nils-herrmann/clean_scirepeval huggingface dataset:
The CXXC motif is more than a redox rheostat.

The CXXC active-site motif of thiol-disulfide oxidoreductases is thought to act as a redox rheostat, the sequence of which determines its reduction potential and functional properties. We tested this idea by selecting for mutants of the CXXC motif in a reducing oxidoreductase (thioredoxin) that complement null mutants of a very oxidizing oxidoreductase, DsbA. We found that altering the CXXC motif affected not only the reduction potential of the protein, but also its ability to function as a disulfide isomerase and also impacted its interaction with folding protein substrates and reoxidants. It is surprising that nearly all of our thioredoxin mutants had increased activity in disulfide isomerization in vitro and in vivo. Our results indicate that the CXXC motif has the remarkable ability to confer a large number of very specific properties on thioredoxin-related proteins.
Different distribution patterns of woody species on a slope in relation to vertical root distribution and dynamics of soil moisture profiles

Distribution patterns along a slope and vertical root distribution were compared among seven major woody species in a secondary forest of the warm-temperate zone in central Japan in relation to differences in soil moisture profiles through a growing season among different positions along the slope. Pinus densiflora, Juniperus rigida, Ilex pedunculosa and Lyonia ovalifolia, growing mostly on the upper part of the slope with shallow soil depth had shallower roots. Quercus serrata and Quercus glauca, occurring mostly on the lower slope with deep soil showed deeper rooting. Styrax japonica, mainly restricted to the foot slope, had shallower roots in spite of growing on the deepest soil. These relations can be explained by the soil moisture profile under drought at each position on the slope. On the upper part of the slope and the foot slope, deep rooting brings little advantage in water uptake from the soil due to the total drying of the soil and no period of drying even in the shallow soil, respectively. However, deep rooting is useful on the lower slope where only the deep soil layer keeps moist. This was supported by better diameter growth of a deep-rooting species on deeper soil sites than on shallower soil sites, although a shallow-rooting species showed little difference between them.
Dogs Can Be Successfully Trained to Alert to Hypoglycemia Samples from Patients with Type 1 Diabetes

Hypoglycemia (Hypo) is the most common side effect of insulin therapy in people with type 1 diabetes (T1D). Over time, patients with T1D become unaware of signs and symptoms of Hypo. Hypo unawareness leads to morbidity and mortality. Diabetes alert dogs (DADs) represent a unique way to help patients with Hypo unawareness. Our group has previously presented data in abstract form which demonstrates the sensitivity and specificity of DADS. The purpose of our current study is to expand evaluation of DAD sensitivity and specificity using a method that reduces the possibility of trainer bias.



We evaluated 6 dogs aging 1-10 years old who had received an average of 6 months of training for Hypo alert using positive training methods. Perspiration samples were collected from patients during Hypo (BG 46-65 mg/dL) and normoglycemia (BG 85-136 mg/dl) and were used in training. These samples were placed in glass vials which were then placed into 7 steel cans (1 Hypo, 2 normal, 4 blank) randomly placed by roll of a dice. The dogs alerted by either sitting in front of, or pushing, the can containing the Hypo sample. Dogs were rewarded for appropriate recognition of the Hypo samples using a food treat via a remote control dispenser. The results were videotaped and statistically evaluated for sensitivity (proportion of lows correctly alerted, "true positive rate") and specificity (proportion of blanks + normal samples not alerted, "true negative rate") calculated after pooling data across all trials for all dogs.



All DADs displayed statistically significant (p value <0.05) greater sensitivity (min 50.0%-max 87.5%) to detect the Hypo sample than the expected random correct alert of 14%. Specificity ranged from a min of 89.6% to a max of 97.9% (expected rate is not defined in this scenario).



