Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of land price regulation on land-use intensity: evidence from Chinese minimum land price regulation policy

ABSTRACT The minimum land price (MLP) regulation that China introduced in 2007 provides a quasi-experiment to investigate how government intervention in the land market influences the intensity with which industrial land is used, which has received limited attention in existing literature. Using a difference-in-difference strategy, our estimates show that an exogenous increase in the price of industrial land induced by the MLP regulation’s introduction significantly increased industrial land-use intensity by putting more capital on same amount of land. The findings from this paper therefore suggest that MLP regulation as an incentive-based policy instrument is effective for promoting land-use intensity.
Housing tenure of young people through a multilevel strategy: differences according to living arrangements

ABSTRACT In this study, we identify and analyse the key determinants of the housing tenure regime and residential independence of young adults in Spain. We use a multilevel strategy that allows us to take into account the heterogeneity amongst regions in both homeownership rate and residential independence rate. Our focus is on young adults as this group have a significant effect on the real estate market. Furthermore, young adults are at an age in their life cycle when important events occur. The results show that demographic factors are more important than economic factors in both processes. Also, for these young adults, the sociocultural norms of each region are not the main criteria in housing tenure and residential independence. Given the recent changes seen in the type of cohabitation, we also analyse the housing tenure for different groups of young adults according to their living arrangements (alone, married couple or common-law couple). For young adults living alone, we find there is little variability among regions. Differences are seen in the factors that influence homeownership among young adults who have formalized their union through marriage and those who are part of a common-law couple.
Polysubstance use among national samples of in-school adolescents in Dominican Republic, Jamaica, and Trinidad and Tobago

This study aimed to provide estimates on current polysubstance use among adolescents in three Caribbean countries. Cross-sectional and national school health survey data (2016-2017) were analysed from 6 269 adolescents (median age = 15 years) in Dominican Republic (n = 1 306), Jamaica (n = 1 455), and Trinidad and Tobago (n = 3 508). More than one in ten of the participating students engaged in current polysubstance use. In the adjusted multinomial logistic regression analysis, the associations with current polysubstance use were stronger than with current single substance use among older adolescents (15-18 years or older), male sex, with passive smoking, high psychological distress, frequent soft drink consumption (≥3 times/day), and frequent fast-food consumption (≥ 3 days/week). Additionally, school truancy and parental tobacco use were associated with current polysubstance use, while adolescents reporting parental support were less likely have current single and polysubstance use. Intervention strategies to prevent and treat substance abuse by teenagers should prioritise psychosocial factors.
Preliminary Study on Gene Regulation and its Pathways in Chinese Holstein Cows with Clinical Mastitis Caused by Staphylococcus Aureus

Abstract Introduction Clinical mastitis (CM) is one of the most common diseases of dairy cows globally, has a complex aetiology and recurs easily. Staphylococcus aureus is a frequently isolated pathogen responsible for bovine mastitis and remains difficult to eradicate. Material and Methods To characterise the transcriptional profiles of dairy cows infected by S. aureus, we performed an RNA-seq analysis of peripheral blood leukocytes in lactating Chinese Holstein dairy cows with CM and did the same with healthy cows’ samples as controls. Results A total of 4,286 genes were detected in the CM cases infected with S. aureus which were differentially expressed compared to the controls, 3,085 of which were upregulated, the remainder being downregulated. Notably, we observed that some differentially expressed genes (DEGs) had strong protein–protein interaction. Of these, six downregulated DEGs (AKR1C4, PTGS2, HNMT, EPHX2, CMBL, and IDH1) were involved in the metabolic pathway, while eight upregulated DEGs (VWF, GP9, MYLK, GP6, F2RL3, ITGB3, GP5, and PRKG1) were associated with the platelet activation pathway. Conclusion The transcriptome dataset of CM cases would be a valuable resource for clinical guidance on anti-inflammatory medication and for deeper understanding of the biological processes of CM response to S. aureus infection, and it would enable us to identify specific genes for diagnostic markers and possibly for targeted therapy.
A review on prioritization techniques in regression testing

Due to dynamic changing business and operating environment, software evolution is inevitable. As software evolves, there is a need for a retest of the system in order to ensure its validity and thus regression testing assures that this happens. Regression Testing has several research categories, one of the most popular areas is Test Case Prioritization which orders test cases based on a particular criteria to be tested in accordance to available resources, and thus ensuring that the most critical test cases are tested first. In this study we examine the different types of test case prioritization and a description of the techniques presented alongside the issue raised in each study. This will give an insight into the main problems currently plaguing test case prioritization and thus pave way for further research areas to tap into.
Workload characterization of a time-series prediction system for spatio-temporal data

