Miracle-dz/UniFAR-FTUs huggingface dataset:
Renormalization approach to the analysis and design of Hermitian and non-Hermitian interfaces

I describe a concrete and efficient real-space renormalization approach that provides a unifying perspective on interface states in a wide class of Hermitian and non-Hermitian models, irrespective of whether they obey a traditional bulk-boundary principle or not. The emerging interface physics are governed by a flow of microscopic interface parameters, and the properties of interface states become linked to the fixed-point topology of this flow. In particular, the quantization condition of interface states converts identically into the question of the convergence to unstable fixed points. As its key merit, the approach can be directly applied to concrete models and utilized to design interfaces that induce states with desired properties, such as states with a predetermined and possibly symmetry-breaking energy. I develop the approach in general, and then demonstrate these features in various settings, including for the design of circular, triangular and square-shaped complex dispersion bands and associated arcs at the edge of a two-dimensional system. Furthermore, I describe how this approach transfers to nonlinear settings, and demonstrate the efficiency, practicability and consistency of this extension for a paradigmatic model of topological mode selection by distributed saturable gain and loss.
Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation

Point cloud registration aims at estimating the geometric transformation between two point cloud scans, in which point-wise correspondence estimation is the key to its success. In addition to previous methods that seek correspondences by hand-crafted or learnt geometric features, recent point cloud registration methods have tried to apply RGB-D data to achieve more accurate correspondence. However, it is not trivial to effectively fuse the geometric and visual information from these two distinctive modalities, especially for the registration problem. In this work, we propose a new Geometry-Aware Visual Feature Extractor (GAVE) that employs multi-scale local linear transformation to progressively fuse these two modalities, where the geometric features from the depth data act as the geometry-dependent convolution kernels to transform the visual features from the RGB data. The resultant visual-geometric features are in canonical feature spaces with alleviated visual dissimilarity caused by geometric changes, by which more reliable correspondence can be achieved. The proposed GAVE module can be readily plugged into recent RGB-D point cloud registration framework. Extensive experiments on 3D Match and ScanNet demonstrate that our method outperforms the state-of-the-art point cloud registration methods even without correspondence or pose supervision. The code is available at https://github.com/514DNA/LLT.
Zero-day DDoS Attack Detection

The ability to detect zero-day (novel) attacks has become essential in the network security industry. Due to ever evolving attack signatures, existing network intrusion detection systems often fail to detect these threats. This project aims to solve the task of detecting zero-day DDoS (distributed denial-of-service) attacks by utilizing network traffic that is captured before entering a private network. Modern feature extraction techniques are used in conjunction with neural networks to determine if a network packet is either benign or malicious.
Few-shot Adaptive Object Detection with Cross-Domain CutMix

In object detection, data amount and cost are a trade-off, and collecting a large amount of data in a specific domain is labor intensive. Therefore, existing large-scale datasets are used for pre-training. However, conventional transfer learning and domain adaptation cannot bridge the domain gap when the target domain differs significantly from the source domain. We propose a data synthesis method that can solve the large domain gap problem. In this method, a part of the target image is pasted onto the source image, and the position of the pasted region is aligned by utilizing the information of the object bounding box. In addition, we introduce adversarial learning to discriminate whether the original or the pasted regions. The proposed method trains on a large number of source images and a few target domain images. The proposed method achieves higher accuracy than conventional methods in a very different domain problem setting, where RGB images are the source domain, and thermal infrared images are the target domain. Similarly, the proposed method achieves higher accuracy in the cases of simulation images to real images.
How many roots of a random polynomial system on a Lie group are real?

A ﬁnite linear combination of matrix elements of a ﬁnite dimensional representation π of a Lie group K is said to be a π -polynomial on K . If K is compact then any π -polynomial uniquely extends to a holomorphic function on the complexiﬁcation K C of K . For a system of n π i -polynomials, where n = dim( K ) , we consider the proportion of real roots, that is the ratio of the number of roots in K to the number of roots in K C . It turns out that for growing representations π i and random system of π i -polynomials, the expected proportion of real roots converges not to 0 , but to a nonzero constant. The limit is calculated in terms of the volumes of some compact convex sets that determine the growth of the representations π i . For a 1 -dimensional torus K the limit is 1 / √ 3 .
Association between Anthropometric-based and Food-based Nutritional Failure among Children in India, 2015.

