nils-herrmann/clean_scirepeval huggingface dataset:
''granična Ličnost I Njena Različita Lica. Šta Su Poremećaji Ličnosti?'' ''borderline Personality and Its Diverse Faces. What Are Personality Disorders?''

''Borderline personality and its diverse faces'' is an excellent, ambitious and comprehensive multidimen-sional book written by Mirjana Divac Jovanovic and Dragan Svrakic. We are all fascinated by illusions when things differ from what they look like. For this reason personality disorders are fascinating because they represent a significant form of psychopathology that is concealed by an outside facade of normalcy. There is a huge number of people impaired by personality disorders , who are unable of sustaining mature, creative and honest relationships. Some of them are very destructive, asocial and even dangerous. On the other side, people with personality disorders can have exceptional capabilities and attractive features and qualities, hold a significant number of positions of responsibility and importance in bussiness, political and public life. Personality disorders lack the professional consensus, respectability and validity that exists with the major mental disorders. The authors of this book decided to take on the probably most complex entity in psychiatry and clinical psychology – personality pathology. The book itself consists of ten chapters: 1. Introduction into the concept of personality and personality disorders; 2. Concep-tualization and classification of personality disorders; 3. Categorial and dimensional models of personality disorders ; 4. Theoretical models of etiology and development of personality disorders; 5. Personality disorders: Adaptation disorders or integration disorders; 6. Clinical evaluation of borderline level of personality functioning ; 7. Psychotherapy of patients with borderline disorder; 8. Empirical studies of psychotherapy; 9. Pharmacotherapy of personality disorders; and 10. Coda or What is our future? A relatively large proportion of this book has been dedicated to the topic of pharmaco-therapy, seemingly in odds with the trend of ''psychologization'' of this field of psychopathology. The psychobiological model of personality presented by the authors is particularly relevant in the context of both causal pharmacotherapy and psychotherapy. More specifically, these experts have based their latest book on the concept of borderline personality, a relatively older term describing general personality disturbances such as identity diffusion and interpersonal instability, that serve as the basis for a multitude of maladaptive expressions of individual personality traits. This line of reasoning is certainly recognized by numerous clinicians around the world who, through their professional experience, have noticed some consistencies among various types of personality disorders (PDs), although at first these patients might seem completely different. Understanding such broader personality disturbances (i.e., borderline personality) and the complex reasons for their heterogeneous manifestations, as explained in …
A 0.45 V, Nano-Watt 0.033% Line Sensitivity MOSFET-Only Sub-Threshold Voltage Reference With no Amplifiers

An ultra-low voltage, low power, low line sensitivity MOSFET-only sub-threshold voltage reference with no amplifiers is presented. The low sensitivity is realized by the difference between two complementary currents and second-order compensation improves the temperature stability. The bulk-driven technique is used and most of the transistors work in the sub-threshold region, which allow a remarkable reduction in the minimum supply voltage and power consumption. Moreover, a trimming circuit is adopted to compensate the process-related reference voltage variation while the line sensitivity is not affected. The proposed voltage reference has been fabricated in the 0.18 μm 1.8 V CMOS process. The measurement results show that the reference could operate on a 0.45 V supply voltage. For supply voltages ranging from 0.45 to 1.8 V the power consumption is 15.6 nW, and the average temperature coefficient is 59.4 ppm/°C across a temperature range of -40 to 85 °C and a mean line sensitivity of 0.033%. The power supply rejection ratio measured at 100 Hz is -50.3 dB. In addition, the chip area is 0.013 mm2.
Sensor Fusion-Based Vacant Parking Slot Detection and Tracking

This paper proposes a vacant parking slot detection and tracking system that fuses the sensors of an Around View Monitor (AVM) system and an ultrasonic sensor-based automatic parking system. The proposed system consists of three stages: parking slot marking detection, parking slot occupancy classification, and parking slot marking tracking. The parking slot marking detection stage recognizes various types of parking slot markings using AVM image sequences. It detects parking slots in individual AVM images by exploiting a hierarchical tree structure of parking slot markings and combines sequential detection results. The parking slot occupancy classification stage identifies vacancies of detected parking slots using ultrasonic sensor data. Parking slot occupancy is probabilistically calculated by treating each parking slot region as a single cell of the occupancy grid. The parking slot marking tracking stage continuously estimates the position of the selected parking slot while the ego-vehicle is moving into it. During tracking, AVM images and motion sensor-based odometry are fused together in the chamfer score level to achieve robustness against inevitable occlusions caused by the ego-vehicle. In the experiments, it is shown that the proposed method can recognize the positions and occupancies of various types of parking slot markings and stably track them under practical situations in a real-time manner. The proposed system is expected to help drivers conveniently select one of the available parking slots and support the parking control system by continuously updating the designated target positions.
Typeface Completion with Generative Adversarial Networks

