in the case of social network graphs), it sometimes makes sense to replace the original graph with a summary, which removes certain details about the original graph topology. However, this summarization process leaves the database owner with the challenge of processing queries that are expressed in terms of the original graph, but are answered using the summary. In this paper, we propose a formal semantics for answering queries on summaries of graph structures. At its core, our formulation is based on a random worlds model. We show that important graph-structure queries (e.g., adjacency, degree, and eigenvector centrality) can be answered efficiently and in closed form using these semantics. Further, based on this approach to query answering, we formulate three novel graph partitioning/compression problems. We develop algorithms for finding a graph summary that least affects the accuracy of query results, and we evaluate our proposed algorithms using both real and synthetic data.
Communication Centrality in Dynamic Networks Using Time-Ordered Weighted Graph

Centrality is an important concept in the study of social network analysis (SNA), which is used to measure the importance of a node in a network. While many different centrality measures exist, most of them are proposed and applied to static networks. However, most types of networks are dynamic that their topology changes over time. A popular approach to represent such networks is to construct a sequence of time windows with a single aggregated static graph that aggregates all edges observed over some time period. In this paper, an approach which overcomes the limitation of this representation is proposed based on the notion of the time-ordered graph, to measure the communication centrality of a node in dynamic networks.
The development of neural stem cells

The discovery of stem cells that can generate neural tissue has raised new possibilities for repairing the nervous system. A rush of papers proclaiming adult stem cell plasticity has fostered the notion that there is essentially one stem cell type that, with the right impetus, can create whatever progeny our heart, liver or other vital organ desires. But studies aimed at understanding the role of stem cells during development have led to a different view — that stem cells are restricted regionally and temporally, and thus not all stem cells are equivalent. Can these views be reconciled?
Calibration of PD Term Structures : To Be Markov Or Not To Be

Term structures of default probabilities are omnipresent in credit risk modeling: time-dynamic credit portfolio models, default times, and multi-year pricing models, they all need the time evolution of default probabilities as a basic model input. Although people tend to believe that from an economic point of view the Markov property as underlying model assumption is kind of questionable it seems to be common market practice to model PD term structures via Markov chain techniques. In this paper we illustrate that the Markov assumption carries us quite far if we allow for nonhomogeneous time behaviour of the Markov chain generating the PD term structures. As a ‘proof of concept’ we calibrate a nonhomogeneous continuous-time Markov chain (NHCTMC) to observed one-year rating migrations and multi-year default frequencies, hereby achieving convincing approximation quality. 1 Markov Chains in Credit Risk Modeling The probability of default (PD) for a client is a fundamental risk parameter in credit risk management. It is common practice to assign to every rating grade in a bank’s masterscale a one-year PD in line with regulatory requirements; see . Table 1 shows an example for default frequencies assigned to rating grades from Standard and Poor’s (S&P). D AAA 0.00% AA 0.01% A 0.04% BBB 0.29% BB 1.28% B 6.24% CCC 32.35% Table 1: One-year default frequencies (D) assigned to S&P ratings; see  Moreover, credit risk modeling concepts like dependent default times, multi-year credit pricing, and multi-horizon economic capital require more than just one-year PDs. For multi-year credit risk modeling, banks need a whole term structure (p R )t≥0 of (cumulative) PDs for every rating grade R; see, e.g.,  for an introduction to PD term structures and [3] for their application to structured credit products. Every bank has its own (proprietary) way to calibrate PD term structures to bank-internal and external data. A look into the literature reveals that for the generation of PD term structures various Markov chain approaches, most often based on homogeneous chains, dominate current market practice. A landmarking paper in this direction is the work by Jarrow, Lando, and Turnbull . Further research has been done by various authors, see, e.g., Kadam , Lando , Sarfaraz et al. , Schuermann and Jafry [14, 15], Trueck and Oezturkmen [18], just to mention a few examples. A new approach via Markov mixtures has been presented recently by Frydman and Schuermann . In Markov chain theory (see [11]) one distinguishes between discrete-time and continuous-time chains. For instance, a discrete-time chain can be specified by a one-year migration or transition 1In the literature, PD term structures are sometimes called credit curves. 2A Markov chain is called homogeneous if transition probabilities do not depend on time.
A low cost MEMS and complementary filter based attitude heading reference system (AHRS) for low speed aircraft

