Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond simple reinforcement learning: the computational neurobiology of reward‐learning and valuation

Neural computational accounts of reward‐learning have been dominated by the hypothesis that dopamine neurons behave like a reward‐prediction error and thus facilitate reinforcement learning in striatal target neurons. While this framework is consistent with a lot of behavioral and neural evidence, this theory fails to account for a number of behavioral and neurobiological observations. In this special issue of EJN we feature a combination of theoretical and experimental papers highlighting some of the explanatory challenges faced by simple reinforcement‐learning models and describing some of the ways in which the framework is being extended in order to address these challenges.
Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords

For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as “bind” or “interact” plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.
Host Platelets and, in Part, Neutrophils Mediate Lung Accumulation of Transfused UVB-Irradiated Human Platelets in a Mouse Model of Acute Lung Injury

We previously reported that ultraviolet light B (UVB)-treated human platelets (hPLTs) can cause acute lung injury (ALI) in a two-event SCID mouse model in which the predisposing event was Lipopolysaccharide (LPS) injection and the second event was infusion of UVB-treated hPLTs. To delineate contributions of host mouse platelets (mPLTs) and neutrophils in the pathogenesis of ALI in this mouse model, we depleted mPLTs or neutrophils and measured hPLT accumulation in the lung. We also assessed lung injury by protein content in bronchoalveolar lavage fluid (BALF). LPS injection followed by infusion of UVB-treated hPLTs resulted in sequestration of both mPLTs and hPLTs in the lungs of SCID mice, although the numbers of neutrophils in the lung were not significantly different from the control group. Depletion of mouse neutrophils caused only a mild reduction in UVB-hPLTs accumulation in the lungs and a mild reduction in protein content in BALF. In comparison, depletion of mPLTs almost completely abolished hPLTs accumulation in the lung and significantly reduced protein content in BALF. UVB-treated hPLTs bound to host mPLTs, but did not bind to neutrophils in the lung. Aspirin treatment of hPLTs in vitro abolished hPLT accumulation in the lung and protected mice from lung injury. Our data indicate that host mPLTs accumulated in the lungs in response to an inflammatory challenge and subsequently mediated the attachment of transfused UVB-hPLTs. Neutrophils also recruited a small percentage of platelets to the lung. These findings may help develop therapeutic strategies for ALI which could potentially result from transfusion of UV illuminated platelets.
Fast phylogenetic DNA barcoding

We present a heuristic approach to the DNA assignment problem based on phylogenetic inferences using constrained neighbour joining and non-parametric bootstrapping. We show that this method performs as well as the more computationally intensive full Bayesian approach in an analysis of 500 insect DNA sequences obtained from GenBank. We also analyse a previously published dataset of environmental DNA sequences from soil from New Zealand and Siberia, and use these data to illustrate the fact that statistical approaches to the DNA assignment problem allow for more appropriate criteria for determining the taxonomic level at which a particular DNA sequence can be assigned.
Improving Paid Microtasks through Gamification and Adaptive Furtherance Incentives

Crowdsourcing via paid microtasks has been successfully applied in a plethora of domains and tasks. Previous efforts for making such crowdsourcing more effective have considered aspects as diverse as task and workflow design, spam detection, quality control, and pricing models. Our work expands upon such efforts by examining the potential of adding gamification to microtask interfaces as a means of improving both worker engagement and effectiveness. We run a series of experiments in image labeling, one of the most common use cases for microtask crowdsourcing, and analyse worker behavior in terms of number of images completed, quality of annotations compared against a gold standard, and response to financial and game-specific rewards. Each experiment studies these parameters in two settings: one based on a state-of-the-art, non-gamified task on CrowdFlower and another one using an alternative interface incorporating several game elements. Our findings show that gamification leads to better accuracy and lower costs than conventional approaches that use only monetary incentives. In addition, it seems to make paid microtask work more rewarding and engaging, especially when sociality features are introduced. Following these initial insights, we define a predictive model for estimating the most appropriate incentives for individual workers, based on their previous contributions. This allows us to build a personalised game experience, with gains seen on the volume and quality of work completed.
Polarization sensitive localization based super-resolution microscopy with a birefringent wedge

