nils-herrmann/clean_scirepeval huggingface dataset:
Unravelling the evolution of neural stem cells in arthropods: notch signalling in neural stem cell development in the crustacean Daphnia magna.

The genetic regulatory networks controlling major developmental processes seem to be conserved in bilaterians regardless of an independent or a common origin of the structures. This has been explained by the employment of a genetic toolkit that was repeatedly used during bilaterian evolution to build the various forms and body plans. However, it is not clear how genetic networks were incorporated into the formation of novel structures and how homologous genes can regulate the disparate morphological processes. Here we address this question by analysing the role of Notch signalling, which is part of the bilaterian toolkit, in neural stem cell evolution in arthropods. Within arthropods neural stem cells have evolved in the last common ancestor of insects and crustaceans (Tetraconata). We analyse here for the first time the role of Notch signalling in a crustacean, the branchiopod Daphnia magna, and show that it is required in neural stem cells for regulating the time of neural precursor production and for binary cell fate decisions in the ventral neuroectoderm. The function of Notch signalling has diverged in the ventral neuroectoderm of insects and crustaceans accompanied by changes in the morphogenetic processes. In the crustacean, Notch controlled mechanisms of neuroblast regulation have evolved that are surprisingly similar to vertebrates and thus present a remarkable case of parallel evolution. These new data on a representative of crustaceans complete the arthropod data set on Notch signalling in the nervous system and allow for reconstructing how the Notch signalling pathway has been co-opted from pre-existing structures to the development of the evolving neural stem cells in the Tetraconata ancestor.
Credit Assignment For Collective Multiagent RL With Global Rewards

Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment. We focus on a multiagent planning model subclass, relevant to urban settings, where agent interactions are dependent on their “collective influence” on each other, rather than their identities. Unlike previous work, we address a general setting where system reward is not decomposable among agents. We develop collective actor-critic RL approaches for this setting, and address the problem of multiagent credit assignment, and computing low variance policy gradient estimates that result in faster convergence to high quality solutions. We also develop difference rewards based credit assignment methods for the collective setting. Empirically our new approaches provide significantly better solutions than previous methods in the presence of global rewards on two real world problems modeling taxi fleet optimization and multiagent patrolling, and a synthetic grid navigation domain.
Power Signatures of High-Performance Computing Workloads

Workload-aware power management and scheduling techniques have the potential to save energy while minimizing negative impact on performance. The effectiveness of these techniques depends on the stability of a workload's power consumption pattern across different input data, resource allocations (e.g. number of cores), and hardware platforms.
 In this paper, we show that the power consumption behavior of HPC workloads can be accurately captured by concise signatures built from their power traces. We validate this approach using 255 traces collected from 13 high-performance computing workloads on 4 different hardware platforms. First, we use both feature-based and time-series-based distance metrics to cluster our traces, and we quantitatively show that feature-based clusterings segregate traces by workload just as effectively as the more compute- and space-intensive time-series-based clusterings. Second, we demonstrate that unlabeled traces can be classified by workload with over 85% accuracy, based only on these concise statistical signatures.
Cultural and generational influences on privacy concerns: a qualitative study in seven European countries

This research examines how European citizens decide to disclose and protect their personal data and thereby reveals cultural and generational divides. Focus group discussions featured either young people, aged 15 to 24 years, or adults, between 25 and 70 years of age, and were conducted in seven EU member states. The results of a computer-aided text analysis with two complementary software packages suggest similarities and differences in participants’ views and privacy concerns. Responsibility is relevant to personal data management, which represents a hotly contested issue. A geographical north–south divide appears for the importance of responsibility as opposed to trust. Moreover, people regard disclosure differently in the south (as a choice) and east (as forced) of Europe. Younger people express more positive attitudes toward data management, feel more responsible, and are more confident in their ability to prevent possible data misuse. Their lower privacy concerns and greater protective behaviours (i.e., a potential reversed privacy paradox) may help explain contradictory results in prior literature. These results offer significant and useful theoretical, managerial, and policy implications.
Performance appraisal and performance management: 100 years of progress?

