Miracle-dz/UniFAR-FTUs huggingface dataset:
HRVGAN: High Resolution Video Generation using Spatio-Temporal GAN

In this paper, we present a novel network for high resolution video generation. Our network uses ideas from Wasserstein GANs by enforcing k-Lipschitz constraint on the loss term and Conditional GANs using class labels for training and testing. We present Generator and Discriminator network layerwise details along with the combined network architecture, optimization details and algorithm used in this work. Our network uses a combination of two loss terms: mean square pixel loss and an adversarial loss. The datasets used for training and testing our network are UCF101, Golf and Aeroplane Datasets. Using Inception Score and Frechet Inception Distance as the evaluation metrics, our network outperforms previous state of the art networks on unsupervised video generation.
A FISTA-Type First Order Algorithm on Composite Optimization Problems that is Adaptable to the Convex Situation

In this note, we propose a FISTA-type first order algorithm, VAR-FISTA, to solve a composite optimization problem. A distinctive feature of VAR-FISTA is its ability to exploit the convexity of the function in the problem, resulting in an improved iteration complexity when the function is convex compared to when it is nonconvex. The iteration complexity result for the convex and nonconvex case obtained in the note are compatible to the best known in the literature so far.
Projected two- and three-point statistics: forecasts and mitigation of non-linear RSDs

The combination of two- and three-point clustering statistics of galaxies and the underlying matter distribution has the potential to break degeneracies between cosmological parameters and nuisance parameters and can lead to significantly tighter constraints on parameters describing the composition of the Universe and the dynamics of inflation. Here we investigate the relation between biases in the estimated parameters and inaccurate modelling of non-linear redshift-space distortions for the power spectrum and bispectrum of projected galaxy density fields and lensing convergence. Non-linear redshift-space distortions are one of the leading systematic uncertainties in galaxy clustering. Projections along the line of sight suppress radial modes and are thus allowing a trade-off between biases due to non-linear redshift-space distortions and statistical uncertainties. We investigate this bias-error trade-off for a CMASS-like survey with a varying number of redshift bins. Improved modelling of the non-linear redshift-space distortions allows the recovery of more radial information when controlling for biases. Not modelling non-linear redshift space distortions inflates error bars for almost all parameters by 20%. The information loss for the amplitude of local non-Gaussianities is smaller, since it is best constrained from large scales. In addition, we show empirically that one can recover more than 99% of the 3D power spectrum information if the depth of the tomographic bins is reduced to 10 $h^{-1}$Mpc.
Theoretical analysis for flattening of a rising bubble in a Hele–Shaw cell

We calculate the shape and the velocity of a bubble rising in an infinitely large and closed Hele–Shaw cell using Park and Homsy’s boundary condition, which accounts for the change of the three dimensional structure in the perimeter zone. We first formulate the problem in the form of a variational problem and discuss the shape change assuming that the bubble takes an elliptic shape. We calculate the shape and the velocity of the bubble as a function of the bubble size, the gap distance, and the inclination angle of the cell. We show that the bubble is flattened as it rises. This result is in agreement with experiments for large Hele–Shaw cells.
Bosch Deep Learning Hardware Benchmark

The widespread use of Deep Learning (DL) applications in science and industry has created a large demand for efficient inference systems. This has resulted in a rapid increase of available Hardware Accelerators (HWAs) making comparison challenging and laborious. To address this, several DL hardware benchmarks have been proposed aiming at a comprehensive comparison for many models, tasks, and hardware platforms. Here, we present our DL hardware benchmark which has been specifically developed for inference on embedded HWAs and tasks required for autonomous driving. In addition to previous benchmarks, we propose a new granularity level to evaluate common submodules of DL models, a twofold benchmark procedure that accounts for hardware and model optimizations done by HWA manufacturers, and an extended set of performance indicators that can help to identify a mismatch between a HWA and the DL models used in our benchmark.
DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data

For sake of reliability, it is necessary for models in real-world applications to be both powerful and globally interpretable. Simple linear classifiers, e.g., Logistic Regression (LR), are globally interpretable, but not powerful enough to model complex nonlinear interactions among features in tabular data. Meanwhile, Deep Neural Networks (DNNs) have shown great effectiveness for modeling tabular data, but is not globally interpretable. Accordingly, it will be promising if we can propose a feature crossing method to find feature interactions in DNN, and use them as cross features in LR. The local piece-wise interpretations in DNN of a specific feature are usually inconsistent in different samples, which is caused by feature interactions in the hidden layers. Inspired by this, we give definition of the interpretation inconsistency in DNN, and accordingly propose a novel feature crossing method called DNN2LR. Extensive experiments have been conducted on five public datasets and two real-world datasets. The final model, a LR model empowered with cross features, generated by DNN2LR can outperform the complex DNN model, as well as several state-of-the-art feature crossing methods. The experimental results strongly verify the effectiveness and efficiency of DNN2LR, especially on real-world datasets with large numbers of feature fields.
Time-Varying and Nonlinearly Scaled Consensus of Multiagent Systems: A Generic Attracting Law Approach

