nils-herrmann/clean_scirepeval huggingface dataset:
Using Smartphones to Detect Car Accidents and Provide Situational Awareness to Emergency Responders

Accident detection systems help reduce fatalities stemming from car accidents by decreasing the response time of emergency responders. Smartphones and their onboard sensors (such as GPS receivers and accelerometers) are promising platforms for constructing such systems. This paper provides three contributions to the study of using smartphone-based accident detection systems. First, we describe solutions to key issues associated with detecting traffic accidents, such as preventing false positives by utilizing mobile context information and polling onboard sensors to detect large accelerations. Second, we present the architecture of our prototype smartphone-based accident detection system and empirically analyze its ability to resist false positives as well as its capabilities for accident reconstruction. Third, we discuss how smartphone-based accident detection can reduce overall traffic congestion and increase the preparedness of emergency responders.
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice

Many currently deployed Reinforcement Learning agents work in an environment shared with humans, be them co-workers, users or clients. It is desirable that these agents adjust to people’s preferences, learn faster thanks to their help, and act safely around them. We argue that most current approaches that learn from human feedback are unsafe: rewarding or punishing the agent a-posteriori cannot immediately prevent it from wrong-doing. In this paper, we extend Policy Gradient to make it robust to external directives, that would otherwise break the fundamentally on-policy nature of Policy Gradient. Our technique, Directed Policy Gradient (DPG), allows a teacher or backup policy to override the agent before it acts undesirably, while allowing the agent to leverage human advice or directives to learn faster. Our experiments demonstrate that DPG makes the agent learn much faster than reward-based approaches, while requiring an order of magnitude less advice. .
Implantable microchip: the futuristic controlled drug delivery system.

There is no doubt that controlled and pulsatile drug delivery system is an important challenge in medicine over the conventional drug delivery system in case of therapeutic efficacy. However, the conventional drug delivery systems often offer a limited by their inability to drug delivery which consists of systemic toxicity, narrow therapeutic window, complex dosing schedule for long term treatment etc. Therefore, there has been a search for the drug delivery system that exhibit broad enhancing activity for more drugs with less complication. More recently, some elegant study has noted that, a new type of micro-electrochemical system or MEMS-based drug delivery systems called microchip has been improved to overcome the problems related to conventional drug delivery. Moreover, micro-fabrication technology has enabled to develop the implantable controlled released microchip devices with improved drug administration and patient compliance. In this article, we have presented an overview of the investigations on the feasibility and application of microchip as an advanced drug delivery system. Commercial manufacturing materials and methods, related other research works and current advancement of the microchips for controlled drug delivery have also been summarized.
Comparing Tweet Classifications by Authors' Hashtags, Machine Learning, and Human Annotators

Over the last years, many papers have been published about how to use machine learning for classifying postings on microblogging platforms like Twitter, e.g., in order to assist users to reach tweets that interest them. Typically, the automatic classification results are then evaluated against a gold standard classification which consists of either (i) the hashtags of the tweets' authors, or (ii) manual annotations of independent human annotators. In this paper, we show that there are fundamental differences between these two kinds of gold standard classifications, i.e., human annotators are more likely to classify tweets like other human annotators than like the tweets' authors. Furthermore, we discuss how these differences may influence the evaluation of automatic classifications, like they may be achieved by Latent Dirichlet Allocation (LDA). We argue that researchers who conduct machine learning experiments for tweet classification should pay particular attention to the kind of gold standard they use. One may even argue that hashtags are not appropriate as a gold standard for tweet classification.
Abnormal event detection in crowded scenes using sparse representation

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given a collection of normal training examples, e.g., an image sequence or a collection of local spatio-temporal patches, we propose the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. To condense the over-completed normal bases into a compact dictionary, a novel dictionary selection method with group sparsity constraint is designed, which can be solved by standard convex optimization. Observing that the group sparsity also implies a low rank structure, we reformulate the problem using matrix decomposition, which can handle large scale training samples by reducing the memory requirement at each iteration from O(k2) to O(k) where k is the number of samples. We use the column wise coordinate descent to solve the matrix decomposition represented formulation, which empirically leads to a similar solution to the group sparsity formulation. By designing different types of spatio-temporal basis, our method can detect both local and global abnormal events. Meanwhile, as it does not rely on object detection and tracking, it can be applied to crowded video scenes. By updating the dictionary incrementally, our 1This work was supported in part by the Nanyang Assistant Professorship (M4080134), JSPSNTU joint project (M4080882), Natural Science Foundation of China (61105013), and National Science and Technology Pillar Program (2012BAI14B03). Part of this work was done when Yang Cong was a research fellow at NTU.  to Pattern Recognition January 30, 2013 method can be easily extended to online event detection. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our method.
Preparation of monodisperse calcium alginate microcapsules via internal gelation in microfluidic-generated double emulsions.

