pwc-archive/papers-with-abstracts huggingface dataset:
MEG abnormalities and mechanisms of surgical failure in neocortical epilepsy
Authors: Thomas W. Owen, Gabrielle M. Schroeder, Vytene Janiukstyte, Gerard R. Hall, Andrew McEvoy, Anna Miserocchi, Jane de Tisi, John S. Duncan, Fergus Rugg-Gunn, Yujiang Wang, Peter N. Taylor
Date: 2022-07-13 00:00:00

Neocortical epilepsy surgery fails to achieve post-operative seizure freedom in 30-40% of cases. It is not fully understood why surgery in some patients is unsuccessful. Comparing interictal MEG bandpower from patients to normative maps, which describe healthy spatial and population variability, we identify patient specific abnormalities relating to surgical failure. We propose three mechanisms contributing to poor surgical outcome; 1) failure to resect abnormalities, 2) failing to remove all epileptogenic abnormalities, and 3) insufficiently impacting the overall cortical abnormality. We develop markers of these mechanisms, validating them against patient outcomes. Resting-state MEG data were acquired for 70 healthy controls and 32 patients with refractory neocortical epilepsy. Relative bandpower maps were computed using source localised recordings from healthy controls. Patient and region-specific bandpower abnormalities were estimated as the maximum absolute z-score, using healthy data as a baseline. Resected regions were identified from post-operative MRI. We hypothesised our mechanism markers would discriminate patient's post-surgery seizure outcomes. Mechanisms of surgical failure discriminate surgical outcome groups (Abnormalities not targeted: AUC=0.80, Partial resection of the epileptogenic zone: AUC=0.68, Insufficient cortical abnormality impact: AUC=0.64). Leveraging all markers together found that 95% of those who were not seizure free had markers of surgical failure in at least one of the three proposed mechanisms. In contrast, of those patients markers for any mechanism, 80% were seizure-free. Abnormality mapping across the brain is important for a wide range of neurological conditions. Here we demonstrated that interictal MEG bandpower mapping has merit for localising pathology and improving our mechanistic understanding of epilepsy.
Analysis of social interactions in group-housed animals using dyadic linear models
Authors: Junjie Han, Janice Siegford, Gustavo de los Campos, Robert J. Tempelman, Cedric Gondro, Juan P. Steibel
Date: 2022-07-14 00:00:00
Tasks: parameter estimation

Understanding factors affecting social interactions among animals is important for applied animal behavior research. Thus, there is a need to elicit statistical models to analyze data collected from pairwise behavioral interactions. In this study, we propose treating social interaction data as dyadic observations and propose a statistical model for their analysis. We performed posterior predictive checks of the model through different validation strategies: stratified 5-fold random cross-validation, block-by-social-group cross-validation, and block-by-focal-animals validation. The proposed model was applied to a pig behavior dataset collected from 797 growing pigs freshly remixed into 59 social groups that resulted in 10,032 records of directional dyadic interactions. The response variable was the duration in seconds that each animal spent delivering attacks on another group mate. Generalized linear mixed models were fitted. Fixed effects included sex, individual weight, prior nursery mate experience, and prior littermate experience of the two pigs in the dyad. Random effects included aggression giver, aggression receiver, dyad, and social group. A Bayesian framework was utilized for parameter estimation and posterior predictive model checking. Prior nursery mate experience was the only significant fixed effect. In addition, a weak but significant correlation between the random giver effect and the random receiver effect was obtained when analyzing the attacking duration. The predictive performance of the model varied depending on the validation strategy, with substantially lower performance from the block-by-social-group strategy than other validation strategies. Collectively, this paper demonstrates a statistical model to analyze interactive animal behaviors, particularly dyadic interactions.
Effect of Demonetisation of on Indian High Denomination Currencies on Indian Stock Market and its Relationship with Foreign Exchange Rate
Authors: N. Suresh, N. R. Bharathi
Date: 2022-07-14 00:00:00

This study examines the impact of the foreign exchange rate, i.e., US Dollar to Indian Rupee (USD/INR) on the Indian Stock Market Index (Nifty 50) during the demonetization of high denomination Indian currencies. A daily rate of return of Foreign exchange rate (USD/INR) and the Indian Stock Market Index (Nifty 50) were considered for the study. The Dummy variable was used to measure the effect of demonetization during Nov/Dec 2016. The period of study was restricted to 243 days from 1st April 2016 to 31st March 2017. The study reveals that there was an upward trend observed in the Indian Stock Market and the Indian currency was strengthened with the decrease in the Foreign exchange rate (USD/INR).
StockBot: Using LSTMs to Predict Stock Prices
Authors: Shaswat Mohanty, Anirudh Vijay, Nandagopan Gopakumar
Date: 2022-07-14 00:00:00
Tasks: Decision Making, Time Series, Time Series Analysis

