nils-herrmann/clean_scirepeval huggingface dataset:
Non-negative Tensor Factorization with missing data for the modeling of gene expressions in the Human Brain

Non-negative Tensor Factorization (NTF) has become a prominent tool for analyzing high dimensional multi-way structured data. In this paper we set out to analyze gene expression across brain regions in multiple subjects based on data from the Allen Human Brain Atlas  with more than 40 % data missing in our problem. Our analysis is based on the non-negativity constrained Canonical Polyadic (CP) decomposition where we handle the missing data using marginalization considering three prominent alternating least squares procedures; multiplicative updates, column-wise, and row-wise updating of the component matrices. We examine three gene expression prediction scenarios based on data missing at random, whole genes missing and whole areas missing within a subject. We find that the column-wise updating approach also known as HALS performs the most efficient when fitting the model. We further observe that the non-negativity constrained CP model is able to predict gene expressions better than predicting by the subject average when data is missing at random. When whole genes and whole areas are missing it is in general better to predict by subject averages. However, we find that when whole genes are missing from all subjects the model based predictions are useful. When analyzing the structure of the components derived for one of the best predicting model orders the components identified in general constitute localized regions of the brain. Non-negative tensor factorization based on marginalization thus forms a promising framework for imputing missing values and characterizing gene expression in the human brain. However, care also has to be taken in particular when predicting the genetic expression levels at a whole region of the brain missing as our analysis indicates that this requires a substantial amount of subjects with data for this region in order for the model predictions to be reliable.
Automation, machine learning, and artificial intelligence in echocardiography: A brave new world.

Automation, machine learning, and artificial intelligence (AI) are changing the landscape of echocardiography providing complimentary tools to physicians to enhance patient care. Multiple vendor software programs have incorporated automation to improve accuracy and efficiency of manual tracings. Automation with longitudinal strain and 3D echocardiography has shown great accuracy and reproducibility allowing the incorporation of these techniques into daily workflow. This will give further experience to nonexpert readers and allow the integration of these essential tools into more echocardiography laboratories. The potential for machine learning in cardiovascular imaging is still being discovered as algorithms are being created, with training on large data sets beyond what traditional statistical reasoning can handle. Deep learning when applied to large image repositories will recognize complex relationships and patterns integrating all properties of the image, which will unlock further connections about the natural history and prognosis of cardiac disease states. The purpose of this review article was to describe the role and current use of automation, machine learning, and AI in echocardiography and discuss potential limitations and challenges of in the future.
Deep Keyframe Detection in Human Action Videos

Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action category from all other categories. To this end, we introduce a deep two-stream ConvNet for key frame detection in videos that learns to directly predict the location of key frames. Our key idea is to automatically generate labeled data for the CNN learning using a supervised linear discriminant method. While the training data is generated taking many different human action videos into account, the trained CNN can predict the importance of frames from a single video. We specify a new ConvNet framework, consisting of a summarizer and discriminator. The summarizer is a two-stream ConvNet aimed at, first, capturing the appearance and motion features of video frames, and then encoding the obtained appearance and motion features for video representation. The discriminator is a fitting function aimed at distinguishing between the key frames and others in the video. We conduct experiments on a challenging human action dataset UCF101 and show that our method can detect key frames with high accuracy.
Interactions between contingency, organizational IT factors, and ERP success

The purpose of this paper is to examine the impact of some organizational information technology (IT) factors (i.e. IT assets, employees’ IT skills, IT resources, and satisfaction with legacy IT systems) and their interacting effects with two contingency factors (i.e. organization’s size and structure) on enterprise resource planning (ERP) system success.  Surveys were conducted in two European countries. Respondents came from diverse, private, and industrial organizations. Relevant hypotheses were developed and tested using a structural equation modeling technique.  The analysis supported – partially or fully – six of the eight hypotheses formulated. For example, the data indicated strong positive relationships between IT assets and IT resources, on the one hand, and ERP success, on the other. Organization’s size and structure were also found to be moderators in some of the relationships. Also, the analysis revealed that satisfaction with legacy IT systems increased with ERP success, which was an unexpected finding.  This study contributes to the literature, being among the few to investigate the effects of organizational IT factors and their interacting effects with relevant contingency factors in the context of ERP system success. Methodologically, the study utilized a “non-deterministic” model to facilitate deeper insights into the effects of variables.
Optimization of PESD implant design for ESD robustness of 5V drain-back N-LDMOSFET