Monodisperse hollow and core-shell calcium alginate microcapsules are successfully prepared via internal gelation in microfluidic-generated double emulsions. Microfluidic emulsification is introduced to generate monodisperse oil-in-water-in-oil (O/W/O) double emulsion templates, which contain Na-alginate, CaCO3 nanoparticles, and photoacid generator in the middle aqueous phase, for synthesizing Ca-alginate microcapsules. The internal gelation of the aqueous middle layer of O/W/O double emulsions is induced by crosslinking alginate polymers with Ca(2+) ions that are released from CaCO3 nanoparticles upon UV exposure of the photoacid generator. The as-prepared hollow and core-shell calcium alginate microcapsules are highly monodisperse and spherical in water. Model proteins Bovine serum albumin (BSA) molecules can be encapsulated into the Ca-alginate microcapsules after the capsule preparation, which demonstrates an alternative route for loading active drugs or chemicals into carriers to avoid the inactivation during the carrier preparation. The proposed technique in this study provides an efficient approach for synthesis of monodisperse hollow or core-shell calcium alginate microcapsules with large cavity or encapsulated lipophilic drugs, chemicals, and nutrients.
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks

Designing shared neural architecture plays an important role in multi-task learning. The challenge is that finding an optimal sharing scheme relies heavily on the expert knowledge and is not scalable to a large number of diverse tasks. Inspired by the promising work of neural architecture search (NAS), we apply reinforcement learning to automatically find possible shared architecture for multi-task learning. Specifically, we use a controller to select from a set of shareable modules and assemble a task-specific architecture, and repeat the same procedure for other tasks. The controller is trained with reinforcement learning to maximize the expected accuracies for all tasks. We conduct extensive experiments on two types of tasks, text classification and sequence labeling, which demonstrate the benefits of our approach.
Searching trajectories by locations: an efficiency study

Trajectory search has long been an attractive and challenging topic which blooms various interesting applications in spatial-temporal databases. In this work, we study a new problem of searching trajectories by locations, in which context the query is only a small set of locations with or without an order specified, while the target is to find the k Best-Connected Trajectories (k-BCT) from a database such that the k-BCT best connect the designated locations geographically. Different from the conventional trajectory search that looks for similar trajectories w.r.t. shape or other criteria by using a sample query trajectory, we focus on the goodness of connection provided by a trajectory to the specified query locations. This new query can benefit users in many novel applications such as trip planning.
 In our work, we firstly define a new similarity function for measuring how well a trajectory connects the query locations, with both spatial distance and order constraint being considered. Upon the observation that the number of query locations is normally small (e.g. 10 or less) since it is impractical for a user to input too many locations, we analyze the feasibility of using a general-purpose spatial index to achieve efficient k-BCT search, based on a simple Incremental k-NN based Algorithm (IKNN). The IKNN effectively prunes and refines trajectories by using the devised lower bound and upper bound of similarity. Our contributions mainly lie in adapting the best-first and depth-first k-NN algorithms to the basic IKNN properly, and more importantly ensuring the efficiency in both search effort and memory usage. An in-depth study on the adaption and its efficiency is provided. Further optimization is also presented to accelerate the IKNN algorithm. Finally, we verify the efficiency of the algorithm by extensive experiments.
An Adaptive GTS Allocation Scheme for IEEE 802.15.4

IEEE 802.15.4 is a new standard uniquely designed for low-rate wireless personal area networks. It targets ultralow complexity, cost, and power for low-rate wireless connectivity among inexpensive, portable, and moving devices. IEEE 802.15.4 provides a guaranteed time slot (GTS) mechanism to allocate a specific duration within a superframe for time-critical transmissions. This paper proposes an adaptive GTS allocation (AGA) scheme for IEEE 802.15.4, which considers low latency and fairness. The scheme is designed based on the existing IEEE 802.15.4 medium access control protocol, and IEEE 802.15.4 devices can receive this AGA service without any modification. A simulation model and an analytical model are developed to investigate the performance of our AGA scheme. The numerical results show that the proposed scheme significantly outperforms the existing IEEE 802.15.4 implementation.
Burnout and Depression: Two Entities or One?

