Miracle-dz/UniFAR-FTUs huggingface dataset:
Using IT to Open Previously Unprofitable Markets

It is now widely accepted that Information Technology can make firms more efficient and has created a host of new markets. Yet IT could also open up previously unprofitable markets. In this paper, we look at this phenomenon. We begin with a traditional economic production model and identify when firms will not enter a market. Specifically, we focus on the problem of high fixed costs and limited market size. Then we introduce technology into the model to identify cases where IT could make it profitable to enter the market. Although IT doesn’t always translate into potential profitability, we find that IT may help firms enter previously unprofitable markets when compared to non-IT enabled firms.
GTM-UVigo Systems for Person Discovery Task at MediaEval 2015

In this paper, we present the systems developed by GTMUVigo team for the Multimedia Person Discovery in Broadcast TV task at MediaEval 2015. The systems propose two different strategies for person discovery in audio through speaker diarization (one based on an online clustering strategy with error correction using OCR information and the other based on agglomerative hierarchical clustering) as well as intrashot and intershot strategies for face clustering.
Temporally-Consistent Video Semantic Segmentation with Bidirectional Occlusion-guided Feature Propagation

Despite recent progress in static image segmentation, video segmentation is still challenging due to the need for an accurate, fast, and temporally consistent model. Conducting per-frame static image segmentation on a video is not acceptable since it is computationally prohibitive and prone to temporal inconsistency. In this paper, we present bidirectional occlusion-guided feature propagation (BOFP) method with the goal of improving temporal consistency of segmentation results without sacrificing segmentation accuracy, while at the same time keeping the operations at a low computation cost. It leverages temporal coherence in the video by feature propagation from keyframes to other frames along the motion paths in both forward and backward directions. We propose an occlusion-based attention network to estimate the distorted areas based on bidirectional optical flows, and utilize them as cues for correcting and fusing the propagated features. Extensive experiments on benchmark datasets demonstrate that the proposed BOFP method achieves superior performance in terms of temporal consistency while maintaining comparable level of segmentation accuracy at a low computation cost, striking a great balance among the three performance metrics essential to evaluate video segmentation solutions.
Bi-Dimensional P2P and MRBD Protocols to Enhance Lookup Performance

Chord is one of the best known lookup protocols for structured peer-to-peer (P2P) networks. Nodes in Chord can be viewed as being placed on a one-dimensional ring. In this paper, we present a novel concept of bi-dimensional P2P, in which all nodes are placed onto a square rather than a ring to enable the flexible configuration of ring(s). Diverse ring configuration schemes form a uniform protocol family called multi-ring bi-dimensional (MRBD) protocols, and Chord can be considered as MRBD-1. Different configurations of rings provide various performance to satisfy diverse user requirements. We study examples of MRBD from both theoretical analysis and simulations. The results validate the effectiveness of MRBD in improving P2P lookup performance.
A parallel and randomized algorithm for large-scale discrete dual-Vt assignment and continuous gate sizing

We propose a parallel and randomized algorithm to solve the problem of discrete dual-Vt assignment combined with continuous gate sizing which is an important low power design technique in high performance domains. This combinatorial optimization problem is particularly difficult to solve on large-sized circuits. We first introduce a hybrid algorithm which combines the existing heuristics and convex formulations for this problem to achieve a better tradeoff between the runtime of the algorithm and the quality of generated solution. We then extend our algorithm to include parallelism and randomization. We introduce a unique utilization of parallelism to better identify the optimization direction. Consequently, we can reduce both the number of iterations in optimization as well as improve the quality of solution. We further use random sampling to avoid being trapped in local minima and to focus the optimization effort on the more "promising" regions of the solution space. Our algorithm improves the average power by 37% compared to an approach which is based on solving a continuous convex program and applying discretization. Power improvement is over 50% for larger benchmarks for an implementation on a grid of 9 computers.
ScholarWorks ScholarWorks

Large scale transformer models, trained with massive datasets have become the standard in natural language processing. The huge size of most transformers make research with these models impossible for those with limited computational resources. Additionally, the enormous pretraining data requirements of trans-formers exclude pretraining them with many smaller datasets that might provide enlightening results. In this study, we show that trans-formers can be significantly reduced in size, with as few as 5.7 million parameters, and still retain most of their downstream capability. Further we show that transformer models can retain comparable results when trained on human-scale datasets, as few as 5 million words of pretraining data. Overall, the results of our study suggest transformers function well as compact, data efficient language models and that complex model compression methods, such as model distillation are not necessarily superior to pretraining reduced size transformer models from scratch.
A Hybrid Neural Network and Genetic Algorithm Based Model for Short Term Load Forecast

Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due to their nonlinear mapping nature. The ANN architecture optimization, the initial weight values of the neurons, selection of training algorithm and critical analysis and selection of the most appropriate input parameters are some important consideration for STLF. Levenberg-Marquardt (LM) algorithm for the training of the neural network is implemented in the first stage. The second stage is based on a hybrid model which combines the ANN and GA.
The influence of chromosome flexibility on chromosome transport during anaphase A.

