nils-herrmann/clean_scirepeval huggingface dataset:
An integrated design and fabrication strategy for entirely soft, autonomous robots

Soft robots possess many attributes that are difficult, if not impossible, to achieve with conventional robots composed of rigid materials. Yet, despite recent advances, soft robots must still be tethered to hard robotic control systems and power sources. New strategies for creating completely soft robots, including soft analogues of these crucial components, are needed to realize their full potential. Here we report the untethered operation of a robot composed solely of soft materials. The robot is controlled with microfluidic logic that autonomously regulates fluid flow and, hence, catalytic decomposition of an on-board monopropellant fuel supply. Gas generated from the fuel decomposition inflates fluidic networks downstream of the reaction sites, resulting in actuation. The body and microfluidic logic of the robot are fabricated using moulding and soft lithography, respectively, and the pneumatic actuator networks, on-board fuel reservoirs and catalytic reaction chambers needed for movement are patterned within the body via a multi-material, embedded 3D printing technique. The fluidic and elastomeric architectures required for function span several orders of magnitude from the microscale to the macroscale. Our integrated design and rapid fabrication approach enables the programmable assembly of multiple materials within this architecture, laying the foundation for completely soft, autonomous robots.
Thin-Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation

We address the problem of learning a pose-aware, compact embedding that projects images with similar human poses to be placed close-by in the embedding space. The embedding function is built on a deep convolutional network, and trained with triplet-based rank constraints on real image data. This architecture allows us to learn a robust representation that captures differences in human poses by effectively factoring out variations in clothing, background, and imaging conditions in the wild. For a variety of pose-related tasks, the proposed pose embedding provides a cost-efficient and natural alternative to explicit pose estimation, circumventing challenges of localizing body joints. We demonstrate the efficacy of the embedding on pose-based image retrieval and action recognition problems.
On error management: lessons from aviation.

Copies of the full protocol and details of training programmes are available from Association of Litigation and Risk Management (ALARM), Royal Society of Medicine, 1 Wimpole Street, London W1.  CV and ST-A carried out the research on which the original protocol was based. All authors participated equally in the development of the protocol, in which successive versions were tested in clinical practice and refined in the light of experience. The writing of the original protocol and present paper was primarily carried out by CV, ST-A, EJC, and DH, but all authors contributed to the final version. CV and DH are the guarantors. Competing interests: CV received funding from Healthcare Risk Resources International to support the work of ST-A during the development of the protocol.
Do facial expressions signal specific emotions? Judging emotion from the face in context.

Certain facial expressions have been theorized to be easily recognizable signals of specific emotions. If so, these expressions should override situationally based expectations used by a person in attributing an emotion to another. An alternative account is offered in which the face provides information relevant to emotion but does not signal a specific emotion. Therefore, in specified circumstances, situational rather than facial information was predicted to determine the judged emotion. This prediction was supported in 3 studies--indeed, in each of the 22 cases examined (e.g., a person in a frightening situation but displaying a reported "facial expression of anger" was judged as afraid). Situational information was especially influential when it suggested a nonbasic emotion (e.g., a person in a painful situation but displaying a "facial expression of fear" was judged as in pain).
IMPACTS DUE TO INTERNET ADDICTION AMONG MALAYSIAN UNIVERSITY STUDENTS Azizah Zainudin

The purpose of this study is to study the impacts due to Internet addiction among Malaysian university students. Research methodology used in this study was by distributing survey questions to 653 university students from five different universities in Malaysia. There were four possible impacts measured in this research study which include Academic Performances, Relationships, Personality and Lifestyle. The finding shows that, Internet addiction cause problems with respondents’ academic performances, having bad personality and practicing an unhealthy lifestyle. There were significantly differences in academic performances, personality and lifestyle between “Average user” and “Excessive user”. The above matter will be further discussed throughout this
RCDA: Recoverable Concealed Data Aggregation for Data Integrity in Wireless Sensor Networks

