nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Play Computer Games with Deep Learning and Reinforcement Learning Final Report

With the integration of deep learning into the traditional field of reinforcement learning in the recent decades, the spectrum of applications that artificial intelligence caters is currently very broad. As using AI to play games is a traditional application of reinforcement learning, the project’s objective is to implement a deep reinforcement learning agent that can defeat a video game. Since it is often difficult to determine which algorithms are appropriate given the wide selection of state-of-the-art techniques in the discipline, proper comparisons and investigations of the algorithms are a prerequisite to implementing such an agent. As a result, this paper serves as a platform for exploring the possibility and effectiveness of using conventional state-of-the-art reinforcement learning methods for playing Pacman maps. In particular, this paper demonstrates that Combined DQN, a variation of Rainbow DQN, is able to attain high performance in small maps such as 506Pacman, smallGrid and mediumGrid. It was also demonstrated that the trained agents could also play Pacman maps similar to training with limited performance. Nevertheless, the algorithm suffers due to its data inefficiency and lack of human-like features, which may be remedied in the future by introducing more human-like features into the algortihm, such as intrinsic motivation and imagination.
Feasibility of Using a Smartwatch to Intensively Monitor Patients With Chronic Obstructive Pulmonary Disease: Prospective Cohort Study

Acute exacerbations of chronic obstructive pulmonary disease (COPD) are associated with accelerated decline in lung function, diminished quality of life, and higher mortality. Proactively monitoring patients for early signs of an exacerbation and treating them early could prevent these outcomes. The emergence of affordable wearable technology allows for nearly continuous monitoring of heart rate and physical activity as well as recording of audio which can detect features such as coughing. These signals may be able to be used with predictive analytics to detect early exacerbations. Prior to full development, however, it is important to determine the feasibility of using wearable devices such as smartwatches to intensively monitor patients with COPD.



We conducted a feasibility study to determine if patients with COPD would wear and maintain a smartwatch consistently and whether they would reliably collect and transmit sensor data.



Patients with COPD were recruited from 3 hospitals and were provided with a smartwatch that recorded audio, heart rate, and accelerations. They were asked to wear and charge it daily for 90 days. They were also asked to complete a daily symptom diary. At the end of the study period, participants were asked what would motivate them to regularly use a wearable for monitoring of their COPD.



Of 28 patients enrolled, 16 participants completed the full 90 days. The average age of participants was 68.5 years, and 36% (10/28) were women. Survey, heart rate, and activity data were available for an average of 64.5, 65.1, and 60.2 days respectively. Technical issues caused heart rate and activity data to be unavailable for approximately 13 and 17 days, respectively. Feedback provided by participants indicated that they wanted to actively engage with the smartwatch and receive feedback about their activity, heart rate, and how to better manage their COPD.



Some patients with COPD will wear and maintain smartwatches that passively monitor audio, heart rate, and physical activity, and wearables were able to reliably capture near-continuous patient data. Further work is necessary to increase acceptability and improve the patient experience.
Effectiveness of mobile phone short message service on diabetes mellitus management; the SMS-DM study.

To test the feasibility of short message service (SMS) usage between the clinic visits and to evaluate its effect on glycemic control in uncontrolled type 2 Diabetes Mellitus (DM) subjects.



34 cases with type 2 Diabetes were followed after fulfilling the inclusion criteria. The interventional group (n=12) had the same conventional approach of the control group but had two mobile numbers (physician and diabetic educator) provided for the SMS support until their next visit in 3 months. Both groups of age, BMI and the pre-study A1c were comparable.



Both groups had a significant reduction in their A1c compared to their baseline visit. However, the interventional group had significantly greater reduction in A1c (p=0.001), 1.16% lower than controls. The service was highly satisfactory to the group.



The results indicate effectiveness in lowering A1c and acceptance by the patients. Further research and large-scale studies are needed.
Overweight/obesity among adults in North-Western Ethiopia: a community-based cross sectional study

Nowadays adulthood overweight/obesity is an emerging public health problem in developing countries. There is no information on magnitude and contributing factors of adulthood overweight/obesity in Ethiopia, particularly in North West region of the country. Thus, the aim of this study was to assess magnitude and contributing factors of adulthood overweight/obesity in North West region of Ethiopia.