A practical method has been presented for polarization sensitive localization based super-resolution microscopy using a birefringent dual wedge. The measurement of the polarization degree at the single molecule level can reveal the chemical and physical properties of the local environment of the fluorescent dye molecule and can hence provide information about the sub-diffraction sized structure of biological samples. Polarization sensitive STORM imaging of the F-Actins proved correlation between the orientation of fluorescent dipoles and the axis of the fibril.
A hierarchical geostatistical model of walking style variety

This paper presents a new method on generating realistic human animation of various styles with given step constraints and a specific skeleton. Given a set of normal walking data captured from different subjects, a hierarchical geostatistical model is automatically learned to encode variety of walking styles by representing human body as a hierarchy of joint groups. For each child hierarchy level, there is a learned geostatistical model, whose low-dimensional control parameters are automatically constructed from the synthesized motion of its parent hierarchy level. The top level is controlled by the given step constraints. Also a realistic transition is finished during each three sequential stances from two interpolated cycles satisfying input step constraints thanks to the local model. We show that the normal walking styles are flexibly controlled by a simple graphlike user interface representing the given skeleton and steps. Our results demonstrate that this method would be helpful to remove the motion clones in group or crowd animation.
Leading to Distraction: Driver distraction, lead car, and road environment

Driver distraction is strongly associated with crashes and near-misses, and despite the attention this topic has received in recent years, the effect of different types of distracting task on driving performance remains unclear. In the case of non-visual distractions, such as talking on the phone or other engaging verbal tasks that do not require a visual input, a common finding is reduced lateral variability in steering and gaze patterns where participants concentrate their gaze towards the centre of the road and their steering control is less variable. In the experiments presented here, we examined whether this finding is more pronounced in the presence of a lead car (which may provide a focus point for gaze) and whether the behaviour of the lead car has any influence on the driver’s steering control. In addition, both visual and non-visual distraction tasks were used, and their effect on different road environments (straight and curved roadways) was assessed. Visual distraction was found to increase variability in both gaze patterns and steering control, non-visual distraction reduced gaze and steering variability in conditions without a lead car; in the conditions where a lead car was present there was no significant difference from baseline. The lateral behaviour of the lead car did not have an effect on steering performance, a finding which indicates that a lead car may not necessarily be used as an information point. Finally, the effects of driver distraction were different for straight and curved roadways, indicating a stronger influence of the road environment in steering than previously thought.
Lossless data hiding method based on MST and topology changes of 3D triangular mesh

For the last five years or so, several 3D data hiding algorithms have been developed. Few of them proposed methods to hide data in 3D objects without changing the position of vertices in the 3D space. In this paper we propose a new approach of data hiding in 3D objects based on the minimum spanning tree (MST). This method is lossless in the sense that the positions of the vertices are unchanged. The method is blind and does not depend on the ordering of the data in the files. Moreover the embedding capacity is significant and can be as high as 2.5% of the size of the 3D object.
Digitized self-oscillating loop for piezoelectric transformer-based power converters

A new method is implemented in designing of self-oscillating loop for driving piezoelectric transformers. The implemented method is based on combining both analog and digital control systems. Digitized delay, or digitized phase shift through the self-oscillating loop results in a very precise frequency control and ensures an optimum operation of the piezoelectric transformer in terms of voltage gain and efficiency. In this work, additional time delay is implemented digitally for the first time through 16 bit digital-to-analog converter to the self-oscillating loop. Delay control setpoints updates at a rate of 417 kHz. This allows the control loop to dynamically follow frequency changes of the transformer in each resonant cycle. The operation principle behind self-oscillating is discussed in this paper. Moreover, experimental results are reported.
The fine structure of the flagellum and kinetoplast-chondriome of Trypanosoma (Schizofrypanum) cruzi in tissue culture.