Recently, several data aggregation schemes based on privacy homomorphism encryption have been proposed and investigated on wireless sensor networks. These data aggregation schemes provide better security compared with traditional aggregation since cluster heads (aggregator) can directly aggregate the ciphertexts without decryption; consequently, transmission overhead is reduced. However, the base station only retrieves the aggregated result, not individual data, which causes two problems. First, the usage of aggregation functions is constrained. For example, the base station cannot retrieve the maximum value of all sensing data if the aggregated result is the summation of sensing data. Second, the base station cannot confirm data integrity and authenticity via attaching message digests or signatures to each sensing sample. In this paper, we attempt to overcome the above two drawbacks. In our design, the base station can recover all sensing data even these data has been aggregated. This property is called “recoverable.” Experiment results demonstrate that the transmission overhead is still reduced even if our approach is recoverable on sensing data. Furthermore, the design has been generalized and adopted on both homogeneous and heterogeneous wireless sensor networks.
Support vector machine approach for protein subcellular localization prediction

Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences.



In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals.



A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.
Truncating Temporal Differences: On the Efficient Implementation of TD(lambda) for Reinforcement Learning

Temporal diierence (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor. Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the eecient and general implementation of TD() for arbitrary , for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suuer from both ineeciency and lack of generality. The TTD (Truncated Temporal Diierences) procedure is proposed as an alternative, that indeed only approximates TD(), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using > 0 with the TTD procedure allows one to obtain a signiicant learning speedup at essentially the same cost as usual TD(0) learning.
Vitamin D: An overview of vitamin D status and intake in Europe

In recent years, there have been reports suggesting a high prevalence of low vitamin D intakes and vitamin D deficiency or inadequate vitamin D status in Europe. Coupled with growing concern about the health risks associated with low vitamin D status, this has resulted in increased interest in the topic of vitamin D from healthcare professionals, the media and the public. Adequate vitamin D status has a key role in skeletal health. Prevention of the well-described vitamin D deficiency disorders of rickets and osteomalacia are clearly important, but there may also be an implication of low vitamin D status in bone loss, muscle weakness and falls and fragility fractures in older people, and these are highly significant public health issues in terms of morbidity, quality of life and costs to health services in Europe. Although there is no agreement on optimal plasma levels of vitamin D, it is apparent that blood 25-hydroxyvitamin D [25(OH)D] levels are often below recommended ranges for the general population and are particularly low in some subgroups of the population, such as those in institutions or who are housebound and non-Western immigrants. Reported estimates of vitamin D status within different European countries show large variation. However, comparison of studies across Europe is limited by their use of different methodologies. The prevalence of vitamin D deficiency [often defined as plasma 25(OH)D <25 nmol/l] may be more common in populations with a higher proportion of at-risk groups, and/or that have low consumption of foods rich in vitamin D (naturally rich or fortified) and low use of vitamin D supplements. The definition of an adequate or optimal vitamin D status is key in determining recommendations for a vitamin D intake that will enable satisfactory status to be maintained all year round, including the winter months. In most European countries, there seems to be a shortfall in achieving current vitamin D recommendations. An exception is Finland, where dietary survey data indicate that recent national policies that include fortification and supplementation, coupled with a high habitual intake of oil-rich fish, have resulted in an increase in vitamin D intakes, but this may not be a suitable strategy for all European populations. The ongoing standardisation of measurements in vitamin D research will facilitate a stronger evidence base on which policies can be determined. These policies may include promotion of dietary recommendations, food fortification, vitamin D supplementation and judicious sun exposure, but should take into account national, cultural and dietary habits. For European nations with supplementation policies, it is important that relevant parties ensure satisfactory uptake of these particularly in the most vulnerable groups of the population.
Calorie restriction extends Saccharomyces cerevisiae lifespan by increasing respiration

Calorie restriction (CR) extends lifespan in a wide spectrum of organisms and is the only regimen known to lengthen the lifespan of mammals. We established a model of CR in budding yeast Saccharomyces cerevisiae. In this system, lifespan can be extended by limiting glucose or by reducing the activity of the glucose-sensing cyclic-AMP-dependent kinase (PKA). Lifespan extension in a mutant with reduced PKA activity requires Sir2 and NAD (nicotinamide adenine dinucleotide). In this study we explore how CR activates Sir2 to extend lifespan. Here we show that the shunting of carbon metabolism toward the mitochondrial tricarboxylic acid cycle and the concomitant increase in respiration play a central part in this process. We discuss how this metabolic strategy may apply to CR in animals.
Novel approaches to crawling important pages early

