Miracle-dz/UniFAR-FTUs huggingface dataset:
EdgeMORE: improving resource allocation with multiple options from tenants

Under the paradigm of Edge Computing (EC), a Network Operator (NO) deploys computational resources at the network edge and let third-party Service Providers (SPs) run on top of them, as tenants. Besides the clear advantages for SPs and final users thanks to the vicinity of computation nodes, a NO aims to allocate edge resources in order to increase its own utility, including bandwidth saving, operational cost reduction, QoE for its users, etc. However, while the number of third-party services competing for edge resources is expected to dramatically grow, the resources deployed cannot increase accordingly, due to physical limitations. Therefore, smart strategies are needed to fully exploit the potential of EC, despite its constrains. To this aim, we propose to leverage service adaptability, a dimension that has mainly been neglected so far: each service can adapt to the amount of resources that the NO has allocated to it, balancing the fraction of service computation performed at the edge and relying on remote servers, e.g., in the Cloud, for the rest. We propose EdgeMORE, a resource allocation strategy in which SPs express their capabilities to adapt to different resource constraints, by declaring the different configurations under which they are able to run, specifying the resources needed and the utility provided to the NO. The NO then chooses the most convenient option per each SP, in order to maximize the total utility. We formalize EdgeMORE as a Integer Linear Program. We show via simulation that EdgeMORE greatly improves EC utility with respect to the standard where no multiple options for running services are allowed.
Supplemental material for : Non-Hermitian boundary state engineering in anomalous Floquet topological insulators

In Hermitian topological systems, the bulk-boundary correspondence strictly constraints boundary transport to values determined by the topological properties of the bulk. We demonstrate that this constraint can be lifted in non-Hermitian Floquet insulators. Provided that the insulator supports an anomalous topological phase, non-Hermiticity allows us to modify the boundary states independently of the bulk, without sacrificing their topological nature. We explore the ensuing possibilities for a Floquet topological insulator with non-Hermitian time-reversal symmetry, where the helical transport via counterpropagating boundary states can be tailored in ways that overcome the constraints imposed by Hermiticity. Non-Hermitian boundary state engineering specifically enables the enhancement of boundary transport relative to bulk motion, helical transport with a preferred direction, and, in direct violation of the standard bulk-boundary correspondence, chiral transport in the same direction on opposite boundaries. We explain the experimental relevance of our findings for the example of photonic waveguide lattices.
Interpretable brain age prediction using linear latent variable models of functional connectivity

Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we seek to build models of brain age based on functional connectivity while prioritizing model interpretability and understanding. This way, the models serve to both provide accurate estimates of brain age as well as allow us to investigate changes in functional connectivity which occur during the ageing process. The methods proposed in this work consist of a two-step procedure: first, linear latent variable models, such as PCA and its extensions, are employed to learn reproducible functional connectivity networks present across a cohort of subjects. The activity within each network is subsequently employed as a feature in a linear regression model to predict brain age. The proposed framework is employed on the data from the CamCAN repository and the inferred brain age models are further demonstrated to generalize using data from two open-access repositories: the Human Connectome Project and the ATR Wide-Age-Range.
Adaloss: Adaptive Loss Function for Landmark Localization

Landmark localization is a challenging problem in computer vision with a multitude of applications. Recent deep learning based methods have shown improved results by regressing likelihood maps instead of regressing the coordinates directly. However, setting the precision of these regression targets during the training is a cumbersome process since it creates a trade-off between trainability vs localization accuracy. Using precise targets introduces a significant sampling bias and hence makes the training more difficult, whereas using imprecise targets results in inaccurate landmark detectors. In this paper, we introduce "Adaloss", an objective function that adapts itself during the training by updating the target precision based on the training statistics. This approach does not require setting problem-specific parameters and shows improved stability in training and better localization accuracy during inference. We demonstrate the effectiveness of our proposed method in three different applications of landmark localization: 1) the challenging task of precisely detecting catheter tips in medical X-ray images, 2) localizing surgical instruments in endoscopic images, and 3) localizing facial features on in-the-wild images where we show state-of-the-art results on the 300-W benchmark dataset.
Modeling Event Propagation via Graph Biased Temporal Point Process

Temporal point process is widely used for sequential data modeling. In this article, we focus on the problem of modeling sequential event propagation in graph, such as retweeting by social network users and news transmitting between websites. Given a collection of event propagation sequences, the conventional point process model considers only the event history, i.e., embed event history into a vector, not the latent graph structure. We propose a graph biased temporal point process (GBTPP) leveraging the structural information from graph representation learning, where the direct influence between nodes and indirect influence from event history is modeled. Moreover, the learned node embedding vector is also integrated into the embedded event history as side information. Experiments on a synthetic data set and two real-world data sets show the efficacy of our model compared with conventional methods and state-of-the-art ones.
Angle rigidity and its usage to stabilize planar formations

