Miracle-dz/UniFAR-FTUs huggingface dataset:
On the development of a Bayesian optimisation framework for complex unknown systems

Bayesian optimisation provides an effective method to optimise expensive black box functions. It has recently been applied to problems in ﬂuid dynamics. This paper studies and compares common Bayesian optimisation algorithms empirically on a range of synthetic test functions. It investigates the choice of acquisition function and number of training samples, exact calculation of acquisition functions and Monte Carlo based approaches and both single-point and multi-point optimisation. The test functions considered cover a wide selection of challenges and therefore serve as an ideal test bed to understand the performance of Bayesian optimisation and to identify general situations where Bayesian optimisation performs well and poorly. This knowledge can be utilised in applications, including those in ﬂuid dynamics, where objective functions are unknown. The results of this investigation show that the choices to be made are less relevant for relatively simple functions, while optimistic acquisition functions such as Upper Conﬁdence Bound should be preferred for more complex objective functions. Furthermore, results from the Monte Carlo approach are comparable to results from analytical acquisition functions. In instances where the objective function allows parallel evaluations, the multi-point approach offers a quicker alternative, yet it may potentially require more objective function evaluations.
Riemannian Stochastic Gradient Method for Nested Composition Optimization

This work considers optimization of composition of functions in a nested form over Riemannian manifolds where each function contains an expectation. This type of problems is gaining popularity in applications such as policy evaluation in reinforcement learning or model customization in meta-learning. The standard Riemannian stochastic gradient methods for non-compositional optimization cannot be directly applied as stochastic approximation of inner functions create bias in the gradients of the outer functions. For two-level composition optimization, we present a Riemannian Stochastic Composition Gradient Descent (R-SCGD) method that finds an approximate stationary point, with expected squared Riemannian gradient smaller than $\epsilon$, in $O(\epsilon^{-2})$ calls to the stochastic gradient oracle of the outer function and stochastic function and gradient oracles of the inner function. Furthermore, we generalize the R-SCGD algorithms for problems with multi-level nested compositional structures, with the same complexity of $O(\epsilon^{-2})$ for the first-order stochastic oracle. Finally, the performance of the R-SCGD method is numerically evaluated over a policy evaluation problem in reinforcement learning.
Neural Greedy Pursuit for Feature Selection

We propose a greedy algorithm to select $N$ impor-tant features among $P$ input features for a non-linear prediction problem. The features are selected one by one sequentially, in an iterative loss minimization procedure. We use neural networks as predictors in the algorithm to compute the loss and hence, we refer to our method as neural greedy pursuit (NGP). NGP is efficient in selecting $N$ features when $N\ll P$, and it provides a notion of feature importance in a descending order following the sequential selection procedure. We experimentally show that NGP provides better performance than several feature selection methods such as DeepLIFT and Drop-one-out loss. In addition, we experimentally show a phase transition behavior in which perfect selection of all $N$ features without false positives is possible when the training data size exceeds a threshold. Index Terms-Feature selection, Deep learning;
A Streamline-guided De-Homogenization Approach for Structural Design

We present a novel de-homogenization approach for efficient design of high-resolution load-bearing structures. The proposed approach builds upon a streamline-based parametrization of the design domain, using a set of space-filling and evenly-spaced streamlines in the two mutually orthogonal direction fields that are obtained from homogenization-based topology optimization. Streamlines in these fields are converted into a graph, which is then used to construct a quad-dominant mesh whose edges follow the direction fields. In addition, the edge width is adjusted according to the density and anisotropy of the optimized orthotropic cells. In a number of numerical examples, we demonstrate the mechanical performance and regular appearance of the resulting structural designs, and compare them with those from classic and contemporary approaches.
Field-orientation dependence of quantum phase transitions in the S=1/2 triangular-lattice antiferromagnet Ba$_3$CoSb$_2$O$_9$

Ba$_3$CoSb$_2$O$_9$ approximates the two-dimensional spin-1/2 triangular-lattice Heisenberg antiferromagnet. This compound displays magnetic-field-induced quantum phase transitions, including the 1/3-magnetization-plateau, but its magnetization processes for the magnetic field $H$ parallel and perpendicular to the $c$ axis are different due to the weak easy-plane anisotropy and the weak interlayer antiferromagnetic exchange interaction. To elucidate how the quantum phase transitions change between these two field directions, we measured the field-angle dependence of the magnetization process in Ba$_3$CoSb$_2$O$_9$ using pulsed high magnetic fields. We compared obtained magnetic field-field angle phase diagram with those obtained by the large-size cluster mean-field method combined with a scaling scheme and the semiclassical theory. We also found a narrow 1/3-magnetization plateau and a high-field transition with a small magnetization jump for $H\,{\parallel}\,c$, not observed in the previous studies.
Bounding generalization error with input compression: An empirical study with infinite-width networks

Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) is an important task that often relies on availability of held-out data. The ability to better predict GE based on a single training set may yield overarching DNN design principles to reduce a reliance on trial-and-error, along with other performance assessment advantages. In search of a quantity relevant to GE, we investigate the Mutual Information (MI) between the input and final layer representations, using the infinite-width DNN limit to bound MI. An existing input compression-based GE bound is used to link MI and GE. To the best of our knowledge, this represents the first empirical study of this bound. In our attempt to empirically falsify the theoretical bound, we find that it is often tight for best-performing models. Furthermore, it detects randomization of training labels in many cases, reflects test-time perturbation robustness, and works well given only few training samples. These results are promising given that input compression is broadly applicable where MI can be estimated with confidence.
Online Learning with Off-Policy Feedback

We study the problem of online learning in adversarial bandit problems under a partial observability model called off-policy feedback. In this sequential decision making problem, the learner cannot directly observe its rewards, but instead sees the ones obtained by another unknown policy run in parallel (behavior policy). Instead of a standard exploration-exploitation dilemma, the learner has to face another challenge in this setting: due to limited observations outside of their control, the learner may not be able to estimate the value of each policy equally well. To address this issue, we propose a set of algorithms that guarantee regret bounds that scale with a natural notion of mismatch between any comparator policy and the behavior policy, achieving improved performance against comparators that are well-covered by the observations. We also provide an extension to the setting of adversarial linear contextual bandits, and verify the theoretical guarantees via a set of experiments. Our key algorithmic idea is adapting the notion of pessimistic reward estimators that has been recently popular in the context of off-policy reinforcement learning.
SphereFed: Hyperspherical Federated Learning

Federated Learning aims at training a global model from multiple decentralized devices (i.e. clients) without exchanging their private local data. A key challenge is the handling of non-i.i. d. (independent identically distributed) data across multiple clients that may induce disparities of their local features. We introduce the Hyperspherical Federated Learning (SphereFed) framework to address the non-i.i. d. issue by constraining learned representations of data points to be on a unit hypersphere shared by clients. Specifically, all clients learn their local representations by minimizing the loss with respect to a fixed classifier whose weights span the unit hypersphere. After federated training in improving the global model, this classifier is further calibrated with a closed-form solution by minimizing a mean squared loss. We show that the calibration solution can be computed efficiently and distributedly without direct access of local data. Extensive experiments indicate that our SphereFed approach is able to improve the accuracy of multiple existing federated learning algorithms by a considerable margin (up to 6% on challenging datasets) with enhanced computation and communication efficiency across datasets and model architectures.
Integer quantum Hall effect and enhanced g-factor in quantum confined Cd3As2 films

We investigate the integer quantum Hall eﬀect in Cd 3 As 2 thin ﬁlms under conditions of strong to moderate quantum conﬁnement (thicknesses of 10 nm, 12 nm, 15 nm). In all the ﬁlms, we observe the integer quantum Hall eﬀect in the spin-polarized lowest Landau level (ﬁlling factor ν = 1) and at spin-degenerate higher index Landau levels with even ﬁlling factors ( ν = 2 , 4 , 6). With increasing quantum conﬁnement, we also observe a lifting of the Landau level spin degeneracy at ν = 3, manifest as the emergence of an anomaly in the longitudinal and Hall resistivity. Tight-binding calculations show that the enhanced g-factor likely arises from a combination of quantum conﬁnement and corrections from nearby subbands. We also comment on the magnetic ﬁeld induced transition from an insulator to a quantum Hall liquid when the chemical potential is near the charge neutrality point. new from four-band tight-binding model calculations carried out for [112]-oriented conﬁned 2 ﬁlms. Our calculations show that quantum conﬁnement results in Landau levels (LLs) derived from both bulk subbands and surface states. This produces a non-monotonic dependence of the g-factor on thickness so that an enhanced Zeeman splitting is possible when the ﬁlm thickness is decreased from 15 nm to 10 nm. This prediction is consistent with the emergence of a ν 3 quantum Hall state in the two thinner samples measured in our experiments. Finally, we brieﬂy quantum transport in Cd 3 As 2 thin ﬁlms when close the charge neutrality (CNP). Here, to a localized at zero magnetic ﬁeld. The ﬁeld leads to the observation of striking direct
Hybrid Belief Pruning with Guarantees for Viewpoint-Dependent Semantic SLAM

