Miracle-dz/UniFAR-FTUs huggingface dataset:
Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band

Reconfigurable intelligent surfaces (RISs) provide an interface between the electromagnetic world of wireless propagation environments and the digital world of information science. Simple yet sufficiently accurate path loss models for RISs are an important basis for theoretical analysis and optimization of RIS-assisted wireless communication systems. In this paper, we refine our previously proposed free-space path loss model for RISs to make it simpler, more applicable, and easier to use. The impact of the antenna’s directivity of the transmitter, receiver, and the unit cells of the RIS on the path loss is explicitly formulated as an angle-dependent loss factor. The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size. Based on the proposed model, the properties of a single unit cell are evaluated in terms of scattering performance, power consumption, and area, which allows us to unveil fundamental considerations for deploying RISs in high frequency bands. Two fabricated RISs operating in the millimeter-wave (mmWave) band are utilized to carry out a measurement campaign. The measurement results are shown to be in good agreement with the proposed path loss model. In addition, the experimental results suggest an effective form to characterize the power radiation pattern of the unit cell for path loss modeling.
Characterizing signal propagation to close the performance gap in unnormalized ResNets

Batch Normalization is a key component in almost all state-of-the-art image classifiers, but it also introduces practical challenges: it breaks the independence between training examples within a batch, can incur compute and memory overhead, and often results in unexpected bugs. Building on recent theoretical analyses of deep ResNets at initialization, we propose a simple set of analysis tools to characterize signal propagation on the forward pass, and leverage these tools to design highly performant ResNets without activation normalization layers. Crucial to our success is an adapted version of the recently proposed Weight Standardization. Our analysis tools show how this technique preserves the signal in networks with ReLU or Swish activation functions by ensuring that the per-channel activation means do not grow with depth. Across a range of FLOP budgets, our networks attain performance competitive with the state-of-the-art EfficientNets on ImageNet.
Synthesis and Characteristics of Novel Poly(Imide Siloxane) Segmented Copolymers

Abstract New poly(imide siloxane) copolymers for possible use as tough environmentally stable structural matrix resins and structure adhesives have been prepared. Thus, 3,3′-4,4′-benzophenone tertracarboxylic dianhydride was reacted with various Mn aminopropyl-terminated polydimethylsiloxane oligomers and a meta-substituted diamine “chain-extender” such as 3,3′-diaminodiphenyl sulfone or 3,3′-diaminobenzophenone to produce the siloxane-modified poly(amic acid). Thin films were cast from the reaction mixtures and subsequent thermal dehydration produced the poly(imide siloxane) block or segmented copolymers. Upper “cure” temperatures of 300°C were used to insure complete imidization. By varying the amount and molecular weight of the siloxane oligomer, a variety of novel copolymers of controlled composition have been synthesized. Tough, transparent, flexible soluble films were produced by this method. The thermal and bulk properties of films having low to moderate siloxane content closely resemble those of t.
Density‐based clustering of social networks

The idea of the modal formulation of density‐based clustering is to associate groups with the regions around the modes of the probability density function underlying the data. The correspondence between clusters and dense regions in the sample space is here exploited to discuss an extension of this approach to the analysis of social networks. Conceptually, the notion of high‐density cluster fits well the one of community in a network, regarded to as a collection of individuals with dense local ties in its neighbourhood. The lack of a probabilistic notion of density in networks is turned into a strength of the proposed method, where node‐wise measures that quantify the role of actors are used to derive different community configurations. The approach allows for the identification of a hierarchical structure of clusters, which may catch different degrees of resolution of the clustering structure. This feature well fits the nature of social networks, disentangling different involvements of individuals in aggregations.
Novel optical neural network architecture with the temporal synthetic dimension

Optical neural networks, employing optical ﬁelds and photonic tools to perform artiﬁcial neural network computations, are rapidly advancing and are generating a broad interest and sparking new applications. We propose a nascent approach for realizing the optical neural network utilizing a single resonator network, where the arrival times of optical pulses are interconnected to construct a synthetic temporal dimension. The set of pulses in each roundtrip therefore provides the sites in each layer in the optical neural network, and can be linearly transformed with splitters and delay lines, including the phase modulators, when pulses circulate inside the network. Such linear transformation can be arbitrarily controlled by applied modulation phases, which serve as the building block of the neural network together with a nonlinear component for pulses. We validate the functionality of the proposed optical neural network using an example of a complicated wine classiﬁcation problem. This proof of principle demonstration opens up an opportunity to develop a photonics-based machine learning in a single ring network utilizing the concept of synthetic dimensions. Our approach holds ﬂexibility and easiness of reconﬁguration with potentially complex functionality in achieving desired optical tasks, pointing towards promisingly perform on-chip optical computations with further miniaturization.
ItNet: iterative neural networks with tiny graphs for accurate and efficient anytime prediction