To facilitate the co-design of next generation hardware architectures, it is critical to characterize the workloads of deep learning (DL) applications and assess their computational patterns on different levels of the execution stack. Time series prediction is one such DL application heavily used in areas that include critical decision making: ensuring power grid resiliency, climate forecasting, transportation infrastructure optimization, stock market prediction, etc. Unlike cross-sectional data (e.g. images), time-series data is inherently sequential, posing challenges to parallelization in the context of deep learning. In this paper, we developed a proxy application for deep learning based time-series application that uses spatio-temporal data from a dynamical system for model training and inference. We study the performance profiles of the associated computational patterns for both training and inference on four different levels: models (Long short-term Memory and Convolutional Neural Network), DL frameworks (Tensorflow, PyTorch), data-types (FP64, mixed-precision), and single-node dense GPU platforms (Nvidia™ DGX A100 and DGX-2 V100). Overall, our findings indicate that, in the context of multiple variants of our time-series prediction proxy application, computational profiles of Tensorflow and PyTorch mostly exhibit divergent overheads across GPU platforms. Our studies also demonstrate that associated data movement, transformation and combination can take more than 50% of the overall execution times. Both, source code and workload profiles are made public for community-use and future studies.
Artificial versus spiking neural networks for reinforcement learning in UAV obstacle avoidance

Spiking Neural Networks (SNN) are gaining more interest from the scientific community thanks to the promise of greater energy-efficient and greater computational power. This poses several challenges as today's SNN training for RL is based on Artificial Neural Network (ANN) training and then conversion from ANN to SNN, which does not leverage SNN event-based processing inherent capabilities. The present work compares an ANN and an SNN in an event-camera-based obstacle avoidance task, trained with Reinforcement Learning (RL) using the Deep Q-Learning (DQL) algorithm. We create an experimental setup composed of Unreal Engine 4, AirSim, and an event camera that simulates a real-world obstacle avoidance environment. Additionally, we train an SNN with a gradient-based training method enabling the use of all their expressiveness even in the training phase, showing comparable performance between the ANN and the SNN. To the best of our knowledge, we are the first that implements an entire realistic pipeline with a photo-realistic simulator (Airsim) and train an SNN without converting it from a pre-trained ANN.
Determination of optimal set of spatio-temporal features for predicting burn probability in the state of California, USA

Wildfires play a critical role in determining ecosystem structure and function and pose serious risks to human life, property and ecosystem services. Burn probability (BP) models the likelihood that a location could burn. Simulation models are typically used to predict BP but are computationally intensive. Machine learning (ML) pipelines can predict BP and reduce computational intensity. In this work, we tested approaches to reduce the set of input features used in an ML model to estimate BP for the state of California, USA, without loss of predictive performance. We used Principal Component Analysis (PCA) to determine the optimal set of features to use in our ML pipeline. Then, we mapped BP and compared model performance when using the reduced set and when using the whole set of features. Models using optimized input achieved similar prediction performance while using less than 50% of the input features.
Probabilistic framework for modeling event shocks to financial time series

In financial market, certain types of stochastic events are intrinsically impactful to the prediction of financial times series, such as stock return, while few existing research attempts have been made to incorporate stochastic event modeling to time series modeling in a principled way. In this paper, we present a pioneering study that fills this gap. In particular, we introduce a generic probabilistic model that captures 1) the inter-dependencies among stochastic events, and 2) the impact of these events on time series. To this end, we extend multivariate Hawkes process (MHP) and proximal graphical event model (PGEM) and apply this framework to modeling two financial events, companies' quarterly revenue releases and updates of consensus prediction of quarterly revenue, and their impacts on the mean and correlation structures of future stock return. Our model not only improves prediction of financial time series, but also promotes AI trust for finance by revealing the causal relationship among the events. Extensive experimental results based on real financial market data validate the effectiveness of our models in learning event impact and improving investment decision by incorporating stochastic event impacts.
ReDPro: an automated technique to detect and regenerate process-level concurrency failures

Process-level concurrency failures occur when multiple processes read and write a shared data item without proper synchronization. Process-level failures are difficult to detect because they are sensitive to the execution order of system calls. These failures can not be detected unless a specific system-call interleaving is exercised during testing. A common practice when testing or debugging concurrency failures is to execute many runs of a program with the hope of exercising interleaving that causes races known as stress testing. However, existing research prove that stress testing is neither efficient nor reproducible. In this paper, we presented an automated technique, RedPro that can detect process-level races and also regenerate the failures. We used a binary instrumentation tool named PIN for run-time monitoring and controlling the order of execution for potential race pairs. We evaluated the performance of RedPro by detecting and regenerating nine real-world bugs. The result demonstrates that our proposed technique is both effective and efficient.
Interactions of market making algorithms: a study on perceived collusion

