Miracle-dz/UniFAR-FTUs huggingface dataset:
Networks with Growth and Preferential Attachment: Modeling and Applications

This article presents a brief overview of the main network models that use growth and preferential attachment. We start with the classical model proposed by Barabási and Albert: nodes are added to the network connecting preferably to nodes that are more connected. We also present models that consider more representative elements from social perspectives, such as the homophily between the nodes and the fitness that each node has, to build connections. Furthermore, we show a version of these models that includes Euclidean distance between the nodes as a preferential attachment component. Our objective is to study the fundamental properties of these networks, as distribution of connectivity, degree correlation, shortest path, cluster coefficient and how these characteristics are affected by the preferential attachment rules. In addition to the review, we also provided an application of these models using real-world networks.
Distortionless Multi-Channel Target Speech Enhancement for Overlapped Speech Recognition

Speech enhancement techniques based on deep learning have brought significant improvement on speech quality and intelligibility. Nevertheless, a large gain in speech quality measured by objective metrics, such as perceptual evaluation of speech quality (PESQ), does not necessarily lead to improved speech recognition performance due to speech distortion in the enhancement stage. In this paper, a multi-channel dilated convolutional network based frequency domain modeling is presented to enhance target speaker in the far-field, noisy and multi-talker conditions. We study three approaches towards distortionless waveforms for overlapped speech recognition: estimating complex ideal ratio mask with an infinite range, incorporating the fbank loss in a multi-objective learning and finetuning the enhancement model by an acoustic model. Experimental results proved the effectiveness of all three approaches on reducing speech distortions and improving recognition accuracy. Particularly, the jointly tuned enhancement model works very well with other standalone acoustic model on real test data.
Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks

The heterogenous wireless services and exponentially growing traffic call for novel spectrum- and energy-efficient wireless communication technologies. Recently, a new technique, called symbiotic radio (SR), is proposed to exploit the benefits and address the drawbacks of cognitive radio (CR) and ambient backscattering communications (AmBC), leading to mutualism spectrum sharing and highly reliable backscattering communications. In particular, the secondary transmitter (STx) in SR transmits messages to the secondary receiver (SRx) over the RF signals originating from the primary transmitter (PTx) based on cognitive backscattering communications, thus the secondary system shares not only the radio spectrum, but also the power, and infrastructure with the primary system. In return, the secondary transmission provides beneficial multipath diversity to the primary system, therefore the two systems form mutualism spectrum sharing. More importantly, joint decoding is exploited at SRx to achieve highly reliable backscattering communications. In this article, to exploit the full potential of SR, we provide a systematic view for SR and address three fundamental tasks in SR: (1) enhancing the backscattering link via active load; (2) achieving highly reliable communications through joint decoding; and (3) capturing PTx’s RF signals using reconfigurable intelligent surfaces. Emerging applications, design challenges and open research problems will also be discussed.
Digestible and metabolizable energy values of some feeds for the growing pig.

Digestible energy (DE), metabolizable energy (ME) and metabolizable energy corrected to nitrogen equilibrium (MEn) were determined for eight feeds. Effects of protein level in the basal ration and of level of feeding certain test feeds were also studied. Protein level of the test ration was found to be inversely correlated with the ratio ME/DE (r = −0.956; P < 0.01). It was postulated that conventional methods for estimating ME and MEn underestimate high protein feeds and that, based on a regression equation, the ME values for swine feeds can be determined most effectively by multiplying DE × 0.98). This provides a metabolizable energy value applicable within the protein levels used in practical swine production. ME values thus derived were: soybean meal 3620, rapeseed meal 3280, herring fishmeal 4550, wheat 3965, barley 3175, oats 2900 and wheat shorts 3040 kcal/kg dry matter.
Channel Compression: Rethinking Information Redundancy Among Channels in CNN Architecture