The evaluation of the financial markets to predict their behaviour have been attempted using a number of approaches, to make smart and profitable investment decisions. Owing to the highly non-linear trends and inter-dependencies, it is often difficult to develop a statistical approach that elucidates the market behaviour entirely. To this end, we present a long-short term memory (LSTM) based model that leverages the sequential structure of the time-series data to provide an accurate market forecast. We then develop a decision making StockBot that buys/sells stocks at the end of the day with the goal of maximizing profits. We successfully demonstrate an accurate prediction model, as a result of which our StockBot can outpace the market and can strategize for gains that are ~15 times higher than the most aggressive ETFs in the market.
Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime
Authors: Daniel Goldfarb, Paul Hand
Date: 2022-06-01 00:00:00
Tasks: Continual Learning, image-classification, Image Classification, Permuted-MNIST

Overparameterization is known to permit strong generalization performance in neural networks. In this work, we provide an initial theoretical analysis of its effect on catastrophic forgetting in a continual learning setup. We show experimentally that in permuted MNIST image classification tasks, the generalization performance of multilayer perceptrons trained by vanilla stochastic gradient descent can be improved by overparameterization, and the extent of the performance increase achieved by overparameterization is comparable to that of state-of-the-art continual learning algorithms. We provide a theoretical explanation of this effect by studying a qualitatively similar two-task linear regression problem, where each task is related by a random orthogonal transformation. We show that when a model is trained on the two tasks in sequence without any additional regularization, the risk gain on the first task is small if the model is sufficiently overparameterized.
Dynamically handling task disruptions by composing together behavior modules
Authors: Thomas E. Portegys
Date: 2022-06-05 00:00:00

Biological neural networks operate in the presence of task disruptions as they guide organisms toward goals. A familiar stream of stimulus-response causations can be disrupted by subtask streams imposed by the environment. For example, taking a familiar path to a foraging area might be disrupted by the presence of a predator, necessitating a "detour" to the area. The detour can be a known alternative path that must be dynamically composed with the original path to accomplish the overall task. In this project, overarching base paths are disrupted by independently learned path modules in the form of insertion, substitution, and deletion modifications to the base paths such that the resulting modified paths are novel to the network. The network's performance is then tested on these paths that have been learned in piecemeal fashion. In sum, the network must compose a new task on the fly. Several network architectures are tested: Time delay neural network (TDNN), Long short-term memory (LSTM), Temporal convolutional network (TCN), and Morphognosis, a hierarchical neural network. LSTM and Morphognosis perform significantly better for this task.
ECG beat classification using machine learning and pre-trained convolutional neural networks
Authors: Neville D. Gai
Date: 2022-06-14 00:00:00
Tasks: BIG-bench Machine Learning

The electrocardiogram (ECG) is routinely used in hospitals to analyze cardiovascular status and health of an individual. Abnormal heart rhythms can be a precursor to more serious conditions including sudden cardiac death. Classifying abnormal rhythms is a laborious process prone to error. Therefore, tools that perform automated classification with high accuracy are highly desirable. The work presented classifies five different types of ECG arrhythmia based on AAMI EC57 standard and using the MIT-BIH data set. These include non-ectopic (normal), supraventricular, ventricular, fusion, and unknown beat. By appropriately transforming pre-processed ECG waveforms into a rich feature space along with appropriate post-processing and utilizing deep convolutional neural networks post fine-tuning and hyperparameter selection, it is shown that highly accurate classification for the five waveform types can be obtained. Performance on the test set indicated higher overall accuracy (98.62%), as well as better performance in classifying each of the five waveforms than hitherto reported in literature.
A Robustly Optimized Long Text to Math Models for Numerical Reasoning On FinQA
Authors: Renhui Zhang, Youwei Zhang, Yao Yu
Date: 2022-06-29 00:00:00
Tasks: Math

Numerical reasoning is required when solving most problems in our life, but it has been neglected in previous artificial intelligence researches. FinQA challenge has been organized to strengthen the study on numerical reasoning where the participants are asked to predict the numerical reasoning program to solve financial question. The result of FinQA will be evaluated by both execution accuracy and program accuracy. In this paper, we present our approach to tackle the task objective by developing models with different specialized capabilities and fusing their strength. Overall, our approach achieves the 1st place in FinQA challenge, with 71.93% execution accuracy and 67.03% program accuracy.
A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT
Authors: Justin Liu
Date: 2022-06-15 00:00:00
Tasks: Prognosis, Transfer Learning