This paper presents the design and analysis of the finite/fixed-time scaled consensus for multiagent systems. A study on a generic attracting law, the certain classes of nonlinear systems that admit attractors with finite/fixed-time convergence, is at first given for the consensus purpose. The estimates for the lower and upper bounds on the settling time functions are provided through the two-phase analysis. The given estimates are initial state dependent, but the durations are finite, without regarding the values that the initial states take. According to the generic attracting law, distributed protocols are proposed for multiagent systems with undirected and detail-balanced directed graphs, respectively, where the scaled strategies, including time-varying and nonlinear scales, are adopted. It is shown that the finite/fixed-time consensus for the multiagent system undertaken can still be achieved, even though both time-varying and nonlinear scales are taken among agents. Numerical simulation of two illustrative examples are given to verify effectiveness of the proposed finite-duration consensus protocols.
A Unified and Fine-Grained Approach for Light Spanners

Seminal works on light spanners from recent years provide near-optimal tradeoffs between the stretch and lightness of spanners in general graphs, minor-free graphs, and doubling metrics. In FOCS'19 the authors provided a ``truly optimal'' tradeoff for Euclidean low-dimensional spaces. Some of these papers employ inherently different techniques than others. Moreover, the runtime of these constructions is rather high. In this work, we present a unified and fine-grained approach for light spanners. Besides the obvious theoretical importance of unification, we demonstrate the power of our approach in obtaining (1) stronger lightness bounds, and (2) faster construction times. Our results include: _ $K_r$-minor-free graphs: A truly optimal spanner construction and a fast construction. _ General graphs: A truly optimal spanner -- almost and a linear-time construction with near-optimal lightness. _ Low dimensional Euclidean spaces: We demonstrate that Steiner points help in reducing the lightness of Euclidean $1+\epsilon$-spanners almost quadratically for $d\geq 3$.
Cross-Modality 3D Object Detection

In this paper, we focus on exploring the fusion of images and point clouds for 3D object detection in view of the complementary nature of the two modalities, i.e., images possess more semantic information while point clouds specialize in distance sensing. To this end, we present a novel two-stage multi-modal fusion network for 3D object detection, taking both binocular images and raw point clouds as input. The whole architecture facilitates two-stage fusion. The first stage aims at producing 3D proposals through sparse pointwise feature fusion. Within the first stage, we further exploit a joint anchor mechanism that enables the network to utilize 2D-3D classification and regression simultaneously for better proposal generation. The second stage works on the 2D and 3D proposal regions and fuses their dense features. In addition, we propose to use pseudo LiDAR points from stereo matching as a data augmentation method to densify the LiDAR points, as we observe that objects missed by the detection network mostly have too few points especially for far-away objects. Our experiments on the KITTI dataset show that the proposed multi-stage fusion helps the network to learn better representations.
Translating Paintings Into Music Using Neural Networks

We propose a system that learns from artistic pairings of music and corresponding album cover art. The goal is to 'translate' paintings into music and, in further stages of development, the converse. We aim to deploy this system as an artistic tool for real time 'translations' between musicians and painters. The system's outputs serve as elements to be employed in a joint live performance of music and painting, or as generative material to be used by the artists as inspiration for their improvisation.
Who ya gonna call? (Alerting Authorities): Measuring Namespaces, Web Certificates, and DNSSEC

During disasters, crisis, and emergencies the public relies on online services provided by authorities to receive timely alerts, trustworthy information, and access to relief programs. It is therefore crucial for the authorities to reduce risks when accessing their Web services. This includes proper naming (e.g., against phishing attacks), name protection (e.g., against forged DNS data), adequate identification (e.g., against spoofing and impersonation), and transport security (e.g., against traffic manipulation). In this paper, we take a first look on Alerting Authorities in the U.S. and measure the deployment of domain names, DNSSEC, and Web certificates related to their websites. Surprisingly, many do not take advantage of existing methods to increase security and reliability. Analyzing 1,388 Alerting Authorities, backed by the Federal Emergency Management Agency (FEMA), we are alarmed by three major findings. First, 50% of the domain names are registered under generic top-level domains, which simplifies phishing. Second, only 8% of the domain names are secured by DNSSEC and about 15% of all hosts fail to provide valid certificates. Third, there is a worrying trend of using shared certificates, which increases dependencies leading to instability in the future.
Detection of biennial rhythm and estimation of repeatability in mango (Mangifera indica L.)