Deep neural networks have usually to be compressed and accelerated for their usage in low-power, e.g. mobile, devices. Recently, massively-parallel hardware accelerators were developed that offer high throughput and low latency at low power by utilizing in-memory computation. However, to exploit these beneﬁts the computational graph of a neural network has to ﬁt into the in-computation memory of these hardware systems that is usually rather limited in size. In this study, we introduce a class of network models that have a tiny memory footprint in terms of their computational graphs. To this end, the graph is designed to contain loops by iteratively executing a single network building block. Furthermore, the trade-off between accuracy and latency of these so-called iterative neural networks is improved by adding multiple intermediate outputs both during training and inference. We show state-of-the-art results for semantic segmentation on the CamVid and Cityscapes datasets that are especially demanding in terms of computational resources. In ablation studies, the improvement of network training by intermediate network outputs as well as the trade-off between weight sharing over iterations and the network size are investigated.
Aesthetics, Personalization and Recommendation: A survey on Deep Learning in Fashion

Machine learning is completely changing the trends in the fashion industry. From big to small every brand is using machine learning techniques in order to improve their revenue, increase customers and stay ahead of the trend. People are into fashion and they want to know what looks best and how they can improve their style and elevate their personality. And since systems are already monitoring every sale and coming trends , why not utilize their power in getting a recommendation regarding outfit. Using Deep learning technology and infusing it with Computer Vision techniques one can do so by utilizing Brain-inspired Deep Networks, and engaging into Neuroaesthetics, working with GAN’s and Training them, playing around with Unstructured Data,and infusing the transformer architecture are just some highlights which can be touched with the Fashion domain. It’s all about designing a system that can tell us information regarding the fashion aspect that can come in handy with the ever growing demand. Personalization is a big factor that impacts the spending choices of customers.The survey also shows remarkable approaches that encroach the subject of achieving that by divulging deep into how visual data can be interpreted and leveraged into different models and approaches. Aesthetics play a vital role in clothing recommendation as users’ decision depends largely on whether the clothing is in line with their aesthetics, however the conventional image features cannot portray this directly. For that the survey also highlights remarkable models like tensor factorization model, conditional random field model among others to cater the need to acknowledge aesthetics as an important factor in Apparel recommendation.These AI inspired deep models can pinpoint exactly which certain style resonates best with their customers and they can have an understanding of how the new designs will set in with the community. With AI and machine learning your businesses can stay ahead of the fashion trends and deliver exactly what your customers want and when they want it.
Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning

We propose self-adaptive training—a unified training algorithm that dynamically calibrates and enhances training processes by model predictions without incurring an extra computational cost—to advance both supervised and self-supervised learning of deep neural networks. We analyze the training dynamics of deep networks on training data that are corrupted by, e.g., random noise and adversarial examples. Our analysis shows that model predictions are able to magnify useful underlying information in data and this phenomenon occurs broadly even in the absence of any label information, highlighting that model predictions could substantially benefit the training processes: self-adaptive training improves the generalization of deep networks under noise and enhances the self-supervised representation learning. The analysis also sheds light on understanding deep learning, e.g., a potential explanation of the recently-discovered double-descent phenomenon in empirical risk minimization and the collapsing issue of the state-of-the-art self-supervised learning algorithms. Experiments on the CIFAR, STL, and ImageNet datasets verify the effectiveness of our approach in three applications: classification with label noise, selective classification, and linear evaluation. To facilitate future research, the code has been made publicly available at https://github.com/LayneH/self-adaptive-training.
Personalised Recommendations in Mental Health Apps: The Impact of Autonomy and Data Sharing

The recent growth of digital interventions for mental well-being prompts a call-to-arms to explore the delivery of personalised recommendations from a user’s perspective. In a randomised placebo study with a two-way factorial design, we analysed the difference between an autonomous user experience as opposed to personalised guidance, with respect to both users’ preference and their actual usage of a mental well-being app. Furthermore, we explored users’ preference in sharing their data for receiving personalised recommendations, by juxtaposing questionnaires and mobile sensor data. Interestingly, self-reported results indicate the preference for personalised guidance, whereas behavioural data suggests that a blend of autonomous choice and recommended activities results in higher engagement. Additionally, although users reported a strong preference of filling out questionnaires instead of sharing their mobile data, the data source did not have any impact on the actual app use. We discuss the implications of our findings and provide takeaways for designers of mental well-being applications.
A fast two-stage algorithm for non-negative matrix factorization in streaming data