Web crawlers are essential to many Web applications, such as Web search engines, Web archives, and Web directories, which maintain Web pages in their local repositories. In this paper, we study the problem of crawl scheduling that biases crawl ordering toward important pages. We propose a set of crawling algorithms for effective and efficient crawl ordering by prioritizing important pages with the well-known PageRank as the importance metric. In order to score URLs, the proposed algorithms utilize various features, including partial link structure, inter-host links, page titles, and topic relevance. We conduct a large-scale experiment using publicly available data sets to examine the effect of each feature on crawl ordering and evaluate the performance of many algorithms. The experimental results verify the efficacy of our schemes. In particular, compared with the representative RankMass crawler, the FPR-title-host algorithm reduces computational overhead by a factor as great as three in running time while improving effectiveness by 5 % in cumulative PageRank.
Cue-based feeding in the NICU: using the infant's communication as a guide.

Although studies have shown cue-based feeding can lead to earlier achievement of full oral feeding, the successful implementation of cue-based feeding has been constrained by the volume-driven culture, which has existed for many years in the NIC U. This culture was built on the notion that a "better" nurse is one who could "get more in," and infants who are "poor feeders" are ones who "can't take enough." The infant who feeds faster is often viewed as more skilled in this task-oriented approach. The feeding relationship and the infant's communication about the experience of feeding may not be nurtured. This article will explain the central role of the preterm infant's communication in successful cue-based feeding. When the infant is perceived as having meaningful behavior (i.e., communicative intent), the focus changes from a volume-driven to a co-regulated approach, through which the infant guides the caregiver. This is cue-based feeding.
Food Recognition using Fusion of Classifiers based on CNNs

With the arrival of convolutional neural networks, the complex problem of food recognition has experienced an important improvement in recent years. The best results have been obtained using methods based on very deep convolutional ceural cetworks, which show that the deeper the model,the better the classification accuracy will be obtain. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on different convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on two public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent CNN models.
Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation

Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem w.r.t. specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the de facto methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE.
Generating Multiple Hypotheses for Human 3D Pose Consistent with 2D Joint Detections

We propose a method to generate multiple hypotheses for human 3D pose all of them consistent with the 2D detection of joints in a monocular RGB image. To generate these pose hypotheses we use a novel generative model defined in the space of anatomically plausible 3D poses satisfying the joint angle limits and limb length ratios. The proposed generative model is uniform in the space of anatomically valid poses and as a result, does not suffer from the dataset bias in existing motion capture datasets such as Human3.6M (H36M), HumanEva, and CMU MoCap. A good model that spans the full variability of human pose and generalizes to unseen poses must be compositional i.e. produce a pose by combining parts. Our model is flexible and compositional and consequently can generalize to every plausible human 3D pose since it is only limited by physical constraints. We discuss sampling from this model and use these samples to generate multiple diverse human 3D pose hypotheses given the 2D detection of joints. We argue that generating multiple pose hypotheses from a monocular RGB image is more reasonable than generating only a single 3D pose given the depth ambiguity and the uncertainty caused by occlusion and imperfect 2D joint detection. To support this argument, we have performed empirical evaluation on the popular Human3.6M dataset that confirms that most often, at least one of our pose hypotheses is closer to the true 3D pose compared to the estimated pose by other recent baseline methods for 3D pose reconstruction from monocular RGB images. The idea of generating multiple consistent and valid pose hypotheses can give rise to a new line of future work that has not previously been addressed in the literature.
An Energy-Efficient Architecture for Binary Weight Convolutional Neural Networks

