nils-herrmann/clean_scirepeval huggingface dataset:
Simulation of Patients Flow in Multiple Healthcare Units using Process and Data Mining Techniques for Model Identification

An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for combination of different techniques. The implementation of the proposed approach for simulation of the acute coronary syndrome (ACS) was developed and used in an experimental study.



A combination of data, text, process mining techniques, and machine learning approaches for the analysis of electronic health records (EHRs) with discrete-event simulation (DES) and queueing theory for the simulation of patient flow was proposed. The performed analysis of EHRs for ACS patients enabled identification of several classes of clinical pathways (CPs) which were used to implement a more realistic simulation of the patient flow. The developed solution was implemented using Python libraries (SimPy, SciPy, and others).



The proposed approach enables more a realistic and detailed simulation of the patient flow within a group of related departments. An experimental study shows an improved simulation of patient length of stay for ACS patient flow obtained from EHRs in Almazov National Medical Research Centre in Saint Petersburg, Russia.



The proposed approach, methods, and solutions provide a conceptual, methodological, and programming framework for the implementation of a simulation of complex and diverse scenarios within a flow of patients for different purposes: decision making, training, management optimization, and others.
ON SOME APPLICATIONS OF AUTOMORPHIC FORMS TO NUMBER THEORY

A basic idea of Dirichlet is to study a collection of interesting quantities {an}n≥1 by means of its Dirichlet series in a complex variable w: ∑ n≥1 ann −w. In this paper we examine this construction when the quantities an are themselves infinite series in a second complex variable s, arising from number theory or representation theory. We survey a body of recent work on such series and present a new conjecture concerning them.
Resolving the Vergence-Accommodation Conflict in Head-Mounted Displays

The vergence-accommodation conflict (VAC) remains a major problem in head-mounted displays for virtual and augmented reality (VR and AR). In this review, I discuss why this problem is pivotal for nearby tasks in VR and AR, present a comprehensive taxonomy of potential solutions, address advantages and shortfalls of each design, and cover various ways to better evaluate the solutions. The review describes how VAC is addressed in monocular, stereoscopic, and multiscopic HMDs, including retinal scanning and accommodation-free displays. Eye-tracking-based approaches that do not provide natural focal cues-gaze-guided blur and dynamic stereoscopy-are also covered. Promising future research directions in this area are identified.
Coherent Keyphrase Extraction via Web Mining

Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. A limitation of previous keyphrase extraction algorithms is that the selected keyphrases are occasionally incoherent. That is, the majority of the output keyphrases may fit together well, but there may be a minority that appear to be outliers, with no clear semantic relation to the majority or to each other. This paper presents enhancements to the Kea keyphrase extraction algorithm that are designed to increase the coherence of the extracted keyphrases. The approach is to use the degree of statistical association among candidate keyphrases as evidence that they may be semantically related. The statistical association is measured using web mining. Experiments demonstrate that the enhancements improve the quality of the extracted keyphrases. Furthermore, the enhancements are not domain-specific: the algorithm generalizes well when it is trained on one domain (computer science documents) and tested on another (physics documents).
Psychiatry & the psychedelic drugs. Past, present & future

The classical psychedelic drugs, including psilocybin, lysergic acid diethylamide and mescaline, were used extensively in psychiatry before they were placed in Schedule I of the UN Convention on Drugs in 1967. Experimentation and clinical trials undertaken prior to legal sanction suggest that they are not helpful for those with established psychotic disorders and should be avoided in those liable to develop them. However, those with so-called 'psychoneurotic' disorders sometimes benefited considerably from their tendency to 'loosen' otherwise fixed, maladaptive patterns of cognition and behaviour, particularly when given in a supportive, therapeutic setting. Pre-prohibition studies in this area were sub-optimal, although a recent systematic review in unipolar mood disorder and a meta-analysis in alcoholism have both suggested efficacy. The incidence of serious adverse events appears to be low. Since 2006, there have been several pilot trials and randomised controlled trials using psychedelics (mostly psilocybin) in various non-psychotic psychiatric disorders. These have provided encouraging results that provide initial evidence of safety and efficacy, however the regulatory and legal hurdles to licensing psychedelics as medicines are formidable. This paper summarises clinical trials using psychedelics pre and post prohibition, discusses the methodological challenges of performing good quality trials in this area and considers a strategic approach to the legal and regulatory barriers to licensing psychedelics as a treatment in mainstream psychiatry. This article is part of the Special Issue entitled 'Psychedelics: New Doors, Altered Perceptions'.
Turbulence Effects on Modified State Observer-Based Adaptive Control : Black Kite Micro Aerial Vehicle

