Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Layout Image Retrieval based on Fast Image Segmentation using K-Means Clustering

An image retrieval system that takes the input query image and retrieves the similar images according to the spatial coordinates and which uses the k means clustering algorithm for its segmentation. Most existing Content Based Image Retrieval based on the images of color, text documents, informative charts, and shape. This paper aims to search the images with similar spatial layouts and the retrieval process which includes the feature extraction methods and image matching criteria. This paper employs K-Means clustering for image segmentation, and the features of small segments of images are extracted based on the segmentation results. To match the segments of two images, the distance between them can be computed by Eucliedean distance to evaluate the similarity of their spatial layouts.
Bootstrapping Ontology Graphs

Ontology graphs are graphs in which the nodes are generic classes and edges have labels that specify the relationships between the classes. In this paper, we address the question: to what extent can automated extraction and crowd sourcing techniques be combined to bootstrap the creation of comprehensive and accurate ontology graphs? By adapting the state-of-the-art language model BERT to this task, and by leveraging a novel crowd sourced relationship selection task, we show that we can use this approach to bootstrap the ontology graph creation for further reﬁnement and improvement through human eﬀort.
fairadapt: Causal Reasoning for Fair Data Pre-processing

Machine learning algorithms are useful for various predictions tasks, but they can also learn how to discriminate, based on gender, race or other sensitive attributes. This realization gave rise to the field of fair machine learning, which aims to measure and mitigate such algorithmic bias. This manuscript describes the R-package fairadapt, which implements a causal inference pre-processing method. By making use of a causal graphical model and the observed data, the method can be used to address hypothetical questions of the form"What would my salary have been, had I been of a different gender/race? ". Such individual level counterfactual reasoning can help eliminate discrimination and help justify fair decisions. We also discuss appropriate relaxations which assume certain causal pathways from the sensitive attribute to the outcome are not discriminatory.
Utility of Range of Motion Measurements on Classification of Athletes

Abstract—In this study, a comparison of Range Of Motion (ROM) of middle and long-distance runners and swimmers has been made. The mobility of the various joints is essential for the quick movement of any sportsman. Knowledge of a ROM helps in preventing injuries, in repeating the movement, and in generating speed and power. ROM varies among individuals, and it is influenced by factors such as gender, age, and whether the motion is performed actively or passively. ROM for running and swimming, both performed with due consideration on speed, plays an important role. The time of generation of speed and mobility of the particular joints are very important for both kinds of athletes. The difficulties that happen during running and swimming in the direction of motion is changed. In this study, data were collected for a total of 102 subjects divided into three groups: control group (22), middle and longdistance runners (40), and swimmers (40), and their ages are between 12 to 18 years. The swimmers have higher ROM in shoulder joint flexion, extension, abduction, and adduction movement. Middle and long-distance runners have significantly greater ROM from Control Group in the left shoulder joint flexion with a 5.82 mean difference. Swimmers have significantly higher ROM from the Control Group in the left shoulder joint flexion with 24.84 mean difference and swimmers have significantly higher ROM from the Middle and Long distance runners in left shoulder flexion with 19.02 mean difference. The picture will be clear after a more detailed investigation.
Forthcoming in Journal of Banking and Finance

This paper investigates whether inclusive banking can boost bank-level performance, using an international sample of 1,740 banks over the period 2004-2015. We find that there is a significant positive association between financial inclusion and bank efficiency. Greater financial inclusion helps banks in reducing the volatility of their deposit-funding share as it provides more stable long-term funds for banks, while also mitigating the adverse effects of their return volatility. The association is stronger in countries with limited restrictions on banking activities or more capital regulation stringency as the deposit channel enables greater flow of low-cost funds for high-return investments. The results are robust to instrumental variable analysis, multiple dimensions of financial inclusion (supply, demand, and pro-access policy), and a difference-in-differences estimator that exploits cross-country and temporal variations in actively promoting an inclusive agenda, further confirming that inclusive financial development can be beneficial for banks. JEL Classification: G15; G21; O16
Mycobacterium chimaera from Cardio Surgery Heating-Cooling Units and from Clinical Samples in Israel are Genetically Unrelated