A community-based cross-sectional study was conducted from September1, 2015 to November 30, 2015 in northwest region of Ethiopia particularly Bahir-Dar city and its rural districts. A total of 1484 adult participants were recruited in the study. Stratified multistage followed by systematic random sampling technique was employed to select participants. Overweight/obesity was determined using center for diseases control cutoff points. A multivariable binary logistic regression model was fitted to identify factors associated with overweight/obesity. Adjusted Odds Ratio (AOR) with the corresponding 95% Confidence Interval (CI) was calculated to show the strength of association.



A total of 1405 adults were participated in the study with a response rate of 94.7%. This study indicated that 11.3 (95% CI: 9.6, 13.1) adults were overweight and obese of which about 9.3% and 2% of adults were overweight and obese, respectively. The higher odds of being Overweight/Obese were noted among urban residents, females and older age. However performing mild to moderate physical activity [AOR = 0.608, 95% CI: 0.37, 0.99] and consumption of fruit and vegetable [AOR = 0.51, 95% CI: 0.34, 0.77] were found to be protective against overweight/obesity.



Though, it was a problem of developed countries adulthood overweight /obesity is emerged as a public health problem among adults in Ethiopia particularly in the study area, northwest region. Hence, preventive interventions focusing on urban residents, females through encouraging Physical activity, fruit and vegetable consumption is essential to prevent emergence of adulthood overweight/obesity.
Machine learning approach for text and document mining

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and industry developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most relevant documents.
The Gut Microbiota of Marine Fish

The body of work relating to the gut microbiota of fish is dwarfed by that on humans and mammals. However, it is a field that has had historical interest and has grown significantly along with the expansion of the aquaculture industry and developments in microbiome research. Research is now moving quickly in this field. Much recent focus has been on nutritional manipulation and modification of the gut microbiota to meet the needs of fish farming, while trying to maintain host health and welfare. However, the diversity amongst fish means that baseline data from wild fish and a clear understanding of the role that specific gut microbiota play is still lacking. We review here the factors shaping marine fish gut microbiota and highlight gaps in the research.
An extended model of natural logic

We propose a model of natural language inference which identifies valid inferences by their lexical and syntactic features, without full semantic interpretation. We extend past work in natural logic, which has focused on semantic containment and monotonicity, by incorporating both semantic exclusion and implicativity. Our model decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical semantic relation for each edit; propagates these relations upward through a semantic composition tree according to properties of intermediate nodes; and joins the resulting semantic relations across the edit sequence. A computational implementation of the model achieves 70% accuracy and 89% precision on the FraCaS test suite. Moreover, including this model as a component in an existing system yields significant performance gains on the Recognizing Textual Entailment challenge.
FastText.zip: Compressing text classification models

We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory. After considering different solutions inspired by the hashing literature, we propose a method built upon product quantization to store word embeddings. While the original technique leads to a loss in accuracy, we adapt this method to circumvent quantization artefacts. Our experiments carried out on several benchmarks show that our approach typically requires two orders of magnitude less memory than fastText while being only slightly inferior with respect to accuracy. As a result, it outperforms the state of the art by a good margin in terms of the compromise between memory usage and accuracy.
Quantifying the Financial Value of Cloud Investments: A Systematic Literature Review

The importance of demonstrating the value achieved from IT investments is long established in the Computer Science (CS) and Information Systems (IS) literature. However, emerging technologies such as the ever-changing complex area of cloud computing present new challenges and opportunities for demonstrating how IT investments lead to business value. This paper conducts a multidisciplinary systematic literature review drawing from CS, IS, and Business disciplines to understand the current evidence on the quantification of financial value from cloud computing investments. The study identified 53 articles, which were coded in an analytical framework across six themes (measurement type, costs, benefits, adoption type, actor and service model). Future research directions were presented for each theme. The review highlights the need for multi-disciplinary research which both explores and further develops the conceptualization of value in cloud computing research, and research which investigates how IT value manifests itself across the chain of service provision and in inter-organizational scenarios.
Attention-guided Unified Network for Panoptic Segmentation