SYNOPSIS. Trypanosoma cruzi in tissue cultures was studied with the electron microscope after double fixation in glutaraldehyde and osmium tetroxide, and embedding in Epon. Previous findings on its fine structure were confirmed, and some new structures were found in the flagellum and kinetoplast-chondriome. In the flagellum, an intraflagellar body was found, similar to that observed in other trypanosomes, beginning at the base of the flagellum and running along the axial fibre bundle thruout its length. The axial fibre bundle is formed by interconnecting tubules, the outer ones apparently smooth, the inner ones with a helical substructure. Lateral extensions from the outer tubules in the flagellar bundle seem to enter the intraflagellar body. The kinetopiast in the leishmania bodies has the same electrondense structure described before. In the trypanosome form it has assumed a large spherical shape, in which the formerly short, compressed fibres have grown in length, are more dispersed and have an irregular shape. They are oriented in the direction of the body's length in parallel array. The whole formation is continuous with a long mitochondrion which begins in the region of the nucleus and extends up almost to the tip of the trypanosome. The matrix of the kinetoplast in these forms is electron-transparent; the matrix of the mitochondria is rather dense. In a few extracellular trypanosomes, a special structure was found in which the kinetoplast is composed of electron-transparent formations, arranged in orderly horizontal lines quite similar to the mitochondrial cristae of the parasite. The significance of this structure is uncertain.
Run , Reward , Repeat : Training Musculoskeletal Models with Deep Deterministic Policy Gradients

Deep reinforcement learning techniques have shown to be promising in highdimensional state and continuous action spaces. We apply deep deterministic policy gradients (DDPG) in a high-dimensional state and action space to train a skeletomuscular human model to run, as simulated by the Stanford NMBL running environment based on the OpenSim simulation and OpenAI Gym environment. We test a variety of different configurations on the sequential actor and critic neural networks of the off-policy model-free DDPG, which learn a policy giving 18-dimensional action vectors from an input of 41-dimensional observation vectors representing the human frame. Our results indicate that the learning configuration best suited toward achieving long-term rapid locomotion uses three-layer fully connected sequential neural networks with leaky reLU activation in both the actor and critic networks. This project has further implications in the fields of robotics and prosthetics, in which robust bipedal locomotion is a longstanding objective.
Simple and rapid method for isolating large plasmid DNA from lactic streptococci

A procedure for the rapid isolation of plasmid DNA larger than 30 megadaltons from lactic streptococci is described. This protocol can be used on a preparative scale to isolate sufficient quantities of plasmid DNA required for restriction analysis, cloning, or transformation experiments. A scaled-down protocol is very useful for rapidly screening the plasmid content of streptococcal strains. With this methodology, previously undetected large plasmids were observed.
Making Sense of Large Network Data : Combining Rich User Interaction and Machine Learning

Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduce Apolo, a system that uses a mixed-initiative approach— combining visualization, rich user interaction and machine learning—to guide the user to incrementally and interactively explore large network data and make sense of it. Apolo engages the user in bottom-up sensemaking to gradually build up an understanding over time by starting small, rather than starting big and drilling down. Apolo also helps users find relevant information by specifying exemplars, and then using a machine learning method called Belief Propagation to infer which other nodes may be of interest. We evaluated Apolo with twelve participants in a between-subjects study, with the task being to find relevant new papers to update an existing survey paper. Using expert judges, participants using Apolo found significantly more relevant papers. Subjective feedback of Apolo was also very positive.
Results of the Enterprise Project

In this paper we present the results of the Enterprise project which was concerned with developing a tool set for enterprise modelling Our approach concentrates on integration communication exibility and support We describe the Enterprise Tool Set which uses executable process models to help users to perform their tasks The Tool Set is implemented using an agent based architecture to integrate o the shelf tools in a plug and play style To ensure e ective interchange of information and knowledge between di erent users tasks and systems we developed the Enterprise Ontology which de nes terms used in organisations The project has been successful and valuable insights have been gained and made available At AIAI at the University of Edinburgh we are interested in pursuing intelligent task management ontologies and enterprise modelling beyond the end of the project
Interpreting Pointing Gestures and Spoken Requests – A Probabilistic, Salience-based Approach

We present a probabilistic, salience-based approach to the interpretation of pointing gestures together with spoken utterances. Our mechanism models dependencies between spatial and temporal aspects of gestures and features of utterances. For our evaluation, we collected a corpus of requests which optionally included pointing. Our results show that pointing information improves interpretation accuracy.
Slim Fly: A Cost Effective Low-Diameter Network Topology