The role of protein flexibility in molecular motor function has previously been studied by considering a Brownian ratchet motor that is connected to its cargo by an elastic spring, with the result that the average velocity of the motor/cargo system is increased by reducing the stiffness of the linkage. Here, we extend this investigation to the case of chromosome transport during anaphase A, in which the relevant flexibility is not primarily in the motor/cargo linkage but rather in the cargo itself, i.e., in the chromosome. We model the motor mechanism as an imperfect Brownian ratchet with a built-in opposing load and the chromosome as a collection of discrete segments linked by an elastic energy function that discretizes the potential energy of an elastic rod. Thermal fluctuations are produced in the model by random forces, as in Brownian dynamics. All of the parameters that characterize the chromosome are known or can be estimated from experimental data, as can all but one of the motor parameters, which is adjusted to give the correct transport velocity of normal-length chromosomes. With the parameters so determined, we then reproduce the experimental finding of Nicklas [Nicklas, R. B. (1965) J. Cell Biol. 25, 119-135] that chromosome speed is essentially independent of chromosome length, even though our model contains no "velocity governor. " We find instead that this effect is a consequence of chromosome flexibility, as it disappears when stiffer than normal chromosomes are considered.
Title : Brain inflammation accompanies amyloid in a majority of mild cognitive impairment cases due to Alzheimer ’ s disease

Complete List of Authors: Parbo, Peter; Aarhus University Hospital, Department of Nuclear Medicine and PET Centre Ismail, Rola; Aarhus University Hospital, Department of Nuclear Medicine and PET Centre Hansen, Kim; Aarhus University Hospital, Department of Nuclear Medicine and PET Centre Amidi, Ali; Aarhus University & Aarhus University Hospital, Department of Psychology and Behavioural Sciences & Department of Oncology Mårup, Frederik; Aarhus University Hospital, Department of Nuclear Medicine and PET Centre Gottrup, Hanne; Aarhus University Hospital, Department of Neurology Braendgaard, Hans; Aarhus University Hospital, Department of Neurology Eriksson, Bengt; Hospitalsenheden Midt, Department of Neurology Eskildsen, Simon; Aarhus Universitet, Center of Functionally Integrative Neuroscience Lund, Torben; Aarhus Universitet, Center of Functionally Integrative Neuroscience Tietze, Anna; Aarhus Universitet, Center of Functionally Integrative Neuroscience Edison, Paul; Imperial College London, Neurology Imaging Unit Pavese, Nicola; Aarhus University Hospital, Department of Nuclear Medicine and PET Centre; Imperial College, Division of Brain Sciences Stokholm, Morten; Aarhus University Hospital, Department of Nuclear Medicine and PET Centre Borghammer, Per; Aarhus University Hospital, Department of Nuclear Medicine and PET Centre Hinz, Rainer; University of Manchester Wolfson Molecular Imaging Centre Aanerud, Joel; Aarhus University Hospital, Department of Nuclear Medicine and PET Centre Brooks, David; Aarhus University Hospital, Department of Nuclear Medicine and PET Centre; Imperial College, Division of Brain Sciences
A Computational Approach to Analyze the Mechanism of Action of the Kinase Inhibitor Bafetinib

Prediction of drug action in human cells is a major challenge in biomedical research. Additionally, there is strong interest in finding new applications for approved drugs and identifying potential side effects. We present a computational strategy to predict mechanisms, risks and potential new domains of drug treatment on the basis of target profiles acquired through chemical proteomics. Functional protein-protein interaction networks that share one biological function are constructed and their crosstalk with the drug is scored regarding function disruption. We apply this procedure to the target profile of the second-generation BCR-ABL inhibitor bafetinib which is in development for the treatment of imatinib-resistant chronic myeloid leukemia. Beside the well known effect on apoptosis, we propose potential treatment of lung cancer and IGF1R expressing blast crisis.
Learning Syntactic Verb Frames using Graphical Models