Motivated by the challenging formation stabilization problem for mobile robotic teams when no distance or relative displacement measurements are available and each robot can only measure some of those angles formed by rays towards its neighbors, we develop the notion of ``angle rigidity" for a multi-point framework, named ``angularity", consisting of a set of nodes embedded in a Euclidean space and a set of angle constraints among them. Different from bearings or angles defined with respect to a global axis, the angles we use do not rely on the knowledge of a global coordinate system and are signed according to the counter-clockwise direction. Here \emph{angle rigidity} refers to the property specifying that under proper angle constraints, the angularity can only translate, rotate or scale as a whole when one or more of its nodes are perturbed locally. We first demonstrate that this angle rigidity property, in sharp comparison to bearing rigidity or other reported rigidity related to angles of frameworks in the literature, is \emph{not} a global property since an angle rigid angularity may allow flex ambiguity. We then construct necessary and sufficient conditions for \emph{infinitesimal} angle rigidity by checking the rank of an angularity's rigidity matrix. We develop a combinatorial necessary condition for infinitesimal minimal angle rigidity. Using the developed theories, a formation stabilization algorithm is designed for a robotic team to achieve a globally angle rigid formation, in which only angle measurements are needed.
Learning to Generalize to Unseen Tasks with Bilevel Optimization

Recent metric-based meta-learning approaches, which learn a metric space that generalizes well over combinatorial number of different classification tasks sampled from a task distribution, have been shown to be effective for few-shot classification tasks of unseen classes. They are often trained with episodic training where they iteratively train a common metric space that reduces distance between the class representatives and instances belonging to each class, over large number of episodes with random classes. However, this training is limited in that while the main target is the generalization to the classification of unseen classes during training, there is no explicit consideration of generalization during meta-training phase. To tackle this issue, we propose a simple yet effective meta-learning framework for metricbased approaches, which we refer to as learning to generalize (L2G), that explicitly constrains the learning on a sampled classification task to reduce the classification error on a randomly sampled unseen classification task with a bilevel optimization scheme. This explicit learning aimed toward generalization allows the model to obtain a metric that separates well between unseen classes. We validate our L2G framework on mini-ImageNet and tiered-ImageNet datasets with two base meta-learning few-shot classification models, Prototypical Networks and Relation Networks. The results show that L2G significantly improves the performance of the two methods over episodic training. Further visualization shows that L2G obtains a metric space that clusters and separates unseen classes well.
Distributed Design for Decentralized Control Using Chordal Decomposition and ADMM

We propose a distributed design method for decentralized control by exploiting the underlying sparsity properties of the problem. Our method is based on the chordal decomposition of sparse block matrices and the alternating direction method of multipliers (ADMM). We first apply a classical parameterization technique to restrict the optimal decentralized control into a convex problem that inherits the sparsity pattern of the original problem. The parameterization relies on a notion of strongly decentralized stabilization, and sufficient conditions are discussed to guarantee this notion. Then, chordal decomposition allows us to decompose the convex restriction into a problem with partially coupled constraints, and the framework of ADMM enables us to solve the decomposed problem in a distributed fashion. Consequently, the subsystems only need to share their model data with their direct neighbors, without needing central computation. Numerical experiments demonstrate the effectiveness of the proposed method.
Control of feedback for assistive listening devices

Acoustic feedback is used to refer to the undesired acoustic coupling between the loudspeaker and microphone in audio reinforcement systems. Systems susceptible to feedback problems include public address systems and hearing aids. It is common practice to use adaptive filters as acoustic feedback cancellers to compensate for the feedback signal. The main challenge of using adaptive filters for feedback cancellation is that the filter estimates may become biased whenever the correlation between the incoming and loudspeaker signals is non-zero. There are two main contributions in this thesis which deals with the biased estimation problem. First, we propose a two microphone method for feedback cancellation where an additional microphone is employed to enhance the canceller’s performance. The second microphone is spatially located further away from the loudspeaker compared to the main microphone so that the feedback signal received is more attenuated. The additional microphone is used to obtain an incoming signal estimate which is then subtracted from the primary microphone signal to create the error signal prior to adapting the canceller’s coefficients. With this method, the biased solution is no longer dependent on the correlation between the incoming and loudspeaker signals, but on the second feedback path. Accordingly, by doing a proper acoustic design based on near field properties of the feedback path and far field properties of the impinging signals significant system benefits are obtained. This thesis also makes a contribution to probe signal injection methods. It is accepted that an unbiased solution is obtained if the canceller bases its estimation solely on the probe signal. However, we show that the solution is biased even if the probe signal is white noise. From this insight, we then derive conditions for obtaining an unbiased estimation. To reduce signal quality degradation probe signals are usually shaped to provide some level of perceptual masking. Thus, it is important to know the impact the shaping filter has on system performance. We present analytically that the shaping filter has a detrimental effect on system performance in terms of convergence rate. Accordingly, we propose a new method which restores convergence rate of the canceller while maintaining the benefits of spectrally shaping the noise.
Research of Processes of Formation and Dissociation of Natural Gas Hydrates