This paper presents the implementation of a modified state observer-based adaptive dynamic inverse controller for the Black Kite micro aerial vehicle. The pitch and velocity adaptations are computed by the modified state observer in the presence of turbulence to simulate atmospheric conditions. This state observer uses the estimation error to generate the adaptations and, hence, is more robust than model reference adaptive controllers which use modeling or tracking error. In prior work, a traditional proportional-integral-derivative control law was tested in simulation for its adaptive capability in the longitudinal dynamics of the Black Kite micro aerial vehicle. This controller tracks the altitude and velocity commands during normal conditions, but fails in the presence of both parameter uncertainties and system failures. The modified state observer-based adaptations, along with the proportional-integral-derivative controller enables tracking despite these conditions. To simulate flight of the micro aerial vehicle with turbulence, a Dryden turbulence model is included. The turbulence levels used are based on the absolute load factor experienced by the aircraft. The length scale was set to 2.0 meters with a turbulence intensity of 5.0 m/s that generates a moderate turbulence. Simulation results for various flight conditions show that the modified state observer-based adaptations were able to adapt to the uncertainties and the controller tracks the commanded altitude and velocity. The summary of results for all of the simulated test cases and the response plots of various states for typical flight cases are presented.
Performance overhead of KVM on Linux 3.9 on ARM cortex-a15

A number of simple performance measurements on network, CPU and disk speed were done on a dual ARM Cortex-A15 machine running Linux inside a KVM virtual machine that uses virtio disk and networking. Unexpected behaviour was observed in the CPU and memory intensive benchmarks, and in the networking benchmarks. The average overhead of running inside KVM is between zero and 30 percent when the host is lightly loaded (running only the system software and the necessary qemu-system-arm virtualization code), but the relative overhead increases when both host and VM is busy. We conjecture that this is related to the scheduling inside the host Linux.
Relevance-based Word Embedding

Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe.
Single Shot Scene Text Retrieval

Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image database. Our experiments demonstrate that the proposed architecture outperforms previous state-of-the-art while it offers a significant increase in processing speed.
Change-Point Detection in Time-Series Data Based on Subspace Identification

In this paper, we propose series of algorithms for detecting change points in time-series data based on subspace identification, meaning a geometric approach for estimating linear state-space models behind time-series data. Our algorithms are derived from the principle that the subspace spanned by the columns of an observability matrix and the one spanned by the subsequences of time-series data are approximately equivalent. In this paper, we derive a batch-type algorithm applicable to ordinary time-series data, i.e. consisting of only output series, and then introduce the online version of the algorithm and the extension to be available with input-output time-series data. We illustrate the effectiveness of our algorithms with comparative experiments using some artificial and real datasets.
The evolution and future of logistics and supply chain management

A evolução e o futuro da logística e do gerenciamento da cadeia de suprimentos  This article will be divided into three sections: past, present, and future. The past section will trace major events that created business logistics as it is practiced today. In particular, do the events portend the future of business logistics and supply chain management? The present section will attempt to summarize the state of business logistics. How business logistics relates to supply chain management will be addressed. The future section will make some predictions as to the issues that need to be addressed and the events that will likely take place in the near term.
Sparse Kneser graphs are Hamiltonian

For integers <i>k</i>≥1 and <i>n</i>≥2<i>k</i>+1, the <em>Kneser graph</em> <i>K</i>(<i>n</i>,<i>k</i>) is the graph whose vertices are the <i>k</i>-element subsets of {1,…,<i>n</i>} and whose edges connect pairs of subsets that are disjoint. The Kneser graphs of the form <i>K</i>(2<i>k</i>+1,<i>k</i>) are also known as the <em>odd graphs</em>. We settle an old problem due to Meredith, Lloyd, and Biggs from the 1970s, proving that for every <i>k</i>≥3, the odd graph <i>K</i>(2<i>k</i>+1,<i>k</i>) has a Hamilton cycle. This and a known conditional result due to Johnson imply that all Kneser graphs of the form <i>K</i>(2<i>k</i>+2<sup><i>a</i></sup>,<i>k</i>) with <i>k</i>≥3 and <i>a</i>≥0 have a Hamilton cycle. We also prove that <i>K</i>(2<i>k</i>+1,<i>k</i>) has at least 2<sup>2<sup><i>k</i>−6</sup></sup> distinct Hamilton cycles for <i>k</i>≥6. Our proofs are based on a reduction of the Hamiltonicity problem in the odd graph to the problem of finding a spanning tree in a suitably defined hypergraph on Dyck words.
A N INFORMATION-THEORETIC ANALYSIS OF DEEP LATENT-VARIABLE MODELS

We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference. This framework emphasizes the need to consider latent-variable models along two dimensions: the ability to reconstruct inputs (distortion) and the communication cost (rate). We derive the optimal frontier of generative models in the two-dimensional rate-distortion plane, and show how the standard evidence lower bound objective is insufficient to select between points along this frontier. However, by performing targeted optimization to learn generative models with different rates, we are able to learn many models that can achieve similar generative performance but make vastly different trade-offs in terms of the usage of the latent variable. Through experiments on MNIST and Omniglot with a variety of architectures, we show how our framework sheds light on many recent proposed extensions to the variational autoencoder family.
Deep and almost unbearable suffering: consequences of childhood sexual abuse for men's health and well-being.

