nils-herrmann/clean_scirepeval huggingface dataset:
Skip-connection convolutional neural network for still image crowd counting

In recent years, crowd counting in still images has attracted many research interests due to its applications in public safety. However, it remains a challenging task for reasons of perspective and scale variations. In this paper, we propose an effective Skip-connection Convolutional Neural Network (SCNN) for crowd counting to overcome the issue of scale variations. The proposed SCNN architecture consists of several multi-scale units to extract multi-scale features. Each multi-scale unit including three convolutional layers builds connections between the input and each convolutional layer. In addition, we propose a scale-related training method to improve the accuracy and robustness of crowd counting. We evaluate our method on three crowd counting benchmarks. Experimental results verify the efficiency of the proposed method, and it achieves superior performance compared with other methods.
Symmetric Spiral Patterns on Spheres

Spiral patterns on the surface of a sphere have been seen in laboratory experiments and in numerical simulations of reaction–diffusion equations and convection. We classify the possible symmetries of spirals on spheres, which are quite different from the planar case since spirals typically have tips at opposite points on the sphere. We concentrate on the case where the system has an additional sign-change symmetry, in which case the resulting spiral patterns do not rotate. Spiral patterns arise through a mode interaction between spherical harmonics degree l and l+1. Using the methods of equivariant bifurcation theory, possible symmetry types are determined for each l. For small values of l, the centre manifold equations are constructed and spiral solutions are found explicitly. Bifurcation diagrams are obtained showing how spiral states can appear at secondary bifurcations from primary solutions, or tertiary bifurcations. The results are consistent with numerical simulations of a model pattern-forming system.
Augmenter of Liver Regeneration (alr) Promotes Liver Outgrowth during Zebrafish Hepatogenesis

Augmenter of Liver Regeneration (ALR) is a sulfhydryl oxidase carrying out fundamental functions facilitating protein disulfide bond formation. In mammals, it also functions as a hepatotrophic growth factor that specifically stimulates hepatocyte proliferation and promotes liver regeneration after liver damage or partial hepatectomy. Whether ALR also plays a role during vertebrate hepatogenesis is unknown. In this work, we investigated the function of alr in liver organogenesis in zebrafish model. We showed that alr is expressed in liver throughout hepatogenesis. Knockdown of alr through morpholino antisense oligonucleotide (MO) leads to suppression of liver outgrowth while overexpression of alr promotes liver growth. The small-liver phenotype in alr morphants results from a reduction of hepatocyte proliferation without affecting apoptosis. When expressed in cultured cells, zebrafish Alr exists as dimer and is localized in mitochondria as well as cytosol but not in nucleus or secreted outside of the cell. Similar to mammalian ALR, zebrafish Alr is a flavin-linked sulfhydryl oxidase and mutation of the conserved cysteine in the CxxC motif abolishes its enzymatic activity. Interestingly, overexpression of either wild type Alr or enzyme-inactive Alr(C131S) mutant promoted liver growth and rescued the liver growth defect of alr morphants. Nevertheless, alr(C131S) is less efficacious in both functions. Meantime, high doses of alr MOs lead to widespread developmental defects and early embryonic death in an alr sequence-dependent manner. These results suggest that alr promotes zebrafish liver outgrowth using mechanisms that are dependent as well as independent of its sulfhydryl oxidase activity. This is the first demonstration of a developmental role of alr in vertebrate. It exemplifies that a low-level sulfhydryl oxidase activity of Alr is essential for embryonic development and cellular survival. The dose-dependent and partial suppression of alr expression through MO-mediated knockdown allows the identification of its late developmental role in vertebrate liver organogenesis.
An efficient and secure data storage in Mobile Cloud Computing through RSA and Hash function