We review 100 years of research on performance appraisal and performance management, highlighting the articles published in JAP, but including significant work from other journals as well. We discuss trends in eight substantive areas: (1) scale formats, (2) criteria for evaluating ratings, (3) training, (4) reactions to appraisal, (5) purpose of rating, (6) rating sources, (7) demographic differences in ratings, and (8) cognitive processes, and discuss what we have learned from research in each area. We also focus on trends during the heyday of performance appraisal research in JAP (1970-2000), noting which were more productive and which potentially hampered progress. Our overall conclusion is that JAP's role in this literature has not been to propose models and new ideas, but has been primarily to test ideas and models proposed elsewhere. Nonetheless, we conclude that the papers published in JAP made important contribution to the filed by addressing many of the critical questions raised by others. We also suggest several areas for future research, especially research focusing on performance management.
Inducing Regular Grammars Using Recurrent Neural Networks

Grammar induction is the task of learning a grammar from a set of examples. Recently, neural networks have been shown to be powerful learning machines that can identify patterns in streams of data. In this work we investigate their effectiveness in inducing a regular grammar from data, without any assumptions about the grammar. We train a recurrent neural network to distinguish between strings that are in or outside a regular language, and utilize an algorithm for extracting the learned finitestate automaton. We apply this method to several regular languages and find unexpected results regarding the connections between the network’s states that may be regarded as evidence for generalization.
Associations between emotional intelligence, depression and suicide risk in nursing students.

The most important factor which predisposes young people to suicide is depression, although protective factors such as self-esteem, emotional adaptation and social support may reduce the probability of suicidal ideation and suicide attempts. Several studies have indicated an elevated risk of suicide for health-related professions. Little is known, however, about the relationship between perceived emotional intelligence and suicide risk among nursing students.



The main goals were to determine the prevalence of suicide risk in a sample of nursing students, to examine the relationship between suicide risk and perceived emotional intelligence, depression, trait anxiety and self-esteem, and to identify any gender differences in relation to these variables.



Cross-sectional study of nursing students (n=93) who completed self-report measures of perceived emotional intelligence (Trait Meta-Mood Scale, which evaluates three dimensions: emotional attention, clarity and repair), suicide risk (Plutchik Suicide Risk Scale), self-esteem (Rosenberg Self-esteem Scale), depression (Zung Self-Rating Depression Scale) and anxiety (Trait scale of the State-Trait Anxiety Inventory).



Linear regression analysis confirmed that depression and emotional attention are significant predictors of suicidal ideation. Moreover, suicide risk showed a significant negative association with self-esteem and with emotional clarity and repair. Gender differences were only observed in relation to depression, on which women scored significantly higher. Overall, 14% of the students were considered to present a substantial suicide risk.



The findings suggest that interventions to prevent suicidal ideation among nursing students should include strategies to detect mood disorders (especially depression) and to improve emotional coping skills. In line with previous research the results indicate that high scores on emotional attention are linked to heightened emotional susceptibility and an increased risk of suicide. The identification and prevention of factors associated with suicidal behaviour in nursing students should be regarded as a priority.
A single GluN2 subunit residue controls NMDA receptor channel properties via intersubunit interaction

NMDA receptors (NMDARs) are glutamate-gated ion channels that are present at most excitatory mammalian synapses. The four GluN2 subunits (GluN2A–D) contribute to four diheteromeric NMDAR subtypes that have divergent physiological and pathological roles. Channel properties that are fundamental to NMDAR function vary among subtypes. We investigated the amino acid residues responsible for variations in channel properties by creating and examining NMDARs containing mutant GluN2 subunits. We found that the NMDAR subtype specificity of three crucial channel properties, Mg2+ block, selective permeability to Ca2+ and single-channel conductance, were all controlled primarily by the residue at a single GluN2 site in the M3 transmembrane region. Mutant cycle analysis guided by molecular modeling revealed that a GluN2-GluN1 subunit interaction mediates the site's effects. We conclude that a single GluN2 subunit residue couples with the pore-forming loop of the GluN1 subunit to create naturally occurring variations in NMDAR properties that are critical to synaptic plasticity and learning.
Effect of homeopathic Lycopodium clavatum on memory functions and cerebral blood flow in memory-impaired rats.

Lycopodium clavatum (Lyc) is a widely used homeopathic medicine for the liver, urinary and digestive disorders. Recently, acetyl cholinesterase (AchE) inhibitory activity has been found in Lyc alkaloid extract, which could be beneficial in dementia disorder. However, the effect of Lyc has not yet been explored in animal model of memory impairment and on cerebral blood flow.



The present study was planned to explore the effect of Lyc on learning and memory function and cerebral blood flow (CBF) in intracerebroventricularly (ICV) administered streptozotocin (STZ) induced memory impairment in rats.