Our results suggest that properly trained DADs can successfully recognize and alert to Hypo in an in vitro setting using smell alone.
Guidance Compliance Behavior on VMS Based on SOAR Cognitive Architecture

SOAR is a cognitive architecture named from state, operator and result, which is adopted to portray the drivers’ guidance compliance behavior on variable message sign VMS in this paper. VMS represents traffic conditions to drivers by three colors: red, yellow, and green. Based on the multiagent platform, SOAR is introduced to design the agent with the detailed description of the working memory, long-term memory, decision cycle, and learning mechanism. With the fixed decision cycle, agent transforms state through four kinds of operators, including choosing route directly, changing the driving goal, changing the temper of driver, and changing the road condition of prediction. The agent learns from the process of state transformation by chunking and reinforcement learning. Finally, computerized simulation program is used to study the guidance compliance behavior. Experiments are simulated many times under given simulation network and conditions. The result, including the comparison between guidance and no guidance, the state transition times, and average chunking times are analyzed to further study the laws of guidance compliance and learning mechanism.
Enhanced Insertion Sort Algorithm

Sorting is integral part of many computer based systems and applications, as it involves rearranging information into either ascending or descending order. There are many sorting algorithms like Quick sort, Heap sort, Merge sort, Insertion sort, Selection sort, Bubble sort and Freezing sort. However, efforts have been made to improve the performance of the algorithm in terms of efficiency, indeed a big issue to be considered. Major Emphasis has been placed on complexity by reducing the Number of comparisons, hence reducing complexity. This paper presents new sorting algorithm EIS, &quot;ENHANCED INSERTION SORT&quot;. It is basically an enhancement toINSERTION SORT (a kind of Hybrid sorting technique) by making it impressively faster algorithm with O(n)complexity as compared to O(n2) of insertion sort in worst case and less than O(n1. 585) in average case which is much better than insertion sort O(n2). It works flawlessly with huge lists of elements. To prove the effectiveness of the algorithm, the new algorithm is analyzed, implemented, tested and results has been carried out and compared with other major sorting algorithms and the results were promising.
Crossbar-Aware Neural Network Pruning

Crossbar architecture has been widely adopted in neural network accelerators due to the efficient implementations on vector-matrix multiplication operations. However, in the case of convolutional neural networks (CNNs), the efficiency is compromised dramatically because of the large amounts of data reuse. Although some mapping methods have been designed to achieve a balance between the execution throughput and resource overhead, the resource consumption cost is still huge while maintaining the throughput. Network pruning is a promising and widely studied method to shrink the model size, whereas prior work for CNNs compression rarely considered the crossbar architecture and the corresponding mapping method and cannot be directly utilized by crossbar-based neural network accelerators. This paper proposes a crossbar-aware pruning framework based on a formulated  $L_{0}$ -norm constrained optimization problem. Specifically, we design an  $L_{0}$ -norm constrained gradient descent with relaxant probabilistic projection to solve this problem. Two types of sparsity are successfully achieved: 1) intuitive crossbar-grain sparsity and 2) column-grain sparsity with output recombination, based on which we further propose an input feature maps reorder method to improve the model accuracy. We evaluate our crossbar-aware pruning framework on the median-scale CIFAR10 data set and the large-scale ImageNet data set with VGG and ResNet models. Our method is able to reduce the crossbar overhead by 44%–72% with insignificant accuracy degradation. This paper significantly reduce the resource overhead and the related energy cost and provides a new co-design solution for mapping CNNs onto various crossbar devices with much better efficiency.
Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts

Neural abstractive summarization has been increasingly studied, where the prior work mainly focused on summarizing single-speaker documents (news, scientific publications, etc). In dialogues, there are diverse interactive patterns between speakers, which are usually defined as dialogue acts. The interactive signals may provide informative cues for better summarizing dialogues. This paper proposes to explicitly leverage dialogue acts in a neural summarization model, where a sentence-gated mechanism is designed for modeling the relationships between dialogue acts and the summary. The experiments show that our proposed model significantly improves the abstractive summarization performance compared to the state-of-the-art baselines on the AMI meeting corpus, demonstrating the usefulness of the interactive signal provided by dialogue
Dupilumab treatment in adults with moderate-to-severe atopic dermatitis.

Dupilumab, a fully human monoclonal antibody that blocks interleukin-4 and interleukin-13, has shown efficacy in patients with asthma and elevated eosinophil levels. The blockade by dupilumab of these key drivers of type 2 helper T-cell (Th2)-mediated inflammation could help in the treatment of related diseases, including atopic dermatitis.