Main call for Cloud computing is that users only utilize what they required and only pay for what they really use. Mobile Cloud Computing refers to an infrastructure where data processing and storage can happen away from mobile device. Portio research estimates that mobile subscribers worldwide will reach 6.9 billion by the end of 2013 and 8 billion by the end of 2016. Ericsson also forecasts that mobile subscriptions will reach 9 billion by 2017. Due to increasing use of mobile devices the requirement of cloud computing in mobile devices arise, which gave birth to Mobile Cloud Computing. Mobile devices do not need to have large storage capacity and powerful CPU speed. Due to storing data on cloud there is an issue of data security. Because of the risk associated with data storage many IT professionals are not showing their interest towards Mobile Cloud Computing. To ensure the correctness of users' data in the cloud, we propose an effective mechanism with salient feature of data integrity and confidentiality. This paper proposed a mechanism which uses the concept of RSA algorithm, Hash function along with several cryptography tools to provide better security to the data stored on the mobile cloud.
Corollary discharge dysfunction in schizophrenia: can it explain auditory hallucinations?

Failure of corollary discharge, a mechanism for distinguishing self-generated from externally generated percepts, has been posited to underlie certain positive symptoms of schizophrenia, including auditory hallucinations. Although originally described in the visual system, corollary discharge may exist in the auditory system, whereby signals from motor speech commands prepare auditory cortex for self-generated speech. While associated with sensorimotor systems, it might also apply to inner speech or thought, regarded as our most complex motor act. In this paper, we describe the results of a series of studies in which we have shown that: (1) event-related brain potentials (ERPs) can be used to demonstrate the corollary discharge phenomenon during talking, (2) corollary discharge is abnormal in patients with schizophrenia, (3) EEG gamma band coherence between frontal and temporal lobes is greater during talking than listening and is disrupted by distorted feedback during talking in normals, and (4) patients with schizophrenia do not show this pattern for EEG gamma coherence. While these studies have identified ERPs and EEG gamma coherence indices of the efference copy/corollary discharge system and documented abnormalities in these systems in patients with schizophrenia, we have so far had limited success in establishing a relationship between these neurobiologic indicators of corollary discharge abnormality and reports of hallucinations in patients.
General Video Game AI: Learning from screen capture

General Video Game Artificial Intelligence is a general game playing framework for Artificial General Intelligence research in the video-games domain. In this paper, we propose for the first time a screen capture learning agent for General Video Game AI framework. A Deep Q-Network algorithm was applied and improved to develop an agent capable of learning to play different games in the framework. After testing this algorithm using various games of different categories and difficulty levels, the results suggest that our proposed screen capture learning agent has the potential to learn many different games using only a single learning algorithm.
Propagating multi-class pixel labels throughout video frames

The effective propagation of pixel labels through the spatial and temporal domains is vital to many computer vision and multimedia problems, yet little attention have been paid to the temporal/video domain propagation in the past. Previous video label propagation algorithms largely avoided the use of dense optical flow estimation due to their computational costs and inaccuracies, and relied heavily on complex (and slower) appearance models. We show in this paper the limitations of pure motion and appearance based propagation methods alone, especially the fact that their performances vary on different type of videos. We propose a probabilistic framework that estimates the reliability of the sources and automatically adjusts the weights between them. Our experiments show that the “dragging effect” of pure optical-flow-based methods are effectively avoided, while the problems of pure appearance-based methods such the large intra-class variance is also effectively handled.
Guest Editors' Introduction: Data Mining in Bioinformatics

B ioinformatics and data mining provide exciting and challenging research and application areas for computational science. Bioinformatics is the science of managing, mining, and interpreting information from biological sequences and structures. Advances such as genome-sequencing initiatives, microarrays, proteomics, and functional and structural genomics have pushed the frontiers of human knowledge. In addition , data mining and machine learning have been advancing in strides in recent years, with high-impact applications from marketing to science. Although researchers have spent much effort on data mining for bioinformatics (see the sidebar), the two areas have largely been developing separately.
Key players associated with tuberization in potato: potential candidates for genetic engineering.