Technogenic hydrates are mostly a negative factor in the oil and gas industry. Their formation in the bottomhole well zone and in wellbores complicates the extraction, reducing their production rate. In the hydrocarbons collection systems, the hydrates in certain thermobaric conditions deposit on the gas pipeline walls and raise their hydraulic resistance, thus increasing energy costs. The presence of hydrates in the streams of the fluid, extracted from subsoil, increases the wear of production headers, reducing their resource. In the systems of field treatment of hydrocarbons, hydratation negatively affects the technological processes. Hydrate formation in machinery and aggregates of gathering facilities and processing systems leads to accidents. Hydrate deposition in gas pipelines decreases work efficiency. For solving the problems related to prevention and elimination of technogenic hydrates, it is necessary to be able to define the basic parameters of hydrate formation and dissociation. This article is devoted to the research of the processes of formation and dissociation of hydrates. The research was performed using the physical and mathematical models, published in several works, from which it is possible to calculate the main parameters of hydrate formation and dissociation in a wide range of thermobaric conditions. The most widespread conditions of formation and dissociation of hydrates in the development and exploitation of gas and condensate fields were analyzed in this article. Keywords—dissociation; elimination; formation; hydrates; natural gas
An improved method for accurate computation of coastal upwelling index using Sea Surface Temperature Images

In this paper we aim to present a new methodology to derive rigorous SST-based coastal upwelling index for the purpose of conducting a saisonal variablity of upwelling area along the Moroccan Atlantic coast. The method is based on the scientific knowledge of upwelling area and its spatial distribution provided by expert oceanographers. The latter consists in automatically identify and extract the region covered by the upwelling waters in the costal ocean of Morocco using the Fuzzy c-means algorithm and finding regions of homogeneous pixels. Then Region Growing process is used to filter out the remaining noisy structures in the offshort waters. The methodology is used to provide a satistical view of the spatial and temporal variability of the Moroccan upwelling activity. The relevance of the proposed Coastal Upwelling Index (CUI) is evaluated by an oceanographer using 86 8-days sea surface temperature images and it is shown to be superior to that of the standard upwelling index.
Introducing prosodic variability

Introduction This collection explores the pervasive variability in speech prosody and its role in linguistic representation and linguistic processing. It contains papers presented at the Third Experimental and Theoretical Approaches to Prosody workshop (supported by an NSF Workshop grant; BCS-1451751), as well as submitted papers reflecting the themes of this workshop. This collection was edited by Mara Breen, Chigusa Kurumada, Michael Wagner, Duane Watson, and Kristine M. Yu.
Von der aktiven Beobachtung bis zur Radiumtherapie

Sonderfall Niedrigrisiko-Prostata-Ca. Diese Patienten haben ein minimales Potenzial für Tumorprogression sowie Metastasierung und somit nur eine geringe tumorspezi sche Mortalität im Zeitraum von 10–15 Jahren [2]. Der PSA-Wert liegt unter 10, der Gleason-Score max. bei 6 und das Tumorstadium bei T1c-2aNoMo. Konzept der „Active Surveillance“ Aus diesen Beobachtungen entwickelte sich die Active Surveillance („aktive Beobachtung“), bei der die Patienten zunächst lediglich aktiv beobachtet und keiner de nitiven erapie zugeführt werden. Neben der Identi zierung geeigneter Patienten ist ein engmaschiges und konsequentes Follow-up essenziell [3]. Die aktuelle europäische Leitlinie emp ehlt eine regelmäßige Kontrolle des PSA-Wertes, digital-rektale Untersuchung und erneute Biopsie [4]. Sollte es im Verlauf zu einer Re-Klassi zierung des Patienten kommen, muss die erapie diskutiert werden. In das Spannungsfeld zwischen möglicher Untertherapie durch die Active Surveillance sowie möglicher Übertherapie Dr. med. Alexander Kretschmer
The volume couple as a source for micro-seismic events