In a worldwide health crisis as exigent as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reverse transcription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. This study aimed to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. First, the dataset utilized in this study was derived from three major sources, comprising a total of 17,698 chest CT slices across 923 patient cases. Image preprocessing algorithms were then developed to reduce noise by excluding irrelevant features. Transfer learning was also implemented with the EfficientNetB7 pre-trained model to provide a backbone architecture and save computational resources. Lastly, several explainability techniques were leveraged to qualitatively validate model performance by localizing infected regions and highlighting fine-grained pixel details. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958. Explainability measures showed that the model correctly distinguished between relevant, critical features pertaining to COVID-19 chest CT images and normal controls. Deep learning frameworks provide efficient, human-interpretable COVID-19 diagnostics that could complement radiologist decisions or serve as an alternative screening tool. Future endeavors may provide insight into infection severity, patient risk stratification, and prognosis.
Analysis of face detection, face landmarking, and face recognition performance with masked face images
Authors: Ožbej Golob
Date: 2022-06-03 00:00:00
Tasks: Face Detection, Face Recognition

Face recognition has become an essential task in our lives. However, the current COVID-19 pandemic has led to the widespread use of face masks. The effect of wearing face masks is currently an understudied issue. The aim of this paper is to analyze face detection, face landmarking, and face recognition performance with masked face images. HOG and CNN face detectors are used for face detection in combination with 5-point and 68-point face landmark predictors and VGG16 face recognition model is used for face recognition on masked and unmasked images. We found that the performance of face detection, face landmarking, and face recognition is negatively impacted by face masks
Study on Image Filtering -- Techniques, Algorithm and Applications
Authors: Bhishman Desai, Manish Paliwal, Kapil Kumar Nagwanshi
Date: 2022-06-04 00:00:00

Image processing is one of the most immerging and widely growing techniques making it a lively research field. Image processing is converting an image to a digital format and then doing different operations on it, such as improving the image or extracting various valuable data. Image filtering is one of the fascinating applications of image processing. Image filtering is a technique for altering the size, shape, color, depth, smoothness, and other image properties. It alters the pixels of the image to transform it into the desired form using different types of graphical editing methods through graphic design and editing software. This paper introduces various image filtering techniques and their wide applications.
A Case Study on the Impact of Similarity Measure on Information Retrieval based Software Engineering Tasks
Authors: Md Masudur Rahman, Saikat Chakraborty, Gail Kaiser, Baishakhi Ray
Date: 2018-08-08 00:00:00
Tasks: Information Retrieval, Retrieval

Information Retrieval (IR) plays a pivotal role in diverse Software Engineering (SE) tasks, e.g., bug localization and triaging, code retrieval, requirements analysis, etc. The choice of similarity measure is the core component of an IR technique. The performance of any IR method critically depends on selecting an appropriate similarity measure for the given application domain. Since different SE tasks operate on different document types like bug reports, software descriptions, source code, etc. that often contain non-standard domain-specific vocabulary, it is essential to understand which similarity measures work best for different SE documents. This paper presents two case studies on the effect of different similarity measure on various SE documents w.r.t. two tasks: (i) project recommendation: finding similar GitHub projects and (ii) bug localization: retrieving buggy source file(s) correspond to a bug report. These tasks contain a diverse combination of textual (i.e. description, readme) and code (i.e. source code, API, import package) artifacts. We observe that the performance of IR models varies when applied to different artifact types. We find that, in general, the context-aware models achieve better performance on textual artifacts. In contrast, simple keyword-based bag-of-words models perform better on code artifacts. On the other hand, the probabilistic ranking model BM25 performs better on a mixture of text and code artifacts. We further investigate how such an informed choice of similarity measure impacts the performance of SE tools. In particular, we analyze two previously proposed tools for project recommendation and bug localization tasks, which leverage diverse software artifacts, and observe that an informed choice of similarity measure indeed leads to improved performance of the existing SE tools.
A Review on Anchor Assignment and Sampling Heuristics in Deep Learning-based Object Detection
Authors: Xuan-Thuy Vo, and Kang-Hyun Jo
Date: 2022-07-16 00:00:00
Proceeding: Neurocomputing 2022 7
Tasks: Object, object-detection, Object Detection

Deep learning-based object detection is a fundamental but challenging problem in computer vision field, has attracted a lot of study in recent years. State-of-the-art object detection methods rely on the selection of positive samples and negative samples, i.e., called sample assignment, and the definition of a useful set for training, i.e., called sample sampling heuristics. This paper presents a comprehensive review of the advanced anchor assignment and sampling approaches in deep learning-based object detection. Each problem is classified and analyzed systematically. According to the problem-based taxonomy, we identify the advantages and disadvantages of each problem in-depth and present open issues regarding the current methods. Furthermore, this paper also reviews the new trends in solving object detection that has not been discussed during the last two years. To track the latest research, a webpage related to the above problems is provided, which is available at \url{https://github.com/VoXuanThuy/ObjectDetectionReview}.
Primordial black holes in the Dark Ages: Observational prospects for future 21cm surveys
Authors: Katherine J. Mack, Daniel H. Wesley
Date: 2008-05-11 00:00:00