The mood of a text and the intention of the writer can be reflected in the typeface. However, in designing a typeface, it is difficult to keep the style of various characters consistent, especially for languages with lots of morphological variations such as Chinese. In this paper, we propose a Typeface Completion Network (TCN) which takes one character as an input, and automatically completes the entire set of characters in the same style as the input characters. Unlike existing models proposed for image-to-image translation, TCN embeds a character image into two separate vectors representing typeface and content. Combined with a reconstruction loss from the latent space, and with other various losses, TCN overcomes the inherent difficulty in designing a typeface. Also, compared to previous image-to-image translation models, TCN generates high quality character images of the same typeface with a much smaller number of model parameters. We validate our proposed model on the Chinese and English character datasets, which is paired data, and the CelebA dataset, which is unpaired data. In these datasets, TCN outperforms recently proposed state-of-the-art models for image-to-image translation. The source code of our model is available at https://github.com/yongqyu/TCN.
Antitumor activity of CEP-751 (KT-6587) on human neuroblastoma and medulloblastoma xenografts.

Neuroblastoma (NBL) and medulloblastoma (MBL) are tumors of the neuroectoderm that occur in children. NBL and MBL express Trk family tyrosine kinase receptors, which regulate growth, differentiation, and cell death. CEP-751 (KT-6587), an indolocarbazole derivative, is an inhibitor of Trk family tyrosine kinases at nanomolar concentrations. This study was designed to determine the effect of CEP-751 on the growth of NBL and MBL cell lines as xenografts. In vivo studies were conducted on four NBL cell lines (IMR-5, CHP-134, NBL-S, and SY5Y) and three MBL cell lines (D283, D341, and DAOY) using two treatment schedules: (a) treatment was started after the tumors were measurable (therapeutic study); or (b) 4-6 days after inoculation, before tumors were palpable (prevention study). CEP-751 was given at 21 mg/kg/dose administered twice a day, 7 days a week; the carrier vehicle was used as a control. In therapeutic studies, a significant difference in tumor size was seen between treated and control animals with IMR-5 on day 8 (P = 0.01), NBL-S on day 17 (P = 0.016), and CHP-134 on day 15 (P = 0.034). CEP-751 also had a significant growth-inhibitory effect on the MBL line D283 (on day 39, P = 0.031). Inhibition of tumor growth of D341 did not reach statistical significance, and no inhibition was apparent with DAOY. In prevention studies, CEP-751 showed a modest growth-inhibitory effect on IMR5 (P = 0.062) and CHP-134 (P = 0.049). Furthermore, inhibition of growth was greater in the SY5Y cell line transfected with TrkB compared with the untransfected parent cell line expressing no detectable TrkB. Terminal deoxynucleotidyl transferase-mediated nick end labeling studies showed CEP-751 induced apoptosis in the treated CHP-134 tumors, whereas no evidence of apoptosis was seen in the control tumors. Finally, there was no apparent toxicity identified in any of the treated mice. These results suggest that CEP-751 may be a useful therapeutic agent for NBL or MBL.
Local search with annealing-like restarts to solve the VRPTW

In this paper, we propose a metaheuristic based on annealing-like restarts to diversify and intensify local searches for solving the vehicle routing problem with time windows (VRPTW). Using the Solomon s benchmark instances for the problem, our method obtained seven new best results and equaled 19 other best results. Extensive comparisons indicate that our method is comparable to the best in published literature. This approach is flexible and can be extended to handle other variants of vehicle routing problems and other combinatorial optimization problems.
Adversarially Regularized Graph Autoencoder for Graph Embedding

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in realworld graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding, two variants of adversarial approaches, adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction, graph clustering, and graph visualization tasks.
Design and implementation of SDN-based lightweight IPv6 mobility manager in ONOS

