nils-herrmann/clean_scirepeval huggingface dataset:
Talking Face Generation by Conditional Recurrent Adversarial Network

Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the entire video clip. Existing works either do not consider temporal dependency on face images across different video frames thus easily yielding noticeable/abrupt facial and lip movement or are only limited to the generation of talking face video for a specific person thus lacking generalization capacity. We propose a novel conditional video generation network where the audio input is treated as a condition for the recurrent adversarial network such that temporal dependency is incorporated to realize smooth transition for the lip and facial movement. In addition, we deploy a multi-task adversarial training scheme in the context of video generation to improve both photo-realism and the accuracy for lip synchronization. Finally, based on the phoneme distribution information extracted from the audio clip, we develop a sample selection method that effectively reduces the size of the training dataset without sacrificing the quality of the generated video. Extensive experiments on both controlled and uncontrolled datasets demonstrate the superiority of the proposed approach in terms of visual quality, lip sync accuracy, and smooth transition of lip and facial movement, as compared to the state-of-the-art.
An Organizational Model for Digital Library Evaluation

Evaluation is a central digital library practice. It provides important data for managing digital libraries and informing strategic decision-making. Digital library evaluation and management are organizational as well as technical practices. What evaluation models can account for these organizational factors, in practice as well as in theory? To address these questions, this paper integrates two models, one from the organizational literature (Porter’s value chain), and one from the evaluation literature (evaluation logic models), into a generic, flexible and extensible evaluation model that supports the goal-oriented evaluation and management of digital libraries in specific sociotechnical contexts. A case study is provided.
Low dose oral cannabinoid therapy reduces progression of atherosclerosis in mice

Atherosclerosis is a chronic inflammatory disease, and is the primary cause of heart disease and stroke in Western countries. Derivatives of cannabinoids such as delta-9-tetrahydrocannabinol (THC) modulate immune functions and therefore have potential for the treatment of inflammatory diseases. We investigated the effects of THC in a murine model of established atherosclerosis. Oral administration of THC (1 mg kg-1 per day) resulted in significant inhibition of disease progression. This effective dose is lower than the dose usually associated with psychotropic effects of THC. Furthermore, we detected the CB2 receptor (the main cannabinoid receptor expressed on immune cells) in both human and mouse atherosclerotic plaques. Lymphoid cells isolated from THC-treated mice showed diminished proliferation capacity and decreased interferon-γ secretion. Macrophage chemotaxis, which is a crucial step for the development of atherosclerosis, was also inhibited in vitro by THC. All these effects were completely blocked by a specific CB2 receptor antagonist. Our data demonstrate that oral treatment with a low dose of THC inhibits atherosclerosis progression in the apolipoprotein E knockout mouse model, through pleiotropic immunomodulatory effects on lymphoid and myeloid cells. Thus, THC or cannabinoids with activity at the CB2 receptor may be valuable targets for treating atherosclerosis.
Biomaterials based strategies for skeletal muscle tissue engineering: existing technologies and future trends.

Skeletal muscles have a robust capacity to regenerate, but under compromised conditions, such as severe trauma, the loss of muscle functionality is inevitable. Research carried out in the field of skeletal muscle tissue engineering has elucidated multiple intrinsic mechanisms of skeletal muscle repair, and has thus sought to identify various types of cells and bioactive factors which play an important role during regeneration. In order to maximize the potential therapeutic effects of cells and growth factors, several biomaterial based strategies have been developed and successfully implemented in animal muscle injury models. A suitable biomaterial can be utilized as a template to guide tissue reorganization, as a matrix that provides optimum micro-environmental conditions to cells, as a delivery vehicle to carry bioactive factors which can be released in a controlled manner, and as local niches to orchestrate in situ tissue regeneration. A myriad of biomaterials, varying in geometrical structure, physical form, chemical properties, and biofunctionality have been investigated for skeletal muscle tissue engineering applications. In the current review, we present a detailed summary of studies where the use of biomaterials favorably influenced muscle repair. Biomaterials in the form of porous three-dimensional scaffolds, hydrogels, fibrous meshes, and patterned substrates with defined topographies, have each displayed unique benefits, and are discussed herein. Additionally, several biomaterial based approaches aimed specifically at stimulating vascularization, innervation, and inducing contractility in regenerating muscle tissues are also discussed. Finally, we outline promising future trends in the field of muscle regeneration involving a deeper understanding of the endogenous healing cascades and utilization of this knowledge for the development of multifunctional, hybrid, biomaterials which support and enable muscle regeneration under compromised conditions.
A Simulink model for an aircraft landing system using energy functions