Non-tuberculous mycobacteria (NTM) are opportunistic pathogens that cause illness primarily in the elderly, in the immunocompromised or in patients with underlying lung disease. Mycobacterium chimaera is a NTM species belonging to the M. avium Complex (MAC) group of species. Since 2013, a global outbreak of M. chimaera infection related to heater-cooler units (HCU) used in cardio-thoracic surgery has been identified. This outbreak was caused by a single strain of M. chimaera. In order to estimate the prevalence of this outbreak strain in Israel, we sampled M. chimaera from several HCU machines in Israel, as well as from patients, sequenced their genomes and compared them to the outbreak strain. The presence of mixed mycobacteria species in the samples complicated the analysis of obtained sequences. By applying a metagenomic binning strategy, we were able to obtain genomes of single strains from the mixed samples, and characterized them. M. chimaera strains were compared to each other and to previously reported genomes from other countries. The strain causing the outbreak related to the HCU machines was identified in several such machines in Israel but not in any of the clinical samples.
Graph Structured Multi-Goal Policy Learning For Robotic Manipulation

: A key challenge in multi-goal policy learning for robotic manipulation 1 is scaling up to a large number of qualitatively different manipulation problems 2 within a single domain. For example, very few methods exist (if any) for learning 3 a goal-conditioned policy that can reliably construct any one of hundreds of differ-4 ent block structures. This paper demonstrates that a simple hierarchical approach 5 can work well as long as we are willing to provide the system with a high-level 6 discrete representation of the domain. We compare against standard baselines and 7 show that the method can be used to learn a single goal-conditioned policy that 8 can build any one of more than a hundred different block structures reliably, both 9 in simulation and on a physical system. 10
On the Visualization of Semantic-based Mappings

The popularity of the semantic web in many domains, such as transportation, has led to an ever-increasing development of standards, vocabularies, and ontologies, which generates problems of heterogeneity and lack of interoperability. To address this issue, a large body of research focused on providing various mapping tools and techniques to translate data from one standard to another to foster smooth communication among them. While valuable advancements in mapping techniques have been achieved so far, the explainability and usability of such tools have been overlooked. Since explainability of software is being recognized as a crucial non-functional requirement for complex systems, the development of self-explaining and user-friendly graphical interfaces is becoming a pressing need. In this paper we present S2SMaT , our contribution to the problem of visualization of mappings. The tool helps users easily navigate the structure of standards, understand the suggested mappings between their terms, and in general more easily interact with the system.
Informing Development of the UK Place-based R&D Strategy (Full Version)

The West-Midlands Regional Economic Development Institute (WMREDI) at the University of Birmingham hosted a closed-forum for Research England (UKRI) to examine the evidence base for a place-based R&D strategy. The following questions guided discussions:  What evidence is available (or is needed) to show that different kinds of regional impact result from different kinds of R&D investments / interventions?  What should the research and innovation system – universities, businesses, research organisation and other intermediaries do more of to support economic growth in their region?  How can we ensure different parts of the UK have the mix of interventions they need to enable R&D-led growth?  What are the major critical dependencies for R&D policy levers at the national and the local level?  Is there evidence to show which different levers, incentives, mechanisms and critical dependencies make a difference to scale of impact and/or outcomes?  What does UKRI, government and local leadership need to do to ensure research and innovation can play a powerful role in levelling up the country?  How can UKRI better support this activity/what more can UKRI do to support these activities?
Inferring precipitation from glacier mass balances

Reconciling high altitude precipitation in the upper Indus Basin with glacier mass balances and runoff W. W. Immerzeel, N. Wanders, A. F. Lutz, J. M. Shea, and M. F. P. Bierkens Utrecht University, Department of Physical Geography, Heidelberglaan 2, P.O. Box 80115, Utrecht, the Netherlands International Centre for Integrated Mountain Development (ICIMOD), P.O. Box 3226, Kathmandu, Nepal FutureWater, Costerweg 1V, 6702 AA Wageningen, the Netherlands
Enhancing Temporal Extrapolation of Wind Speed Using a Hybrid Technique: A Case Study in West Coast of Denmark

The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models. Keywords—Data fusion, Gaussian process regression, signal denoise, temporal extrapolation.
Disentangled Predictive Representation for Meta-Reinforcement Learning