Tuberization in potato (Solanum tuberosum L.) is a complex biological phenomenon which is affected by several environmental cues, genetic factors and plant nutrition. Understanding the regulation of tuber induction is essential to devise strategies to improve tuber yield and quality. It is well established that short-day photoperiods promote tuberization, whereas long days and high-temperatures inhibit or delay tuberization. Worldwide research on this complex biological process has yielded information on the important bio-molecules (proteins, RNAs, plant growth regulators) associated with the tuberization process in potato. Key proteins involved in the regulation of tuberization include StSP6A, POTH1, StBEL5, StPHYB, StCONSTANS, Sucrose transporter StSUT4, StSP5G, etc. Biomolecules that become transported from "source to sink" have also been suggested to be important signaling candidates regulating the tuberization process in potatos. Four molecules, namely StSP6A protein, StBEL5 RNA, miR172 and GAs, have been found to be the main candidates acting as mobile signals for tuberization. These biomolecules can be manipulated (overexpressed/inhibited) for improving the tuberization in commercial varieties/cultivars of potato. In this review, information about the genes/proteins and their mechanism of action associated with the tuberization process is discussed.
Importance of Intrusion Detection System ( IDS )

Intruders computers, who are spread across the Internet have become a major threat in our world, The researchers proposed a number of techniques such as (firewall, encryption) to prevent such penetration and protect the infrastructure of computers, but with this, the intruders managed to penetrate the computers. IDS has taken much of the attention of researchers, IDS monitors the resources computer and sends reports on the activities of any anomaly or strange patterns The aim of this paper is to explain the stages of the evolution of the idea of IDS and its importance to researchers and research centres, security, military and to examine the importance of intrusion detection systems and categories , classifications, and where can put IDS to reduce the risk to the network.
Neck Rejuvenation with Mastoid-Spanning Barbed Tensor Threads (MST Operation)

Face rejuvenation with barbed tensor threads has been used in a variety of techniques by many authors focused mainly on cheek rejuvenation. Nevertheless, there is a scarcity of detailed techniques and descriptions of surgical steps in barbed-thread neck rejuvenation. Here we describe our technique and analyze the results of this approach to minimally invasive neck rejuvenation with barbed threads. The main aspect of this technique is placement of a mastoid-spanning barbed thread, which deepens the cervicomental angle and provides more tension than other techniques that use barbed threads but that do not cross the neck midline. A retrospective study was conducted on a group of 67 consecutive cases who underwent this technique in a 34-month period starting in August 2009. The patients’ average age was 48 years old and the average follow-up was 18 months. Patient satisfaction was high as evidenced by a minimal request for secondary operations. Complications were few and mild, with no cases requiring reoperation for thread removal. The study showed that neck rejuvenation with barbed threads that cross the neck midline is a powerful minimally invasive rejuvenation technique. The best indication is in patients with initial signs of facial aging. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
A New Born with Lamellar ichthyosis(Collodion Baby).

Ichthyosisis an infrequent clinical entity worldwide with an incidence of 1:600,000 births. It can be one of the two types: collodion baby and Harlequin fetus or malignant keratoma (most severe form). The clinical manifestations in either form are thick and hard skin with deep splits. Affected babies are born in a collodion membrane, a shiny waxy outer layer to the skin that is shed 10 - 14 days after birth, revealing the main symptom of the disease. The reported case is of a neonate, born to primigravida mother at seven and a half month's gestation with a birth weight of 2160 grams and Apgar score of 6/10 and 8/10 at 1 and 5 minutes respectively. Conclusively, early diagnosis of this condition can help cope and prevent serious morbidity or even mortality at time. These newborns should be monitored carefully in intensive care units by a multi-disciplinary team.
Fluid dynamics topics in bloodstain pattern analysis: comparative review and research opportunities.

