Miracle-dz/UniFAR-FTUs huggingface dataset:
Foreword: Ultrafast Phenomena in condensed matter physics

Matter is dynamic and changes under the effect of various external constraints, in particular light. The development of ultra-fast techniques now makes it possible to study the elementary mechanisms of ultrafast photoinduced phenomena, in particular by following the dynamics of electronic and structural degrees of freedom of matter. These studies provide a better understanding of these non-equilibrium processes in order to control the physical properties of molecules and materials by light. In this introductory article we discuss different aspects of studying ultra-fast condensed matter phenomena. Résumé. La matière est dynamique et se transforme sous l’effet de différentes contraintes extérieures, en particulier la lumière. Le développement de techniques ultra-rapides permet à présent d’étudier les mécanismes élémentaires de phénomènes photoinduits ultra-rapides, en particulier en suivant les dynamiques des degrés de libertés électroniques et structuraux de la matière. Ces études permettent de mieux comprendre ces processus hors équilibre pour envisager un contrôle des propriétés physiques de molécules et matériaux par la lumière. Dans cet article d’introduction nous abordons différents aspects de l’étude de phénomènes ultrarapide en matière condensée.
Dual‐wideband branch‐line coupler based on a loaded N‐segment SIR

In this study, a novel topology of loaded N‐segment stepped‐impedance resonator (SIR) is introduced to control the frequency ratio between the second and first resonance frequencies of such resonator. The fundamental characteristic of such resonators, the frequency ratio between the second and first resonance frequencies, is targeted as a key factor in arbitrary tuning the resonance frequencies. It is shown that this factor can be determined in a ratio of 6.7: 2.7: 2.25 for the proposed, the conventional dual‐section and T‐shaped SIRs using a five‐segment SIR. This concept can be applied in the design of any dual‐band microwave components where a wide branch‐line couplers is chosen as a sample. It is demonstrated that the proposed topology not only provides more design flexibilities but also improves the performance of such components in terms of bandwidth enhancement, better return loss, and size reduction around 56%. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:2300–2303, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27076
The Criticality of Transferable Skills Development and Virtual Learning Environments Used in the Teaching of Project Management

Studies have found the development of transferable skills and the provision of virtual learning environments (VLEs) to be crucial to students who are learning project management. Taking these factors into consideration, this article explores why project management students have emphasized the importance of these two critical learning and teaching parameters in their developmental needs. The study was conducted utilizing focus groups consisting of 88 students in a UK higher education institution. Overall, we found the existence of five critical and applied themes grouped against transferable skills and the provision of VLEs under which students’ needs could be classified.
SciGen: a Dataset for Reasoning-Aware Text Generation from Scientific Tables

We introduce SciGen, a new challenge dataset consisting of tables from scien-tiﬁc articles and their corresponding descriptions, for the task of reasoning-aware data-to-text generation. Describing scientiﬁc tables goes beyond the surface realization of the table content and requires reasoning over table values. The unique properties of SciGen are that (1) tables mostly contain numerical values, and (2) the corresponding descriptions require arithmetic reasoning. SciGen is the ﬁrst dataset that assesses the arithmetic reasoning capabilities of generation models on complex input structures, such as tables from scientiﬁc articles, and thus it opens new avenues for future research in reasoning-aware text generation and evaluation. The core part of SciGen, including the test data, is annotated by one of the authors of the corresponding articles. Such expert annotations do not scale to large training data sizes. To tackle this, we propose a pipeline for automatically extracting high-quality table-description pairs from the LaTeX sources of scientiﬁc articles. We study the effectiveness of state-of-the-art data-to-text generation models on SciGen and evaluate the results using common generation metrics and human evaluation. Our results and analyses show that adding high-quality unsupervised training data improves the correctness and reduces the hallucination in generated descriptions, however, the ability of state-of-the-art models is still severely limited on this task. 2
Processing of data streams in the detection of retroreflective objects

This work is devoted to the study of methods for detecting retroreflective objects (RRO), including optical and optoelectronic observation devices, based on the search for spatial anomalies in images formed in pulse laser location systems. Algorithms and software for detecting RRO have been developed. At the same time, special attention is paid to various methods of forming difference frames with periodic illumination, including preliminary replacing each pixel of the background images with the maximum value for the corresponding neighborhood. The effectiveness of the proposed methods for detecting retroreflective objects in conditions of intense sunlight, despite the presence of specular and diffuse reflecting surfaces in the field of view, is demonstrated.
DEBAGREEMENT: A comment-reply dataset for (dis)agreement detection in online debates