We present a novel approach for building verb subcategorization lexicons using a simple graphical model. In contrast to previous methods, we show how the model can be trained without parsed input or a predefined subcategorization frame inventory. Our method outperforms the state-of-the-art on a verb clustering task, and is easily trained on arbitrary domains. This quantitative evaluation is complemented by a qualitative discussion of verbs and their frames. We discuss the advantages of graphical models for this task, in particular the ease of integrating semantic information about verbs and arguments in a principled fashion. We conclude with future work to augment the approach.
Energy Harvesting Based Body Area Networks for Smart Health

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device’s battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive.
Efficient Algorithms for Some Variants of the Farthest String Problem

The farthest string problem (FARTHEST STRING) is one of the core problems in the field of consensus word analysis and several biological problems such as discovering potential drugs, universal primers, or unbiased consensus sequences. Given k strings of the same length L and a nonnegative integer d, FARTHEST STRING is to find a string s such that none of the given strings has a Hamming distance that is smaller than d from s. It has been shown to be NP-complete. In this paper, we study two variants of FARTHEST STRING. One is to search for a string s satisfying the condition that the hamming distances between s and all the given strings are greater than d. We give an O((|Σ|(L-d)) )-time algorithm, where | Σ| is the alphabet size. The other variant is to find a string s such that the sum of the hamming distances between s and all the given strings are maximized. We solve this problem in O( kL ) time.
A strong bout of natural cooling in 2008

A precipitous drop in North American temperature in 2008, commingled with a decade‐long fall in global mean temperatures, are generating opinions contrary to the inferences drawn from the science of climate change. We use an extensive suite of model simulations and appraise factors contributing to 2008 temperature conditions over North America. We demonstrate that the anthropogenic impact in 2008 was to warm the region's temperatures, but that it was overwhelmed by a particularly strong bout of naturally‐induced cooling resulting from the continent's sensitivity to widespread coolness of the tropical and northeastern Pacific sea surface temperatures. The implication is that the pace of North American warming is likely to resume in coming years, and that climate is unlikely embarking upon a prolonged cooling.
Rett Syndrome: A Model of Genetic Neurodevelopmental Disorders

Neurodevelopmental disorders, sometimes referred to as disorders of intellectual disability (ID), are a large family of conditions of genetic, acquired, or environmental origin that are characterized by deficiencies in cognitive and behavioral functions. While many of these dis‐ orders share similar behavioral phenotypes, they are often accompanied with other features specific to each disorder. One disorder with a unique progression is Rett Syndrome (RTT; Online Mendelian Inheritance in Man #312750; http://www.ncbi.nlm.nih.gov/omim/). RTT is the leading cause of severe ID in females, with approximately 1:10,000 females worldwide affected by this disorder. Mutations in the gene encoding the transcriptional regulator, methyl-CpG-binding protein 2 (MeCP2), located on the X chromosome (Xq28), have been confirmed in more than 95% of individuals meeting diagnostic criteria for RTT. RTT is char‐ acterized by an uneventful early infancy followed by stagnation and regression of growth, motor, language, and social skills later in development.
On Induction of Morphology Grammars and its Role in Bootstrapping

Different Alignment Based Learning (ABL) algorithms have been proposed for unsupervised grammar induction, e. g. Zaanen (2001) and Déjean (1998), in particular for the induction of syntactic rules. However, ABL seems to be better suited for the induction of morphological rules. In this paper we show how unsupervised hypothesis generation with ABL algorithms can be used to induce a lexicon and morphological rules for various types of languages, e. g. agglutinative or polysynthetic languages. The resulting morphological rules and structures are optimized with the use of conflicting constraints on the size and statistical properties of the grammars, i. e. Minimium Description Length and Minimum Relative Entropy together with Maximum Average Mutual Information. Further, we discuss how the resulting (optimal and regular) grammar can be used for lexical clustering/classification for the induction of syntactic (context free) rules.
RESEARCH OUTPUTS / RÉSULTATS DE RECHERCHE

Blockchain technology has the potential to facilitate the development and improvement of cross-organizational business processes. When it comes to developing smart contracts with this purpose, model-driven engineering is a promising approach that has been adopted by a number of solutions. This paper presents B-MERODE, a novel approach relying on model-driven engineering and artifact-centric business processes to generate smart contracts supporting cross-organizational collaborations. The feasibility of the approach is demonstrated by modeling the case of a rice supply chain. Compared to other solutions, B-MERODE provides more flexibility, more reusability and further leverages the automation potential offered by model-driven engineering.
Department of Economics