The widespread use of market making algorithms and the associated feedback effects may have unexpected consequences which need to be better understood. In particular the phenomenon of 'tacit collusion' in which the interaction of algorithms leads to an outcome similar to a collusion among market makers, has increasingly received regulatory scrutiny. We propose a game-theoretic model of a financial market in which multiple market makers compete for market share and learn from market data to adjust their spreads. We model this learning process through a decentralized multi-agent reinforcement learning algorithm and show that, even in absence of price information sharing, under specific mechanism through which market makers compete for market shares, market prices may converge to levels which are similar to a collusion situation, resulting in 'tacit collusion'. We briefly discuss implications of our research for market regulators.
Permutation Coding for Vertex-Blend Attribute Compression

Compression of vertex attributes is crucial to keep bandwidth requirements in real-time rendering low. We present a method that encodes any given number of blend attributes for skinning at a fixed bit rate while keeping the worst-case error small. Our method exploits that the blend weights are sorted. With this knowledge, no information is lost when the weights get shuffled. Our permutation coding thus encodes additional data, e.g. about bone indices, into the order of the weights. We also transform the weights linearly to ensure full coverage of the representable domain. Through a thorough error analysis, we arrive at a nearly optimal quantization scheme. Our method is fast enough to decode blend attributes in a vertex shader and also to encode them at runtime, e.g. in a compute shader. Our open source implementation supports up to 13 weights in up to 64 bits.
Structure Diversity-Induced Anchor Graph Fusion for Multi-View Clustering

The anchor graph structure has been widely used to speed up large-scale multi-view clustering and exhibited promising performance. How to effectively integrate the anchor graphs on multiple views to achieve enhanced clustering performance still remains a challenging task. Existing fusing strategies ignore the structure diversity among anchor graphs and restrict the anchor generation to be same on different views, which degenerates the representation ability of corresponding fused consensus graph. To overcome these drawbacks, we propose a novel structural fusion framework to integrate the multi-view anchor graphs for clustering. Different from traditional integration strategies, we merge the anchors and edges of all the view-specific anchor graphs into a single graph for the structural optimal graph learning. Benefiting from the structural fusion strategy, the anchor generation of each view is not forced to be same, which greatly improves the representation capability of the target structural optimal graph, since the anchors of each view capture the diverse structure of different views. By leveraging the potential structural consistency among each anchor graph, a connectivity constraint is imposed on the target graph to indicate clusters directly without any post-processing such as k-means in classical spectral clustering. Substantial experiments on real-world datasets are conducted to verify the superiority of the proposed method, as compared with the state-of-the-arts over the clustering performance and time expenditure.
Interactive Physics-Based Virtual Sculpting with Haptic Feedback

Sculpting is an art form that relies on both the visual and tactile senses. A faithful simulation of sculpting, therefore, requires interactive, physically accurate haptic and visual feedback. We present an interactive physics-based sculpting framework with faithful haptic feedback. We enable cutting of the material by designing a stable, remeshing-free cutting algorithm called Improved stable eXtended Finite Element Method. We present a simulation framework to enable stable visual and haptic feedback at interactive rates. We evaluate the performance of our framework quantitatively and quantitatively through an extensive user study.
Opportunities and challenges of AI on satellite processing units

Higher autonomy in satellite operation is seen as the key game changer for the space systems market in the next decade, with a considerable amount of agencies and startups focusing on bringing machine learning to space. The adoption of Artificial Intelligence on-board of satellites is still limited due to the processing capabilities of radiation hardened hardware, which requires flight-heritage and extensive qualification. At the same time, the satellite market is undergoing a major paradigm shift from a hardware equipment perspective. Classical approaches, which aim at realizing satellites compliant with mission profiles including a long-lasting operational life and an extremely high reliability are ill-suited for many of the new market segments. The satellite-manufacturing industry is gradually adapting to these new mission requirements by identifying segments where components-off-the-shelf (COTS) can be employed. The latest generation of commercial components offer the unique possibility to integrate AI-algorithms with relative ease with tool assisted design and a much higher performance in parallel processing. In this position paper, the authors introduce the state of art of on-board AI and present the approach that is currently being researched in Airbus Defence and Space to perform neural network inference in various mission scenarios.
Micro BTB: a high performance and storage efficient last-level branch target buffer for servers