In this paper, we introduce DEBAGREEMENT, a dataset of 42,894 comment-reply pairs from the popular discussion website reddit , annotated with agree , neutral or disagree labels. We collect data from ﬁve forums on reddit : r/BlackLivesMatter , r/Brexit , r/climate , r/democrats , r/Republican . For each forum, we select comment pairs such that they form altogether a user interaction graph. DEBAGREEMENT presents a challenge for Natural Language Processing (NLP) systems, as it contains slang, sarcasm and topic-speciﬁc jokes, often present in online exchanges. We evaluate the performance of state-of-the-art language models on a (dis)agreement detection task, and investigate the use of contextual information available (graph, authorship, and temporal information). Since recent research has shown that context, such as social context or knowledge graph information, enables language models to better perform on downstream NLP tasks, DEBAGREEMENT provides novel opportunities for combining graph-based and text-based machine learning techniques to detect (dis)agreements online.
Lower Extremity Joint Moments during Uphill Cycling

Lower extremity joint moments were investigated in three cycling conditions: level seated, uphill seated and uphill standing. Based on a previous study (Caldwell, Li, McCole, & Hagberg, 1998), it was hypothesized that joint moments in the uphill standing condition would be altered in both magnitude and pattern. Eight national caliber cyclists were filmed while riding their own bicycles mounted to a computerized ergometer. Applied forces were measured with an instrumented pedal, and inverse dynamics were used to calculate joint moments. In the uphill seated condition the joint moments were similar in profile to the level seated but with a modest increase in magnitude. In the uphill standing condition the peak ankle plantarflexor moment was much larger and occurred later in the downstroke than in the seated conditions. The extensor knee moment that marked the first portion of the down-stroke for the seated trials was extended much further into the downstroke while standing, and the subsequent knee flexor mo.
Survey on Peer to Peer Decentralize Ridesharing using Blockchain Technology

: Ride sharing services conveniently allows the drivers to share short to long distances trips with other riders, contributing to the benefits of travel cost, reducing traffic congestion and climate change mitigations. The existing ridesharing services depends on the centralized trust based system for the regulation of the ride set up, fare calculation, tracking, cancellation etc. These central third party authorities control and maintain data about the users, raising the issue about the data reliability and service policies. In case, this centralized based system make them subject to single point of failure and privacy disclosure concerns. Besides they demand high fees for providing the services. Blockchain based ridesharing gives us a chance to shift from the traditional centralized platforms to the decentralized ones. This work explores smart contracts to build and deploy the functionalities such as create ride, deposit transfer, cancel and complete ride method for our decentralized ride sharing application. The work also studies on how to automate the ridesharing service by using the blockchain and smart contract to harvest the benefits of decentralized collaboration and blockchain perspectives.
Urban food sovereignty: Food, land and democracy in Kampala

In urban centres across East Africa, the combination of the Global Food Crisis and unprecedented rates of urbanisation has resulted in chronic food insecurity for the urban poor. Urban agriculture is widely practiced across the region, particularly by low-income groups. The purpose of this paper is to consider how one response to the prevailing, inequitable world food system, Food Sovereignty, might be realised through urban agriculture and the ways that the realities of urban agriculture might be used to strengthen the Food Sovereignty Framework as it is currently conceived. In this paper I explore the current and potential significance of urban agriculture in Kampala and review the evidence for the contribution of urban food production to the realisation of Food Sovereignty with particular focus on the Right to Food, land reform and democratic control. I also outline some of the practical, social and legal challenges facing urban food producers and identify some opportunities for the city authorities to improve the support for pro-poor urban
Nonconscious cognitive reasoning: A neglected ability shaping economic behavior

This paper argues for the inclusion of both conscious and nonconscious reasoning in economic decision making. Vast differences in the capacities of these two sources of cognitive reasoning results in a greater reliance on nonconscious reasoning resources, although the most complex decisions depend on an automatic intermingling of conscious and nonconscious resources. Each decision resource is capable of independently processing data, determining relationships, learning about the underlying structure, and making economic decisions. In contrast to conventional analysis which implicitly views mental activity as costless, the binding limits on conscious reasoning entails high opportunity costs, and the need to make an enormous number of decisions in a timely manner in order to avoid losses due to foregone decisions. People engage in a maximization process to optimize the efficiency and accuracy of their mental resources for decision making. People choose the most efficient and least costly resources that will maximize overall utility. JEL Classification: D83; D87; D90
Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks

Bayesian deep learning seeks to equip deep neural networks with the ability to precisely quantify their predictive uncertainty, and has promised to make deep learning more reliable for safety-critical real-world applications. Yet, existing Bayesian deep learning methods fall short of this promise; new methods continue to be evaluated on unrealistic test beds that do not reﬂect the complexities of downstream real-world tasks that would beneﬁt most from reliable uncertainty quantiﬁcation. We propose the R ETINA Benchmark , a set of real-world tasks that accurately reﬂect such complexities and are designed to assess the reliability of predictive models in safety-critical scenarios. Speciﬁcally, we curate two publicly available datasets of high-resolution human retina images exhibiting varying degrees of diabetic retinopathy, a medical condition that can lead to blindness, and use them to design a suite of automated diagnosis tasks that require reliable predictive uncertainty quantiﬁcation. We use these tasks to benchmark well-established and state-of-the-art Bayesian deep learning methods on task-speciﬁc evaluation metrics. We provide an easy-to-use codebase for fast and easy benchmarking following reproducibility and software design principles. We provide implementations of all methods included in the benchmark as well as results computed over 100 TPU days, 20 GPU days, 400 hyperparameter conﬁgurations, and evaluation on at least 6 random seeds each.
I3D: An Improved Three-Dimensional CNN Model on Hyperspectral Remote Sensing Image Classification

Hyperspectral image data are widely used in real life because it contains rich spectral and spatial information. Hyperspectral image classiﬁcation is to distinguish diﬀerent functions based on diﬀerent features. The computer performs quantitative analysis through the captured image and classiﬁes each pixel in the image. However, the traditional deep learning-based hyperspectral image classiﬁcation technology, due to insuﬃcient spatial-spectral feature extraction, too many network layers, and complex calculations, leads to large parameters and optimizes hyperspectral images. For this reason, I proposed the I3D-CNN model. The number of classiﬁcation parameters is large, and the network is complex. This method uses hyperspectral image cubes to directly extract spectral-spatial coupling features, adds depth separable convolution to 3D convolution to reextract spatial features, and extracts the parameter amount and calculation time at the same time. In addition, the model removes the pooling layer to achieve fewer parameters, smaller model scale, and easier training eﬀects. The performance of the I3D-CNN model on the test datasets is better than other deep learning-based methods after comparison. The results show that the model still exhibits strong classiﬁcation performance, reduces a large number of learning parameters, and reduces complexity. The accuracy rate, average classiﬁcation accuracy rate, and kappa coeﬃcient are all stable above 95%.
Third-Party Sale of Information

We characterise the optimal selling mechanism by a third party adviser who has sole access to information on the continuous value of the good for the buyer in a seller-buyer relationship. The adviser sells binary advice (buy or not) contingent on the seller’s price and, with positive probability, recommends a buy decision when the price exceeds the value. We extend the analysis to the case that the buyer has additional private information on the value of the good. If the buyer has private information the adviser may improve the welfare of the trading parties but, if not, the adviser is detrimental to them. University of Cambridge and St John’s College, Cambridge, UK. University of Bristol, UK.
Debugging deep neural networks

This project thesis argues that interpretability of Artiﬁcial Neural Networks (ANN) will be key to its adoption, especially in high-stakes applications where human lives or livelihoods are at risk. It then provides a bird’s eye view at the fundamentals of ANNs and one of its special-isations, Convolutional Neural Networks (CNN). To dispell the notion of ANNs as complete black boxes, a review of current visualisation methods for CNNs is done. Finally a pre-processing pipeline for an image classiﬁcation and visualisation system to answer the question “Are ﬁnal grades predictable from resume photos? ”
nanoMOS 2.5: A two-dimensional simulator for quantum transport in double-gate MOSFETs

A program to numerically simulate quantum transport in double gate metal oxide semiconductor field effect transistors (MOSFETs) is described. The program uses a Green's function approach and a simple treatment of scattering based on the idea of so-called Buttiker probes. The double gate device geometry permits an efficient mode space approach that dramatically lowers the computational burden and permits use as a design tool. Also implemented for comparison are a ballistic solution of the Boltzmann transport equation and the drift-diffusion approaches. The program is described and some examples of the use of nanoMOS for 10 nm double gate MOSFETs are presented.
Blue, white, and red ocean planets-Simulations of orbital variations in flux and polarization colors

Context. An exoplanet’s habitability will depend strongly on the presence of liquid water. Flux and/or polarization measurements of starlight that is reflected by exoplanets could help to identify exo-oceans. Aims. We investigate which broadband spectral features in flux and polarization phase functions of reflected starlight uniquely identify exo-oceans. Methods. With an adding-doubling algorithm, we computed total fluxes F and polarized fluxes Q of starlight that is reflected by cloud-free and (partly) cloudy exoplanets, for wavelengths from 350 to 865 nm. The ocean surface has waves composed of Fresnel reflecting wave facets and whitecaps, and scattering within the water body is included. Results. Total flux F, polarized flux Q, and degree of polarization P of ocean planets change color from blue, through white, to red at phase angles α ranging from ∼134◦ to ∼108◦ for F, and from ∼123◦ to ∼157◦ for Q, with cloud coverage fraction fc increasing from 0.0 (cloud-free) to 1.0 (completely cloudy) for F, and to 0.98 for Q. The color change in P only occurs for fc ranging from 0.03 to 0.98, with the color crossing angle α ranging from ∼88◦ to ∼161◦. The total flux F of a cloudy, zero surface albedo planet can also change color, and for fc = 0.0, an ocean planet’s F will not change color for surface pressures ps ' 8 bars. Polarized flux Q of a zero surface albedo planet does not change color for any fc. Conclusions. The color change of P of starlight reflected by an exoplanet, from blue, through white, to red with increasing α above 88◦, appears to identify a (partly) cloudy exo-ocean. The color change of polarized flux Q with increasing α above 123◦ appears to uniquely identify an exo-ocean, independent of surface pressure or cloud fraction. At the color changing phase angle, the angular distance between a star and its planet is much larger than at the phase angle where the glint appears in reflected light. The color change in polarization thus offers better prospects for detecting an exo-ocean.
Optimal Transport Methods for Aligning Knowledge Graph Triples with Natural Language in Unsupervised Settings