This comparative review highlights the relationships between the disciplines of bloodstain pattern analysis (BPA) in forensics and that of fluid dynamics (FD) in the physical sciences. In both the BPA and FD communities, scientists study the motion and phase change of a liquid in contact with air, or with other liquids or solids. Five aspects of BPA related to FD are discussed: the physical forces driving the motion of blood as a fluid; the generation of the drops; their flight in the air; their impact on solid or liquid surfaces; and the production of stains. For each of these topics, the relevant literature from the BPA community and from the FD community is reviewed. Comments are provided on opportunities for joint BPA and FD research, and on the development of novel FD-based tools and methods for BPA. Also, the use of dimensionless numbers is proposed to inform BPA analyses.
Quality of Experience for video streaming: A contemporary survey

the recent advancement of networking technology has enabled the streaming of video content over wired/wireless network to a great extent. Video streaming includes various types of video content, namely, IP television (IPTV), Video on demand (VOD), Peer-to-Peer (P2P) video sharing, Voice (and video) over IP (VoIP) etc. The consumption of the video contents has been increasing a lot these days and promises a huge potential for the network provider, content provider and device manufacturers. However, from the end user's perspective there is no universally accepted existing standard metric, which will ensure the quality of the application/utility to meet the user's desired experience. In order to fulfill this gap, a new metric, called Quality of Experience (QoE), has been proposed in numerous researches recently. Our aim in this paper is to research the evolution of the term QoE, find the influencing factors of QoE metric especially in video streaming and finally QoE modelling and methodologies in practice.
An OAuth based authentication mechanism for IoT networks

Internet of things (IoT) has rapidly become one of the most familiar and perhaps most discussed topic on the research field. The attention for the Internet of Things is mainly due to the new connected products intended to bring greater efficiencies and simplicity to life. Variety of IoT applications lead to equally wide variety of security issues. In this paper, we propose an approach to provide secure authentication mechanism for an IoT network which consists of various kinds of constrained devices using a security manager. This proposed approach protects IoT network from unauthenticated users with security manager using OAuth 2.0 protocol. Moreover, this approach provides flexibility in managing IoT networks. The security manager provides authentication service for multiple IoT networks, which can also help to reduce the cost overhead to maintain secure database in IoT networks.
Toward a Computational Model of Narrative

Narratives structure our understanding of the world and of ourselves. They exploit the shared cognitive structures of human motivations, goals, actions, events, and outcomes. We report on a computational model that is motivated by results in neural computation and captures fine-grained, context sensitive information about human goals, processes, actions, policies, and outcomes. We describe the use of the model in the context of a pilot system that is able to interpret simple stories and narrative fragments in the domain of international politics and economics. We identify problems with the pilot system and outline extensions required to incorporate several crucial dimensions of narrative structure.
3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations

The ability to interact and understand the environment is a fundamental prerequisite for a wide range of applications from robotics to augmented reality. In particular, predicting how deformable objects will react to applied forces in real time is a significant challenge. This is further confounded by the fact that shape information about encountered objects in the real world is often impaired by occlusions, noise and missing regions e.g. a robot manipulating an object will only be able to observe a partial view of the entire solid. In this work we present a framework, 3D-PhysNet, which is able to predict how a three-dimensional solid will deform under an applied force using intuitive physics modelling. In particular, we propose a new method to encode the physical properties of the material and the applied force, enabling generalisation over materials. The key is to combine deep variational autoencoders with adversarial training, conditioned on the applied force and the material properties. We further propose a cascaded architecture that takes a single 2.5D depth view of the object and predicts its deformation. Training data is provided by a physics simulator. The network is fast enough to be used in real-time applications from partial views. Experimental results show the viability and the generalisation properties of the proposed architecture.
A CMOS MEMS Pirani vacuum gauge with complementary bump heat sink and cavity heater