High-performance branch target buffers (BTBs) and the L1I cache are key to high-performance front-end. Modern branch predictors are highly accurate, but with an increase in code footprint in modern-day server workloads, BTB and L1I misses are still frequent. Recent industry trend shows usage of large BTBs (100s of KB per core) that provide performance closer to the ideal BTB along with a decoupled front-end that provides efficient fetch-directed L1I instruction prefetching. On the other hand, techniques proposed by academia, like BTB prefetching and using retire order stream for learning, fail to provide significant performance with modern-day processor cores that are deeper and wider. We solve the problem fundamentally by increasing the storage density of the last-level BTB. We observe that not all branch instructions require a full branch target address. Instead, we can store the branch target as a branch offset, relative to the branch instruction. Using branch offset enables the BTB to store multiple branches per entry. We reduce the BTB storage in half, but we observe that it increases skewness in the BTB. We revisit the need for skewed indexing and propose a skewed indexed and compressed last-level BTB design called MicroBTB (MBTB) that stores multiple branches per BTB entry. We evaluate MBTB on 100 industry-provided server workloads. A 4K-entry MBTB provides 17.61% performance improvement compared to an 8K-entry baseline BTB design with a storage savings of 47.5KB per core.
Financial misstatement detection: a realistic evaluation

In this work, we examine the evaluation process for the task of detecting financial reports with a high risk of containing a mis-statement. This task is often referred to, in the literature, as "mis-statement detection in financial reports". We provide an extensive review of the related literature. We propose a new, realistic evaluation framework for the task which, unlike a large part of the previous work: (a) focuses on the misstatement class and its rarity, (b) considers the dimension of time when splitting data into training and test and (c) considers the fact that misstatements can take a long time to detect. Most importantly, we show that the evaluation process significantly affects system performance, and we analyze the performance of different models and feature types in the new realistic framework.
Median Filters on FPGAs for Infinite Data and Large, Rectangular Windows

Efficient architectures and implementations of median filters have been well investigated in the past. In this article, we focus on median filters for very big scientific applications with very large windows and an infinite stream of data, inspired by big data needs in the Square Kilometre Array (SKA) pulsar search engine, but transferable to other big data domains. We propose a novel approach for very large rectangular windows on an FPGA accelerator device able to support the processing of infinite streams of data. OpenCL is used for rapid parameter sweeping and design space exploration based on a pipelined model of the system. Evaluation on a host/accelerator system with an Arria 10 device surpassed 64 million values processed per second considered for the SKA real time requirement, achieving 83.4M value/s while reading from/writing to disk. These results are compared with a state-of-the-art software implementation only achieving 41M value/s for over twice the total system energy cost.
Multi-agent malicious behaviour detection

This research presents a novel technique termed Multi-Agent Malicious Behaviour Detection. The goal of Multi-Agent Malicious Behaviour Detection is to provide infrastructure to allow for the detection and observation of malicious multi-agent systems in computer network environments. This research explores combinations of machine learning techniques and fuses them with a multi-agent approach to malicious behaviour detection that effectively blends human expertise from network defenders with modern artificial intelligence. Success of the approach depends on the Multi-Agent Malicious Behaviour Detection system's capability to adapt to evolving malicious multi-agent system communications, even as the malicious software agents in network environments vary in their degree of autonomy and intelligence. This thesis research involves the design of this framework, its implementation into a working tool, and its evaluation using network data generated by an enterprise class network appliance to simulate both a standard educational network and an educational network containing malware traffic.
Performance Analysis of ANN Prediction for Groundwater Level Considering Regional‐Specific Influence Components

Groundwater level (GWL) varies periodically or non‐periodically with various factors including precipitation, river stage (RS) change, sea level, and dewatering activities. In this study, the effect of influence components on the prediction of GWL using an artificial neural network (ANN) was investigated. Six regions with different hydrologic and geologic conditions were collected and adopted in the investigation using various input combinations. In urban areas with a high surface paved ratio, GWL was mainly affected by RS. In rural areas, the permeability of ground showed a significant impact on GWL. For such cases, the moving average (MA) was a suitable component as it could reflect both time lag and the effect of preceding precipitation. It was shown that site‐specific influence component should be firstly identified and introduced into input for more enhanced and reliable prediction of GWL using ANN. The effect of learning data length (LDL) was less significant. In urban and rural areas, the introduction of RS and MA into ANN input significantly improved the prediction performance, respectively, which was consistent with the correlation analysis of GWL influence components.