Memory deficit was induced by ICV administration of STZ (3 mg/kg) in rats on 1st and 3rd day. Male SD rats were treated with Lyc Mother Tincture (MT) 30, 200 and 1000 for 17 days. Learning and memory was evaluated by Morris water maze test on 14th, 15th and 16th day. CBF was measured by Laser Doppler flow meter on 17th day.



STZ (ICV) treated rats showed impairment in learning and memory along with reduced CBF. Lyc MT and 200 showed improvement in learning and memory. There was increased CBF in STZ (ICV) treated rats at all the potencies of Lyc studied.



The above study suggests that Lyc may be used as a drug of choice in condition of memory impairment due to its beneficial effect on CBF.
Moldenhawera intermedia (Leguminosae: Caesalpinioideae), a new species from the Brazilian state of Bahia

A new species of Moldenhawera is described from the state of Bahia in Brazil. Moldenhawera intermedia differs from all other species in the genus by its combination of pinnate leaves with acute to caudate apices, its pink petals, pentamerous flowers, and the staminode filaments 1.5 to 2.5 × longer than the anthers which dehisce by slits not pores. The species has a suite of morphological characters that place it intermediate between all three described sections of the genus, suggesting that the supra-specific taxonomy of Moldenhawera will benefit from a future reappraisal.
Fast Efficient Object Detection Using Selective Attention

Deep learning object detectors achieve state-of-the-art accuracy at the expense of high computational overheads, impeding their utilization on embedded systems such as drones. A primary source of these overheads is the exhaustive classification of typically 10 − 10 regions per image. Given that most of these regions contain uninformative background, the detector designs seem extremely superfluous and inefficient. In contrast, biological vision systems leverage selective attention for fast and efficient object detection. Recent neuroscientific findings shedding new light on the mechanism behind selective attention allowed us to formulate a new hypothesis of object detection efficiency and subsequently introduce a new object detection paradigm. To that end, we leverage this knowledge to design a novel region proposal network and empirically show that it achieves high object detection performance on the COCO dataset. Moreover, the model uses two to three orders of magnitude fewer computations than state-of-the-art models and consequently achieves inference speeds exceeding 500 frames/s, thereby making it possible to achieve object detection on embedded systems.
Overview of Environment Perception for Intelligent Vehicles

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.
Hybrid NOrec: a case study in the effectiveness of best effort hardware transactional memory

Transactional memory (TM) is a promising synchronization mechanism for the next generation of multicore processors. Best-effort Hardware Transactional Memory (HTM) designs, such as Sun's prototype Rock processor and AMD's proposed Advanced Synchronization Facility (ASF), can efficiently execute many transactions, but abort in some cases due to various limitations. Hybrid TM systems can use a compatible software TM (STM) in such cases.
 We introduce a family of hybrid TMs built using the recent NOrec STM algorithm that, unlike existing hybrid approaches, provide both low overhead on hardware transactions and concurrent execution of hardware and software transactions. We evaluate implementations for Rock and ASF, exploring how the differing HTM designs affect optimization choices. Our investigation yields valuable input for designers of future best-effort HTMs.
White blood cell counts and neutrophil to lymphocyte ratio in the diagnosis of testicular cancer: a simple secondary serum tumor marker.

The aim of the study was to investigate white blood cell counts and neutrophil to lymphocyte ratio (NLR) as markers of systemic inflammation in the diagnosis of localized testicular cancer as a malignancy with initially low volume.



Thirty-six patients with localized testicular cancer with a mean age of 34.22±14.89 years and 36 healthy controls with a mean age of 26.67±2.89 years were enrolled in the study. White blood cell counts and NLR were calculated from complete blood cell counts.



White blood cell counts and NLR were statistically significantly higher in patients with testicular cancer compared with the control group (p<0.0001 for all).



Both white blood cell counts and NLR can be used as a simple test in the diagnosis of testicular cancer besides the well-known accurate serum tumor markers as AFP (alpha fetoprotein), hCG (human chorionic gonadotropin) and LDH (lactate dehydrogenase).
Tensorial Mixture Models