In this article, we study algorithms for nonnegative matrix factorization (NMF) in various applications involving streaming data. Utilizing the continual nature of the data, we develop a fast two-stage algorithm for highly efficient and accurate NMF. In the first stage, an alternating non-negative least squares (ANLS) framework is used, in combination with active set method with warm-start strategy for the solution of subproblems. In the second stage, an interior point method is adopted to accelerate the local convergence. The convergence of the proposed algorithm is proved. The new algorithm is compared with some existing algorithms in benchmark tests using both real-world data and synthetic data. The results demonstrate the advantage of our algorithm in finding high-precision solutions.
Dive into Decision Trees and Forests: A Theoretical Demonstration

Based on decision trees, many fields have arguably made tremendous progress in recent years. In simple words, decision trees use the strategy of"divide-and-conquer"to divide the complex problem on the dependency between input features and labels into smaller ones. While decision trees have a long history, recent advances have greatly improved their performance in computational advertising, recommender system, information retrieval, etc. We introduce common tree-based models (e.g., Bayesian CART, Bayesian regression splines) and training techniques (e.g., mixed integer programming, alternating optimization, gradient descent). Along the way, we highlight probabilistic characteristics of tree-based models and explain their practical and theoretical benefits. Except machine learning and data mining, we try to show theoretical advances on tree-based models from other fields such as statistics and operation research. We list the reproducible resource at the end of each method.
A Note on Connectivity of Sublevel Sets in Deep Learning

It is shown that for deep neural networks, a single wide layer of width $N+1$ ($N$ being the number of training samples) suffices to prove the connectivity of sublevel sets of the training loss function. In the two-layer setting, the same property may not hold even if one has just one neuron less (i.e. width $N$ can lead to disconnected sublevel sets).
Effect of Deep Learning Feature Inference Techniques on Respiratory Sounds

Analysis of respiratory sounds increases its importance every day. Many different methods are available in the analysis, and new techniques are continuing to be developed to further improve these methods. Features are extracted from audio signals and trained using different machine learning techniques. The use of deep learning, which is a different method and has increased in recent years, also shows its influence in this field. Deep learning techniques applied to the image of audio signals give good results and continue to be developed. In this study, image filters were applied to the values obtained from audio signals and the results of the features formed from this were examined in machine learning and deep learning techniques. Their results were compared with the results of methods that had previously achieved good results.
When the ends don't justify the means: Learning a treatment strategy to prevent harmful indirect effects

There is a growing literature on finding so-called optimal treatment rules, which are rules by which to assign treatment to individuals based on an individual’s characteristics, such that a desired outcome is maximized. A related goal entails identifying individuals who are predicted to have a harmful indirect effect (the effect of treatment on an outcome through mediators) even in the presence of an overall beneficial effect of the treatment on the outcome. In some cases, the likelihood of a harmful indirect effect may outweigh a likely beneficial overall effect, and would be reason to caution against treatment for indicated individuals. We build on both the current mediation and optimal treatment rule literature to propose a method of identifying a subgroup for which the treatment effect through the mediator is harmful. Our approach is nonparametric, incorporates post-treatment variables that may confound ∗The authors gratefully acknowledge R00DA042127 from the National Institute on Drug Abuse 1 ar X iv :2 10 1. 08 59 0v 1 [ st at . A P] 2 1 Ja n 20 21 the mediator-outcome relationship, and does not make restrictions on the distribution of baseline covariates, mediating variables (considered jointly), or outcomes. We apply the proposed approach to identify a subgroup of boys in the Moving to Opportunity housing voucher experiment who are predicted to have harmful indirect effects, though the average total effect is beneficial.
Regularization via Deep Generative Models: an Analysis Point of View

This paper proposes a new way of regularizing an inverse problem in imaging (e.g., deblurring or inpainting) by means of a deep generative neural network. Compared to end-to-end models, such approaches seem particularly interesting since the same network can be used for many different problems and experimental conditions, as soon as the generative model is suited to the data. Previous works proposed to use a synthesis framework, where the estimation is performed on the latent vector, the solution being obtained afterwards via the decoder. Instead, we propose an analysis formulation where we directly optimize the image itself and penalize the latent vector. We illustrate the interest of such a formulation by running experiments of inpainting, deblurring and super-resolution. In many cases our technique achieves a clear improvement of the performance and seems to be more robust, in particular with respect to initialization.
Programming Boundary Deformation Patterns in Active Networks

