Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory of Extreme Optical Concentration in All-Dielectric Waveguides

We introduce transversely structured all-dielectric waveguides which exploit the vectorial nature of light to achieve extreme sub-wavelength confinement in high index dielectrics, enabling characteristic mode dimensions below $\lambda_0^2$/1,000 without metals or plasmonics. We also derive the metric of optical concentration and show its convenient usage in characterizing enhanced linear and non-linear interactions at the nanoscale. This work expands the toolbox of nanophotonics and opens the door to new types of ultra-efficient and record performing linear and nonlinear devices with broad applications spanning classical and quantum optics.
Information Closure Theory of Consciousness

Information processing in neural systems can be described and analyzed at multiple spatiotemporal scales. Generally, information at lower levels is more fine-grained but can be coarse-grained at higher levels. However, only information processed at specific scales of coarse-graining appears to be available for conscious awareness. We do not have direct experience of information available at the scale of individual neurons, which is noisy and highly stochastic. Neither do we have experience of more macro-scale interactions, such as interpersonal communications. Neurophysiological evidence suggests that conscious experiences co-vary with information encoded in coarse-grained neural states such as the firing pattern of a population of neurons. In this article, we introduce a new informational theory of consciousness: Information Closure Theory of Consciousness (ICT). We hypothesize that conscious processes are processes which form non-trivial informational closure (NTIC) with respect to the environment at certain coarse-grained scales. This hypothesis implies that conscious experience is confined due to informational closure from conscious processing to other coarse-grained scales. Information Closure Theory of Consciousness (ICT) proposes new quantitative definitions of both conscious content and conscious level. With the parsimonious definitions and a hypothesize, ICT provides explanations and predictions of various phenomena associated with consciousness. The implications of ICT naturally reconcile issues in many existing theories of consciousness and provides explanations for many of our intuitions about consciousness. Most importantly, ICT demonstrates that information can be the common language between consciousness and physical reality.
MPC-based Controller with Terrain Insight for Dynamic Legged Locomotion

We present a novel control strategy for dynamic legged locomotion in complex scenarios that considers information about the morphology of the terrain in contexts when only on-board mapping and computation are available. The strategy is built on top of two main elements: first a contact sequence task that provides safe foothold locations based on a convolutional neural network to perform fast and continuous evaluation of the terrain in search of safe foothold locations; then a model predictive controller that considers the foothold locations given by the contact sequence task to optimize target ground reaction forces. We assess the performance of our strategy through simulations of the hydraulically actuated quadruped robot HyQReal traversing rough terrain under realistic on-board sensing and computing conditions.
Kashmir: A Computational Analysis of the Voice of Peace

The recent Pulwama terror attack (February 14, 2019, Pulwama, Kashmir) triggered a chain of escalating events between India and Pakistan adding another episode to their 70-year-old dispute over Kashmir. The present era of ubiquitious social media has never seen nuclear powers closer to war. In this paper, we analyze this evolving international crisis via a substantial corpus constructed using comments on YouTube videos (921,235 English comments posted by 392,460 users out of 2.04 million overall comments by 791,289 users on 2,890 videos). Our main contributions in the paper are three-fold. First, we present an observation that polyglot word-embeddings reveal precise and accurate language clusters, and subsequently construct a document language-identification technique with negligible annotation requirements. We demonstrate the viability and utility across a variety of data sets involving several low-resource languages. Second, we present an extensive analysis on temporal trends of pro-peace and pro-war intent through a manually constructed polarity phrase lexicon. We observe that when tensions between the two nations were at their peak, pro-peace intent in the corpus was at its highest point. Finally, in the context of heated discussions in a politically tense situation where two nations are at the brink of a full-fledged war, we argue the importance of automatic identification of user-generated web content that can diffuse hostility and address this prediction task, dubbed \emph{hope-speech detection}.
Device-independent verification of Einstein-Podolsky-Rosen steering

If the presence of entanglement could be certified in a device-independent (DI) way, it is likely to provide various quantum information processing tasks with unconditional security. Recently, it was shown that a DI protocol, combining measurement-device-independent techniques with self-testing, is able to verify all entangled states, however, it imposes demanding requirements on its experimental implementation. In this work, we propose a much less-demanding protocol based on Einstein-Podolsky-Rosen (EPR) steering to certify entanglement. We establish a complete framework for DI verification of EPR steering in which all steerable states could be verified. We then analyze its robustness towards noise and imperfections of self-testing by considering the measurement scenario with three settings at each side. Finally, a four-photon experiment is implemented to demonstrate that even Bell local states can be device-independently verified. Our work may pave the way for realistic applications of secure quantum information tasks.
An Optimal Algorithm for Multiplayer Multi-Armed Bandits