The purpose of this study was to examine the overlap in burnout and depression.



The sample comprised 1,386 schoolteachers (mean [M]age = 43; Myears taught = 15; 77% women) from 18 different U.S. states. We assessed burnout, using the Shirom-Melamed Burnout Measure, and depression, using the depression module of the Patient Health Questionnaire.



Treated dimensionally, burnout and depressive symptoms were strongly correlated (.77; disattenuated correlation, .84). Burnout and depressive symptoms were similarly correlated with each of 3 stress-related factors, stressful life events, job adversity, and workplace support. In categorical analyses, 86% of the teachers identified as burned out met criteria for a provisional diagnosis of depression. Exploratory analyses revealed a link between burnout and anxiety.



This study provides evidence that past research has underestimated burnout-depression overlap. The state of burnout is likely to be a form of depression. Given the magnitude of burnout-depression overlap, treatments for depression may help workers identified as "burned out."
Angelman syndrome in an inbred family

Angelman syndrome (AS) is characterized by severe mental retardation, absent speech, puppet-like movements, inappropriate laughter, epilepsy, and abnormal electroencephalogram. The majority of AS patients (≈ 65%) have a maternal deficiency within chromosomal region 15q11–q13, caused by maternal deletion or paternal uniparental disomy (UPD). Approximately 35% of AS patients exhibit neither detectable deletion nor UPD, but a subset of these patients have abnormal methylation at several loci in the 15q11–q13 interval. We describe here three patients with Angelman syndrome belonging to an extended inbred family. High resolution chromosome analysis combined with DNA analysis using 14 marker loci from the 15q11-q13 region failed to detect a deletion in any of the three patients. Paternal UPD of chromosome 15 was detected in one case, while the other two patients have abnormal methylation atD15S9, D15S63, andSNRPN. Although the three patients are distantly related, the chromosome 15q11-q13 haplotypes are different, suggesting that independent mutations gave rise to AS in this family.
Advancing government-wide Enterprise Architecture - A meta-model approach

Enterprise Architecture (EA) is regarded as one of the means to ensure information technology interoperability, standard and reuse within an organizational boundary. In the public sector, however, there rises another kind of issues on how to integrate agency EA practices into the whole of government view. The conception of Government-wide EA (GEA) is the results of this consideration, and how to implement GEA has become a major challenge among IT policymakers form many countries. The present article deals with the applied case of modeling EA in the public sector. More specifically the article focuses on the methods and applications of modeling information integration architecture at government-wide level. A meta-modeling approach is used to design GEA database and to communicate with agencies regarding how they should manage agency-level EA information. The article consists of brief history of the EA program of the Korean Government, introduction of the KGEA Meta-model and its evolutionary history, and current status with some major outcomes. Methodological and managerial implications are also discussed.
A cyber-physical systems approach to energy management in data centers

This paper presents a new control strategy for data centers that aims to optimize the trade-off between maximizing the payoff from the provided quality of computational services and minimizing energy costs for computation and cooling. The data center is modeled as two interacting dynamic networks: a computational (cyber) network representing the distribution and flow of computational tasks, and a thermal (physical) network characterizing the distribution and flow of thermal energy. To make the problem tractable, the control architecture is decomposed hierarchically according to time-scales in the thermal and computational network dynamics, and spatially, reflecting weak coupling between zones in the data center. Simulation results demonstrate the effectiveness of the proposed coordinated control strategy relative to traditional approaches in which the cyber and physical resources are controlled independently.
Cardiac Signatures of Personality

There are well-established relations between personality and the heart, as evidenced by associations between negative emotions on the one hand, and coronary heart disease or chronic heart failure on the other. However, there are substantial gaps in our knowledge about relations between the heart and personality in healthy individuals. Here, we investigated whether amplitude patterns of the electrocardiogram (ECG) correlate with neurotisicm, extraversion, agreeableness, warmth, positive emotion, and tender-mindedness as measured with the Neuroticism-Extraversion-Openness (NEO) personality inventory. Specifically, we investigated (a) whether a cardiac amplitude measure that was previously reported to be related to flattened affectivity (referred to as Eκ values) would explain variance of NEO scores, and (b) whether correlations can be found between NEO scores and amplitudes of the ECG.