A Simulink model for an aircraft landing system using Lyapunov function is discussed. This paper also illustrates the concepts of the Nonlinear Energy Based Control Method (NEM) and modifying the aircraft energy to automatically land an aircraft. The aircraft dynamics are expressed in the form of the Euler- Lagrange (EL) equations of motion, which are derived from the energy functions. The structure of a controller consists of an inner loop, energy loop and a tracking loop which controls the aircraft model dynamics. Using Matlab, Simulink and S functions, perform simulation for the control structure. The system considered in this paper is a Research Civil Aircraft Model (RCAM) developed by the Group for Aeronautical Research and Technology in Europe. (GARTEUR).
Grasping and Manipulation in Humanoid Robotics

Humanoid robotics is attracting the interest of many research groups world-wide. In particular, developing humanoids requires the implementation of manipulation capabilities, which is still a most complex problem in robotics. This paper presents an overview of current activities in the development of humanoid robots, with special focus on manipulation. Then we discuss our current approach to the design and development of anthropomorphic sensorized hand and of anthropomorphic control and sensory-motor coordination schemes. Current achievements in the development of a robotic human hand prosthesis are described, together with preliminary experimental results, as well as in the implementation of biologically-inspired schemes for control and sensory-motor co-ordination in manipulation, derived from models of well-identified human brain areas.
Language modeling for bag-of-visual words image categorization

In this paper, we propose two ways of improving image classification based on bag-of-words representation . Two shortcomings of this representation are the loss of the spatial information of visual words and the presence of noisy visual words due to the coarseness of the vocabulary building process. On the one hand, we propose a new representation of images that goes further in the analogy with textual data: visual sentences, that allows us to "read" visual words in a certain order, as in the case of text. We can therefore consider simple spatial relations between words. We also present a new image classification scheme that exploits these relations. It is based on the use of language models, a very popular tool from speech and text analysis communities. On the other hand, we propose new techniques to eliminate useless words, one based on geometric properties of the keypoints, the other on the use of probabilistic Latent Semantic Analysis (pLSA). Experiments show that our techniques can significantly improve image classification, compared to a classical Support Vector Machine-based classification.
Single stage surgical treatment of amniotic band syndrome – Case report.

Amniotic band syndrome is a rare congenital disorder with clinical presentation of constricting
bands in different parts of extremities or whole extremities. Conservative or surgical treatment
is provided depending on the type and severity of the anomaly.


Case Outline
The paper presents the case of a neonate patient with constriction bands localized on the
left leg. During the second week of life, a surgery was indicated, and a single-stage multiple Z-plasty was
performed to correct the anomalies on the left lower leg. Postoperative edema in the distal part of the
lower leg was easily managed by incisions and drainage. Two months later, the correction of the stricture
of the left thigh was managed using the same procedure. The postoperative course was uneventful and
the outcome was satisfactory after a two-year follow-up.



Evaluation of a patient with amniotic band syndrome, as well as diagnosis, monitoring, treatment
and postoperative care, should always be multidisciplinary. A single-stage correction approach
provided satisfactory both functional and aesthetic results. Given many morphological variations of the
syndrome, a decision on the strategy of treatment should be made individually for each patient.
Learning Deep Policies for Physics-Based Manipulation in Clutter

Uncertainty in modeling real world physics makes transferring traditional open-loop motion planning techniques from simulation to the real world particularly challenging. Available closed-loop policy learning approaches, for physicsbased manipulation tasks, typically either focus on single object manipulation, or rely on imitation learning, which inherently constrains task generalization and performance to the available demonstrations. In this work, we propose an approach to learn a policy for physics-based manipulation in clutter, which enables the robot to react to the uncertain dynamics of the real world. We start with presenting an imitation learning technique which compiles demonstrations from a sampling-based planner into an action-value function encoded as a deep neural network. We then use the learned action-value function to guide a look-ahead planner, giving us a control policy. Lastly, we propose to refine the deep action-value function through reinforcement learning, taking advantage of the look-ahead planner. We evaluate our approach in a physics-enabled simulation environment with artificially injected uncertainty, as well as in a real world task of manipulation in clutter.
A Survey and Taxonomy of GALS Design Styles

Single-clocked digital systems are largely a thing of the past. Although most digital circuits remain synchronous, many designs feature multiple clock domains, often running at different frequencies. Using an asynchronous interconnect decouples the timing issues for the separate blocks. Systems employing such schemes are called globally asynchronous, locally synchronous (GALS). To minimize time to market, large SoC designs must integrate many functional blocks with minimal design effort. These blocks are usually designed using standard synchronous methods and often have different clocking requirements. A GALS approach can facilitate fast block reuse by providing wrapper circuits to handle interblock communication across clock domain boundaries. SoCs may also achieve power savings by clocking different blocks at their minimum speeds. For example, Scott et al. describe the advantages of GALS design for an embedded-processor peripheral bus.
A Measure of Visual Clutter : Feature Congestion When is a desk cluttered ?