We introduce a high-performance cost-effective network topology called Slim Fly that approaches the theoretically optimal network diameter. Slim Fly is based on graphs that approximate the solution to the degree-diameter problem. We analyze Slim Fly and compare it to both traditional and state-of the-art networks. Our analysis shows that Slim Fly has significant advantages over other topologies in latency, bandwidth, resiliency, cost, and power consumption. Finally, we propose deadlock-free routing schemes and physical layouts for large computing centres as well as a detailed cost and power model. Slim Fly enables constructing cost effective and highly resilient data enter and HPC networks that offer low latency and high bandwidth under different HPC workloads such as stencil or graph computations.
Conditional Entropy Based User Selection for Multiuser MIMO Systems

We consider the problem of user subset selection for maximizing the sum rate of downlink multi-user MIMO systems. The brute-force search for the optimal user set becomes impractical as the total number of users in a cell increase. We propose a user selection algorithm based on conditional differential entropy. We apply the proposed algorithm on Block diagonalization scheme. Simulation results show that the proposed conditional entropy based algorithm offers better alternatives than the existing user selection algorithms. Furthermore, in terms of sum rate, the solution obtained by the proposed algorithm turns out to be close to the optimal solution with significantly lower computational complexity than brute-force search.
Ensemble Learning of Named Entity Recognition Algorithms using Multilayer Perceptron for the Multilingual Web of Data

Implementing the multilingual Semantic Web vision requires transforming unstructured data in multiple languages from the Document Web into structured data for the multilingual Web of Data. We present the multilingual version of FOX, a knowledge extraction suite which supports this migration by providing named entity recognition based on ensemble learning for five languages. Our evaluation results show that our approach goes beyond the performance of existing named entity recognition systems on all five languages. In our best run, we outperform the state of the art by a gain of 32.38% F1-Score points on a Dutch dataset. More information and a demo can be found at http://fox.aksw. org as well as an extended version of the paper descriping the evaluation in detail.
Fabrication of nanowire-based magnetic structures for magnetic resonance applications

The development and fabrication of novel magnetic nanowire devices is presented. These devices are used both to explore the fundamental physics of single domain particles, and to provide signal amplification and increased resolution in magnetic resonance imaging. Fabrication protocols for the creation of nickel nanowires were developed using both electron beam lithography and electroplating into nanoporous templates. The templates for electroplating were created by anodizing aluminum in either oxalic or sulfuric acids. The templates are 15 to 25 $mu$m thick and composed of highly ordered pores of 40 nm and 20 nm in diameter respectively. Nanowire samples formed by each protocol are characterized using an alternating gradient magnetometer to measure magnetic hysteresis loops. The magnets formed by electroplating were found to be much closer to ideal single domain magnets than those written via electron beam. Coercivities over to 1000 Oe were observed. Individual cylindrical nanowires of 70 nm diameter were contacted using focused ion beam assisted platinum deposition. A contacted nanowire was tested in a cryostat to determine the temperature dependence of the magneto-resistive properties of the wire. Sections of plated nanowires still in the anodized aluminum template were examined for their reversible transverse susceptibility for applications in signal amplification in magnetic resonance imaging systems. A process of selectively plating into the aluminum templates to create shape magnets with interesting magnetic fields was developed for creating magnetic "lenses' with focal points above the plane of the substrate. Finally, an inductive stripe loop array was fabricated for use in stripe sensor tomography. These developments will enable future work on magnetic resonance imaging using a background of patterned templates for amplification.
Learning Markov networks: maximum bounded tree-width graphs

Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a joint probability distribution. Once one has chosen to use a Markov network model, one aims to choose the model that “best explains” the data that has been observed—this model can then be used to make predictions about future data. We show that the problem of learning a maximum likelihood Markov network given certain observed data can be reduced to the problem of identifying a maximum weight low-treewidth graph under a given input weight function. We give the first constant factor approximation algorithm for this problem. More precisely, for any fixed treewidth objective k, we find a treewidth-k graph with an f(k) fraction of the maximum possible weight of any treewidth-k graph.