Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepGamble: Towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection

Annually the gaming industry spends approximately $15 billion in marketing reinvestment. However, this amount is spent without any consideration for the skill and luck of the player. For a casino, an unskilled player could fetch ~4× more revenue than a skilled player. This paper describes a video recognition system that is based on an extension of the Mask R-CNN model. Our system digitizes the game of blackjack by detecting cards and player bets in real time and processes decisions they took in order to create accurate player personas. Our proposed supervised learning approach consists of a specialized three-stage pipeline that takes images from two viewpoints of the casino table and does instance segmentation to generate masks on proposed regions of interests. These predicted masks along with derivative features are used to classify image attributes that are passed onto the next stage to assimilate the gameplay understanding. Our end-to-end model yields an accuracy of ∼95% for main bet detection and ∼98% for card detection in a controlled environment trained using transfer learning approach with 900 training examples. Our approach is generalizable and scalable and shows promising results in varied gaming scenarios and test data. Such granular level gathered data, helped in understanding player’s deviation from optimal strategy and thereby separate the skill of the player from the luck of the game. Our system also assesses the likelihood of card counting by correlating player’s betting pattern to the deck’s scaled count. Such a system lets casinos flag fraudulent activity and calculate expected personalized profitability for each player and tailor their marketing reinvestment decisions.
Variable Selection With Second-Generation P-Values

Abstract Many statistical methods have been proposed for variable selection in the past century, but few balance inference and prediction tasks well. Here, we report on a novel variable selection approach called penalized regression with second-generation p-values (ProSGPV). It captures the true model at the best rate achieved by current standards, is easy to implement in practice, and often yields the smallest parameter estimation error. The idea is to use an penalization scheme with second-generation p-values (SGPV), instead of traditional ones, to determine which variables remain in a model. The approach yields tangible advantages for balancing support recovery, parameter estimation, and prediction tasks. The ProSGPV algorithm can maintain its good performance even when there is strong collinearity among features or when a high-dimensional feature space with p > n is considered. We present extensive simulations and a real-world application comparing the ProSGPV approach with smoothly clipped absolute deviation (SCAD), adaptive lasso (AL), and minimax concave penalty with penalized linear unbiased selection (MC+). While the last three algorithms are among the current standards for variable selection, ProSGPV has superior inference performance and comparable prediction performance in certain scenarios.
Hard x-ray standing-wave photoemission study of the interfaces in a BiFeO$_3$/La$_{0.7}$Sr$_{0.3}$MnO$_3$ superlattice.

Hybrid multiferroics such as BiFeO$_3$ (BFO) and La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO) heterostructures are highly interesting functional systems due to their complex electronic and magnetic properties. One of the key parameters influencing the emergent properties is the quality of interfaces, where varying interdiffusion lengths can give rise to different chemistry and distinctive electronic states. Here we report high-resolution depth resolved chemical and electronic investigation of BFO/LSMO superlattice using standing-wave hard X-ray photoemission spectroscopy in the first-order Bragg as well as near-total-reflection geometry. Our results show that the interfaces of BFO on top of LSMO are atomically abrupt, while the LSMO on top of BFO interfaces show an interdiffusion length of around 1.2 unit cells. The two interfaces also exhibit different chemical gradients, with the BFO/LSMO interface being Sr-terminated by a spectroscopically distinctive high binding energy component in Sr 2p core-level spectra, which is spatially contained within 1 unit cell from the interface. From the electronic point of view, unique valence band features were observed for bulk-BFO, bulk-LSMO and their interfaces. Our X-ray optical analysis revealed a unique electronic signature at the BFO/LSMO interface, which we attribute to the coupling between those respective layers. Valence band decomposition based on the Bragg-reflection standing-wave measurement also revealed the band alignment between BFO and LSMO layers. Our work demonstrates that standing-wave hard x-ray photoemission is a reliable non-destructive technique for probing depth-resolved electronic structure of buried layers and interfaces with sub-unit-cell resolution. Equivalent investigations can be successfully applied to a broad class of material such as perovskite complex oxides with emergent interfacial phenomena.
Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions

In recent years, the field of recommendation systems has attracted increasing attention to developing predictive models that provide explanations of why an item is recommended to a user. The explanations can be either obtained by post-hoc diagnostics after fitting a relatively complex model or embedded into an intrinsically interpretable model. In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI). This model architecture is intrinsically interpretable, as it additively consists of the user and item main effects, the manifest user-item interactions based on observed features, and the latent interaction effects from residuals. Unlike conventional collaborative filtering methods, the group effect of users and items are considered in GAMMLI. It is beneficial for enhancing the model interpretability, and can also facilitate the cold-start recommendation problem. A new Python package GAMMLI is developed for efficient model training and visualized interpretation of the results. By numerical experiments based on simulation data and real-world cases, the proposed method is shown to have advantages in both predictive performance and explainable recommendation.
Compliation Techniques for Graphs Algorithms on GPUs