A major challenge in reinforcement learning is the design of agents that are able to generalise across tasks under common dynamics. A viable solution is meta-reinforcement learning, which identiﬁes common structures among past tasks to be then generalised to new tasks (meta-test). Prior works learn meta-representation jointly while solving a task, resulting in representations that do not generalise well across different policies, leading to sampling-inefﬁciency during meta-test phases. In this work, we introduce state2vec , an efﬁcient and low-complexity unsupervised framework for learning disentangled representation that are more general. The state embedding vectors learned with state2vec capture the geometry of the underlying state space, resulting in high-quality basis functions for linear value function approximation.
On Dialogue Systems Based on Deep Learning

—Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Speciﬁcally, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions.
[Re] Reproducing ’Identifying through flows for recovering latent representations’

To reproduce the results of the paper, the main experiments are reproduced and the figures are recreated. To do so, we largely worked with code from the repository belonging to the original paper. We added plotting functionality as well as various bug fixes and optimisations. Additionally, attempts weremade to improve the iVAE bymaking it more complex and fixing a mistake in its implementation.We also tried to investigate possible correlation between the structure of the dataset and the performance. All code used is publicly available at https://github.com/HiddeLekanne/Reproducibility-Challenge-iFlow.
Statistical pattern recognition for automatic writer identification and verification

The thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document analysis fields.We describes a number of new and very effective techniques that we have developed in recent years for automatic writer identification and verification. Writer individuality is encoded using probability distribution functions (PDFs) extracted handwritten text blocks. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of the individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common codebook of graphemes obtained by clustering. There are two distinguishing characteristics of our approach: human intervention is minimized in the writer identification process and we encode individual handwriting style using features designed to be independent of the textual content of the handwritten sample. In our methods the computer is completely unaware of what has been written in the sample. The handwriting is merely seen as a texture characterized by some directional probability distributions or as a simple stochastic shape-emission process characterized by a grapheme occurrence probability. Our methods were statistically evaluated using datasets with handwriting samples collected from up to 900 subjects. The development of our writer identification techniques takes place at a time when many biometric methods (e.g. iris, fingerprint, face) undergo a transition from research to real full-scale deployment. Our methods also have practical feasibility and hold the promise of direct applicability. The main chapters of the thesis are based on a number of scientific papers that were published during the duration of the PhD project. Here is an outline with short abstracts for the main chapters and references to the corresponding papers.
Whole genome sequence and genome-wide distributed single nucleotide polymorphisms (SNPs) of the Black Bengal goat [version 1; peer review: 2 approved with reservations]

The Black Bengal goat (BBG) is a dwarf sized heritage goat (Capra hircus) breed from Bangladesh, and is well known for its high fertility, excellent meat and skin quality. Here we present the first whole genome sequence and genome-wide distributed single nucleotide polymorphisms (SNPs) of the BBG. A total of 833,469,900 raw reads consisting of 125,020,485,000 bases were obtained by sequencing one male BBG sample. The reads were aligned to the San Clemente and the Yunnan black goat genome which resulted in 98.65% (properly paired, 94.81%) and 98.50% (properly paired, 97.10%) of the reads aligning, respectively. Notably, the estimated sequencing coverages were 48.22X and 44.28X compared to published San Clemente and the Yunnan black goat genomes respectively. On the other hand, a total of 9,497,875 high quality SNPs (Q ≥ 20) along with 1,023,359 indels, and 8,746,849 high quality SNPs along with 842,706 indels were identified in BBG against the San Clemente and Yunnan black goat genomes respectively. The dataset is publicly available from NCBI BioSample (SAMN10391846), Sequence Read Archive (SRR8182317, SRR8549413 and SRR8549904), with BioProject ID PRJNA504436. These data might be useful genomic resources in conducting genome wide association studies, identification of quantitative trait loci (QTLs) and functional genomic analysis of the Black Bengal goat.
Cra and cAMP Receptor Protein Have Opposing Roles in the Regulation of <italic toggle='yes'>fruB</italic> in <named-content content-type='genus-species'>Vibrio cholerae</named-content>

