Miracle-dz/UniFAR-FTUs huggingface dataset:
Local context is not enough! Towards Query Semantic and Knowledge Guided Multi-Span Medical Question Answering

Medical Question Answering (MedQA) is one of the most popular and significant tasks in developing healthcare assistants. When humans extract an answer to a question from a document, they first (a) understand the question itself in detail and (b) utilize relevant knowledge/experiences to determine the answer segments. In multi-span question answering, it becomes increasingly important to comprehend the query accurately and possess relevant knowledge, as the interrelationship among different answer segments is essential for achieving completeness. Motivated by this, we first propose a transformer-based query semantic and knowledge ( Que-SemKnow ) guided multi-span question-answering model. The proposed QueSemKnow works in a two-phased manner; in the first stage, a multi-task model is proposed to extract query semantics: (i) intent identification and (ii) question type prediction. In the second stage, QueSemKnow selects a relevant subset of the knowledge graph as the underlying context/document and extracts answers depending on the semantic information extracted from the first stage and context. We build a multi-task query semantic extraction model for query intent and query type identification to investigate the co-relation among these tasks. Furthermore, we created a semantically aware medical question-answering corpus named QueSeMSpan MedQA wherein each question is annotated with its corresponding semantic information. The proposed model outperforms several baselines and existing state-of-the-art models by a large margin on multiple datasets, which firmly demonstrates the effectiveness of the human-inspired multi-span question-answering methodology.
Fair Latent Deep Generative Models (FLDGMs) for Syntax-Agnostic and Fair Synthetic Data Generation

Deep Generative Models (DGMs) for generating synthetic data with properties such as quality, diversity, ﬁdelity, and privacy is an important research topic. Fairness is one particular aspect that has not received the attention it deserves. One difﬁculty is training DGMs with an in-process fairness objective, which can disturb the global convergence characteristics. To address this, we propose Fair Latent Deep Generative Models (FLDGMs) as enablers for more ﬂexible and stable training of fair DGMs, by ﬁrst learning a syntax-agnostic, model-agnostic fair latent representation (low dimensional) of the data. This separates the fairness optimization and data generation processes thereby boosting stability and optimization performance. Moreover, data generation in the low dimensional space enhances the accessibility of models by reducing computational demands. We conduct extensive experiments on image and tabular domains us-ing Generative Adversarial Networks (GANs) and Diffusion Models (DMs) and compare them to the state-of-the-art in terms of fairness and utility. Our proposed FLDGMs achieve superior performance in generating high-quality, high-ﬁdelity, and high-diversity fair synthetic data compared to the state-of-the-art fair generative models.
A collaborative virtual geographic environment for emergency dam-break simulation and risk analysis

Since barrier lakes are affected by a highly changing and complex environment, it is always a great challenge to determine the proper actions that must be performed within a limited amount of time. Existing methods have some restrictions, such as the dispersed data resources and personnel, the loose model integration, and the complicated and unfriendly user interface, which results in low efficiency of dam-break risk assessment under emergency response conditions. These issues were addressed in this paper by constructing a collaborative virtual geographic environment (CVGE), which supports three levels of deeper applications including sharing of distributed resources, tightly geographic process simulation and visualisation analysis, and geographic collaboration. A virtual resource environment and mobile computing service were designed for the distributed resource sharing. A spatiotemporal process model based on cellular automata and parallel computing optimisation was proposed and integrated for real-time simulation and analysis of dam-break risk assessment of barrier lakes. A distributed virtual geographic scene and collaborative workflow were designed to improve the operational efficiency of the models with respect to initiation, computation, output visualisation and analysis. A prototype system was developed to support collaborative dam-break risk assessment of the Xiaojiaqiao barrier lake in Anxian County, Sichuan Province, China. The experimental results indicate that the proposed method could efficiently improve the efficiency of dam-break risk assessment on data supporting, model computation, and visualisation analysis.
Towards Abstractive Timeline Summarisation Using Preference-Based Reinforcement Learning

This paper introduces a novel pipeline for summarising timelines of events reported by multiple news sources. Transformer-based models for abstractive summarisation generate coherent and concise summaries of long documents but can fail to outperform established extractive methods on specialised tasks such as timeline summarisation (TLS). While extractive summaries are more faithful to their sources, they may be less readable and contain redundant or unnecessary information. This paper proposes a preference-based reinforcement learning (PBRL) method for adapting pretrained abstractive summarisers to TLS, which can overcome the drawbacks of extractive timeline summaries. We define a compound reward function that learns from keywords of interest and pairwise preference labels, which we use to fine-tune a pretrained abstractive summariser via offline reinforcement learning. We carry out both automated and human evaluation on three datasets, finding that our method outperforms a comparable extractive TLS method on two of the three benchmark datasets, and participants prefer our method's summaries to those of both the extractive TLS method and the pretrained abstractive model. The method does not require expensive reference summaries and needs only a small number of preferences to align the generated summaries with human preferences.
TECTONOMAGMATIC CHARACTERISTICS OF THE ETIVE IGNEOUS COMPLEX, SOUTH WEST SCOTLAND