The performance of graph programs depends highly on the algorithm, size, and structure of the input graphs, as well as the features of the underlying hardware. No single set of optimizations or one hardware platform works well across all settings. To achieve high performance, the programmer must carefully select which set optimizations as well as the hardware platform to use. However, currently when switching between CPUs and GPUs, programmer must re-implement the entire algorithm with different optimizations in order to achieve high performance. We propose the GG GPU compiler, a new graph processing framework that achieves high performance on both CPUs and GPUs using the same algorithm specification. GG significantly expands the optimization space of GPU graph processing frameworks with a novel GPU scheduling language and compiler that enables combining load balancing, edge traversal direction, active vertex set creation, active vertex set processing ordering, and kernel fusion optimizations. GG also introduces two performance optimizations, Edge-based Thread Warps CTAs load balancing (ETWC) and EdgeBlocking, to expand the optimization space for GPUs. ETWC improves load balance by dynamically partitioning the edges of each vertex into blocks that are assigned to threads, warps, and CTAs for execution. EdgeBlocking improves the locality of the program by reordering the edges and restricting random memory accesses to fit within L2 cache. We evaluate GG on 5 algorithms and 9 input graphs on both Pascal and Volta generation NVIDIA GPUs, and show that it achieves up to 5.11x speedup over state-of-the-art GPU graph processing frameworks, and is the fastest on 62 out of the 90 experiment
Detection of Anomalies in a Time Series Data using InfluxDB and Python

Analysis of water and environmental data is an important aspect of many intelligent water and environmental system applications where inference from such analysis plays a significant role in decision making. Quite often these data that are collected through sensible sensors can be anomalous due to different reasons such as systems breakdown, malfunctioning of sensor detectors, and more. Regardless of their root causes, such data severely affect the results of the subsequent analysis. This paper demonstrates data cleaning and preparation for time-series data and further proposes cost-sensitive machine learning algorithms as a solution to detect anomalous data points in time-series data. The following models: Logistic Regression, Random Forest, Support Vector Machines have been modified to support the cost-sensitive learning which penalizes misclassified samples thereby minimizing the total misclassification cost. Our results showed that Random Forest outperformed the rest of the models at predicting the positive class (i.e anomalies). Applying predictive model improvement techniques like data oversampling seems to provide little or no improvement to the Random Forest model. Interestingly, with recursive feature elimination, we achieved a better model performance thereby reducing the dimensions in the data. Finally, with Influxdb and Kapacitor the data was ingested and streamed to generate new data points to further evaluate the model performance on unseen data, this will allow for early recognition of undesirable changes in the drinking water quality and will enable the water supply companies to rectify on a timely basis whatever undesirable changes abound.
On Continuous Local BDD-Based Search for Hybrid SAT Solving

We explore the potential of continuous local search (CLS) in SAT solving by proposing a novel approach for finding a solution of a hybrid system of Boolean constraints. The algorithm is based on CLS combined with belief propagation on binary decision diagrams (BDDs). Our framework accepts all Boolean constraints that admit compact BDDs, including symmetric Boolean constraints and small-coefficient pseudo-Boolean constraints as interesting families. We propose a novel algorithm for efficiently computing the gradient needed by CLS. We study the capabilities and limitations of our versatile CLS solver, GradSAT, by applying it on many benchmark instances. The experimental results indicate that GradSAT can be a useful addition to the portfolio of existing SAT and MaxSAT solvers for solving Boolean satisfiability and optimization problems.
Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features

Automated vehicles require a comprehensive understanding of traffic situations to ensure safe and anticipatory driving. In this context, the prediction of pedestrians is particularly challenging as pedestrian behavior can be influenced by multiple factors. In this paper, we thoroughly analyze the requirements on pedestrian behavior prediction for automated driving via a system-level approach. To this end we investigate real-world pedestrian-vehicle interactions with human drivers. Based on human driving behavior we then derive appropriate reaction patterns of an automated vehicle and determine requirements for the prediction of pedestrians. This includes a novel metric tailored to measure prediction performance from a system-level perspective. The proposed metric is evaluated on a large-scale dataset comprising thousands of real-world pedestrian-vehicle interactions. We furthermore conduct an ablation study to evaluate the importance of different contextual cues and compare these results to ones obtained using established performance metrics for pedestrian prediction. Our results highlight the importance of a system-level approach to pedestrian behavior prediction.
Machine Learning for MU-MIMO Receive Processing in OFDM Systems