A novel CMOS-MEMS Pirani vacuum gauge with complementary bump heat-sink and cavity heater design has been proposed and demonstrated. This design using CMOS-MEMS process to offer the following advantages for Pirani gauge: (1) The bump heat-sink vertical integrates with cavity heater increases the dynamic range and sensitivity without changing device footprint size, (2) The cavity in heater reduces the thermal mass for low-power operation, and (3) Easy integration with packaged CMOS-MEMS devices for pressure monitoring . The design is implemented using the standard TSMC 0.18μm 1P6M CMOS process. A 120μm×120μm die size with 0.53μm sensing gap is demonstrated. Measurement indicates the gauge has sensing range 0.3-100torr with sensitivity of 1.53×104(K/W)/torr. The power consumption is 67μW for 1% resistance change. In comparison, the gauge with typical heat-sink/heater design has sensing range 1-100torr with sensitivity of 0.99×104(K/W)/torr and power consumption of 119μW.
Stress Memorization Technique (SMT) Optimization for 45nm CMOS

In this paper, we present an optimization path of stress memorization technique (SMT) for 45nm node and below using a nitride capping layer. We demonstrate that the understanding of coupling between nitride properties, dopant activation and poly-silicon gate mechanical stress allows enhancing nMOS performance by 7% without pMOS degradation. In contrast to previously reported works on SMT (Chen et al., 2004) - (Singh et al., 2005), a low-cost process compatible with consumer electronics requirements has been successfully developed
Long-term green tea extract supplementation does not affect fat absorption, resting energy expenditure, and body composition in adults.

Green tea (GT) extract may play a role in body weight regulation. Suggested mechanisms are decreased fat absorption and increased energy expenditure.



We examined whether GT supplementation for 12 wk has beneficial effects on weight control via a reduction in dietary lipid absorption as well as an increase in resting energy expenditure (REE).



Sixty Caucasian men and women [BMI (in kg/m²): 18-25 or >25; age: 18-50 y] were included in a randomized placebo-controlled study in which fecal energy content (FEC), fecal fat content (FFC), resting energy expenditure, respiratory quotient (RQ), body composition, and physical activity were measured twice (baseline vs. week 12). For 12 wk, subjects consumed either GT (>0.56 g/d epigallocatechin gallate + 0.28-0.45 g/d caffeine) or placebo capsules. Before the measurements, subjects recorded energy intake for 4 consecutive days and collected feces for 3 consecutive days.



No significant differences between groups and no significant changes over time were observed for the measured variables. Overall means ± SDs were 7.2 ± 3.8 g/d, 6.1 ± 1.2 MJ/d, 67.3 ± 14.3 kg, and 29.8 ± 8.6% for FFC, REE, body weight, and body fat percentage, respectively.



GT supplementation for 12 wk in 60 men and women did not have a significant effect on FEC, FFC, REE, RQ, and body composition.
Systems Biology-Derived Discoveries of Intrinsic Clocks

A systems approach to studying biology uses a variety of mathematical, computational, and engineering tools to holistically understand and model properties of cells, tissues, and organisms. Building from early biochemical, genetic, and physiological studies, systems biology became established through the development of genome-wide methods, high-throughput procedures, modern computational processing power, and bioinformatics. Here, we highlight a variety of systems approaches to the study of biological rhythms that occur with a 24-h period-circadian rhythms. We review how systems methods have helped to elucidate complex behaviors of the circadian clock including temperature compensation, rhythmicity, and robustness. Finally, we explain the contribution of systems biology to the transcription-translation feedback loop and posttranslational oscillator models of circadian rhythms and describe new technologies and "-omics" approaches to understand circadian timekeeping and neurophysiology.
Metalearning with Hebbian Fast Weights

We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning. Our model learns jointly to represent data and to bind class labels to representations in a single shot. It builds representations via slow weights, learned across tasks through SGD, while fast weights constructed by a Hebbian learning rule implement one-shot binding for each new task. On the Omniglot, Mini-ImageNet, and Penn Treebank one-shot learning benchmarks, our model achieves state-of-the-art results.