Binary weight convolutional neural networks (BCNNs) can achieve near state-of-the-art classification accuracy and have far less computation complexity compared with traditional CNNs using high-precision weights. Due to their binary weights, BCNNs are well suited for vision-based Internet-of-Things systems being sensitive to power consumption. BCNNs make it possible to achieve very high throughput with moderate power dissipation. In this paper, an energy-efficient architecture for BCNNs is proposed. It fully exploits the binary weights and other hardware-friendly characteristics of BCNNs. A judicious processing schedule is proposed so that off-chip I/O access is minimized and activations are maximally reused. To significantly reduce the critical path delay, we introduce optimized compressor trees and approximate binary multipliers with two novel compensation schemes. The latter is able to save significant hardware resource, and almost no computation accuracy is compromised. Taking advantage of error resiliency of BCNNs, an innovative approximate adder is developed, which significantly reduces the silicon area and data path delay. Thorough error analysis and extensive experimental results on several data sets show that the approximate adders in the data path cause negligible accuracy loss. Moreover, algorithmic transformations for certain layers of BCNNs and a memory-efficient quantization scheme are incorporated to further reduce the energy cost and on-chip storage requirement. Finally, the proposed BCNN hardware architecture is implemented with the SMIC 130-nm technology. The postlayout results demonstrate that our design can achieve an energy efficiency over 2.0TOp/s/W when scaled to 65 nm, which is more than two times better than the prior art.
GaN MMIC amplifiers for W-band transceivers

This paper presents W-band monolithic microwave integrated circuit (MMIC) amplifiers with grounded coplanar waveguide (GCPW) in 0.12µm GaN HEMT technology. A fabricated four-stage low-noise amplifier (LNA) exhibited a record gain of 23 dB at 76.5 GHz and a first reported noise figure (NF) of 3.8 dB at 80 GHz for any W-band GaN  Another MMIC power amplifier (PA) delivered an output power of 25.4 dBm at 76.5 GHz with continuous wave (CW) operation. To our knowledge, this is the first demonstration of GaN LNA as well as GaN MMICs with GCPW in the W-band. In addition, a practical design technique to prevent instability of the W-band MMIC is described.
Learning Robust Features using Deep Learning for Automatic Seizure Detection

We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures. This is a challenging problem because seizure manifestations on EEG are extremely variable both interand intra-patient. By simultaneously capturing spectral, temporal and spatial information our recurrent convolutional neural network learns a general spatially invariant representation of a seizure. The proposed approach exceeds significantly previous results obtained on cross-patient classifiers both in terms of sensitivity and false positive rate. Furthermore, our model proves to be robust to missing channel and variable electrode montage.
A Cloud Computing Resource Allocation Model Based on Combinatorial Double Auction

As the rate of information growth intensifies, so has the study of cloud resource management because cloud computing can generate powerful online resource convergence. This paper presents a new cloud computing resource allocation model based on combinatorial double auction mechanism (CDA-CCRA) for more effective resource utilization. The CDA-CCRA model can simultaneously satisfy the requirements of users and providers and significantly reduce transactions. Simulations show that the CDA-CCRA model allocates resources well in a cloud computing environment.
Honey-based therapy for paediatric burns and dermal trauma compared to standard hospital protocol

To compare honey-based ointments and dressings for the treatment of paediatric partial-thickness burns and skin lesions with results from cases treated with conventional methods.  Eight children (eight months–13 years) were included in the study when admitted to the emergency room (ER). Five had partial-thickness burns (2–35% total body surface area [TBSA]). The three others had necrotic ulcers, circular skin lesions and deep cervical trauma. After stabilising the patients in the ER, honey-based products were applied. Parents were given instructions on how to apply the honey ointment and dressings at home. Patients were followed up weekly on an out-patient basis. Similar cases treated with povidone iodine were collated from hospital archives for comparison.  All honey cases had complete epithelialisation by the end of the second week, without any adverse effects. The povidone iodine group needed on average three weeks for epithelialisation and had prolonged hospitalisation. The home care by the parents provided stress-free healing for both children and parents.  The honey-based therapy indicates a fast (19 days vs 25–31 days), cost-effective and patient-friendlier treatment method compared to povidone iodine. Conflict of interest: None.