The Etive igneous complex has intruded into Dalradian and Moine metasedimentary rocks in Devonian. Main rock types of the complex are granodiorite, monzodiorite, monzogranite, granite and diorite. Geochemical studies show that the magmas were of high K calc-alkaline, I-type and metaluminous nature. Application of discriminant diagrams to the Etive rocks indicates a possible within-plate tectonic setting for the complex. Considering the magma type and trace element geochemistry along with behaviour of studied samples on tectonomagmatic discriminant diagrams, contributions of subduction related materials and materials with within-plate affinities are likely in generation of the Etive igneous complex.
High-quality Shading and Lighting for Hardware-accelerated Rendering

With fast 3D graphics becoming more and more available even on low end platforms, the focus in developing new graphics hardware is beginning to shift towards higher quality rendering and additional functionality instead of simply higher performance implementations of the traditional graphics pipeline. On this search for improved quality it is important to identify a powerful set of orthogonal features to be implemented in hardware, which can then be flexibly combined to form new algorithms. This dissertation introduces a set of new algorithms for high quality shading and lighting using computer graphics hardware. It is mostly concerned with algorithms for generating various local shading and lighting effects and for visualizing global illumination solutions. In particular, we discuss algorithms for shadows, bump mapping, alternative material models, mirror reflections and glossy reflections off curved surfaces, as well as more realistic lens systems and a complex model for light sources. In the course of developing these algorithms, we identify building blocks that are important for future generations of graphics hardware. Some of these are established features of graphics hardware used in a new, unexpected way, some are experimental features not yet widely used, and some are completely new features that we propose. When introducing new functionality, we make sure that it is orthogonal to existing stages in the graphics pipeline. Thus, the contribution of this dissertation is twofold: on the one hand, it introduces a set of new algorithms for realistic image synthesis using computer graphics hardware. These methods are capable of providing image qualities similar to those of simple ray-tracers, but provide interactive frame rates on contemporary graphics systems. On the other hand, this thesis identifies and introduces features and building blocks that are important for realistic shading and lighting, and thus contributes to the development of better graphics hardware in the future.
Automatic Parallel Portfolio Selection

Algorithms to solve hard combinatorial problems often exhibit complementary performance, i.e. where one algorithm fails, another shines. Algorithm portfolios and algorithm selection take advantage of this by running all algorithms in parallel or choosing the best one to run on a problem instance. In this paper, we show that neither of these approaches gives the best possible performance and propose the happy medium of running a subset of all algorithms in parallel. We propose a method to choose this subset automatically for each problem instance, and demonstrate empirical improvements of up to 19% in terms of runtime, 81% in terms of misclassiﬁcation penalty, and 26% in terms of penalized averaged runtime on scenarios from the ASlib benchmark library. Unlike all other algorithm selection and scheduling approaches in the literature, our performance measures are based on the actual performance for algorithms running in parallel rather than assuming overhead-free parallelization based on sequential performance. Our approach is easy to apply in practice and does not require to solve hard problems to obtain a schedule, unlike other techniques in the literature, while still delivering superior performance.
Cost-Awareness in Multi-Agent Active Search

Multi-agent active search requires autonomous agents to choose sensing actions that efficiently locate targets. In a realistic setting, agents also must consider the costs that their decisions incur. Previously proposed active search algorithms simplify the problem by ignoring uncertainty in the agent’s environment, using myopic decision making, and/or overlooking costs. In this paper, we introduce an online active search algorithm to detect targets in an unknown environment by making adaptive cost-aware decisions regarding the agent’s actions. Our algorithm proposes an online lookahead planner that combines priniciples from Monte Carlo Tree Search, Thompson sampling and pareto-optimal confidence bounds for decentralized multi-agent multi-objective optimization in an unknown environment. We analyze the algorithm’s performance in simulation to show its efficacy in cost-aware active search.
Grouped Multi-Task Learning with Hidden Tasks Enhancement