The Gram-negative bacterium Vibrio cholerae adapts to changes in the environment by selectively producing the necessary machinery to take up and metabolize available carbohydrates. The import of fructose by the fructose-specific phosphoenolpyruvate (PEP) phosphotransferase system (PTS) is of particular interest because of its putative connection to cholera pathogenesis and persistence. Here, we describe the expression and regulation of fruB, which encodes an EIIA-FPr fusion protein as part of the fructose-specific PTS in V. cholerae. Using a series of transcriptional reporter fusions and additional biochemical and genetic assays, we identified Cra (catabolite repressor/activator) and cAMP receptor protein (CRP) as regulators of fruB expression and determined that this regulation is dependent upon the presence or absence of PTS sugars. Cra functions as a repressor, downregulating fruB expression in the absence of fructose when components of PTSFru are not needed. CRP functions as an activator of fruB expression. We also report that Cra and CRP can affect fruB expression independently; however, CRP can modulate cra expression in the presence of fructose and glucose. Evidence from this work provides the foundation for continued investigations into PTSFru and its relationship to the V. cholerae life cycle. IMPORTANCE Vibrio cholerae is the causative agent of cholera disease. While current treatments of care are accessible, we still lack an understanding of the molecular mechanisms that allow V. cholerae to survive in both aquatic reservoirs and the human small intestine, where pathogenesis occurs. Central to V. cholerae’s survival is its ability to use available carbon sources. Here, we investigate the regulation of fruB, which encodes a protein central to the import and metabolism of fructose. We show that fruB expression is controlled by the transcriptional regulators Cra and CRP. This work contributes toward a clearer understanding of how carbon source availability impacts the physiology and, potentially, the persistence of the pathogen.
Redundancy Resolution as Action Bias in Policy Search for Robotic Manipulation

: We propose a novel approach that biases actions during policy search by lifting the concept of redundancy resolution from multi-DoF robot kinematics to the level of the reward in deep reinforcement learning and evolution strategies. The key idea is to bias the distribution of executed actions in the sense that the immediate reward remains unchanged. The resulting biased actions favor secondary objectives yielding policies that are safer to apply on the real robot. We demonstrate the feasibility of our method, considered as policy search with redundant action bias (PSRAB), in a reaching and a pick-and-lift task with a 7-DoF Franka robot arm trained in RLBench – a recently introduced benchmark for robotic manipulation – using state-of-the-art TD3 deep reinforcement learning and OpenAI’s evolutionary strategy. We show that it is a ﬂexible approach without the need of signiﬁcant ﬁne-tuning and interference with the main objective even across different policy search methods and tasks of different complexity. We evaluate our approach in simulation and on the real robot. Our project website with videos and further results can be found at: https://sites.google. com/view/redundant-action-bias
Reproduction study: Towards Transparent and Explainable Attention Models

Mohankumar et al. (2020) claim that current attention mechanisms in LSTM based encoders can neither provide a 3 faithful nor a plausible explanation of the model’s predictions in Natural Language Processing tasks. To make attention 4 mechanisms more faithful and plausible, the authors propose two modified LSTM models with a diversity-driven 5 training objective that ensures that the hidden representations learned at different time steps are diverse: the Orthogonal 6 LSTM and the Diversity LSTM. The authors claim that the resulting attention distributions from these diversity-driven 7 LSTMs offer more explainability and transparency in contrast to a Vanilla LSTM. 8
CS 224S Final Report: Compression of Deep Speech Recognition Networks

Automatic speech recognition (ASR) has been a subject of research by linguists and computer scientists for decades. In recent years, state-of-theart speech recognition systems have moved from HMM-based systems to end-to-end neural systems. These systems usually run on powerful machines or clusters. However one of the biggest usages of automatic speech recognition is in applications running on mobile and wearable devices, requiring acoustic data to be sent from the device to data centers. In many situations, it is desirable to have a speech recognition system that performs relatively well offline as well. Offline ASR systems have the benefits of reduced latency as well as improved reliability, but face the challenges of a memory, compute, and power-constrained environment. In our project, we focus on investigating model compression methods for reducing the memory and computational footprint of deep speech recognition networks.
Native Language Identiﬁcation from i-vectors and Speech Transcriptions

Native Language Identiﬁcation (NLI) is the task of identifying a speaker’s native language (L1) from their speech or writing in a secondary language (L2). This paper details a novel approach to NLI. The authors propose several independent meth-ods for approaching the task, along with an ensemble method that achieves greater performance by combining results from individual models. The models in this paper were trained and tested using data from the Native Language Identiﬁcation Shared Task data set.