Summary A case is presented that the volume couple source mechanism is a likely description of fracturing induced seismic events. Finite-difference modelling is then introduced as a valid description of the double couple by comparing its wavefield predictions to the well established theory results developed within the Aki and Richards text. The finite-difference method is than applied to the volume couple case to show the nature of the predicted wavefields. Some comparisons are then made to some published results which are consistent with the model predictions made here.
Head Motion parallax effect on driving performances when using an AR-HUD: Simulation Study on Renault’s CARDs Simulator

Augmented Reality information on Head-Up display (AR-HUD) in a car can be relevant for visual aid and for strengthening the safety of the driver. However, some display Parameters are necessary to guarantee the good perception of these information and the driving environment. In this study, we are interested on head motion parallax, and specifically the ones generated with lateral head movements of the driver. In fact, during natural observation this cue physiologically strengthens depth perception and its absence may impact driver perception. Our goal is to demonstrate the impact of the generated movements and the projection distance on driver’s perception, using an AR-HUD. This was investigated in terms of eye-comfort and driver preferences. In this article, we focus on the primary driving task with basic lane marking highlight, and we observe how the different conditions affect subject’s perception especially during curves negotiation. Results show the importance of eye-tracking when using an AR-HUD for alignment accuracy and better comfort which directly driver performance and safety.
David James Purslove Barker. 29 June 1938—27 August 2013

Professor David James Purslove Barker was a physician and one of the most influential medical scientists of our time. His fetal programming hypothesis (known as the Barker Hypothesis) transformed thinking about what causes chronic diseases that are the scourge of modern society: cancer, cardiovascular disease and diabetes. The Barker Hypothesis proposed that the environment of the fetus and infant determined by maternal nutrition and exposure to infection subsequently predisposes the pathologies of later life. He challenged the idea that chronic diseases result from a combination of bad genes and unhealthy adult lifestyle. The environment of the fetus and infant, he suggested, permanently set or ‘programmed’ the body's metabolism and growth, and thereby pathologies of old age. His initially controversial, but now widely accepted, ideas have produced an explosion of research worldwide into the complex processes of nutrition and growth during intrauterine and early post-natal life and how these cause adult diseases. His discoveries created a new field of research, developmental origins of health and disease (DOHaD), influencing global scientific thinking. David believed that ‘the poorer health of people in lower socio-economic groups or living in impoverished places was linked to past and present neglect of the welfare of mothers and babies’. Tackling the epidemics of diabetes and heart disease in the Western world and in developing countries would require, he said, a shift in focus to prioritize the health and nutrition of adolescent girls, pregnant women and infants. This focus has subsequently been enshrined in global health policies and priorities.
Three Novel Players: PTK2B, SYK, and TNFRSF21 Were Identified to Be Involved in the Regulation of Bovine Mastitis Susceptibility via GWAS and Post-transcriptional Analysis

Bovine mastitis is a common inflammatory disease caused by multiple factors in early lactation or dry period. Genome wide association studies (GWAS) can provide a convenient and effective strategy for understanding the biological basis of mastitis and better prevention. 2b-RADseq is a high-throughput sequencing technique that offers a powerful method for genome-wide genetic marker development and genotyping. In this study, single nucleotide polymorphisms (SNPs) of the immune-regulated gene correlative with mastitis were screened and identified by two stage association analysis via GWAS-2b-RADseq in Chinese Holstein cows. We have screened 10,058 high quality SNPs from 7,957,920 tags and calculated their allele frequencies. Twenty-seven significant SNPs were co-labeled in two GWAS analysis models [Bayesian (P < 0.001) and Logistic regression (P < 0.01)], and only three SNPs (rs75762330, C > T, PIC = 0.2999; rs88640083, A > G, PIC = 0.1676; rs20438858, G > A, PIC = 0.3366) were annotated to immune-regulated genes (PTK2B, SYK, and TNFRSF21). Identified three SNPs are located in non-coding regions with low or moderate genetic polymorphisms. However, independent sample population validation (Case-control study) data showed that three important SNPs (rs75762330, P < 0.025, OR > 1; rs88640083, P < 0.005, OR > 1; rs20438858, P < 0.001, OR < 1) were significantly associated with clinical mastitis trait. Importantly, PTK2B and SYK expression was down-regulated in both peripheral blood leukocytes (PBLs) of clinical mastitis cows and in vitro LPS (E. coli)–stimulated bovine mammary epithelial cells, while TNFRSF21 was up-regulated. Under the same conditions, expression of Toll-like receptor 4 (TLR4), AKT1, and pro-inflammatory factors (IL-1β and IL-8) were also up-regulated. Interestingly, network analysis indicated that PTK2B and SYK are co-expressed in innate immune signaling pathway of Chinese Holstein. Taken together, these results provided strong evidence for the study of SNPs in bovine mastitis, and revealed the role of SYK, PTK2B, and TNFRSF21 in bovine mastitis susceptibility/tolerance.