Inadequate dietary intake is a critical underlying determinant of child undernutrition. This study examined the association between anthropometric-based and food-based nutritional failure among children in India. We used the 2015-2016 National Nutrition Monitoring Bureau data where anthropometric outcomes and food intake were both measured for each child. We followed the World Health Organization child growth reference standards to define anthropometric failures (i.e., height-for-age z score < -2 SD for stunting, weight-for-age z score < -2 SD for underweight, and weight-for-height z score < -2 SD for wasting), and the Indian Council of Medical Research recommended dietary allowance (RDA) to define adequacy in intake of calorie, protein, and fat. We used descriptive and regression-based assessments to test the association between the two indicators of nutritional failure and also computed the area under the receiver operating characteristic curve (AUC). The prevalence of stunting, underweight, and wasting was 28.6%, 24.3%, and 12.8%, respectively, whereas 78.2%, 27.4%, and 50.8% of the children had below RDA norms consumption of calorie, protein, and fat, respectively. We found weak-to-null correlation between anthropometric failures and food failures (Pearson correlation ranging from -0.013 to 0.147) and poor discriminatory accuracy (AUC < 0.62), suggesting that in the Indian context, anthropometric failures are not directly associated with food intake. This finding highlights the need for improving adequate intake of macronutrients and draws attention toward adopting a multifactorial approach to improve child nutrition in India. Poor food intake itself merits exclusive policy focus as it is an important nutrition and health concern.
Accelerating Deep Unrolling Networks via Dimensionality Reduction

In this work we propose a new paradigm for designing efficient deep unrolling networks using dimensionality reduction schemes, including minibatch gradient approximation and operator sketching. The deep unrolling networks are currently the state-of-the-art solutions for imaging inverse problems. However, for high-dimensional imaging tasks, especially X-ray CT and MRI imaging, the deep unrolling schemes typically become inefficient both in terms of memory and computation, due to the need of computing multiple times the high-dimensional forward and adjoint operators. Recently researchers have found that such limitations can be partially addressed by unrolling the stochastic gradient descent (SGD), inspired by the success of stochastic first-order optimization. In this work, we explore further this direction and propose first a more expressive and practical stochastic primal-dual unrolling, based on the state-of-the-art Learned Primal-Dual (LPD) network, and also a further acceleration upon stochastic primal-dual unrolling, using sketching techniques to approximate products in the high-dimensional image space. The operator sketching can be jointly applied with stochastic unrolling for the best acceleration and compression performance. Our numerical experiments on X-ray CT image reconstruction demonstrate the remarkable effectiveness of our accelerated unrolling schemes.
CMB lensing with shear-only reconstruction on the full sky

Reconstruction of gravitational lensing effects in the CMB from current and upcoming surveys is still dominated by temperature anisotropies. Extragalactic foregrounds in temperature maps can induce significant biases in the lensing power spectrum obtained with the standard quadratic estimators. Techniques such as masking cannot remove these foregrounds fully, and the residuals can still lead to large biases if unaccounted for. In this paper, we study the"shear-only"estimator, an example of a class of geometric methods that suppress extragalactic foreground contamination while making only minimal assumptions about foreground properties. The shear-only estimator has only been formulated in the flat-sky limit and so is not easily applied to wide surveys. Here, we derive the full-sky version of the shear-only estimator and its generalisation to an $m=2$ multipole estimator that has improved performance for lensing reconstruction on smaller scales. The multipole estimator is generally not separable, and so is expensive to compute. We explore separable approximations based on a singular-value decomposition, which allow efficient evaluation of the estimator with real-space methods. Finally, we apply these estimators to simulations that include extragalactic foregrounds and verify their efficacy in suppressing foreground biases.
Generating Intermediate Steps for NLI with Next-Step Supervision

The Natural Language Inference (NLI) task often requires reasoning over multiple steps to reach the conclusion. While the necessity of generating such intermediate steps (instead of a summary explanation) has gained popular support, it is unclear how to generate such steps without complete end-to-end supervision and how such generated steps can be further utilized. In this work, we train a sequence-to-sequence model to generate only the next step given an NLI premise and hypothesis pair (and previous steps); then enhance it with external knowledge and symbolic search to generate intermediate steps with only next-step supervision. We show the correctness of such generated steps through automated and human verification. Furthermore, we show that such generated steps can help improve end-to-end NLI task performance using simple data augmentation strategies, across multiple public NLI datasets.
Optimization-based Motion Planning for Multirotor Aerial Vehicles: a Review