This paper studies panoptic segmentation, a recently proposed task which segments foreground (FG) objects at the instance level as well as background (BG) contents at the semantic level. Existing methods mostly dealt with these two problems separately, but in this paper, we reveal the underlying relationship between them, in particular, FG objects provide complementary cues to assist BG understanding. Our approach, named the Attention-guided Unified Network (AUNet), is an unified framework with two branches for FG and BG segmentation simultaneously. Two sources of attentions are added to the BG branch, namely, RPN and FG segmentation mask to provide object-level and pixel-level attentions, respectively. Our approach is generalized to different backbones with consistent accuracy gain in both FG and BG segmentation, and also sets new stateof-the-arts in the MS-COCO (46.5% PQ) benchmarks.
Awe the Audience: How the Narrative Trajectories Affect Audience Perception in Public Speaking

Telling a great story often involves a deliberate alteration of emotions. In this paper, we objectively measure and analyze the narrative trajectories of stories in public speaking and their impact on subjective ratings. We conduct the analysis using the transcripts of over 2000 TED talks and estimate potential audience response using over 5 million spontaneous annotations from the viewers. We use IBM Watson Tone Analyzer to extract sentence-wise emotion, language, and social scores. Our study indicates that it is possible to predict (with AUC as high as 0.88) the subjective ratings of the audience by analyzing the narrative trajectories. Additionally, we find that some trajectories (for example, a flat trajectory of joy) correlate well with some specific ratings (e.g. "Longwinded') assigned by the viewers. Such an association could be useful in forecasting audience responses using objective analysis.
Self-Compassion : Conceptualizations , Correlates , & Interventions

Within American psychology, there has been a recent surge of interest in self-compassion, a construct from Buddhist thought. Self-compassion entails: (a) being kind and understanding toward oneself in times of pain or failure, (b) perceiving one’s own suffering as part of a larger human experience, and (c) holding painful feelings and thoughts in mindful awareness. In this article we review findings from personality, social, and clinical psychology related to self-compassion. First, we define self-compassion and distinguish it from other self-constructs such as self-esteem, self-pity, and self-criticism. Next, we review empirical work on the correlates of self-compassion, demonstrating that self-compassion has consistently been found to be related to well-being. These findings support the call for interventions that can raise self-compassion. We then review the theory and empirical support behind current interventions that could enhance self-compassion including compassionate mind training (CMT), imagery work, the gestalt two-chair technique, mindfulness based stress reduction (MBSR), dialectical behavior therapy (DBT), and acceptance and commitment therapy (ACT). Directions for future research are also discussed.
Bilingual and Monolingual Brains Compared: A Functional Magnetic Resonance Imaging Investigation of Syntactic Processing and a Possible Neural Signature of Bilingualism

Does the brain of a bilingual process language differently from that of a monolingual? We compared how bilinguals and monolinguals recruit classic language brain areas in response to a language task and asked whether there is a neural signature of bilingualism. Highly proficient and early-exposed adult Spanish-English bilinguals and English monolinguals participated. During functional magnetic resonance imaging (fMRI), participants completed a syntactic sentence judgment task [Caplan, D., Alpert, N., & Waters, G. Effects of syntactic structure and propositional number on patterns of regional cerebral blood flow. Journal of Cognitive Neuroscience, 10, 541552, 1998]. The sentences exploited differences between Spanish and English linguistic properties, allowing us to explore similarities and differences in behavioral and neural responses between bilinguals and monolinguals, and between a bilingual's two languages. If bilinguals' neural processing differs across their two languages, then differential behavioral and neural patterns should be observed in Spanish and English. Results show that behaviorally, in English, bilinguals and monolinguals had the same speed and accuracy, yet, as predicted from the Spanish-English structural differences, bilinguals had a different pattern of performance in Spanish. fMRI analyses revealed that both monolinguals (in one language) and bilinguals (in each language) showed predicted increases in activation in classic language areas (e.g., left inferior frontal cortex, LIFC), with any neural differences between the bilingual's two languages being principled and predictable based on the morphosyntactic differences between Spanish and English. However, an important difference was that bilinguals had a significantly greater increase in the blood oxygenation level-dependent signal in the LIFC (BA 45) when processing English than the English monolinguals. The results provide insight into the decades-old question about the degree of separation of bilinguals' dual-language representation. The differential activation for bilinguals and monolinguals opens the question as to whether there may possibly be a neural signature of bilingualism. Differential activation may further provide a fascinating window into the language processing potential not recruited in monolingual brains and reveal the biological extent of the neural architecture underlying all human language.
Civil Engineering in Disaster Management