In multi-task learning (MTL), multiple prediction tasks are learned jointly, such that generalization performance is improved by transferring information across the tasks. However, not all tasks are related, and training unrelated tasks together can worsen the prediction performance because of the phenomenon of negative transfer. To overcome this problem, we propose a novel MTL method that can robustly group correlated tasks into clusters and allow useful information to be transferred only within clusters. The proposed method is based on the assumption that the task clusters lie in the low-rank subspaces of the parameter space, and the number of them and their dimensions are both unknown. By applying subspace clustering to task parameters, parameter learning and task grouping can be done in a uniﬁed framework. To relieve the error induced by the basic linear learner and robustify the model, the effect of hidden tasks is exploited. Moreover, the framework is extended to a multi-layer architecture so as to progressively extract hierarchical subspace structures of tasks, which helps to further improve generalization. The optimization algorithm is proposed, and its effectiveness is validated by experimental results on both synthetic and real-world datasets.
Reconciling SHACL and Ontologies: Semantics and Validation via Rewriting

OWL and SHACL are two prominent W3C standards for managing RDF graphs, the data model of the Web. They are used for different purposes and make different assumptions about the completeness of data: SHACL is used for expressing integrity constraints on complete data, while OWL allows inferring implicit facts from incomplete data; SHACL reasoners perform validation, while OWL reasoners do logical inference. Integrating these two tasks into one uniform approach is a relevant but challenging problem. The SHACL standard envisions graph validation in combination with OWL entailment, but it does not provide technical guidance on how to realize this. To address this problem, we propose a new intuitive semantics for validating SHACL constraints with OWL 2 QL ontologies based on a suitable notion of the chase . We propose an algo-rithm that rewrites a set of recursive SHACL constraints (with strat-iﬁed negation) and an OWL 2 QL ontology into a stand-alone set of SHACL constraints that preserves validation for every input graph, which can in turn be evaluated using an off-the-shelf SHACL valida-tor. We show that validation in this setting is E XP T IME -complete in combined complexity, but only PT IME -complete in data complexity, i.e., if the constraints and the ontology are ﬁxed.
Assessment of sampling techniques and fish assemblage structure in large western river systems

A comparison of sampling techniques and investigation of fish assemblage structure is greatly needed to improve management of western river systems. The objective of this thesis was to compare the efficacy of several gears (hoop nets, boat-mounted electrofishing, benthic trawls) for sampling fish assemblages of western rivers (Green River, Wyoming; Kootenai River, Idaho) and to investigate patterns of fish assemblage shifts in relation to habitat in the Kootenai River. In general, species-specific detectability varied by system and habitat, but was most influenced by gear. Electrofishing and hoop nets had the highest detection probabilities for most species, whereas detectability of most fishes was low with trawls. Transitions in fish assemblage and habitat structure were apparent among river sections in the Kootenai River. This thesis provides information that will improve sampling efficiency and our understanding of factors that structure fish assemblages and populations in coldwater rivers.
Design and Implementation of 8-bit Vedic-Wallace Multiplier

: The processors used in the electronic devices spend more time on multiplication operation compared to other arithmetic operations like addition and subtraction. Generally, multiplying two numbers includes basic shift and add operations which are used in Array multipliers. To speed up the multiplication process Conventional Wallace Tree Multiplier (WTM) is used, which is pipelined process and uses carry save adders to decrease the delay. To further increase the speed of the WTM, higher order compressors like 3-2, 4-2, 5-2, 7-2 etc., are used. In this paper we are designing an 8-bit Wallace Multiplier which uses an optimized 4-2 compressor circuit to decrease the power and delay of the multiplier. The design and simulation of the multiplier circuit is done on Tanner EDA tool version 16.0
Kirstenbosch Botanical Art Biennale 2013Early Detection and Rapid Response Programme

The Biennale (two-yearly exhibition) was delayed by a year in order to be part of the Kirstenbosch/BotSoc Centenary celebrations this year. The theme chosen for 2013 is medicinal and traditional-use plants of southern Africa, which includes plants used for food, shelter, decoration, adornment, health, devotional rituals and rites of passage. Plants have been utilized by all human societies throughout the world, stretching back approximately 60 000 years ago as archaeolgical evidence indicates. The first written records, generally agreed to be a series of clay tablets found in Sumeria around 4000 years ago, detail plant species and their uses. Many of the invasive species being addressed by Working for Water have been proliferating across South Africa for decades, and are thus no longer feasible eradication targets. It is the role of SANBI's Early Detection and Rapid Response Programme (EDRR) to focus upon Invasive Alien Plants (IAPs), which are still considered to be feasible eradication targets.
Detecting and disrupting criminal networks