Previous studies indicate that childhood sexual abuse can have extensive and serious consequences. The aim of this research was to do a qualitative study of the consequences of childhood sexual abuse for Icelandic men's health and well-being. Phenomenology was the methodological approach of the study. Totally 14 interviews were conducted, two per individual, and analysed based on the Vancouver School of Phenomenology. The main results of the study showed that the men describe deep and almost unbearable suffering, affecting their entire life, of which there is no alleviation in sight. The men have lived in repressed silence most of their lives and have come close to taking their own lives. What stopped them from committing suicide was revealing to others what happened to them which set them free in a way. The men experienced fear- or rage-based shock at the time of the trauma and most of them endured the attack by dissociation, disconnecting psyche and body and have difficulties reconnecting. They had extremely difficult childhoods, living with indisposition, bullying, learning difficulties and behavioural problems. Some have, from a young age, numbed themselves with alcohol and elicit drugs. They have suffered psychologically and physically and have had relational and sexual intimacy problems. The consequences of the abuse surfaced either immediately after the shock or many years later and developed into complex post-traumatic stress disorder. Because of perceived societal prejudice, it was hard for the men to seek help. This shows the great need for professionals to be alert to the possible consequences of childhood sexual abuse in their practice to reverse the damaging consequences on their health and well-being. We conclude that living in repressed silence after a trauma, like childhood sexual abuse, can be dangerous for the health, well-being and indeed the very life of the survivor.
Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.
A Robust System for Noisy Image Classification Combining Denoising Autoencoder and Convolutional Neural Network

Image classification, a complex perceptual task with many real life important applications, faces a major challenge in presence of noise. Noise degrades the performance of the classifiers and makes them less suitable in real life scenarios. To solve this issue, several researches have been conducted utilizing denoising autoencoder (DAE) to restore original images from noisy images and then Convolutional Neural Network (CNN) is used for classification. The existing models perform well only when the noise level present in the training set and test set are same or differs only a little. To fit a model in real life applications, it should be independent to level of noise. The aim of this study is to develop a robust image classification system which performs well at regular to massive noise levels. The proposed method first trains a DAE with low-level noise-injected images and a CNN with noiseless native images independently. Then it arranges these two trained models in three different combinational structures: CNN, DAE-CNN, and DAE-DAECNN to classify images corrupted with zero, regular and massive noises, accordingly. Final system outcome is chosen by applying the winner-takes-all combination on individual outcomes of the three structures. Although proposed system consists of three DAEs and three CNNs in different structure layers, the DAEs and CNNs are the copy of same DAE and CNN trained initially which makes it computationally efficient as well. In DAE-DAECNN, two identical DAEs are arranged in a cascaded structure to make the structure well suited for classifying massive noisy data while the DAE is trained with low noisy image data. The proposed method is tested with MNIST handwritten numeral dataset with different noise levels. Experimental results revealed the effectiveness of the proposed method showing better results than individual structures as well as the other related methods. Image denoising; denoising autoencoder; cascaded denoising autoencoder; convolutional neural network
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS) methods are computationally expensive. ConvNet architecture optimality depends on factors such as input resolution and target devices. However, existing approaches are too expensive for case-by-case redesigns. Also, previous work focuses primarily on reducing FLOPs, but FLOP count does not always reflect actual latency. To address these, we propose a differentiable neural architecture search (DNAS) framework that uses gradient-based methods to optimize ConvNet architectures, avoiding enumerating and training individual architectures separately as in previous methods. FBNets, a family of models discovered by DNAS surpass state-of-the-art models both designed manually and generated automatically. FBNet-B achieves 74.1% top-1 accuracy on ImageNet with 295M FLOPs and 23.1 ms latency on a Samsung S8 phone, 2.4x smaller and 1.5x faster than MobileNetV21.3[16] with similar accuracy. Despite higher accuracy and lower latency than MnasNet[19], we estimate FBNet-B’s search cost is 420x smaller than MnasNet’s, at only 216 GPU-hours. Searched for different resolutions and channel sizes, FBNets achieve 1.5% to 6.4% higher accuracy than MobileNetV2. The smallest FBNet achieves 50.2% accuracy and 2.9 ms latency (345 frames per second) on a Samsung S8. Over a Samsung-optimized FBNet, the iPhone-Xoptimized model achieves a 1.4x speedup on an iPhone X.
EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning

The Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detection avoidance techniques in order to hide their malicious activities from analysis tools. These include a wide range of anti-emulator techniques, where the malware programs attempt to hide their malicious activities by detecting the emulator. For this reason, countermeasures against anti-emulation are becoming increasingly important in Android malware detection. Analysis and detection based on real devices can alleviate the problems of anti-emulation as well as improve the effectiveness of dynamic analysis. Hence, in this paper we present an investigation of machine learning based malware detection using dynamic analysis on real devices. A tool is implemented to automatically extract dynamic features from Android phones and through several experiments, a comparative analysis of emulator based vs. device based detection by means of several machine learning algorithms is undertaken. Our study shows that several features could be extracted more effectively from the on-device dynamic analysis compared to emulators. It was also found that approximately 24% more apps were successfully analysed on the phone. Furthermore, all of the studied machine learning based detection performed better when applied to features extracted from the on-device dynamic analysis.