Suppose individuals get utilities from the total amount of wealth they hold and from their wealth relative to those immediately below them. This paper studies the distribution of wealth that maximizes an additive welfare function made up of these utilities. It interprets wealth distribution in a control theory framework to show that the welfare maximizing distribution may have unexpected properties. In some circumstances it requires that inequality be maximized at the poorest and richest ends of the distribution. In other circumstances it requires that all wealth be given to a single individual.
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition

Latent fingerprints are one of the most widely used forensic evidence by law enforcement agencies. However, latent recognition performance is far from the exemplary performance of sensor fingerprint recognition due to deformations and artifacts within these images. In this study, we propose a fusion based local matching approach towards latent fingerprint recognition. Recent latent recognition studies typically relied on local descriptor generation methods, in which either handcrafted minutiae features or deep neural network features are extracted around a minutia of interest, in the latent recognition process. Proposed approach would integrate these handcrafted features with a recently proposed deep neural network embedding features in a multi-stage fusion approach to significantly improve latent recognition results. Effectiveness of the proposed approach has been shown on several public and private data sets. As demonstrated in our experimental results, proposed method improves rank-1 identification accuracy by considerably for real-world datasets when compared to either the single usage of these features or existing state-of-the-art methods in the literature.
CaloPointFlow II Generating Calorimeter Showers as Point Clouds

The simulation of calorimeter showers presents a significant computational challenge, impacting the efficiency and accuracy of particle physics experiments. While generative ML models have been effective in enhancing and accelerating the conventional physics simulation processes, their application has predominantly been constrained to fixed detector readout geometries. With CaloPointFlow we have presented one of the first models that can generate a calorimeter shower as a point cloud. This study describes CaloPointFlow II, which exhibits several significant improvements compared to its predecessor. This includes a novel dequantization technique, referred to as CDF-Dequantization, and a normalizing flow architecture, referred to as DeepSet- Flow. The new model was evaluated with the fast Calorimeter Simulation Challenge (CaloChallenge) Dataset II and III.
Near-Optimal differentially private low-rank trace regression with guaranteed private initialization

We study differentially private (DP) estimation of a rank-$r$ matrix $M \in \mathbb{R}^{d_1\times d_2}$ under the trace regression model with Gaussian measurement matrices. Theoretically, the sensitivity of non-private spectral initialization is precisely characterized, and the differential-privacy-constrained minimax lower bound for estimating $M$ under the Schatten-$q$ norm is established. Methodologically, the paper introduces a computationally efficient algorithm for DP-initialization with a sample size of $n \geq \widetilde O (r^2 (d_1\vee d_2))$. Under certain regularity conditions, the DP-initialization falls within a local ball surrounding $M$. We also propose a differentially private algorithm for estimating $M$ based on Riemannian optimization (DP-RGrad), which achieves a near-optimal convergence rate with the DP-initialization and sample size of $n \geq \widetilde O(r (d_1 + d_2))$. Finally, the paper discusses the non-trivial gap between the minimax lower bound and the upper bound of low-rank matrix estimation under the trace regression model. It is shown that the estimator given by DP-RGrad attains the optimal convergence rate in a weaker notion of differential privacy. Our powerful technique for analyzing the sensitivity of initialization requires no eigengap condition between $r$ non-zero singular values.
Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network

The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States. Many strategies have been developed by the federal and local governments and health communities to combat this crisis. Among them, improving our understanding of the epidemic through better health surveillance is one of the top priorities. In addition to direct testing, machine learning approaches may also allow us to detect opioid users by analyzing data from social media because many opioid users may choose not to do the tests but may share their experiences on social media anonymously. In this paper, we take advantage of recent advances in machine learning, collect and analyze user posts from a popular social network Reddit with the goal to identify opioid users. Posts from more than 1,000 users who have posted on three sub-reddits over a period of one month have been collected. In addition to the ones that contain keywords such as opioid, opiate, or heroin, we have also collected posts that contain slang words of opioid such as black or chocolate. We apply an attention-based bidirectional long short memory model to identify opioid users. Experimental results show that the approaches significantly outperform competitive algorithms in terms of F1-score. Furthermore, the model allows us to extract most informative words, such as opiate, opioid, and black, from posts via the attention layer, which provides more insights on how the machine learning algorithm works in distinguishing drug users from non-drug users.