An N-LDMOS ESD protection device with drain back and PESD optimization design is proposed. With PESD layer enclosing the N+ drain region, a parasitic SCR is created to achieve high ESD level. When PESD is close to gate, the turn-on efficiency can be further improved (Vt1: 11.2 V reduced to 7.2 V) by the punch-through path from N+/PESD to PW. The proposed ESD N-LDMOS can sustain over 8KV HBM with low trigger behavior without extra area cost.
A 7nm CMOS technology platform for mobile and high performance compute application

We present a fully integrated 7nm CMOS platform featuring a 3rd generation finFET architecture, SAQP for fin formation, and SADP for BEOL metallization. This technology reflects an improvement of 2.8X routed logic density and >40% performance over the 14nm reference technology described in [1-3]. A full range of Vts is enabled on-chip through a unique multi-workfunction process. This enables both excellent low voltage SRAM response and highly scaled memory area simultaneously. The HD 6-T bitcell size is 0.0269um2. This 7nm technology is fully enabled by immersion lithography and advanced optical patterning techniques (like SAQP and SADP). However, the technology platform is also designed to leverage EUV insertion for specific multi-patterned (MP) levels for cycle time benefit and manufacturing efficiency. A complete set of foundation and complex IP is available in this advanced CMOS platform to enable both High Performance Compute (HPC) and mobile applications.
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders

We introduce adversarial neural networks for representation learning as a novel approach to transfer learning in brain-computer interfaces (BCIs). The proposed approach aims to learn subject-invariant representations by simultaneously training a conditional variational autoencoder (cVAE) and an adversarial network. We use shallow convolutional architectures to realize the cVAE, and the learned encoder is transferred to extract subject-invariant features from unseen BCI users’ data for decoding. We demonstrate a proof-of-concept of our approach based on analyses of electroencephalographic (EEG) data recorded during a motor imagery BCI experiment.
Driver Fatigue Detection Using Mouth and Yawning Analysis

Driver fatigue is an important factor in large number of accidents. There has been much work done in driver fatigue detection. This paper presents driver fatigue detection based on tracking the mouth and to study on monitoring and recognizing yawning. The authors proposed a method to locate and track driver’s mouth using cascade of classifiers proposed by Viola-Jones for faces. SVM is used to train the mouth and yawning images. During the fatigue detection mouth is detected from face images using cascade of classifiers. Then, SVM is used to classify the mouth and to detect yawning then alert Fatigue.
Deep Unsupervised Domain Adaptation for Face Recognition

Face recognition is challenge task which involves determining the identity of facial images. With availability of a massive amount of labeled facial images gathered from Internet, deep convolution neural networks(DCNNs) have achieved great success in face recognition tasks. Those images are gathered from unconstrain environment, which contain people with different ethnicity, age, gender and so on. However, in the actual application scenario, the target face database may be gathered under different conditions compered with source training dataset, e.g. different ethnicity, different age distribution, disparate shooting environment. These factors increase domain discrepancy between source training database and target application database which makes the learnt model degenerate in target database. Meanwhile, for the target database where labeled data are lacking or unavailable, directly using target data to fine-tune pre-learnt model becomes intractable and impractical. In this paper, we adopt unsupervised transfer learning methods to address this issue. To alleviate the discrepancy between source and target face database and ensure the generalization ability of the model, we constrain the maximum mean discrepancy (MMD) between source database and target database and utilize the massive amount of labeled facial images of source database to training the deep neural network at the same time. We evaluate our method on two face recognition benchmarks and significantly enhance the performance without utilizing the target label.
Penile squamous cell carcinoma: a review of the literature and case
report treated with Mohs micrographic surgery*


The majority of penile carcinoma is squamous cell carcinoma. Although uncommon in the United States, it represents a larger proportion of cancers in the underdeveloped world. Invasive squamous cell carcinoma may arise from precursor lesions or de novo , and has been associated with lack of circumcision and HPV infection. Early diagnosis is imperative as lymphatic spread is associated with a poor prognosis. Radical surgical treatment is no longer the mainstay, and penile sparing treatments now are often used, including Mohs micrographic surgery. Therapeutic decisions should be made with regard to the size and location of the tumor, as well as the functional desires of the patient. It is critical for the dermatologist to be familiar with the evaluation, grading/staging, and treatment advances of penile squamous cell carcinoma. Herein, we present a review of the literature regarding penile squamous cell carcinoma, as well as a case report of invasive squamous cell carcinoma treated with Mohs micrographic surgery.
Diagnostic performance of dual-energy contrast-enhanced subtracted mammography in dense breasts compared to mammography alone: interobserver blind-reading analysis