NEO scores and rest ECGs were obtained from 425 healthy individuals. Neuroticism and positive emotion significantly differed between individuals with high and low Eκ values. In addition, stepwise cross-validated regressions indicated correlations between ECG amplitudes and (a) agreeableness, as well as (b) positive emotion.



These results are the first to demonstrate that ECG amplitude patterns provide information about the personality of an individual as measured with NEO personality scales and facets. These findings open new perspectives for a more efficient personality assessment using cardiac measures, as well as for more efficient risk-stratification and pre-clinical diagnosis of individuals at risk for cardiac, affective and psychosomatic disorders.
Cutaneous nonepitheliotropic B-cell lymphoma in a Golden retriever Cutaan niet-epitheliotroop B-cellymfoma bij een Golden retriever

The vast majority of cutaneous canine nonepitheliotropic lymphomas are of T cell origin. Nonepithelial Bcell lymphomas are extremely rare. The present case report describes a 10-year-old male Golden retriever that was presented with slowly progressive nodular skin lesions on the trunk and limbs. Histopathology of skin biopsies revealed small periadnexal dermal nodules composed of rather pleomorphic round cells with round or contorted nuclei. The diagnosis of nonepitheliotropic cutaneous B-cell lymphoma was based on histopathological morphology and case follow-up, and was supported immunohistochemically by CD79a positivity.
Deep Image Clustering Using Convolutional Autoencoder Embedding with Inception-Like Block

Image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.
The logical primitives of thought: Empirical foundations for compositional cognitive models.

The notion of a compositional language of thought (LOT) has been central in computational accounts of cognition from earliest attempts (Boole, 1854; Fodor, 1975) to the present day (Feldman, 2000; Penn, Holyoak, & Povinelli, 2008; Fodor, 2008; Kemp, 2012; Goodman, Tenenbaum, & Gerstenberg, 2015). Recent modeling work shows how statistical inferences over compositionally structured hypothesis spaces might explain learning and development across a variety of domains. However, the primitive components of such representations are typically assumed a priori by modelers and theoreticians rather than determined empirically. We show how different sets of LOT primitives, embedded in a psychologically realistic approximate Bayesian inference framework, systematically predict distinct learning curves in rule-based concept learning experiments. We use this feature of LOT models to design a set of large-scale concept learning experiments that can determine the most likely primitives for psychological concepts involving Boolean connectives and quantification. Subjects' inferences are most consistent with a rich (nonminimal) set of Boolean operations, including first-order, but not second-order, quantification. Our results more generally show how specific LOT theories can be distinguished empirically.
Combined metformin-associated lactic acidosis and euglycemic ketoacidosis

In renal failure metformin can lead to lactic acidosis. Additional inhibition of hepatic gluconeogenesis by accumulation of the drug may aggravate fasting-induced ketoacidosis. We report the occurrence of metformin-associated lactic acidosis (MALA) with concurrent euglycemic ketoacidosis (MALKA) in three patients with renal failure. Patient 1: a 78-year-old woman (pH = 6.89, lactic acid 22 mmol/l, serum ketoacids 7.4 mmol/l and blood glucose 63 mg/dl) on metformin and insulin treatment. Patient 2: a 79-year-old woman on metformin treatment (pH = 6.80, lactic acid 14.7 mmol/l, serum ketoacids 6.4 mmol/l and blood glucose 76 mg/dl). Patient 3: a 71-year-old man on metformin, canagliflozin and liraglutide treatment (pH = 7.21, lactic acid 5.9 mmol/l, serum ketoacids 16 mmol/l and blood glucose 150 mg/dl). In all patients, ketoacidosis receded on glucose infusion and renal replacement therapy. This case series highlights the parallel occurrence of MALA and euglycemic ketoacidosis, the latter exceeding ketosis due to starvation, suggesting a metformin-triggered inhibition of gluconeogenesis. Affected patients benefit from glucose infusion counteracting suppressed hepatic gluconeogenesis.