We consider the signatures of a population of primordial black holes (PBHs) in future observations of 21cm radiation from neutral hydrogen at high redshift. We focus on PBHs in the mass range $5 \times 10^{10} kg \lesssim M_{PBH} \lesssim 10^{14} kg$, which primarily influence the intergalactic medium (IGM) by heating from direct Hawking radiation. Our computation takes into account the black hole graybody factors and the detailed energy dependence of photon and e+/- absorption by the IGM. We find that for black holes with initial masses between $5 \times 10^{11} kg \lesssim M_{PBH} \lesssim 10^{14} kg$, the signal mimics that of a decaying dark matter species. For black holes in the range $5 \times 10^{10} kg \lesssim M_{PBH} \lesssim 5 \times 10^{11} kg$, the late stages of evaporation produce a characteristic feature in the 21cm brightness temperature that provides a unique signature of the black hole population. If no signal is observed, then 21cm observations will provide significantly better constraints on PBHs in the mass range $5 \times 10^{10} kg \lesssim M_{PBH} \lesssim 10^{12} kg$ than are currently available from the diffuse $\gamma$-ray background.
Cosmic X-ray background and Earth albedo Spectra with Swift/BAT
Authors: M. Ajello, J. Greiner, G. Sato, D. R. Willis, G. Kanbach, A. W. Strong, R. Diehl, G. Hasinger, N. Gehrels, C. B. Markwardt, J. Tueller
Date: 2008-08-25 00:00:00

We use Swift/BAT Earth occultation data at different geomagnetic latitudes to derive a sensitive measurement of the Cosmic X-ray background (CXB) and of the Earth albedo emission in the 15--200 keV band. We compare our CXB spectrum with recent (INTEGRAL, BeppoSAX) and past results (HEAO-1) and find good agreement. Using an independent measurement of the CXB spectrum we are able to confirm our results. This study shows that the BAT CXB spectrum has a normalization ~8(+/-3)% larger than the HEAO-1 measurement. The BAT accurate Earth albedo spectrum can be used to predict the level of photon background for satellites in low Earth and mid inclination orbits.
Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text Recognition
Authors: Zilong Fu, Guoqing Jin, Hongtao Xie, Junbo Guo
Date: 2021-08-01 00:00:00
Proceeding: ICMR 2021 8
Tasks: Language Modelling, Position, Scene Text Recognition

Nowadays, it is a trend that using a parallel-decoupled encoderdecoder (PDED) framework in scene text recognition for its flexibility and efficiency. However, due to the inconsistent information content between queries and keys in the parallel positional attention module (PPAM) used in this kind of framework(queries: position information, keys: context and position information), visual misalignment tends to appear when confronting hard samples(e.g., blurred texts, irregular texts, or low-quality images). To tackle this issue, in this paper, we propose a dual parallel attention network (DPAN), in which a newly designed parallel context attention module (PCAM) is cascaded with the original PPAM, using linguistic contextual information to compensate for the information inconsistency between queries and keys. Specifically, in PCAM, we take the visual features from PPAM as inputs and present a bidirectional language model to enhance them with linguistic contexts to produce queries. In this way, we make the information content of the queries and keys consistent in PCAM, which helps to generate more precise visual glimpses to improve the entire PDED framework’s accuracy and robustness. Experimental results verify the effectiveness of the proposed PCAM, showing the necessity of keeping the information consistency between queries and keys in the attention mechanism. On six benchmarks, including regular text and irregular text, the performance of DPAN surpasses the existing leading methods by large margins, achieving new state-of-the-art performance. The code is available on https://github.com/Jackandrome/DPAN.
Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation
Authors: Wencan Cheng, Jong Hwan Ko
Date: 2022-07-15 00:00:00
Tasks: Scene Flow Estimation

Scene flow estimation, which extracts point-wise motion between scenes, is becoming a crucial task in many computer vision tasks. However, all of the existing estimation methods utilize only the unidirectional features, restricting the accuracy and generality. This paper presents a novel scene flow estimation architecture using bidirectional flow embedding layers. The proposed bidirectional layer learns features along both forward and backward directions, enhancing the estimation performance. In addition, hierarchical feature extraction and warping improve the performance and reduce computational overhead. Experimental results show that the proposed architecture achieved a new state-of-the-art record by outperforming other approaches with large margin in both FlyingThings3D and KITTI benchmarks. Codes are available at https://github.com/cwc1260/BiFlow.