Model compression and acceleration are attracting increasing attention due to the demand for embedded devices and mobile applications. Research on efficient convolutional neural networks (CNNs) aims at removing feature redundancy by decomposing or optimizing the convolutional calculation. In this work, feature redundancy is assumed to exist among channels in CNN architectures, which provides some leeway to boost calculation efficiency. Aiming at channel compression, a novel convolutional construction named compact convolution is proposed to embrace the progress in spatial convolution, channel grouping and pooling operation. Specifically, the depth-wise separable convolution and the point-wise interchannel operation are utilized to efficiently extract features. Different from the existing channel compression method which usually introduces considerable learnable weights, the proposed compact convolution can reduce feature redundancy with no extra parameters. With the point-wise interchannel operation, compact convolutions implicitly squeeze the channel dimension of feature maps. To explore the rules on reducing channel redundancy in neural networks, the comparison is made among different point-wise interchannel operations. Moreover, compact convolutions are extended to tackle with multiple tasks, such as acoustic scene classification, sound event detection and image classification. The extensive experiments demonstrate that our compact convolution not only exhibits high effectiveness in several multimedia tasks, but also can be efficiently implemented by benefitting from parallel computation.
ANALYSIS OF IMMUNOREGULATORY BIOMARKERS IN NON-SMALL CELL LUNG CANCER

[EN] Lung cancer is the leading cause of cancer-related death worldwide, and is the third most common cancer type; it can be classified into two subgroups based on histology: non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). The 5-year survival still remains poor and despite the existence of several distinct tumour phenotypes, therapeutic decisions are mainly based on clinical features such as stage or performance status. This highlights the need for new diagnostic and prognostic biomarkers in different types of samples (such as blood, fresh-frozen tissue or formalin-fixed, paraffin-embedded samples). The field of tumour immunology has changed in the last decade, and it is now accepted that the immune system plays a pivotal role in cancer. Although the immune cells that infiltrate the tumour microenvironment are potentially capable of eliminating tumour cells, they cannot prevent tumour development and progression. Tumours acquire mechanisms to regulate their immune microenvironment such as the release of a series of factors to subvert normal reaction mechanisms, the modulation of co-stimulatory pathways, also known as immune checkpoints, and the induction and attraction of suppressor cells (myeloid-derived suppressor cells, tumour-associated macrophages, and regulatory T cells). The potential effect of the patient's immune system on clinical outcome is important for the identification of prognostic markers as well as markers that predict treatment responses. The study of immune-related markers, especially those implicated in immunoregulatory processes, could provide valuable prognostic information that could help in many applications in future clinical practice. Thus, the objective of this thesis is to characterise cancer immunoregulation biomarkers and to evaluate the possible correlation between these biomarkers and clinicopathological and prognostic variables in patients with NSCLC by the use of well-tested and accurate techniques such as quantitative PCR and immunohistochemistry. Furthermore, this study will provide information about the immunological features of the tumour microenvironment in NSCLCs. ; [ES] El cancer de pulmon es una de las principales causas de muerte relacionada con cancer en el mundo, siendo el tercer tipo de cancer mas comun. El cancer de pulmon no microcitico (CPNM) representa casi el 85% de todos los canceres de pulmon y la supervivencia a los 5 anos va desde el 50% en estadios IA hasta el 15% en estadios IIIA. Hasta el momento, no se han descubierto biomarcadores capaces de predecir la progresion de la enfermedad en pacientes tanto en estadios resecables como en estadios avanzados, por lo que existe una clara necesidad de realizar estudios centrados en la busqueda de biomarcadores pronosticos y diagnosticos en los diferentes tipos de muestra disponibles, como por ejemplo sangre, tejido fresco y tejido parafinado. El campo de la inmunologia tumoral ha cambiado en la ultima decada y actualmente se sabe que el sistema inmune juega un papel clave en cancer. Las celulas…
Generating Adversarial Examples withControllable Non-transferability