Background: criminal networks are essentially defined as sets of actors that are connected by one or more ties. While many empirical studies of criminal networks have focused on the properties of actors (i.e. key-players), less attention is paid to the importance of ties in terms of tie-strength and its influence on emerging criminal networks. Aim: The aim of this study is to empirically study the associations between the structural, temporal and demographical dimensions of the strength of ties within organized criminal networks Method: We use a combination of social network-, statistical-and qualitative analysis to study the associations between three dimensions of tie-strength: the structural, temporal-and demographical-dimension. Network-data: A criminal meso-network consisting of N= 5178 actors Results and conclusions: Our findings suggest that weak ties are important for the exchange of information particularly at a meso-level, but not exclusively due to a small-world effect. The majority of these ties are fluid, instrumental in nature, and often based on trust in a third party instead of between actors at both ends. ‘Freelance’ co-operation emerges out of the fluid pools of potential co-offenders. The strong ties in the network rely heavily on kinship and attachment to local neighborhoods. Different clusters of strong ties are associated with an overarching ‘old-boys’ network’ that remains more durable over time and forms a sustainable pool of criminal cooperation within the overall fluid criminal network.
Electrochemical Performance of Screen-Printed Composite Electrochemical Performance of Screen-Printed Composite Coatings of Conducting Polymers and Carbon Nanotubes on Coatings of Conducting Polymers and Carbon Nanotubes on Titanium Bipolar Plates in Aqueous Asymmetrical Titanium Bipolar Plates in A

: Composites of conducting polymers (polypyrrole and polyaniline) with acid treated multi-walled carbon nanotubes were formulated into printable aqueous inks, with the aid of functional additives (benzethonium chloride as a surfactant with or without polyvinyl alcohol as a binder). The inks were screen-printed as fairly uniform coatings of various mass loading densities and areas (up to 75 mg 窑 cm -2 and 100 cm 2 ) on thin titanium plates (0.1 mm in thickness). These screen-printed plates were used to fabricate both unit cell and multi-cell stack of asymmetrical supercapacitors with screen-printed negative electrodes of activated carbon (pigment black) in aqueous electrolytes (3.0 mol 窑 L -1 KCl or 1.0 mol 窑 L -1 HCl). In particular, a three-cell stack with two bipolar Ti plates of 100 cm 2 in screen-printed area was constructed, demonstrating promising technical specifications: 3.0 V in stack voltage, 1.29 ~ 1.83 F 窑 cm -2 in electrode capacitance, 2.30 ~ 3.24 Wh 窑 kg -1 in specific energy, and 1.04 kW 窑 kg -1 in maximum specific power. Cyclic voltammetry, galvanostatic charging and discharging, and electrochemical impedance spectrometry were applied to study the composites, screen-printed coatings and individual and bipolarly stacked cells, assisted by optical and electron microscopy.
Evolution of snow cover and dynamics of atmospheric deposits in the snow in the Antarctica. Data report

Field programme on the surface layer of the ice sheet in the Dronning Maud Land, Antarctica has been performed in 2009-2011. The objectives were to examine the annual accumulation and sublimation history, snow melting, chemistry of snow impurities, and life history of supraglacial and epiglacial lakes in blue ice regions. Fieldwork was done during FINNARP 2009 and 2010 expeditions. The sites were at the Finnish research station Aboa (73◦ 02.5’S, 013◦ 24.4’W), a snow line from Rampen at the edge of the ice shelf pass Aboa to the station Svea, and blue ice at Basen and neighboring nunataks. Snow measurements were made using classical snow pit method, ice and snow sampling, and with automatic observation stations (surface radiation balance, penetration of sunlight into snow and ice, and temperature within the surface layer of snow and ice). Life history, physics, and ecological state of lakes were mapped. This document is the data report including a brief project introduction, descriptions of the experiments, and the data obtained.
, metabolic, and endocannabinoid alterations in rats exhibiting high anxiety-related behaviors

Anxiety disorders are major risk factors for obesity. However, the mechanisms accounting for this susceptibility remain unclear. Animal models have proved to be useful tools for understanding the role of emotional functioning in the development and maintenance of metabolic alterations implicated in obesity. Here we sought to determine the predictive value of behavioral indices of anxiety for hormonal and metabolic imbalances in rats. Adult Lewis rats were screened on the elevated plus maze (EPM). K-means clustering was used to divide the rats into two groups based on their anxiety index in the EPM: low (LA) and high anxiety (HA) rats. This proxy of anxiety combines individual EPM parameters and accepted ratios into a single score. Four weeks later, we measured markers of endocrine and metabolic function. We found that relative LA rats, the HA rats exhibited reduced latencies to exit a modified light-dark conflict test. Our results show that the HA rats displayed increased corticosterone levels when compared to LA rats. Furthermore, the HA rats weighted more and exhibited an enhanced glycemic response to exogenously administered glucose during the glucose tolerance test, indicating glucose intolerance. Notably, when compared to LA rats, the HA rats showed higher circulating levels of the endogenous cannabinoid, 2-arachidonoyl-sn-glycerol (2-AG). Together, these data indicate that patterns of emotional reactivity associated with anxiety may share common pathological pathways with metabolic complications implicated in obesity. Uncovering metabolic risk factors for anxiety disorders have the potential to strongly impact how we treat mental illnesses.