The paper addresses the Multiplayer Multi-Armed Bandit (MMAB) problem, where $M$ decision makers or players collaborate to maximize their cumulative reward. When several players select the same arm, a collision occurs and no reward is collected on this arm. Players involved in a collision are informed about this collision. We present DPE (Decentralized Parsimonious Exploration), a decentralized algorithm that achieves the same regret as that obtained by an optimal centralized algorithm. Our algorithm has better regret guarantees than the state-of-the-art algorithm SIC-MMAB \cite{boursier2019}. As in SIC-MMAB, players communicate through collisions only. An additional important advantage of DPE is that it requires very little communication. Specifically, the expected number of rounds where players use collisions to communicate is finite.
Colloquium
: Ice rule and emergent frustration in particle ice and beyond

Complex behavior in soft materials comes from the interactions between its components and geometrical constraints associated with lattice structures. Frustration is one classical example of such complex behavior leading to a plethora of phases and new phenomena. In this Colloquium the case of the ice rule is discussed and how it reflects in other soft materials.
Voting for Distortion Points in Geometric Processing

Low isometric distortion is often required for mesh parameterizations. A configuration of some vertices, where the distortion is concentrated, provides a way to mitigate isometric distortion, but determining the number and placement of these vertices is non-trivial. We call these vertices distortion points. We present a novel and automatic method to detect distortion points using a voting strategy. Our method integrates two components: candidate generation and candidate voting. Given a closed triangular mesh, we generate candidate distortion points by executing a three-step procedure repeatedly: (1) randomly cut an input to a disk topology; (2) compute a low conformal distortion parameterization; and (3) detect the distortion points. Finally, we count the candidate points and generate the final distortion points by voting. We demonstrate that our algorithm succeeds when employed on various closed meshes with a genus of zero or higher. The distortion points generated by our method are utilized in three applications, including planar parameterization, semi-automatic landmark correspondence, and isotropic remeshing. Compared to other state-of-the-art methods, our method demonstrates stronger practical robustness in distortion point detection.
Identifying supportive contexts for mindset interventions: A two-model machine learning approach

Growth mindset interventions (which foster students' beliefs that their abilities can grow through effort) are more effective in some contexts than others; however, relatively few variables have been explored that could identify contexts in which growth mindset interventions are most effective. In this study, we utilized machine learning methods to predict growth mindset effectiveness in a nationwide experiment in the U.S. with over 10,000 students. These methods enable analysis of arbitrarily-complex interactions between combinations of student-level predictor variables and intervention outcome, defined as the improvement in grade point average (GPA) during the transition from high school. We utilized two separate machine learning models: one to control for complex relationships between 51 student-level predictors and GPA, and one to predict the change in GPA due to the intervention. We analyzed the trained models to discover which features influenced model predictions most, finding that prior academic achievement, intervention compliance, self-reported reasons for learning, and race/ethnicity were the most important predictors in the model for predicting intervention effectiveness. Unique to this study, we found that low intervention compliance (attempting to navigate through the intervention software without completing all steps) resulted in as much as -0.2 difference in predicted intervention effect on GPA. Our findings have implications for the design of computer-administered growth mindset interventions, especially for students who do not properly complete the intervention.
Enhancing Object Detection in Adverse Conditions using Thermal Imaging

Autonomous driving relies on deriving understanding of objects and scenes through images. These images are often captured by sensors in the visible spectrum. For improved detection capabilities we propose the use of thermal sensors to augment the vision capabilities of an autonomous vehicle. In this paper, we present our investigations on the fusion of visible and thermal spectrum images using a publicly available dataset, and use it to analyze the performance of object recognition on other known driving datasets. We present an comparison of object detection in night time imagery and qualitatively demonstrate that thermal images significantly improve detection accuracy.
A dynamic bandwidth allocation algorithm based on neural network prediction-correction model and software defined TDM-PON