Adversarial attacks against Deep Neural Networks have been widely studied. One significant feature that makes such attacks particularly powerful is transferability, where the adversarial examples generated from one model can be effective against other similar models as well. A large number of works have been done to increase the transferability. However, how to decrease the transferability and craft malicious samples only for specific target models are not explored yet. In this paper, we design novel attack methodologies to generate adversarial examples with controllable non-transferability. With these methods, an adversary can efficiently produce precise adversarial examples to attack a set of target models he desires, while keeping benign to other models. The first method is Reversed Loss Function Ensemble, where the adversary can craft qualified examples from the gradients of a reversed loss function. This approach is effective for the white-box and gray-box settings. The second method is Transferability Classification: the adversary trains a transferability-aware classifier from the perturbations of adversarial examples. This classifier further provides the guidance for the generation of non-transferable adversarial examples. This approach can be applied to the black-box scenario. Evaluation results demonstrate the effectiveness and efficiency of our proposed methods. This work opens up a new route for generating adversarial examples with new features and applications.
New Classes of Quantum Codes Associated with Surface Maps

If the cyclic sequences of {face types} {at} all vertices in a map are the same, then the map is said to be a semi-equivelar map. In particular, a semi-equivelar map is equivelar if the faces are the same type. Homological quantum codes represent a subclass of topological quantum codes. In this article, we introduce {thirteen} new classes of quantum codes. These codes are associated with the following: (i) equivelar maps of type $ [k^k]$, (ii) equivelar maps on the double torus along with the covering of the maps, and (iii) semi-equivelar maps on the surface of \Echar{-1}, along with {their} covering maps. The encoding rate of the class of codes associated with the maps in (i) is such that $ \frac{k}{n}\rightarrow 1 $ as $ n\rightarrow\infty $, and for the remaining classes of codes, the encoding rate is $ \frac{k}{n}\rightarrow \alpha $ as $ n\rightarrow \infty $ with $ \alpha< 1 $.
Explainable Deep One-Class Classification

Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, finding interpretations poses a significant challenge. In this paper we present an explainable deep one-class classification method, Fully Convolutional Data Description (FCDD), where the mapped samples are themselves also an explanation heatmap. FCDD yields competitive detection performance and provides reasonable explanations on common anomaly detection benchmarks with CIFAR-10 and ImageNet. On MVTec-AD, a recent manufacturing dataset offering ground-truth anomaly maps, FCDD meets the state of the art in an unsupervised setting, and outperforms its competitors in a semi-supervised setting. Finally, using FCDD's explanations we demonstrate the vulnerability of deep one-class classification models to spurious image features such as image watermarks.
A variational formulation for motion design of adaptive compliant structures

Adaptive structures are characterized by their ability to adjust their geometrical and other properties to changing loads or requirements during service. This contribution deals with a method for the design of quasi‐static motions of structures between two prescribed geometrical configurations that are optimal with regard to a specified quality function while taking large deformations into account. It is based on a variational formulation and the solution by two finite element discretizations, the spatial discretization (the standard finite element mesh) and an additional discretization of the deformation path or trajectory. For the investigations, an exemplary objective function, the minimization of the internal energy, integrated along the deformation path, is used. The method for motion design presented herein uses the Newton‐Raphson method as a second‐order optimization algorithm and allows for analytical sensitivity analysis. The proposed method is verified and its properties are investigated by benchmark examples including rigid body motions, instability phenomena, and determination of inextensible deformations of shells.
Online learning in MDPs with linear function approximation and bandit feedback