AHRS is an important system that provides the 3-dimensional orientation (roll, pitch and yaw or heading) of an aircraft from which the flying performance can be evaluated. The avionics of aircraft are very sophisticated and majority of them do not allow any external interface for logging. As such, a standalone external AHRS is needed which should be comprised of low cost devices so that it becomes affordable for other aircrafts. This paper proposes a low cost Micro-Electrical-Mechanical system (MEMS) based AHRS for low speed aircraft to evaluate the flying performance. MEMS based 3-axis accelerometer, gyroscope and magnetometer have been used to build the AHRS. Low cost MEMS based sensors suffer from bias, drift and sudden spike. Low pass filter has been utilized to reduce the spikes. Complementary filter has been used to pin down the drift of the gyroscope from accelerometer reading. The system has been implemented in Arduino environment. Simulation and real time results have shown correct functioning of the system. Besides using as an AHRS in low speed aircraft, the system can be used for control and stabilization of other real time system.
Run-time Partial Reconfiguration speed investigation and architectural design space exploration

Run-time Partial Reconfiguration (PR) speed is significant in applications especially when fast IP core switching is required. In this paper, we propose to use Direct Memory Access (DMA), Master (MST) burst, and a dedicated Block RAM (BRAM) cache respectively to reduce the reconfiguration time. Based on the Xilinx PR technology and the Internal Configuration Access Port (ICAP) primitive in the FPGA fabric, we discuss multiple design architectures and thoroughly investigate their performance with measurements for different partial bitstream sizes. Compared to the reference OPB HWICAP and XPS HWICAP designs, experimental results showthatDMA HWICAP and MST HWICAP reduce the reconfiguration time by one order of magnitude, with little resource consumption overhead. The BRAM HWICAP design can even approach the reconfiguration speed limit of the ICAP primitive at the cost of large Block RAM utilization.
A weighted least-norm solution based scheme for avoiding joint limits for redundant joint manipulators

It is proposed to use weighted least-norm solution to avoid joint limits for redundant joint manipulators. A comparison is made with the gradient projection method for avoiding joint limits. While the gradient projection method provides the optimal direction for the joint velocity vector within the null space, its magnitude is not unique and is adjusted by a scalar coefficient chosen by trial and error. It is shown in this paper that one fixed value of the scalar coefficient is not suitable even in a small workspace. The proposed manipulation scheme automatically chooses an appropriate magnitude of the self-motion throughout the workspace. This scheme, unlike the gradient projection method, guarantees joint limit avoidance, and also minimizes unnecessary self-motion. It was implemented and tested for real-time control of a seven-degree-offreedom (7-DOF) Robotics Research Corporation (RRC) manipulator.
Paroxysmal hemicrania as the clinical presentation of giant cell arteritis

Head pain is the most common complaint in patients with giant cell arteritis but the headache has no distinct diagnostic features. There have been no published reports of giant cell arteritis presenting as a trigeminal autonomic cephalalgia. We describe a patient who developed a new onset headache in her fifties, which fit the diagnostic criteria for paroxysmal hemicrania and was completely responsive to corticosteroids. Removal of the steroid therapy brought a reemergence of her headaches. Giant cell arteritis should be considered in the evaluation of secondary causes of paroxysmal hemicrania; in addition giant cell arteritis needs to be ruled out in patients who are over the age of 50 years with a new onset trigeminal autonomic cephalalgia.
Modeling Color Difference for Visualization Design

Color is frequently used to encode values in visualizations. For color encodings to be effective, the mapping between colors and values must preserve important differences in the data. However, most guidelines for effective color choice in visualization are based on either color perceptions measured using large, uniform fields in optimal viewing environments or on qualitative intuitions. These limitations may cause data misinterpretation in visualizations, which frequently use small, elongated marks. Our goal is to develop quantitative metrics to help people use color more effectively in visualizations. We present a series of crowdsourced studies measuring color difference perceptions for three common mark types: points, bars, and lines. Our results indicate that peoples' abilities to perceive color differences varies significantly across mark types. Probabilistic models constructed from the resulting data can provide objective guidance for designers, allowing them to anticipate viewer perceptions in order to inform effective encoding design.