Visual clutter concerns designers of user interfaces and information visualizations. This should not surprise visual perception researchers, since excess and/or disorganized display items can cause crowding, masking, decreased recognition performance due to occlusion, greater difficulty at both segmenting a scene and performing visual search, and so on. Given a reliable measure of the visual clutter in a display, designers could optimize display clutter. Furthermore, a measure of visual clutter could help generalize models like Guided Search (Wolfe, 1994) by providing a substitute for "setsize" more easily computable on more complex and natural imagery. In this paper, we present and test several measures of visual clutter, which operate on arbitrary images as input. The first is a new version of the Feature Congestion measure of visual clutter presented in (Rosenholtz, Li, Mansfield, & Jin, 2006). This Feature Congestion measure of visual clutter is based on the analogy that a display or scene is more cluttered the more difficult it would be to add a new item which would reliably draw attention. A second, subband entropy, measure of visual clutter is based on the notion that clutter is related to the visual information in the display. Finally, we test a third measure, edge density, used by Mack & Oliva (2004) as a measure of subjective visual complexity. We explore the use of these measures as stand-ins for set-size in visual search models, and demonstrate that they correlate well with search performance in complex imagery. This includes the search-in-clutter displays of Wolfe, Oliva, Horowitz, Butcher, & Bompas (2002) and Bravo & Farid (2004), as well as new search experiments. An additional experiment suggests that color variability, accounted for by Feature Congestion but not the edge density measure or the subband entropy measure, does matter for visual clutter.
Multi-Task CNN Model for Attribute Prediction

This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multi-task learning allows CNN models to simultaneously share visual knowledge among different attribute categories. Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes. In our multi-task framework, we propose a method to decompose the overall model's parameters into a latent task matrix and combination matrix. Furthermore, under-sampled classifiers can leverage shared statistics from other classifiers to improve their performance. Natural grouping of attributes is applied such that attributes in the same group are encouraged to share more knowledge. Meanwhile, attributes in different groups will generally compete with each other, and consequently share less knowledge. We show the effectiveness of our method on two popular attribute datasets.
Chaotic mixing in three-dimensional microvascular networks fabricated by direct-write assembly.

The creation of geometrically complex fluidic devices is a subject of broad fundamental and technological interest. Here, we demonstrate the fabrication of three-dimensional (3D) microvascular networks through direct-write assembly of a fugitive organic ink. This approach yields a pervasive network of smooth cylindrical channels (approximately 10-300 microm) with defined connectivity. Square-spiral towers, isolated within this vascular network, promote fluid mixing through chaotic advection. These vertical towers give rise to dramatic improvements in mixing relative to simple straight (1D) and square-wave (2D) channels while significantly reducing the device planar footprint. We envisage that 3D microvascular networks will provide an enabling platform for a wide array of fluidic-based applications.
On the Two-Dimensional Simplification of Three-Dimensional Cementless Hip Stem Numerical Models.

Three-dimensional (3D) finite element (FE) models are commonly used to analyze the mechanical behavior of the bone under different conditions (i.e., before and after arthroplasty). They can provide detailed information but they are numerically expensive and this limits their use in cases where large or numerous simulations are required. On the other hand, 2D models show less computational cost, but the precision of results depends on the approach used for the simplification. Two main questions arise: Are the 3D results adequately represented by a 2D section of the model? Which approach should be used to build a 2D model that provides reliable results compared to the 3D model? In this paper, we first evaluate if the stem symmetry plane used for generating the 2D models of bone-implant systems adequately represents the results of the full 3D model for stair climbing activity. Then, we explore three different approaches that have been used in the past for creating 2D models: (1) without side-plate (WOSP), (2) with variable thickness side-plate and constant cortical thickness (SPCT), and (3) with variable thickness side-plate and variable cortical thickness (SPVT). From the different approaches investigated, a 2D model including a side-plate best represents the results obtained with the full 3D model with much less computational cost. The side-plate needs to have variable thickness, while the cortical bone thickness can be kept constant.
Robust Face Detection via Learning Small Faces on Hard Images