Most of the perennial crops exhibit biennial rhythm in fruiting and growth. Among the perennial crops, mango is an important fruit crop not only in India but also across the world. This crop acquires bienniality, a complex phenomenon, by carrying a heavy fruiting in one year called ‘on ’year and little or no fruiting in the next year called ‘off ’year. The presence of bienniality in the data may affect genetic parameter estimates like repeatability. In this paper, the extent and intensity of bienniality in mango has been studied by graphical as well as quantitative approaches. Besides, a test of significance for bienniality has been obtained for each tree using binomial distribution. On this basis, the percentage trees in ‘on ’years and in ‘off ’years has been quantified for four different locations. Also, variety-wise biennial rhythm has been studied in these locations. The results reveal that there exists a moderate to strong bienniality in mango crop. Besides, the errors become auto-correlated when bienniality is removed by taking moving average. Moving average methods have been applied to estimate the repeatability. The results reveal that the mango yield was moderate to highly repeatable.
Social Cybersecurity Chapter 13: Casestudy with COVID-19 Pandemic

The purpose of this case study is to leverage the concepts and tools presented in the preceding chapters and apply them in a real world social cybersecurity context. With the COVID-19 pandemic emerging as a defining event of the 21st Century and a magnet for disinformation maneuver, we have selected the pandemic and its related social media conversation to focus our efforts on. This chapter therefore applies the tools of information operation maneuver, bot detection and characterization, meme detection and characterization, and information mapping to the COVID-19 related conversation on Twitter. This chapter uses these tools to analyze a stream containing 206 million tweets from 27 million unique users from 15 March 2020 to 30 April 2020. Our results shed light on elaborate information operations that leverage the full breadth of the BEND maneuvers and use bots for important shaping operations.
Towards Super Teichm\"uller Spin TQFT

The quantization of the Teichm\"uller theory has led to the formulation of the so-called Teichm\"uller TQFT for 3-manifolds. In this paper we initiate the study of "supersymmetrization" of the Teichm\"uller TQFT, which we call the super Teichm\"uller spin TQFT. We obtain concrete expressions for the partition functions of the super Teichm\"uller spin TQFT for a class of spin 3-manifold geometries, by taking advantage of the recent results on the quantization of the super Teichm\"uller theory. We then compute the perturbative expansions of the partition functions, to obtain perturbative invariants of spin 3-manifolds. We also comment on the relations of the super Teichm\"uller spin TQFT to 3-dimensional Chern-Simons theories with complex gauge groups, and to a class of 3d N=2 theories arising from the compactifications of the M5-branes.
Training Sparse Neural Networks using Compressed Sensing

Pruning the weights of neural networks is an effective and widely-used technique for reducing model size and inference complexity. We develop and test a novel method based on compressed sensing which combines the pruning and training into a single step. Specifically, we utilize an adaptively weighted $\ell^1$ penalty on the weights during training, which we combine with a generalization of the regularized dual averaging (RDA) algorithm in order to train sparse neural networks. The adaptive weighting we introduce corresponds to a novel regularizer based on the logarithm of the absolute value of the weights. Numerical experiments on the CIFAR-10 and CIFAR-100 datasets demonstrate that our method 1) trains sparser, more accurate networks than existing state-of-the-art methods; 2) can also be used effectively to obtain structured sparsity; 3) can be used to train sparse networks from scratch, i.e. from a random initialization, as opposed to initializing with a well-trained base model; 4) acts as an effective regularizer, improving generalization accuracy.
RANSIP : From noisy point clouds to complete ear models, unsupervised

Ears are a particularly difficult region of the human face to model, not only due to the non-rigid deformations existing between shapes but also to the challenges in processing the retrieved data. The first step towards obtaining a good model is to have complete scans in correspondence, but these usually present a higher amount of occlusions, noise and outliers when compared to most face regions, thus requiring a specific procedure. Therefore, we propose a complete pipeline taking as input unordered 3D point clouds with the aforementioned problems, and producing as output a dataset in correspondence, with completion of the missing data. We provide a comparison of several state-of-the-art registration methods and propose a new approach for one of the steps of the pipeline, with better performance for our data.
Coinductive Invertibility in Higher Categories