We consider an online learning problem where the learner interacts with a Markov decision process in a sequence of episodes, where the reward function is allowed to change between episodes in an adversarial manner and the learner only gets to observe the rewards associated with its actions. We allow the state space to be arbitrarily large, but we assume that all action-value functions can be represented as linear functions in terms of a known low-dimensional feature map, and that the learner has access to a simulator of the environment that allows generating trajectories from the true MDP dynamics. Our main contribution is developing a computationally efficient algorithm that we call MDP-LinExp3, and prove that its regret is bounded by $\widetilde{\mathcal{O}}\big(H^2 T^{2/3} (dK)^{1/3}\big)$, where $T$ is the number of episodes, $H$ is the number of steps in each episode, $K$ is the number of actions, and $d$ is the dimension of the feature map. We also show that the regret can be improved to $\widetilde{\mathcal{O}}\big(H^2 \sqrt{TdK}\big)$ under much stronger assumptions on the MDP dynamics. To our knowledge, MDP-LinExp3 is the first provably efficient algorithm for this problem setting.
Identifying Molecular Genetic Markers Associated with Seedlessness in Grape

Additional index words. preselection, breeding, stenospermocarpy, random amplified polymorphic DNA, PCR Abstract. Excluding seeded offspring at an early stage could be of great value to the breeder concerned with the development of seedless grapes (Vitis vinifera L.). We used the random amplified polymorphic DNA (RAPD) technique to identify molecular genetic markers, analyzing 82 individuals of a progeny resulting from a cross between 'Early Muscat' (seeded) and 'Flame Seedless'. Seven variables representing the traits of seedlessness were analyzed: mean fresh weight of one seed, total fresh weight of seeds per berry, perception of seed content, seed size categories evaluated visually, degree of hardness of the seed coat, degree of development of the endosperm, and degree of development of the embryo. Among 160 10mer primers, 110 gave distinct band patterns. Twelve markers yielded significant correlations with several subtraits of seedlessness, mainly with the mean fresh weight of one seed and the total fresh weight of seeds per berry. Multiple linear regression analysis resulted in high coefficients, such as R = 0.779 for fresh weight of seeds per berry, when the seven markers were included as independent variables in the model. Most of the seeded individuals, about 44% of the progeny, could be excluded using a two-step process of marker assisted selection.
The size and pervasiveness of Ly α–UV spatial offsets in star-forming galaxies at z ∼ 6

Author(s): Lemaux, BC; Fuller, S; Bradac, M; Pentericci, L; Hoag, A; Strait, V; Treu, T; Alvarez, C; Bolan, P; Gandhi, PJ; Jones, T; Mason, C; Pelliccia, D; Ribeiro, B; Ryan, RE; Schmidt, KB; Vanzella, E; Khusanova, Y; Fevre, O Le; Guaita, L; Hathi, NP; Koekemoer, A; Pforr, J | Abstract: We study the projected spatial offset between the ultraviolet continuum and Ly$\alpha$ emission for a sample of 65 lensed and unlensed galaxies in the Epoch of Reionization (5
Epidemiology of exposure to mixtures: we cant be casual about causality when using or testing methods

Background: There is increasing interest in approaches for analyzing the effect of exposure mixtures on health. A key issue is how to simultaneously analyze often highly collinear components of the mixture, which can create problems such as confounding by co-exposure and co-exposure amplification bias (CAB). Evaluation of novel mixtures methods, typically using synthetic data, is critical to their ultimate utility. Objectives: This paper aims to answer two questions. How do causal models inform the interpretation of statistical models and the creation of synthetic data used to test them? Are novel mixtures methods susceptible to CAB? Methods: We use directed acyclic graphs (DAGs) and linear models to derive closed form solutions for model parameters to examine how underlying causal assumptions affect the interpretation of model results. Results: The same beta coefficients estimated by a statistical model can have different interpretations depending on the assumed causal structure. Similarly, the method used to simulate data can have implications for the underlying DAG (and vice versa), and therefore the identification of the parameter being estimated with an analytic approach. We demonstrate that methods that can reproduce results of linear regression, such as Bayesian kernel machine regression and the new quantile g-computation approach, will be subject to CAB. However, under some conditions, estimates of an overall effect of the mixture is not subject to CAB and even has reduced uncontrolled bias. Discussion: Just as DAGs encode a priori subject matter knowledge allowing identification of variable control needed to block analytic bias, we recommend explicitly identifying DAGs underlying synthetic data created to test statistical mixtures approaches. Estimates of the total effect of a mixture is an important but relatively underexplored topic that warrants further investigation.
Minimal Modification to Nosé-Hoover Barostat Enables Correct NPT Sampling