In general, optimal motion planning can be performed both as local and as global. In such a planning, the choice in favor of either local or global planning technique mainly depends on whether the environmental conditions are dynamic or static. Hence, the most adequate choice is to use local planning or local planning alongside global planning. When designing optimal motion planning both as local or as global, the key metrics to bear in mind are execution time, asymptotic optimality, quick reaction on dynamic obstacles. Such planning approaches can address the aforesaid target metrics more efficiently compared to other approaches such as path planning followed by smoothing. Thus, the foremost objective of this study is to analyse related literature in order to understand how the motion planning, specially trajectory planning, problem is formulated, when being applied for generating optimal trajectories in real-time for Multirotor Aerial Vehicles (MAVs), impact the listed metrics. As result of the research, trajectory planning problem was broken down into a set of subproblems, the lists of methods for addressing each of the problems were identified and described in detail. Subsequently, the most prominent results from 2010 to 2022 were summarized and presented in the form of a timeline.
Averaging over codes and an $SU(2)$ modular bootstrap

Error-correcting codes are known to define chiral 2d lattice CFTs where all the $U(1)$ symmetries are enhanced to $SU(2)$. In this paper, we extend this construction to a broader class of length-$n$ codes which define full (non-chiral) CFTs with $SU(2)^n$ symmetry, where $n=c+\bar c$. We show that codes give a natural discrete ensemble of 2d theories in which one can compute averaged observables. The partition functions obtained from averaging over all codes weighted equally is found to be given by the sum over modular images of the vacuum character of the full extended symmetry group, and in this case the number of modular images is finite. This averaged partition function has a large gap, scaling linearly with $n$, in primaries of the full $SU(2)^n$ symmetry group. Using the sum over modular images, we conjecture the form of the genus-2 partition function. This exhibits the connected contributions to disconnected boundaries characteristic of wormhole solutions in a bulk dual.
Slave particle study of the strongly correlated electrons

Until three decades ago, our understanding of the condensed matter systems were based on two frameworks developed by Russian physicist Lev Landau: his theory of phase transition, and Fermi liquids. The Landau theory of phase transition and the Fermi liquid theory together, can successfully explain a wide range of phenomena from ferromagnetism and antiferromagnetism to the conventional superconductivity. However, in the last thirty years, many experiments including the fractional quantum Hall effect (QHE) have revolutionized our view of nature. For a system of electrons that is subject to a very strong interactions and/or strong correlations between electrons, these two frameworks may break down. There many phases of matter, e.g. spin liquids, that do not break any classical symmetry, but are separated by phase transition. These states has the so called topological order. Also, many of these states do not follow predictions of the Fermi liquid theory and have many exotic behaviors. A rather powerful technique to handle with these issues is the slave particle method. In the first part of this thesis, using a more general slave particle method we study the strongly correlated Hubbard model, whose ground state may represent a Fermi liquid state at two spatial dimensions. We study the phase diagram of this model and show that the gapped spin liquid can be realized on the both honeycomb and square lattices, within mean-field. We also investigate the effective low energy theory of these states. Some of them are subject to compact gauge fluctuations. We study instanton effect in them and show that instanton proliferation can destabilize some of them. Another interesting problem in which we are interested in is the copper based high temperature superconductors (HTSC). The parent state of cuprates materials (undoped case) is a Mott insulator whose ground state is proposed to be a spin liquid. Upon doping, many exotic phases appear, from high temperature superconductivity to the pseudogap phase with disjoint Fermi segments (Fermi arcs) instead of a closed Fermi surface, or the strange metal phase where the Fermi liquid theory breaks down and exhibits very unusual transport properties. The isotope effect in these materials is also very different from that of conventional superconductors. In the second part of this thesis we study the above mentioned problem in detail and explain them by appealing to the slave particle method. Thesis Supervisor: Xiao-Gang Wen Title: Cecil and Ida green Professor of Physics
An Anonymous Overlay Routing Protocol for Uplink-Intensive Applications

Sender anonymity in network communication is an important problem, widely addressed in the literature. Mixnets, combined with onion routing, represent certainly the most concrete and effective approach achieving the above goal. In general, the drawback of these approaches is that anonymity has a price in terms of traffic overhead and latency. On the Internet, to achieve scalability and not to require relevant infrastructure and network-protocol changes, only P2P overlay protocols can be adopted. Among these, the most representative proposal is certainly Tarzan, which is designed to obtain strong anonymity still preserving low-latency applications. In recent years, we are witnessing a change in Internet traffic. Due to IoT, cloud storage, WSN, M2M, uplink traffic is more and more increasing. An interesting question is whether this new traffic configuration may enable new strategies to improve the effectiveness of Tarzan-like approaches. In this paper, we investigate this problem, by proposing \textit{C-Tarzan}, an anonymous overlay P2P routing protocol. Through a deep experimental analysis, we show that C-Tarzan outperforms Tarzan in the case of uplink-intensive applications.
Listen to your heart: A self-supervised approach for detecting murmur in heart-beat sounds for the Physionet 2022 challenge