Machine learning (ML) starts to be widely used to enhance the performance of multi-user multiple-input multiple-output (MU-MIMO) receivers. However, it is still unclear if such methods are truly competitive with respect to conventional methods in realistic scenarios and under practical constraints. In addition to enabling accurate signal reconstruction on realistic channel models, MU-MIMO receive algorithms must allow for easy adaptation to a varying number of users without the need for retraining. In contrast to existing work, we propose an machine learning (ML)-enhanced MU-MIMO receiver that builds on top of a conventional linear minimum mean squared error (LMMSE) architecture. It preserves the interpretability and scalability of the LMMSE receiver, while improving its accuracy in two ways. First, convolutional neural networks (CNNs) are used to compute an approximation of the second-order statistics of the channel estimation error which are required for accurate equalization. Second, a CNN-based demapper jointly processes a large number of orthogonal frequency-division multiplexing (OFDM) symbols and subcarriers, which allows it to compute better log likelihood ratios (LLRs) by compensating for channel aging. The resulting architecture can be used in the up- and downlink and is trained in an end-to-end manner, removing the need for hard-to-get perfect channel state information (CSI) during the training phase. Simulation results demonstrate consistent performance improvements over the baseline which are especially pronounced in high mobility scenarios.
Growth, Yields, and Nitrogen-Use Efficiency of Maize (Zea mays L.) and Mungbean (Vigna radiata L. Wilczek) as Affected by Potassium Fertilizer in Tropical South Asia

Application of chemical fertilizers in smallholder tropical farming systems of South Asia has become mandatory because of soil degradation, and nitrogen (N) is the most abundantly used nutrient. However, integrated management of N with potassium (K) could enhance productivity. Field studies tested the impact of fertilizer K on root development, seed yields, harvest indices, and N-use efficiencies of maize and mungbean, two popular smallholder crops over major and minor seasons. Application of 120 kg K ha−1 optimized all parameters of maize in the major wet season, whereas the requirement was 80 kg K ha−1 in the minor season. Optimal growth yields and N-use efficiencies of mungbean was with 80 kg K ha−1 in both seasons. Information regarding rates of fertilizer K that optimized N use and yield of maize and mungbean during each of the two tropical monsoonal seasons of South Asia is presented.
Chronic Rhinosinusitis with Nasal Polyps: Targeting IgE with Anti-IgE Omalizumab Therapy

Abstract Chronic rhinosinusitis with nasal polyps (CRSwNP) is a complex, clinically heterogeneous and persistent inflammatory disorder of the upper airway. Detailed mechanistic insights into disease pathogenesis are lacking, but it is now accepted that local tissue IgE driven T2-high inflammatory pathways are critical to disease. The recent CRSwNP Phase 3 POLYP1 and POLYP2 replicate studies of blocking IgE with omalizumab confirmed rapid improvements in all clinical parameters of sinonasal disease, confirming a pivotal role for IgE driven inflammatory pathways in CRSwNP. This review summarises the biology of IgE in relation to CRSwNP. Insight into how IgE may drive CRSwNP is evaluated in the context of clinical improvements seen with omalizumab. The need for further studies using a broader patient and biomarker specific groups to aid more precise drug-patient selection alongside more detailed mechanistic studies of omalizumab in CRSwNP is highlighted.
Serum MACC-1: a new biomarker for breast cancer

Metastasis-associated in colon cancer-1 (MACC-1) is a newly identified tumor marker, found to express in various normal and cancerous tissue. This study is conducted to evaluate the serum MACC-1 level as a diagnostic marker for breast cancer (BC). Sixty new BC patients were included in this study. Patients who received neoadjuvant chemotherapy or with metastatic disease were excluded. Eighty patients of benign disease were taken as control group. All the patients were females with the mean age of 46.7 ± 10.6 years in study group and 40.2 ± 8.4 years in control group (p = 0.0001). The mean serum MACC-1 level in BC patients was 3.46 ± 1.3 ng/ml which was significantly higher than control mean serum MACC-1 level (1.90 ± 0.2 ng/ml) (p < 0.0001). On ROC analysis, the AUC was 0.98 (p ≤ 0.0001; 95% CI = 0.97–1.0) i.e., a good predictor for breast cancer. At the cut-off value of 2.12 ng/ml, the sensitivity and the specificity of serum MACC-1 were 96.7% and 92.5%, respectively. This study showed that serum MACC-1 can be a potential biomarker for diagnosis and tumor progression in patients with breast cancer.
Vascular Plant Herbarium at the Kandalaksha Strict Nature Reserve (KAND), Russia