To analyse the accuracy of dual-energy contrast-enhanced spectral mammography in dense breasts in comparison with contrast-enhanced subtracted mammography (CESM) and conventional mammography (Mx). CESM cases of dense breasts with histological proof were evaluated in the present study. Four radiologists with varying experience in mammography interpretation blindly read Mx first, followed by CESM. The diagnostic profiles, consistency and learning curve were analysed statistically. One hundred lesions (28 benign and 72 breast malignancies) in 89 females were analysed. Use of CESM improved the cancer diagnosis by 21.2 % in sensitivity (71.5 % to 92.7 %), by 16.1 % in specificity (51.8 % to 67.9 %) and by 19.8 % in accuracy (65.9 % to 85.8 %) compared with Mx. The interobserver diagnostic consistency was markedly higher using CESM than using Mx alone (0.6235 vs. 0.3869 using the kappa ratio). The probability of a correct prediction was elevated from 80 % to 90 % after 75 consecutive case readings. CESM provided additional information with consistent improvement of the cancer diagnosis in dense breasts compared to Mx alone. The prediction of the diagnosis could be improved by the interpretation of a significant number of cases in the presence of 6 % benign contrast enhancement in this study. • DE-CESM improves the cancer diagnosis in dense breasts compared with mammography. • DE-CESM shows greater consistency than mammography alone by interobserver blind reading. • Diagnostic improvement of DE-CESM is independent of the mammographic reading experience.
Abandoned Object Detection Using Pixel-Based Finite State Machine and Single Shot Multibox Detector

This paper proposes a robust, scalable framework for automatic detection of abandoned, stationary objects in real time surveillance videos that can pose a security threat. We use the sViBe background modeling method to generate a long-term and a short-term background model to extract foreground objects. Subsequently, a pixel-based FSM detects stationary candidate objects based on the temporal transition of code patterns. In order to classify the stationary candidate objects, we use deep learning method (SSD: Single Shot MultiBox Detector) to detect person and some suspected type of objects which include backpack, handbag. In order to suppress any false alarm, we remove other stationary candidate objects other than the suspected stationary objects. After stationary object detection, we also check if there is no person near by the suspected detected objects for a particular time. We tested the system on four standard public datasets. The results show that our method outperforms the performance of existing results while also being robust to temporary occlusions and illumination changes.
Modelling User's Theory of AI's Mind in Interactive Intelligent Systems

Many interactive intelligent systems, such as recommendation and information retrieval systems, treat users as a passive data source. Yet, users form mental models of systems and instead of passively providing feedback to the queries of the system, they will strategically plan their actions within the constraints of the mental model to steer the system and achieve their goals faster. We propose to explicitly account for the user’s theory of the AI’s mind in the user model: the intelligent system has a model of the user having a model of the intelligent system. We study a case where the system is a contextual bandit and the user model is a Markov decision process that plans based on a simpler model of the bandit. Inference in the model can be reduced to probabilistic inverse reinforcement learning, with the nested bandit model defining the transition dynamics, and is implemented using probabilistic programming. Our results show that improved performance is achieved if users can form accurate mental models that the system can capture, implying predictability of the interactive intelligent system is important not only for the user experience but also for the design of the system’s statistical models.
TiQi: answering unstructured natural language trace queries

Software traceability is a required element in the development and certification of safety-critical software systems. However, trace links, which are created at significant cost and effort, are often underutilized in practice due primarily to the fact that project stakeholders often lack the skills needed to formulate complex trace queries. To mitigate this problem, we present a solution which transforms spoken or written natural language queries into structured query language (SQL). TiQi includes a general database query mechanism and a domain-specific model populated with trace query concepts, project-specific terminology, token disambiguators, and query transformation rules. We report results from four different experiments exploring user preferences for natural language queries, accuracy of the generated trace queries, efficacy of the underlying disambiguators, and stability of the trace query concepts. Experiments are conducted against two different datasets and show that users have a preference for written NL queries. Queries were transformed at accuracy rates ranging from 47 to 93 %.
Innovation in Ghana : Insight of Students ’ Adoption and Diffusion