Heart murmurs are abnormal sounds present in heart-beats, caused by turbulent blood ﬂow through the heart. The PhysioNet 2022 challenge targets automatic detection of murmur from audio recordings of the heart and automatic detection of normal vs. abnormal clinical outcome. The recordings are captured from multiple locations around the heart. Our participation investigates the effectiveness of self-supervised learning for murmur detection. We evaluate the use of a backbone CNN, whose layers are trained in a self-supervised way with data from both this year’s and the 2016 challenge. We use two different augmentations on each training sample, and normalized temperature-scaled cross-entropy loss. We experiment with different augmentations to learn effective phonocardiogram representations. To build the ﬁnal detectors we train two classiﬁcation heads, one for each challenge task. We present evaluation results for all combinations of the available augmentations, and for our multiple-augmentation approach.
Radiation statistics of a degenerate parametric oscillator at threshold

As a function of the driving strength, a degenerate parametric oscillator exhibits an instability at which spontaneous oscillations occur. Close to threshold, both the nonlinearity as well as fluctuations are vital to the accurate description of the dynamics. We study the statistics of the adiation that is emitted by the degenerate parametric oscillator at threshold. For a weak nonlinearity, we can employ a quasiclassical description. We identify a universal Liouvillian that captures the relevant long-time dynamics for large photon-numbers. We find that the cumulants obey a universal power-law scaling as a function of the nonlinearity. The Fano factor shows a maximum close, but not coinciding, with the threshold. Moreover, we predict a certain ratio of the first three cumulants to be independent of the microscopic details of the system and connect the results to experimental platforms.
The Elements of Continuum Biomechanics

This book presents the elements of Continuum Mechanics to people interested in applications to biological systems. It is divided into two parts, the first of which introduces the basic concepts within a strictly one-dimensional spatial context. This policy has been adopted so as to allow the newcomer to Continuum Mechanics to appreciate how the theory can be applied to important issues in Biomechanics from the very beginning. These include mechanical and thermodynamical balance, materials with fading memory and chemically reacting mixtures.
Deep Reinforcement Learning for Uplink Multi-Carrier Non-Orthogonal Multiple Access Resource Allocation Using Buffer State Information

—For orthogonal multiple access (OMA) systems, the number of served user equipments (UEs) is limited to the number of available orthogonal resources. On the other hand, non-orthogonal multiple access (NOMA) schemes allow multiple UEs to use the same orthogonal resource. This extra degree of freedom introduces new challenges for resource allocation. Buffer state information (BSI), like the size and age of packets waiting for transmission, can be used to improve scheduling in OMA systems. In this paper, we investigate the impact of BSI on the performance of a centralized scheduler in an uplink multi- carrier NOMA scenario with UEs having various data rate and latency requirements. To handle the large combinatorial space of allocating UEs to the resources, we propose a novel scheduler based on actor-critic reinforcement learning incorporating BSI. Training and evaluation are carried out using Nokia’s “wireless suite”. We propose various novel techniques to both stabilize and speed up training. The proposed scheduler outperforms benchmark schedulers.
Models of Bounded Arithmetic and variants of Pigeonhole Principle

We give elementary proof that theory $T^1_2(R)$ augmented by the weak pigeonhole principle for all $\Delta^b_1(R)$-definable relations does not prove the bijective pigeonhole principle for $R$. This can be derived from known more general results but our proof yields a model of $T^1_2(R)$ in which $ontoPHP^{n+1}_n(R)$ fails for some nonstandard element $n$ while $PHP^{m+1}_m$ holds for all $\Delta^b_1(R)$-definable relations and all $m \leq n^{1-\epsilon}$, where $\epsilon>0$ is a fixed standard rational parameter. This can be seen as a step towards solving an open question posed by M. Ajtai.
Dynamic Global Sensitivity for Differentially Private Contextual Bandits

Bandit algorithms have become a reference solution for interactive recommendation. However, as such algorithms directly interact with users for improved recommendations, serious privacy concerns have been raised regarding its practical use. In this work, we propose a differentially private linear contextual bandit algorithm, via a tree-based mechanism to add Laplace or Gaussian noise to model parameters. Our key insight is that as the model converges during online update, the global sensitivity of its parameters shrinks over time (thus named dynamic global sensitivity). Compared with existing solutions, our dynamic global sensitivity analysis allows us to inject less noise to obtain (ϵ, δ)-differential privacy with added regret caused by noise injection in . We provide a rigorous theoretical analysis over the amount of noise added via dynamic global sensitivity and the corresponding upper regret bound of our proposed algorithm. Experimental results on both synthetic and real-world datasets confirmed the algorithm’s advantage against existing solutions.