In the future, with multiple services and large-capacity access network scenarios, the network load is often high but the bandwidth is limited. On the situation, based on the software-defined TDM-PON access network architecture and network traffic prediction-correction model, a dynamic bandwidth allocation algorithm is proposed. In the algorithm, a prediction model is used to predict traffic information and a correction mechanism is used to correct the prediction model. After analyzing the global information of the network, the algorithm provide corresponding bandwidth management policies based on business priorities according to different network load conditions. We compare this algorithm with IPACT algorithm, unused prediction algorithm and neural network prediction without correction. It proves that the algorithm guarantees the service quality requirements of different priority services when the bandwidth is limited and the network load is high, and it performs better in terms of average packet delay, bandwidth utilization, etc. Simulation shows, compared with the traditional strategy, the average packet delay is reduced by 70%, and the bandwidth utilization is increased by 19%.
Feedback stabilization of the three-dimensional Navier-Stokes equations using generalized Lyapunov equations

The approximation of the value function associated to a stabilization problem formulated as optimal control problem for the Navier-Stokes equations in dimension three by means of solutions to generalized Lyapunov equations is proposed and analyzed. The specificity, that the value function is not differentiable on the state space must be overcome. For this purpose a new class of generalized Lyapunov equations is introduced. Existence of unique solutions to these equations is demonstrated. They provide the basis for feedback operators, which approximate the value function, the optimal states and controls, up to arbitrary order.
DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision

We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independently controlled respectively, yet using no tuple supervision. The setting is significant since tuple supervision is costly and sometimes even unavailable. However, the task is highly unconstrained and thus ill-posed. To address this problem, we propose to introduce an auxiliary domain which shares a common underlying-structure space with the target domain, and we make a partially shared latent space assumption. The key idea is to encourage the partially shared latent variable to represent the similar underlying spatial structures in both domains, while the two domain-specific latent variables will be unavoidably arranged to present renderings of two domains respectively. This is achieved by designing two parallel generative networks with a common Progressive Rendering Architecture (PRA), which constrains both generative networks' behaviors to model shared underlying structure and to model spatially dependent relation between rendering and underlying structure. Thus, we propose DSRGAN (GANs for Disentangling Underlying Structure and Rendering) to instantiate our method. We also propose a quantitative criterion (the Normalized Disentanglability) to quantify disentanglability. Comparison to the state-of-the-art methods shows that DSRGAN can significantly outperform them in disentanglability.
Interference-Precancelled Pilot Design for LMMSE Channel Estimation of GFDM

Generalized frequency division multiplexing (GFDM) is a promising candidate waveform for next-generation wireless communication systems. However, GFDM channel estimation is still challenging due to the inherent interference. In this paper, we formulate a pilot design framework with linear minimum mean square error (LMMSE) channel estimation for GFDM, and propose a novel pilot design to achieve interference precancellation during pilot generation with the fixed transmit sample values at selected frequency bins. Numerical results demonstrate that the proposed method reduces the channel estimation mean square error and the symbol error rate (SER) in high signal-to-noise ratio (SNR) regions, compared with the conventional methods.
Efficiently Maximizing a Homogeneous Polynomial over Unit Sphere without Convex Relaxation

This work studies the problem of maximizing a higher degree real homogeneous multivariate polynomial over the unit sphere. This problem is equivalent to finding the leading eigenvalue of the associated symmetric tensor of higher order, which is nonconvex and NP-hard. Recent advances show that semidefinite relaxation is quite effective to find a global solution. However, the solution methods involve full/partial eigenvalue decomposition during the iterates, which heavily limits its efficiency and scalability. On the other hand, for odd degree (odd order) cases, the order has to be increased to even, which potentially reduces the efficiency. To find the global solutions, instead of convexifying the problem, we equivalently reformulate the problem as a nonconvex matrix program based on an equivalence property between symmetric rank-1 tensors and matrices of any order, which is a generalization of the existing results. The program is directly solved by a vanilla alternating direction method, which only involves the computation of leading eigenvalue/singular value of certain matrices, benefiting from the special structure of the program. Although being nonconvex, under certain hypotheses, it is proved that the algorithm converges to a leading eigenvalue of the associated tensor. Numerical experiments on different classes of tensors demonstrate that the proposed approach has a significant improvement in efficiency and scalability, while it can keep the effectiveness of semidefinite relaxation as much as possible. For instance, the proposed method finds the leading eigenpair of a third-order 500 dimensional Hilbert tensor in a personal computer within 100 seconds.
Selection of UML Models for Test Case Generation: A Discussion on Techniques to Generate Test Cases