Active materials take advantage of their internal sources of energy to self-organize in an automated manner. This feature provides a novel opportunity to design micron-scale machines with minimal required control. However, self-organization goes hand in hand with predetermined dynamics that are hardly susceptible to environmental perturbations. Therefore utilizing this feature of active systems requires harnessing and directing the macroscopic dynamics to achieve specific functions; which in turn necessitates understanding the underlying mechanisms of active forces. Here we devise an optical control protocol to engineer the dynamics of active networks composed of microtubules and light-activatable motor proteins. The protocol enables carving activated networks of different shapes, and isolating them from the embedding solution. Studying a large set of shapes, we observe that the active networks contract in a shape-preserving manner that persists over the course of contraction. We formulate a coarse-grained theory and demonstrate that self-similarity of contraction is associated with viscous-like active stresses. These findings help us program the dynamics of the network through manipulating the light intensity in space and time, and maneuver the network into bending in specific directions, as well as temporally alternating directions. Our work improves understanding the active dynamics in contractile networks, and paves a new path towards engineering the dynamics of a large class of active materials.
Massive White Dwarfs in Young Star Clusters

We have carried out a search for massive white dwarfs (WDs) in the direction of young open star clusters using the Gaia DR2 database. The aim of this survey was (1) to provide robust data for new and previously known high-mass WDs regarding cluster membership, (2) to highlight WDs previously included in the initial final mass relation (IFMR) that are unlikely members of their respective clusters according to Gaia astrometry, and (3) to select an unequivocal WD sample that could then be compared with the host clusters’ turnoff masses. All promising WD candidates in each cluster color–magnitude diagram were followed up with spectroscopy from Gemini in order to determine whether they were indeed WDs and derive their masses, temperatures, and ages. In order to be considered cluster members, white dwarfs were required to (1) have proper motions and parallaxes within 2σ, 3σ, or 4σ of those of their potential parent cluster based on how contaminated the field was in their region of the sky, (2) have a cooling age that was less than the cluster age, and (3) have a mass that was broadly consistent with the IFMR. A number of WDs included in current versions of the IFMR turned out to be nonmembers, and a number of apparent members, based on Gaia’s astrometric data alone, were rejected, as their mass and/or cooling times were incompatible with cluster membership. In this way, we developed a highly selected IFMR sample for high-mass WDs that, surprisingly, contained no precursor masses significantly in excess of ∼ 6 M ⊙.
Finger Vein Recognition by Generating Code

Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishes ability, security and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images or patterns and then compare features to find the best match. However, the features extracted from images contain much redundant data, while the features extracted from patterns are greatly influenced by image segmentation methods. To tack these problems, this paper proposes a new finger vein recognition by generating code. The proposed method does not require an image segmentation algorithm, is simple to calculate and has a small amount of data. Firstly, the finger vein images were divided into blocks to calculate the mean value. Then the centrosymmetric coding is performed by using the generated eigenmatrix. The obtained codewords are concatenated as the feature codewords of the image. The similarity between vein codes is measured by the ratio of minimum Hamming distance to codeword length. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed method. The results indicate that our method outperforms the state-of-theart methods and has competitive potential in performing the matching task.
Calculating the polarization in bipartite lattice models: Application to an extended Su-Schrieffer-Heeger model

We address the question of different representation of Bloch states for lattices with a basis, with a focus on topological systems. The representations differ in the relative phase of the Wannier functions corresponding to the diffferent basis members. We show that the phase can be chosen in such a way that the Wannier functions for the different sites in the basis both become eigenstates of the position operator in a particular band. A key step in showing this is the extension of the Brillouin zone. When the distance between sites within a unit cell is a rational number, $p/q$, the Brillouin extends by a factor of $q$. For irrational numbers, the Brillouin zone extends to infinity. In the case of rational distance, $p/q$, the Berry phase"lives"on a cyclic curve in the parameter space of the Hamiltonian, on the Brillouin zone extended by a factor of $q$. For irrational distances the most stable way to calculate the polarization is to approximate the distance as a rational sequence, and use the formulas derived here for rational numbers. The use of different bases are related to unitary transformations of the Hamiltonian, as such, the phase diagrams of topological systems are not altered, but each phase can acquire different topological characteristics when the basis is changed. In the example we use, an extended Su-Schrieffer-Heeger model, the use of the diagonal basis leads to toroidal knots in the Hamiltonian space, whose winding numbers give the polarization.