Since Software-Defined Networking (SDN) gains popularity, mobile/wireless support is mentioned with importance to be handled as one of the crucial aspects in SDN. SDN introduces a centralized entity called SDN controller with the holistic view of the topology on the separated control/data plane architecture. Leveraging the features provided in the SDN controller, mobility management can be simply designed and lightweight, thus there is no need to define and rely on new mobility entities such as given in the traditional IP mobility management architectures. In this paper, we design and implement lightweight IPv6 mobility management in Open Network Operating System (ONOS) that is an open-source SDN control platform for service providers. For the lightweight mobility management, we implement the Neighbor Discovery Proxy (ND Proxy) function into the OpenFlow-enabled AP and switches, and ONOS controller module to handle the receiving ICMPv6 message and to send the unique home network prefix address to an IPv6 host. Thus this approach enables mobility management without bringing or integrating on traditional IP mobility protocols. The proposed idea was experimentally evaluated in the ONOS controller and Raspberry Pi based testbed, identifying the obtained handoff signaling latency is in the acceptable performance range.
3d object modeling and recognition in photographs and video

This thesis introduces a novel representation for three-dimensional (3D) objects in terms of local affine-invariant descriptors of their appearance and the spatial relationships between the corresponding affine regions. Geometric constraints associated with different views of the same surface patches are combined with a normalized representation of their appearance to guide matching and reconstruction, allowing the acquisition of true 3D models from multiple unregistered images, as well as their recognition in photographs and image sequences. The proposed approach is applied to two domains: 1) Photographs – Models of rigid objects are constructed from photos and recognized in highly cluttered shots taken from arbitrary viewpoints. 2) Video – Dynamic scenes containing multiple moving objects observed by a moving camera are segmented into rigid components, and the 3D models constructed from these components are matched across different image sequences, with application to shot matching.
Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation

We compared four automated methods for hippocampal segmentation using different machine learning algorithms: 1) hierarchical AdaBoost, 2) support vector machines (SVM) with manual feature selection, 3) hierarchical SVM with automated feature selection (Ada-SVM), and 4) a publicly available brain segmentation package (FreeSurfer). We trained our approaches using T1-weighted brain MRIs from 30 subjects [10 normal elderly, 10 mild cognitive impairment (MCI), and 10 Alzheimer's disease (AD)], and tested on an independent set of 40 subjects (20 normal, 20 AD). Manually segmented gold standard hippocampal tracings were available for all subjects (training and testing). We assessed each approach's accuracy relative to manual segmentations, and its power to map AD effects. We then converted the segmentations into parametric surfaces to map disease effects on anatomy. After surface reconstruction, we computed significance maps, and overall corrected p-values, for the 3-D profile of shape differences between AD and normal subjects. Our AdaBoost and Ada-SVM segmentations compared favorably with the manual segmentations and detected disease effects as well as FreeSurfer on the data tested. Cumulative p-value plots, in conjunction with the false discovery rate method, were used to examine the power of each method to detect correlations with diagnosis and cognitive scores. We also evaluated how segmentation accuracy depended on the size of the training set, providing practical information for future users of this technique.
SECTION 1 : WOMEN ’ S EMPOWERMENT AND MICRO-FINANCE : COMPETING PARADIGMS AND UNDERLYING ASSUMPTIONS

. Micro-finance programmes targeting women became a major plank of donor poverty alleviation and gender strategies in the 1990s. Increasing evidence of the centrality of gender equality to poverty reduction and women’s higher credit repayment rates led to a general consensus on the desirability of targeting women. Not only ‘reaching’ but also ‘empowering’ women became the second official goal of the Micro-credit Summit Campaign.
MODELING MULTILEVEL DATA STRUCTURES

Although integrating multiple levels of data into an analysis can often yield better inferences about the phenomenon under study, traditional methodologies used to combine multiple levels of data are problematic. In this paper, we discuss several methodologies under the rubric of multil evel analysis. Multil evel methods, we argue, provide researchers, particularly researchers using comparative data, substantial leverage in overcoming the typical problems associated with either ignoring multiple levels of data, or problems associated with combining lower-level and higherlevel data (including overcoming implicit assumptions of fixed and constant effects). The paper discusses several variants of the multil evel model and provides an application of individual-level support for European integration using comparative politi cal data from Western Europe.
Online user survey on current mobile augmented reality applications