Abstract Background The present-day demand for digital availability of distributional data in biodiversity studies requires a special effort in assembling and editing the data otherwise scattered in paper literature and herbarium collections, which can be poorly accessible or little understood to present-day users and especially automatic data processors. Our project on developing the information resource for the vascular plant flora of Murmansk Region, Russia, includes processing and making digitally available all the data on the taxonomy and distribution of this flora. So far, published distribution maps are limited to the old set in the Flora of Murmansk Region (published in 1953–1966) and the Red Data Book of Murmansk Region (ed. 2, published in 2014). These publications did not take into account the main part of the herbarium collections kept at the Kandalaksha Strict Nature Reserve, which are the basis for numerous local publications that appear scattered and, therefore, little accessible nowadays. New information We present a complete dataset of all holdings of vascular plants in the Herbarium of the Kandalaksha Strict Nature Reserve, totalling 10,218 specimens collected during 1947–2019, which are referable to 764 species and 19 subspecies. All specimens were georeferenced with the utmost precision available. This dataset offers a complete and dense coverage of the Nature Reserve's territory (islands and adjacent mainland coastal areas of the Barents and White Seas, Murmansk Region and Republic of Karelia, Russia); these data are little represented in herbarium collections elsewhere.
In-water observations highlight the effects of provisioning on whale shark behaviour at the world's largest whale shark tourism destination

The whale shark is the world's largest fish that forms predictable aggregations across its range, many of which support tourism industries. The largest non-captive provisioned whale shark destination globally is at Oslob, Philippines, where more than 500 000 tourists visit yearly. There, the sharks are provisioned daily, year-round, allowing the human–shark interaction in nearshore waters. We used in-water behavioural observations of whale sharks between 2015 and 2017 to understand the relationship between external stimuli and shark behaviour, whether frequency of visits at the site can act as a predictor of behaviour, and the tourist compliance to the code of conduct. Mixed effects models revealed that the number of previous visits at the site was a strong predictor of whale shark behaviour, and that provisioned sharks were less likely to exhibit avoidance. Compliance was poor, with 93% of surveys having people less than 2 m from the animal, highlighting overcrowding of whale sharks at Oslob. Given the behavioural implications to whale sharks highlighted here and the local community's reliance on the tourism industry, it is imperative to improve management strategies to increase tourist compliance and strive for sustainable tourism practices.
Coastal Image Classification under Noisy Labels

ABSTRACT Tai, X. ; Wang, G. ; Grecos, C. , and Ren, P., 2020. Coastal image classification under noisy labels. In: Jung, H. -S. ; Lee, S. ; Ryu, J.-H. , and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 151-156. Coconut Creek (Florida), ISSN 0749-0208. Remote sensing coastal images commonly suffer from noisy labels, which tend to reduce the benefits of training coastal image classifiers and thus deteriorate the classification performance. This article explores the feasibility of classifying coastal images under noisy labels. The state-of-the-art co-teaching paradigm in computer vision is exploited for processing remote sensing coastal images that are partially incorrectly labeled. The data with noisy labels are hard to learn and induce large loss values for a classifier. In the light of this observation, the co-teaching paradigm trains two identically structured but randomly initialized convolutional neural network (CNN) classifiers with stochastic optimization. Training of the two CNN classifiers is performed on mini-batches in an interactive and iterative manner. Specifically, the data with small loss values of one CNN classifier are used for updating the other CNN classifier in the next training iteration. The two CNN classifiers turn robust to noisy labels through the iterative process of teaching each other. Experimental evaluations on a coastal image dataset, i.e., the Moye Island remote sensing dataset, validate the effectiveness of the proposed coastal image classification strategy.
Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images

We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images.
Environment‐sensitive carbon nanotube/polymer composite microhydrogels synthesized via a microfluidic reactor

Multiwalled carbon nanotubes (MWCNTs)/poly(N-isopropylacrylamide-co-acrylic acid) composite microhydrogels are simply synthesized with controllable size distribution via a microfluidic reactor system. Monomers (N-isopropylacrylamide and acrylic acid) are rapidly copolymerized (about 3 s) with the embedding of nanoscaled inorganic materials (MWCNTs and hectorites) in microfluidic channels. MWCNTs/hectorites act as “molecular heater” and inorganic crosslinkers in this hydrogel system. As a result, microminiaturization, multifunctionalizaion, and modification of traditional polymer hydrogels are realized simultaneously. Fourier transform infrared spectroscopy is used to confirm polymerization and environment-sensitive tests are done as well. The resultant microgels exhibit dual near-infrared and pH response with good reversibility, indicating their potential applications in microreactor fields. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013