We introduce a generative model, we call Tensorial Mixture Models (TMMs) based on mixtures of basic component distributions over local structures (e.g. patches in an image) where the dependencies between the local-structures are represented by a ”priors tensor” holding the prior probabilities of assigning a component distribution to each local-structure. In their general form, TMMs are intractable as the prior tensor is typically of exponential size. However, when the priors tensor is decomposed it gives rise to an arithmetic circuit which in turn transforms the TMM into a Convolutional Arithmetic Circuit (ConvAC). A ConvAC corresponds to a shallow (single hidden layer) network when the priors tensor is decomposed by a CP (sum of rank-1) approach and corresponds to a deep network when the decomposition follows the Hierarchical Tucker (HT) model. The ConvAC representation of a TMM possesses several attractive properties. First, the inference is tractable and is implemented by a forward pass through a deep network. Second, the architectural design of the model follows the deep networks community design, i.e., the structure of TMMs is determined by just two easily understood factors: size of pooling windows and number of channels. Finally, we demonstrate the effectiveness of our model when tackling the problem of classification with missing data, leveraging TMMs unique ability of tractable marginalization which leads to optimal classifiers regardless of the missingness distribution.
Lateral Control of Vehicle Platoons

Vehicle platoons are fully automated vehicles driving in close proximity of each other, where both distance keeping and steering is under automatic control. This paper is aiming at a variant of vehicle platoons, where the lateral control is using forward looking sensors, i.e. camera, radar. Such a system solution implies that the vehicle dynamics are coupled together laterally, in contrast to the classical look-down solutions. For such a platoon, lateral string stability is an important property that the controller needs to guarantee. This article proposes a method for designing such a distributed controller. It also examines the effect of model uncertainties on the lateral string stability of the platoon for the proposed method.
22.5 A 93%-power-efficiency photovoltaic energy harvester with irradiance-aware auto-reconfigurable MPPT scheme achieving >95% MPPT efficiency across 650µW to 1W and 2.9ms FOCV MPPT transient time

With more and more functions in modern battery-powered mobile devices, enabling light-harvesting in the power management system can extend battery usage time . For both indoor and outdoor operations of mobile devices, the output power range of the solar panel with the size of a touchscreen can vary from 100s of µW to a Watt due to the irradiance-level variation. An energy harvester is thus essential to achieve high maximum power-point tracking efficiency (ηT) over this wide power range. However, state-of-the-art energy harvesters only use one maximum power-point tracking (MPPT) method under different irradiance levels as shown in Fig. 22.5.1 [2–5]. Those energy harvesters with power-computation-based MPPT schemes for portable [2,3] and standalone [4] systems suffer from low ηT under low input power due to the limited input dynamic range of the MPPT circuitry. Other low-power energy harvesters with the fractional open-cell voltage (FOCV) MPPT scheme are confined by the fractional-constant accuracy to only offer high ηT across a narrow power range . Additionally, the conventional FOCV MPPT scheme requires long transient time of 250ms to identify MPP , thereby significantly reducing energy capture from the solar panel. To address the above issues, this paper presents an energy harvester with an irradiance-aware hybrid algorithm (IAHA) to automatically switch between an auto-zeroed pulse-integration based MPPT (AZ PI-MPPT) and a slew-rate-enhanced FOCV (SRE-FOCV) MPPT scheme for maximizing ηT under different irradiance levels. The SRE-FOCV MPPT scheme also enables the energy harvester to shorten the MPPT transient time to 2.9ms in low irradiance levels.
Transition-based Neural Constituent Parsing

Constituent parsing is typically modeled by a chart-based algorithm under probabilistic context-free grammars or by a transition-based algorithm with rich features. Previous models rely heavily on richer syntactic information through lexicalizing rules, splitting categories, or memorizing long histories. However enriched models incur numerous parameters and sparsity issues, and are insufficient for capturing various syntactic phenomena. We propose a neural network structure that explicitly models the unbounded history of actions performed on the stack and queue employed in transition-based parsing, in addition to the representations of partially parsed tree structure. Our transition-based neural constituent parsing achieves performance comparable to the state-of-the-art parsers, demonstrating F1 score of 90.68% for English and 84.33% for Chinese, without reranking, feature templates or additional data to train model parameters.
A dual-motor robot joint mechanism with epicyclic gear train

This paper presents the concept of a new robotic joint composed of two electric motors as inputs, an epicyclic gearing system for the transmission, and a single output. The proposed joint mechanism has a wider range of speed and torque performances comparatively to a traditional robot joint using a single motor and gearbox. The dynamic equations for the mechanical transmission system are given and a dual-motor joint mechanism is designed and prototyped to test this new concept of robotic joint. Also, the potential advantages of this joint concept for the design of manipulators for which a wide range of performances are desired are discussed. This work is motivated by the development of field robots designed for the operation and maintenance tasks in power distribution lines.