For a country prone to disasters such as India, disaster management is a crucial subject. For successful management, capacity building along with understanding the topography of the land and proper organization and planning of cities is vital. Nonstructural elements though will help in the preparedness, the importance of structural elements cannot be overlooked. The structural aspect of disaster management is where the role of Civil Engineers come in. This paper deals with the interrelationship between disaster management and civil engineering. This paper includes a brief introduction about disasters in the country and the role played by specific civil engineers. The Gujarat Earthquake of 2001 was studied and how modern technologies such as base isolation methods and with more stress on forensic and earthquake engineering could have reduced the damage. Structural flood control measures have been mentioned along with the science behind them. Occurrences of landslides can also be reduced by implication of studies conducted by geotechnical and hydraulic engineers. Latest developments in technologies which support civil engineering in disaster management, in particular to analyze the health of structure, which is very crucial in determining the way it will behave in times if disasters, have also been looked into. The paper also includes a case study on the flooding of the Brahmaputra, why even after almost half a century of flood control measures, the river is yet to be controlled. Reasons for its failure have been cited, along with the unfortunate facts of the land and the situation. Also, a possible solution has been proposed. Since dams cannot be built on the river due to various reasons, and most of the conventional means are powerless against the mighty river, a possibility could be channelization of the river, which has never been taken up by the Government.
Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

Very high resolution (VHR) remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.
I Couldn't Agree More: The Role of Conversational Structure in Agreement and Disagreement Detection in Online Discussions

Determining when conversational participants agree or disagree is instrumental for broader conversational analysis; it is necessary, for example, in deciding when a group has reached consensus. In this paper, we describe three main contributions. We show how different aspects of conversational structure can be used to detect agreement and disagreement in discussion forums. In particular, we exploit information about meta-thread structure and accommodation between participants. Second, we demonstrate the impact of the features using 3-way classification, including sentences expressing disagreement, agreement or neither. Finally, we show how to use a naturally occurring data set with labels derived from the sides that participants choose in debates on createdebate.com. The resulting new agreement corpus, Agreement by Create Debaters (ABCD) is 25 times larger than any prior corpus. We demonstrate that using this data enables us to outperform the same system trained on prior existing in-domain smaller annotated datasets.
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks

Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly. We present a gradient normalization (GradNorm) algorithm that automatically balances training in deep multitask models by dynamically tuning gradient magnitudes. We show that for various network architectures, for both regression and classification tasks, and on both synthetic and real datasets, GradNorm improves accuracy and reduces overfitting across multiple tasks when compared to single-task networks, static baselines, and other adaptive multitask loss balancing techniques. GradNorm also matches or surpasses the performance of exhaustive grid search methods, despite only involving a single asymmetry hyperparameter α. Thus, what was once a tedious search process that incurred exponentially more compute for each task added can now be accomplished within a few training runs, irrespective of the number of tasks. Ultimately, we will demonstrate that gradient manipulation affords us great control over the training dynamics of multitask networks and may be one of the keys to unlocking the potential of multitask learning.
Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators

The authors demonstrate the key role dataflows play in the optimization of energy efficiency for deep neural network (DNN) accelerators. By introducing a systematic approach to analyze the problem and a new dataflow, called Row-Stationary, which is up to 2.5 times more energy efficient than existing dataflows in processing a state-of-the-art DNN, this work provides guidelines for future DNN accelerator designs.