The Nose-Hoover dynamics for isothermal-isobaric (NPT) computer simulations do not generate the appropriate partition function for ergodic systems. The present paper points out that this can be corrected with a simple addition of a constant term to only one of the equations of motion. The solution proposed is much simpler than previous modifications done towards the same goal. The present modification is motivated by the work virial theorem, which has been derived for the special case of an infinitely periodic system in the first part of this paper.
In Search of Lost Domain Generalization

The goal of domain generalization algorithms is to predict well on distributions different from those seen during training. While a myriad of domain generalization algorithms exist, inconsistencies in experimental conditions -- datasets, architectures, and model selection criteria -- render fair and realistic comparisons difficult. In this paper, we are interested in understanding how useful domain generalization algorithms are in realistic settings. As a first step, we realize that model selection is non-trivial for domain generalization tasks. Contrary to prior work, we argue that domain generalization algorithms without a model selection strategy should be regarded as incomplete. Next, we implement DomainBed, a testbed for domain generalization including seven multi-domain datasets, nine baseline algorithms, and three model selection criteria. We conduct extensive experiments using DomainBed and find that, when carefully implemented, empirical risk minimization shows state-of-the-art performance across all datasets. Looking forward, we hope that the release of DomainBed, along with contributions from fellow researchers, will streamline reproducible and rigorous research in domain generalization.
A narrow hybrid zone between two crayfish species from a Mexican cave

In Northern Chiapas (Mexico), two newly discovered species of Procambarus crayfish inhabit a subterranean stream. These species can be morphologically distinguished only by comparing extreme phenotypes (dark, thick‐eyed, surface dwelling‐like individuals vs light, elongated, microphtalmic, cave dwelling‐like individuals). Individuals with intermediate phenotypes co‐occur with those exhibiting extreme phenotypes.
Mini-Program Design and Evaluation to Create Online-Merge-Offline Service for Retailers

As the new retail has come, a lot of retailers try to make a digital transformation. The new retail begins with the development of an online to offline (O2O) mechanism to extend their online e-commerce to offline retailers. Nowadays, leading new retail players have been actively investing in offline retailers. Furthermore, they also evolve around technology-driven sales and cost efficiency to create online merge offline (OMO) services. This study used the Buynow Shanghai flagship store as an example and presents how to design and evaluate mini-programs for creating new retail OMO services for retailers. This study begins with the focus group interview to the three main business departments of the Buynow Shanghai flagship shopping mall. Then, we implemented a quality function deployment (QFD) analysis by referring to the interview results. Thirdly, we proposed the main functions, which can create the OMO service, of the mini-program prototypes by referring to the development priority proposed in QFD analysis. Finally, we design the user interface of the mini-programs and edit a simulated operational video to provide comprehensive introduction of the mini-programs and implemented the system usability scale (SUS) test to sample users. The results of this study show that the mini-programs can enable offline retailers to create omni-channel to customers. They also simplify and save time for the consumer's shopping experience. Furthermore, it allows for better data collection and data analytics for big retailers. The future studies will focus on using Kano's model to evaluate the customers' satisfaction of the services provided by the mini-programs.
Multi-Domain Information Exposure using ALTO: The Good, the Bad and the Solution

Multi-domain applications can benefit from network information exposure using the ALTO protocol framework. ALTO provides network state and capabilities to applications to be more flexible in terms of rate adaptation, transmission time, and server/path selection. However, different key issues arise when designing an ALTO solution for multi-domain environments. In this talk, we summarize such issues along with basic mechanisms to be considered to allow ALTO to expose network information across multiple domains.