Software development life cycle (SDLC) depicts the distinct phases a software needs to go through, starting off with the requirement engineering phase through the testing phase. Requirement engineering and testing hold a very important place in the development of the software. Testing plays such a crucial part in SDLC that more than development some projects spend more time on testing. This helps in finding bugs and ensure that a quality product is shipped. Test case generation is the toughest step in the testing process. It involves a lot of effort to find errors in the code. To eliminate the tedious process of finding errors in the code and improve the efficiency of the software, an innovative approach called Model-Based Testing (MBT) has been evolved. In the MBT approach, testing begins at the design phase and is thus helpful in identifying the faults early. Unified Modeling Language (UML) has been used to generate test cases using MBT. There has been a lot of research and proposals made for MBT using different UML diagrams. But the problem with these approaches is that a single UML diagram like activity diagram or sequence diagram is not enough to generate test cases in all the scenarios. There might be situation where multiple models might be used to generate test case. This paper discusses about different existing MBT methodologies used for testing and discusses the guidelines for choosing an efficient UML diagram to generate test cases. It also discusses about the way of improving the models by using multiple UML models or by extending the models.
Continuous-time Dynamic Realization for Nonlinear Stabilization via Control Contraction Metrics

Nonlinear stabilization using control contraction metric (CCM) method usually involves an online optimization problem to compute a minimal geodesic (a shortest path) between pair of states, which is not desirable for real-time applications. This paper introduces a continuous-time dynamic realization which distributes the computational cost of the optimization problem over the time domain. The basic idea is to force the internal state of the dynamic controller to converge to a geodesic using covariant derivative information. A numerical example illustrates the proposed approach.
Libraries of hidden layer activity patterns can lead to better understanding of operating principles of deep neural networks

Deep neural networks (DNNs) can outperform human brains in specific tasks, but due to their lack of transparency in decision-making processes, it remains uncertain whether we can completely rely on DNNs when it comes to high-risk problems requiring rigorous decisions. As DNNs' operations rely on a massive number of linear/nonlinear computations in both parallel and sequential, it is impossible to distinguish every factor influencing their decision. Further, DNNs cannot be empirically tested in every potential condition. Therefore, we should consider the possibility that DNNs' decisions can be corrupted by unexpected factors; for instance, adversarial attacks can deceive DNNs quite effectively. Before DNNs are deployed to solve high-stakes problems, such vulnerability must be overcome. In our study, we propose an algorithm to provide better insights into DNNs' decision-making processes. Our experiments suggest that this algorithm can effectively trace DNNs' decision processes on a layer-by-layer basis and be used to detect adversarial attacks.
Gyratory Shearing Compaction of Granular Materials

In this letter, instead of investigating the compaction of granular materials under tapping or cyclic shearing, we focused more on the gyratory shearing compaction, where particles are subjected to constant pressure and shear rate continuously. Such a phenomenon is crucial to the compaction of asphalt mixtures or soil in civil engineering and can be extended to other areas, such as powder processing and pharmaceutical engineering. In this study, we found that the gyratory speed or interstitial fluid viscosity has almost no impact on the compaction behavior, while the pressure plays a more important role. Additionally, it is the inertial time scale which dictates the compaction behavior under gyratory shearing in most cases, whereas the viscous time scale can also have influence in some conditions.
A generalized perfect vortex beam with controllable impulse ring profile

Perfect optical vortices (POVs) provide an enabling solution to address the predicament induced by the strong dependence of classical optical vortices on theirs carried topological charges. Here, a type of generalized POVs with controllable impulse ring profile was proposed and demonstrated. Especially, a type of "absolute" dark POVs surrounded by two bright ringlobes in each side was presented, which provides a perfect annular potential well along those dark impulse rings for trapping steadily low-index particles, cells, or quantum gas, etc. In further, several POVs with different impulse ring profiles, including conventional POVs with bright rings, dark POVs mentioned above, and also POVs with controllable impulse ring profile, were demonstrated. This work opens up new possibilities to reshape arbitrarily the impulse ring profile for perfect vortices, and this type of novel POVs will enrich functions of optical vortices and it should be of high interest for its potential applications in optical manipulation, both quantum and classical optical communications, enhanced optical imaging, and also novel structured pumping lasers, etc.
On the degree growth of iterated birational maps.

We construct a family of birational maps acting on two dimensional projective varieties, for which the growth of the degrees of the iterates is cubic. It is known that this growth can be bounded, linear, quadratic or exponential for such maps acting on two dimensional compact Kahler varieties. The example we construct goes beyond this limitation, thanks to the presence of a singularity on the variety where the maps act. We provide all details of the calculations.