Invertibility is an important concept in category theory. In higher category theory, it becomes less obvious what the correct notion of invertibility is, as extra coherence conditions can become necessary for invertible structures to have desirable properties. We define some properties we expect to hold in any reasonable definition of a weak $\omega$-category. With these properties we define three notions of invertibility inspired by homotopy type theory. These are quasi-invertibility, where a two sided inverse is required, bi-invertibility, where a separate left and right inverse is given, and half-adjoint inverse, which is a quasi-inverse with an extra coherence condition. These definitions take the form of coinductive data structures. Using coinductive proofs we are able to show that these three notions are all equivalent in that given any one of these invertibility structures, the others can be obtained. The methods used to do this are generic and it is expected that the results should be applicable to any reasonable model of higher category theory. Many of the results of the paper have been formalised in Agda using coinductive records and the machinery of sized types.
Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning

Temporal-Difference (TD) learning with nonlinear smooth function approximation for policy evaluation has achieved great success in modern reinforcement learning. It is shown that such a problem can be reformulated as a stochastic nonconvex-strongly-concave optimization problem, which is challenging as naive stochastic gradient descent-ascent algorithm suffers from slow convergence. Existing approaches for this problem are based on two-timescale or double-loop stochastic gradient algorithms, which may also require sampling large-batch data. However, in practice, a single-timescale single-loop stochastic algorithm is preferred due to its simplicity and also because its step-size is easier to tune. In this paper, we propose two single-timescale single-loop algorithms which require only one data point each step. Our first algorithm implements momentum updates on both primal and dual variables achieving an $O(\varepsilon^{-4})$ sample complexity, which shows the important role of momentum in obtaining a single-timescale algorithm. Our second algorithm improves upon the first one by applying variance reduction on top of momentum, which matches the best known $O(\varepsilon^{-3})$ sample complexity in existing works. Furthermore, our variance-reduction algorithm does not require a large-batch checkpoint. Moreover, our theoretical results for both algorithms are expressed in a tighter form of simultaneous primal and dual side convergence.
Network-Aware Demand-Side Management Framework With A Community Energy Storage System Considering Voltage Constraints

This paper studies the feasibility of integrating a community energy storage (CES) system with rooftop photovoltaic (PV) power generation for demand-side management of a neighbourhood while maintaining the distribution network voltages within allowed limits. To this end, we develop a decentralized energy trading system between a CES provider and users with rooftop PV systems. By leveraging a linearized branch flow model for radial distribution networks, a voltage-constrained leader-follower Stackelberg game is developed wherein the CES provider maximizes revenue and the users minimize their personal energy costs by trading energy with the CES system and the grid. The Stackelberg game has a unique equilibrium at which the CES provider maximizes revenue and the users minimize energy costs at a unique Nash equilibrium. A case study, with realistic PV power generation and demand data, confirms that the energy trading system can reduce peak energy demand and prevent network voltage excursions, while delivering financial benefits to the users and the CES provider. Further, simulations highlight that, in comparison with a centralized system, the decentralized energy trading system provides greater economic benefits to the users with less energy storage capacity.
Accurate Alignment Inspection System for Low-resolution Automotive and Mobility LiDAR

A misalignment of LiDAR as low as a few degrees could cause a significant error in obstacle detection and mapping that could cause safety and quality issues. In this paper, an accurate inspection system is proposed for estimating a LiDAR alignment error after sensor attachment on a mobility system such as a vehicle or robot. The proposed method uses only a single target board at the fixed position to estimate the three orientations (roll, tilt, and yaw) and the horizontal position of the LiDAR attachment with sub-degree and millimeter level accuracy. After the proposed preprocessing steps, the feature beam points that are the closest to each target corner are extracted and used to calculate the sensor attachment pose with respect to the target board frame using a nonlinear optimization method and with a low computational cost. The performance of the proposed method is evaluated using a test bench that can control the reference yaw and horizontal translation of LiDAR within ranges of 3 degrees and 30 millimeters, respectively. The experimental results for a low-resolution 16 channel LiDAR (Velodyne VLP-16) confirmed that misalignment could be estimated with accuracy within 0.2 degrees and 4 mm. The high accuracy and simplicity of the proposed system make it practical for large-scale industrial applications such as automobile or robot manufacturing process that inspects the sensor attachment for the safety quality control.
Black hole shadows in M-theory scenarios

We study the shadows of four dimensional black holes in M-theory inspired models. We first inspect the influence of M2-branes on such optical aspects for non-rotating solutions. In particular, we show that the M2-brane number can control the circular shadow size. This geometrical behavior is distorted for rotating solutions exhibiting cardioid shapes in certain moduli space regions. Implementing a rotation parameter, we analyze the geometrical shadow deformations. Among others, we recover the circular behaviors for a large M2-brane number. In addition, we propose a possible connection with observations (from Event Horizon Telescope or future devices) for a particular M-theory compactification by deriving naive constraints on the M2-brane number in the light of the $M87^\star$ observational parameters. Investigating the energy emission rate at high energies, we find, in a well-defined approximation, that the associated peak decreases with the M2-brane number.