We performed randomized, double-blind, placebo-controlled trials involving adults who had moderate-to-severe atopic dermatitis despite treatment with topical glucocorticoids and calcineurin inhibitors. Dupilumab was evaluated as monotherapy in two 4-week trials and in one 12-week trial and in combination with topical glucocorticoids in another 4-week study. End points included the Eczema Area and Severity Index (EASI) score, the investigator's global assessment score, pruritus, safety assessments, serum biomarker levels, and disease transcriptome.



In the 4-week monotherapy studies, dupilumab resulted in rapid and dose-dependent improvements in clinical indexes, biomarker levels, and the transcriptome. The results of the 12-week study of dupilumab monotherapy reproduced and extended the 4-week findings: 85% of patients in the dupilumab group, as compared with 35% of those in the placebo group, had a 50% reduction in the EASI score (EASI-50, with higher scores in the EASI indicating greater severity of eczema) (P<0.001); 40% of patients in the dupilumab group, as compared with 7% in the placebo group, had a score of 0 to 1 (indicating clearing or near-clearing of skin lesions) on the investigator's global assessment (P<0.001); and pruritus scores decreased (indicating a reduction in itch) by 55.7% in the dupilumab group versus 15.1% in the placebo group (P<0.001). In the combination study, 100% of the patients in the dupilumab group, as compared with 50% of those who received topical glucocorticoids with placebo injection, met the criterion for EASI-50 (P=0.002), despite the fact that patients who received dupilumab plus glucocorticoids used less than half the amount of topical glucocorticoids used by those who received placebo plus the topical medication (P=0.16). Adverse events, such as skin infection, occurred more frequently with placebo; nasopharyngitis and headache were the most frequent adverse events with dupilumab.



Patients treated with dupilumab had marked and rapid improvement in all the evaluated measures of atopic dermatitis disease activity. Side-effect profiles were not dose-limiting.
Control of linear switched reluctance motor for sliding door application

This paper deals with the modelling and control of a single sided linear switched reluctance actuator. This study provide a presentation of modelling and proposes a study on open and closed loop controls for the studied motor. From the proposed model, its dynamic behavior is described and discussed in detail. In addition, a simpler controller based on PID regulator is employed to upgrade the dynamic behavior of the motor. The simulation results in closed loop show a significant improvement in dynamic response compared with open loop. In fact, this simple type of controller offers the possibility to improve the dynamic response for sliding door application.
GPER functions as a tumor suppressor in triple-negative breast cancer cells

The orphan, membrane-bound estrogen receptor (GPER) is expressed at high levels in a large fraction of breast cancer patients and its expression is favorable for patients’ survival. We investigated the role of GPER as a potential tumor suppressor in triple-negative breast cancer cells MDA-MB-231 and MDA-MB-468 using cell cycle analysis and apoptosis assay. The constitutive activity of GPER was investigated. GPER-specific activation with G-1 agonist inhibited breast cancer cell growth in concentration-dependent manner via induction of the cell cycle arrest in G2/M phase, enhanced phosphorylation of histone H3 and caspase-3-mediated apoptosis. Analysis of the methylation status of the GPER promoter in the triple-negative breast cancer cells and in tissues derived from breast cancer patients revealed that GPER amount is regulated by epigenetic mechanisms and GPER expression is inactivated by promoter methylation. Furthermore, GPER expression was induced by stress factors, such as radiation, and GPER amount inversely correlated with the p53 expression level. Overall, our results establish the protective role in breast cancer tumorigenesis, and the cell surface expression of GPER makes it an excellent potential therapeutic target for triple-negative breast cancer.
Semi-supervised Speech Act Recognition in Emails and Forums

In this paper, we present a semi-supervised method for automatic speech act recognition in email and forums. The major challenge of this task is due to lack of labeled data in these two genres. Our method leverages labeled data in the SwitchboardDAMSL and the Meeting Recorder Dialog Act database and applies simple domain adaptation techniques over a large amount of unlabeled email and forum data to address this problem. Our method uses automatically extracted features such as phrases and dependency trees, called subtree features, for semi-supervised learning. Empirical results demonstrate that our model is effective in email and forum speech act recognition.
Robust Predictive Feedback Control for Constrained Systems