Augmented reality (AR) as an emerging technology in the mobile computing domain is becoming mature enough to engender publicly available applications for end users. Various commercial applications have recently been emerging in the mobile consumer domain at an increasing pace — Layar, Junaio, Google Goggles, and Wikitude are perhaps the most prominent ones. However, the research community lacks an understanding of how well such timely applications have been accepted, what kind of user experiences they have evoked, and what the users perceive as the weaknesses of the various applications overall. During the spring of 2011 we conducted an online survey to study the overall acceptance and user experience of the mobile AR-like consumer applications currently existing on the market. This paper reports the first analyses of the qualitative and quantitative survey data of 90 respondents. We highlight an extensive set of user-oriented issues to be considered in developing the applications further, as well as in directing future user research in AR. The results indicate that the experiences have been inconsistent: generally positive evaluations are overshadowed by mentions of applications' pragmatic uselessness in everyday life and technical unreliability, as well as excessive or limited and irrelevant content.
Using Spatial Pooler of Hierarchical Temporal Memory for object classification in noisy video streams

This paper focuses on analyzing a Spatial Pooler (SP) of Hierarchical Temporal Memory (HTM) ability for facilitating object classification in noisy video streams. In particular, we seek to determine whether employing SP as a component of the video system increases overall robustness to noise. We have implemented our own version of HTM and applied it to object recognition tasks under various testing conditions. The system is composed of a video preprocessing block, a dimensionality reduction section which contains SP, a histograms collecting module and SVM classifier. Our experiments involve assessing performance of two different system setups (i.e. a version featuring SP and one without it) under various noise conditions with 32-frame video files. In order to make tests fair and repeatable the videos of several 3-D geometric shapes were artificially generated. Subsequently, Gaussian noise of a different intensity was introduced to the videos making them more indistinct. Such an approach mimics real-life scenarios where the system is taught ideal objects and then faces in its normal working conditions the challenge of detecting noisy ones. The results of the experiments reveal the superiority of the solution featuring Spatial Pooler over the one without it. Furthermore, the system with SP performed better also in the experiment without a noise component introduced and achieved a mean F1-score of 0.91 in ten trials.
Completely Distributed Power Allocation using Deep Neural Network for Device to Device communication Underlaying LTE

Device to device (D2D) communication underlaying LTE can be used to distribute traffic loads of eNBs. However, a conventional D2D link is controlled by an eNB, and it still remains burdens to the eNB. We propose a completely distributed power allocation method for D2D communication underlaying LTE using deep learning. In the proposed scheme, a D2D transmitter can decide the transmit power without any help from other nodes, such as an eNB or another D2D device. Also, the power set, which is delivered from each D2D node independently, can optimize the overall cell throughput. We suggest a distirbuted deep learning architecture in which the devices are trained as a group, but operate independently. The deep learning can optimize total cell throughput while keeping constraints such as interference to eNB. The proposed scheme, which is implemented model using Tensorflow, can provide same throughput with the conventional method even it operates completely on distributed manner.
Transfer Learning for Speech Recognition on a Budget

End-to-end training of automated speech recognition (ASR) systems requires massive data and compute resources. We explore transfer learning based on model adaptation as an approach for training ASR models under constrained GPU memory, throughput and training data. We conduct several systematic experiments adapting a Wav2Letter convolutional neural network originally trained for English ASR to the German language. We show that this technique allows faster training on consumer-grade resources while requiring less training data in order to achieve the same accuracy, thereby lowering the cost of training ASR models in other languages. Model introspection revealed that small adaptations to the network’s weights were sufficient for good performance, especially for inner layers.
Multimodal Human Activity Recognition for Industrial Manufacturing Processes in Robotic Workcells

We present an approach for monitoring and interpreting human activities based on a novel multimodal vision-based interface, aiming at improving the efficiency of human-robot interaction (HRI) in industrial environments. Multi-modality is an important concept in this design, where we combine inputs from several state-of-the-art sensors to provide a variety of information, e.g. skeleton and fingertip poses. Based on typical industrial workflows, we derived multiple levels of human activity labels, including large-scale activities (e.g. assembly) and simpler sub-activities (e.g. hand gestures), creating a duration- and complexity-based hierarchy. We train supervised generative classifiers for each activity level and combine the output of this stage with a trained Hierarchical Hidden Markov Model (HHMM), which models not only the temporal aspects between the activities on the same level, but also the hierarchical relationships between the levels.
Hierarchical evolution of linear regressors

We propose an algorithm for function approximation that evolves a set of hierarchical piece-wise linear regressors. The algorithm, named HIRE-Lin, follows the iterative rule learning approach. A genetic algorithm is iteratively called to find a partition of the search space where a linear regressor can accurately fit the objective function. The resulting ruleset performs an approximation to the objective function formed by a hierarchy of locally trained linear regressors. The approach is evaluated in a set of objective functions and compared to other regression techniques.