Recent anchor-based deep face detectors have achieved promising performance, but they are still struggling to detect hard faces, such as small, blurred and partially occluded faces. A reason is that they treat all images and faces equally, without putting more effort on hard ones; however, many training images only contain easy faces, which are less helpful to achieve better performance on hard images. In this paper, we propose that the robustness of a face detector against hard faces can be improved by learning small faces on hard images. Our intuitions are (1) hard images are the images which contain at least one hard face, thus they facilitate training robust face detectors; (2) most hard faces are small faces and other types of hard faces can be easily converted to small faces by shrinking. We build an anchor-based deep face detector, which only output a single feature map with small anchors, to specifically learn small faces and train it by a novel hard image mining strategy. Extensive experiments have been conducted on WIDER FACE, FDDB, Pascal Faces, and AFW datasets to show the effectiveness of our method. Our method achieves APs of 95.7, 94.9 and 89.7 on easy, medium and hard WIDER FACE val dataset respectively, which surpass the previous state-of-the-arts, especially on the hard subset. Code and model are available at https://github.com/bairdzhang/smallhardface.
ICK classification system for partially edentulous arches.

Several methods of classification of partially edentulous arches have been proposed and are in use. The most familiar classifications are those originally proposed by Kennedy, Cummer, and Bailyn. None of these classification systems include implants, simply because most of them were proposed before implants became widely accepted. At this time, there is no classification system for partially edentulous arches incorporating implants placed or to be placed in the edentulous spaces for a removable partial denture (RPD). This article proposes a simple classification system for partially edentulous arches with implants based on the Kennedy classification system, with modification, to be used for RPDs. It incorporates the number and positions of implants placed or to be placed in the edentulous areas. A different name, Implant-Corrected Kennedy (ICK) Classification System, is given to the new classification system to be differentiated from other partially edentulous arch classification systems.
Design of a Tubular Permanent Magnet Actuator for Active Lateral Secondary Suspension of a Railway Vehicle

This paper describes the finite element (FE)-based design of a slotted tubular permanent magnet actuator (TPMA) used in railway vehicle active lateral secondary suspension that improves the actuator’s thrust and lowers its cogging force under thermal and geometric constraints. To consider the electromagnetic and thermal fields and the complex interactions among the design variables, design was carried out in an electromagnet and thermal field environment using accurate and time-effective FE analysis. A six-slot prototype model was fabricated to estimate critical thermal parameters, which are difficult to compute without experiments. Three-dimensional FE analysis using the determined thermal parameters was adopted to calculate the precise thermal distribution of the TPMA and verify the forced air-cooling effect. A prototype TPMA with a quasi-Halbach array of permanent magnets and a moving magnet was manufactured through the FE-based design process; the dynamic, electromagnetic, and thermal characteristics of the prototype TPMA were validated experimentally.
Realtime object detection in IoT (Internet of Things) devices

IoT (Internet of Things) is acommunication network that connects physical or things to each other or with a group all together. The use is widely popular nowadays and its usage has expanded into interesting subjects. Especially, it is getting more popular to research in cross subjects such as mixing smart systems with computer sciences and engineering applications together. Object detection is one of these subjects. Realtime object detection is one of the foremost interesting subjects because of its compute costs. Gaps in methodology, unknown concepts and insufficiency in mathematical modeling makes it harder for designing these computing algorithms. Algortihms in these applications can be developed with in machine learning and/or numerical methods that are available in scientific literature. These operations are possible only if communication of objects within theirselves in physical space and awareness of the objects nearby. Artificial Neural Networks may help in these studies. In this study, yolo algorithm which is seen as a key element for real-time object detection in IoT is researched. It is realized and shown in results that optimization of computing and analyzation of system aside this research which takes Yolo algorithm as a foundation point [10]. As a result, it is seen that our model approach has an interesting potential and novelty.
AN EMERGING TREND OF FEATURE EXTRACTION METHOD IN VIDEO PROCESSING

Recently the progress in technology and flourishing applications open up new forecast and defy for the image and video processing community. Compared to still images, video sequences afford more information about how objects and scenarios change over time. Quality of video is very significant before applying it to any kind of processing techniques. This paper deals with two major problems in video processing they are noise reduction and object segmentation on video frames. The segmentation of objects is performed using foreground segmentation based and fuzzy c-means clustering segmentation is compared with the proposed method Improvised fuzzy c – means segmentation based on color. This was applied in the video frame to segment various objects in the current frame. The proposed technique is a powerful method for image segmentation and it works for both single and multiple feature data with spatial information. The experimental result was conducted using various noises and filtering methods to show which is best suited among others and the proposed segmentation approach generates good quality segmented frames.