Frameworks for aligning embeddings of text and embeddings of knowledge graphs (KG) have been used for generating mappings for test-to-text alignment and KG-to-KG alignment, but little has been done for alignment between these two domains. In this dissertation proposal, I aim to create a framework for KG-to-text alignment that utilizes little to no training data to learn these correspondences. Additionally, motivated by the semantic geometries of these embedding spaces, I propose a new line of research into generating explicit embeddings of triples from a knowledge graph.
Dynamic Environments with Deformable Objects

We propose a set of environments with dynamic tasks that involve highly deformable topologically non-trivial objects. These environments facilitate easy experimentation: offer fast runtime, support large-scale parallel data generation, are easy to connect to reinforcement learning frameworks with OpenAI Gym API. We offer several types of benchmark tasks with varying levels of complexity, provide variants with procedurally generated cloth objects and randomized material textures. Moreover, we allow users to customize the tasks: import custom objects and textures, adjust size and material properties of deformable objects. We prioritize dynamic aspects of the tasks: forgoing 2D tabletop manipulation in favor of 3D tasks, with gravity and inertia playing a non-negligible role. Such advanced challenges require insights from multiple ﬁelds: machine learning and computer vision to process high-dimensional inputs, methods from computer graphics and topology to inspire structured and interpretable representations, insights from robotics to learn advanced control policies. We aim to help researches from these ﬁelds contribute their insights and simplify establishing interdisciplinary collaborations.
Improved analysis and design of coupled-line phase shifters

An analysis of coupled-line microstrip phase shifters is presented which shows that significant differences in theoretical performance are obtained by using an exact analysis in terms of odd and even mode propagation velocities rather than the approach in which the velocities are averaged, measured data are presented and compared with theory over the frequency range 8-12 GHz and the agreement with theory is good. >
Incentive-Based Trafﬁc Management and QoS Measurements

—We argue that the current Best Effort Quality of Service (QoS) strategy of the Internet is one of the reasons why meaningful quality indicators and measurements are hard to deﬁne over the Internet. At the same time, Session-based QoS has emerged for mobile communication, VoIP and IPTV services. However, it is way too complex for widespread usage and cannot out-weight the simplicity being the primary advantage of Internet’s success. Incentive-based strategies have been proposed as lightweight alternatives to traditional solutions in the past decade. As an example, we explore the Core Stateless QoS strategy and show that it is simple, it still provides a ﬁne control of QoS, and that it can also provide a network wide congestion metric. It enables inter-domain policy translation by implementing hierarchical resource sharing policies of arbitrary depth. Furthermore, it also enables network operators and subscribers to carry out meaningful measurements, ensure and verify Service Level Agreements (SLAs) and support network- endpoints signals. We propose future work in this area, including the integration of measurements into the Trafﬁc Management strategies, that are needed for the widespread deployment.
Exploiting Temporal Difference for Energy Disaggregation via Discriminative Sparse Coding

This thesis analyzes one hour based energy disaggregation using Sparse Coding by exploiting temporal differences. Energy disaggregation is the task of taking a whole-home energy signal and separating it into its component appliances. Studies have shown that having device-level energy information can cause users to conserve significant amounts of energy, but current electricity meters only report whole-home data. Thus, developing algorithmic methods for disaggregation presents a key technical challenge in the effort to maximize energy conservation. In Energy Disaggregation or sometimes called Non- Intrusive Load Monitoring (NILM) most approaches are based on high frequent monitored appliances, while households only measure their consumption via smart-meters, which only account for one-hour measurements. This thesis aims at implementing key algorithms from J. Zico Kotler, Siddarth Batra and Andrew Ng paper "Energy Disaggregation via Discriminative Sparse Coding" and try to replicate the results by exploiting temporal differences that occur when dealing with time series data. The implementation was successful, but the results were inconclusive when dealing with large datasets, as the algorithm was too computationally heavy for the resources available. The work was performed at the Swedish company Greenely, who develops visualizations based on gamification for energy bills via a mobile application.