A new method for the design of predictive controllers for SISO systems is presented. The proposed technique allows uncertainties and constraints to be concluded in the design of the control law. The goal is to design, at each sample instant, a predictive feedback control law that minimizes a performance measure and guarantees of constraints are satisfied for a set of models that describes the system to be controlled. The predictive controller consists of a finite horizon parametric-optimization problem with an additional constraint over the manipulated variable behavior. This is an end-constraint based approach that ensures the exponential stability of the closed-loop system. The inclusion of this additional constraint, in the on-line optimization algorithm, enables robust stability properties to be demonstrated for the closedloop system. This is the case even though constraints and disturbances are present. Finally, simulation results are presented using a nonlinear continuous stirred tank reactor model.
A Deep Learning Based Fast Image Saliency Detection Algorithm

In this paper, we propose a fast deep learning method for object saliency detection using convolutional neural networks. In our approach, we use a gradient descent method to iteratively modify the input images based on the pixel-wise gradients to reduce a pre-defined cost function, which is defined to measure the class-specific objectness and clamp the class-irrelevant outputs to maintain image background. The pixel-wise gradients can be efficiently computed using the back-propagation algorithm. We further apply SLIC superpixels and LAB color based low level saliency features to smooth and refine the gradients. Our methods are quite computationally efficient, much faster than other deep learning based saliency methods. Experimental results on two benchmark tasks, namely Pascal VOC 2012 and MSRA10k, have shown that our proposed methods can generate high-quality salience maps, at least comparable with many slow and complicated deep learning methods. Comparing with the pure low-level methods, our approach excels in handling many difficult images, which contain complex background, highly-variable salient objects, multiple objects, and/or very small salient objects.
Temperature-dependent Dielectric and Thermal Properties of Whey Protein Gel and Mashed Potato

Temperature-dependent dielectric properties (dielectric constant and dielectric loss factor) and thermal properties (thermal conductivity and specific heat capacity) of whey protein gel and mashed potato were measured from -20°C to 100°C. A dielectric properties measurement system and a multipoint temperature calibration protocol were developed. The system consists of an impedance analyzer, a high-temperature coaxial cable, a high-temperature coaxial probe, a micro-climatic chamber, and a metal sample holder. Calibrations at two temperatures (25°C and 85°C) were sufficient to accurately measure the dielectric properties of foods from frozen to hot temperatures. Dielectric constant and dielectric loss factor both rapidly increased from -20°C to 0°C. Thereafter, dielectric constant linearly decreased from 0°C to 100°C, while dielectric loss factor decreased first and then linearly increased. The thermal conductivity values of whey protein gel and mashed potato decreased with increasing temperature in the frozen range and did not change considerably after thawing. The latent heat of fusion values of whey protein gel and mashed potato were 219.1 and 186.8 kJ·kg, respectively. The temperature-dependent material properties can be used in microwave heat transfer models for improving heating performance of foods in domestic microwave ovens.
Inferring social ties from geographic coincidences.

We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals' physical locations over time.
Robot Enhanced Therapy for Children with Autism (DREAM): A Social Model of Autism

The development of social robots for children with autism has been a growth field for the past 15 years. This article reviews studies in robots and autism as a neurodevelopmental disorder that impacts socialcommunication development, and the ways social robots could help children with autism develop social skills. Drawing on ethics research from the EU-funded Development of Robot-Enhanced Therapy for Children with Autism (DREAM) project (framework 7), this paper explores how ethics evolves and developed in this European project.
Bi-directional flip-chip 28 GHz phased-array core-chip in 45nm CMOS SOI for high-efficiency high-linearity 5G systems

This paper presents a 0 mW two-channel 28 GHz bi-directional phased-array chip packaged using flip-chip interconnects in 45nm CMOS SOI. The design alternates switched-LC phase shifters with switched attenuators to result in 5-bit phase control with an rms gain and phase error <0.8 dB and 5°, respectively at 25–33 GHz. In the RX mode, the measured gain is −10 dB and the NF is 10 dB with an input P1dB of 5 dBm. In the TX mode, the measured output P1dB is −2 dBm. This work presents an efficient solution for the construction of high-linearity and high-power phased-array base-stations by combining GaAs front-ends with a passive silicon core chip.
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising

Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.