This study seeks to determine a set of attributes that influence the adoption mobile banking innovation among university students in Ghana since most banks have started reaching their customers directly via mobile phones. A quantitative approach was used to obtain data collect from cohort of 550 undergraduate students who are frequent users of mobile phone devices. Data was analysed with SPSS software which measured factors such as relative advantage, compatibility, observability, complexity, perceived risk, trialability and service satisfaction as critical factors influencing the adoption of mobile banking in Ghana. Further, the study revealed that mobile banking was compatible with their life styles. This research adds value to existing studies on mobile banking especially in Sub Saharan African where adoption of mobile phone innovation JIBC December 2013,  Mobile banking projects Ghana as an emerging innovation adopter and a catalyst for Africa’s socioeconomic development. Finally, it provides opportunity for marketers at strategic positions in telecommunication and banking sectors to focus on student cohort as essential target market for rapid growth.
A U-shape bed localization method on embedded vision system

In order to dock an embedded intelligent wheelchair into a U-shape bed automatically through visual servo, this paper proposes a real-time U-shape bed localization method on an embedded vision system based on FPGA and DSP. This method locates the U-shape bed through finding its line contours. The task can be done in a parallel way with FPGA does line extraction and DSP does line contour finding. Experiments show that, the speed and precision of the U-shape bed localization method proposed in this paper which based on an embedded vision system can satisfy the needs of the system.
Haptic Palpation for Medical Simulation in Virtual Environments

Palpation is a physical examination technique where objects, e.g., organs or body parts, are touched with fingers to determine their size, shape, consistency and location. Many medical procedures utilize palpation as a supplementary interaction technique and it can be therefore considered as an essential basic method. However, palpation is mostly neglected in medical training simulators, with the exception of very specialized simulators that solely focus on palpation, e.g., for manual cancer detection. In this article we propose a novel approach to enable haptic palpation interaction for virtual reality-based medical simulators. The main contribution is an extensive user study conducted with a large group of medical experts. To provide a plausible simulation framework for this user study, we contribute a novel and detailed interaction algorithm for palpation with tissue dragging, which utilizes a multi-object force algorithm to support multiple layers of anatomy and a pulse force algorithm for simulation of an arterial pulse. Furthermore, we propose a modification for an off-the-shelf haptic device by adding a lightweight palpation pad to support a more realistic finger grip configuration for palpation tasks. The user study itself has been conducted on a medical training simulator prototype with a specific procedure from regional anesthesia, which strongly depends on palpation. The prototype utilizes a co-rotational finite-element approach for soft tissue simulation and provides bimanual interaction by combining the aforementioned techniques with needle insertion for the other hand. The results of the user study suggest reasonable face validity of the simulator prototype and in particular validate medical plausibility of the proposed palpation interaction algorithm.
Sex steroids and the construction and conservation of the adult skeleton.

Here we review and extend a new unitary model for the pathophysiology of involutional osteoporosis that identifies estrogen (E) as the key hormone for maintaining bone mass and E deficiency as the major cause of age-related bone loss in both sexes. Also, both E and testosterone (T) are key regulators of skeletal growth and maturation, and E, together with GH and IGF-I, initiate a 3- to 4-yr pubertal growth spurt that doubles skeletal mass. Although E is required for the attainment of maximal peak bone mass in both sexes, the additional action of T on stimulating periosteal apposition accounts for the larger size and thicker cortices of the adult male skeleton. Aging women undergo two phases of bone loss, whereas aging men undergo only one. In women, the menopause initiates an accelerated phase of predominantly cancellous bone loss that declines rapidly over 4-8 yr to become asymptotic with a subsequent slow phase that continues indefinitely. The accelerated phase results from the loss of the direct restraining effects of E on bone turnover, an action mediated by E receptors in both osteoblasts and osteoclasts. In the ensuing slow phase, the rate of cancellous bone loss is reduced, but the rate of cortical bone loss is unchanged or increased. This phase is mediated largely by secondary hyperparathyroidism that results from the loss of E actions on extraskeletal calcium metabolism. The resultant external calcium losses increase the level of dietary calcium intake that is required to maintain bone balance. Impaired osteoblast function due to E deficiency, aging, or both also contributes to the slow phase of bone loss. Although both serum bioavailable (Bio) E and Bio T decline in aging men, Bio E is the major predictor of their bone loss. Thus, both sex steroids are important for developing peak bone mass, but E deficiency is the major determinant of age-related bone loss in both sexes.
utonomous robotic weed control systems : A review .

Autonomous robotic weed control systems hold promise toward the automation of one of agriculture’s few remaining unmechanized and drudging tasks, hand weed control. Robotic technology may also provide a means of reducing agriculture’s current dependency on herbicides, improving its sustainability and reducing its environmental impact. This review describes the current status of the four core technologies (guidance, detection and identification, precision in-row weed control, and mapping) required for the successful development of a general-purpose robotic system for weed control. Of the four, detection and identifica-