Semantic simultaneous localization and mapping is a subject of increasing interest in robotics and AI that directly influences the autonomous vehicles industry, the army industries, and more. One of the challenges in this field is to obtain object classification jointly with robot trajectory estimation. Considering view-dependent semantic measurements, there is a coupling between different classes, resulting in a combinatorial number of hypotheses. A common solution is to prune hypotheses that have a sufficiently low probability and to retain only a limited number of hypotheses. However, after pruning and renormalization, the updated probability is overconfident with respect to the original probability. This is especially problematic for systems that require high accuracy. If the prior probability of the classes is independent, the original normalization factor can be computed efficiently without pruning hypotheses. To the best of our knowledge, this is the first work to present these results. If the prior probability of the classes is dependent, we propose a lower bound on the normalization factor that ensures cautious results. The bound is calculated incrementally and with similar efficiency as in the independent case. After pruning and updating based on the bound, this belief is shown empirically to be close to the original belief.
A Novel Seven-Level Active Neutral-Point-Clamped Converter With Reduced Active Switching Devices and DC-Link Voltage

This paper presents a novel seven-level inverter topology for medium-voltage high-power applications. It consists of eight active switches and two inner flying capacitor (FC) units forming a similar structure as in a conventional active neutral-point-clamped (ANPC) inverter. This unique arrangement reduces the number of active and passive components. A simple modulation technique reduces cost and complexity in the control system design without compromising reactive power capability. In addition, compared to major conventional seven-level inverter topologies, such as the neutral point clamped, FC, cascaded H-bridge, and ANPC topologies, the new topology reduces the dc-link voltage requirement by 50%. This recued dc-link voltage makes the new topology appealing for various industrial applications. Experimental results from a 2.2-kVA prototype are presented to support the theoretical analysis presented in this paper. The prototype demonstrates a conversion efficiency of around 97.2% ± 1% for a wide load range.
Fundamental constraints on the observability of non-Hermitian effects in passive systems

Utilizing scattering theory, we quantify the consequences of physical constraints that limit the visibility of non-Hermitian effects in passive devices. The constraints arise from the fundamental requirement that the system obeys causality, and can be captured concisely in terms of an internal time-delay operator, which furthermore provides a direct quantitative measure of the visibility of specific non-Hermitian phenomena in the density of states. We illustrate the implications by contrasting different symmetry classes and non-Hermitian effects, including exceptional points and the non-Hermitian skin effect, whose underlying extreme mode nonorthogonality turns out to be effectively disguised.
A sharp uniform-in-time error estimate for Stochastic Gradient Langevin Dynamics

We establish a sharp uniform-in-time error estimate for the Stochastic Gradient Langevin Dynamics (SGLD), which is a popular sampling algorithm. Under mild assumptions, we obtain a uniform-in-time $O(\eta^2)$ bound for the KL-divergence between the SGLD iteration and the Langevin diffusion, where $\eta$ is the step size (or learning rate). Our analysis is also valid for varying step sizes. Based on this, we are able to obtain an $O(\eta)$ bound for the distance between the SGLD iteration and the invariant distribution of the Langevin diffusion, in terms of Wasserstein or total variation distances.
Data-Centric Epidemic Forecasting: A Survey

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing previously unobservable facets and also due to initiatives from government public health and funding agencies like forecasting challenges and large scale team science initiatives. This has resulted, in particular, in a spate of work on ’data-centered’ solutions which have shown potential in enhancing our forecasting capabilities by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This survey delves into various data-driven methodological and practical advancements and introduces a conceptual framework to navigate through them. First, we enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting, capturing various factors like symptomatic online surveys, retail and commerce, mobility, genomics data and more. Next, we discuss methods and modeling paradigms focusing on the recent data-driven statistical and deep-learning based methods as well as on the novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline.
TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation

Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive frames. State-of-the-art approaches usually adopt a two-step solution, which includes 1) generating locally-warped pixels by calculating the optical flow based on pre-defined motion patterns (e.g., uniform motion, symmetric motion), 2) blending the warped pixels to form a full frame through deep neural synthesis networks. However, for various complicated motions (e.g., non-uniform motion, turn around), such improper assumptions about pre-defined motion patterns introduce the inconsistent warping from the two consecutive frames. This leads to the warped features for new frames are usually not aligned, yielding distortion and blur, especially when large and complex motions occur. To solve this issue, in this paper we propose a novel Trajectory-aware Transformer for Video Frame Interpolation (TTVFI). In particular, we formulate the warped features with inconsistent motions as query tokens, and formulate relevant regions in a motion trajectory from two original consecutive frames into keys and values. Self-attention is learned on relevant tokens along the trajectory to blend the pristine features into intermediate frames through end-to-end training. Experimental results demonstrate that our method outperforms other state-of-the-art methods in four widely-used VFI benchmarks. Both code and pre-trained models will be released at https://github.com/ChengxuLiu/TTVFI.
Analysis of SIR epidemic models with sociological phenomenon

We propose two SIR models which incorporate sociological behavior of groups of individuals. It is these differences in behaviors which impose different infection rates on the individual susceptible populations, rather than biological differences. We compute the basic reproduction number for each model, as well as analyze the sensitivity of $R_0$ to changes in sociological parameter values.
I2I: Image to Icosahedral Projection for SO(3) Object Reasoning from Single-View Images

Reasoning about 3D objects based on 2D images is challenging due to large variations in appearance caused by viewing the object from different orientations. Ideally, our model would be invariant or equivariant to changes in object pose. Unfortunately, this is typically not possible with 2D image input because we do not have an a priori model of how the image would change under out-of-plane object rotations. The only SO(3) -equivariant models that currently exist require point cloud input rather than 2D images. In this paper, we propose a novel model architecture based on icosahedral group convolution that reasons in SO(3) by projecting the input image onto an icosahedron. As a result of this projection, the model is approximately equivariant to rotation in SO(3) . We apply this model to an object pose estimation task and ﬁnd that it outperforms reasonable baselines.
On Regularity Lemma and Barriers in Streaming and Dynamic Matching

We present a new approach for finding matchings in dense graphs by building on Szemerédi’s celebrated Regularity Lemma. This allows us to obtain non-trivial albeit slight improvements over longstanding bounds for matchings in streaming and dynamic graphs. In particular, we establish the following results for n-vertex graphs: A deterministic single-pass streaming algorithm that finds a (1−o(1))-approximate matching in o(n2) bits of space. This constitutes the first single-pass algorithm for this problem in sublinear space that improves over the 1/2-approximation of the greedy algorithm. A randomized fully dynamic algorithm that with high probability maintains a (1−o(1))-approximate matching in o(n) worst-case update time per each edge insertion or deletion. The algorithm works even against an adaptive adversary. This is the first o(n) update-time dynamic algorithm with approximation guarantee arbitrarily close to one. Given the use of regularity lemma, the improvement obtained by our algorithms over trivial bounds is only by some (log* n)Θ(1) factor. Nevertheless, in each case, they show that the “right” answer to the problem is not what is dictated by the previous bounds. Finally, in the streaming model, we also present a randomized (1−o(1))-approximation algorithm whose space can be upper bounded by the density of certain Ruzsa-Szemerédi (RS) graphs. While RS graphs by now have been used extensively to prove streaming lower bounds, ours is the first to use them as an upper bound tool for desigining improved streaming algorithms.
The ionizing and heating power of ultraluminous X-ray sources under the geometrical beaming model

While there is now a consensus that X-ray binaries (XRBs) are the dominant X-ray sources in the early Universe and play a signif- icant role during the epoch of heating of the intergalactic medium (IGM), recent studies report contradicting results regarding their contribution in the nebular emission of local Universe galaxies. Ultraluminous X-ray sources (ULXs), which dominate the X-ray budget of normal galaxies, may be important interstellar-medium (ISM) ionizing sources. However, their output in the extreme UV (EUV) and soft–X-ray part of the spectrum remains observationally unconstrained. In this paper, we predict the ionizing and heating power from ULX populations under the geometrical beaming scenario, and three models describing the emission from super-critical accretion disks. We ﬁnd that our theoretical spectra for ULX populations cannot (can) explain the He ii (Ne v ) emission observed in some galaxies, with their contribution being less (more) important than the underlying stellar population. Stochastic ﬂuctuations in the number of ULXs may allow for equal contributions in the He ii emission, in a fraction of galaxies. We provide average spectra of ULX populations as an input to local, and early-Universe studies. We ﬁnd that the soft–X-ray emission arising from super-critical accretion is signiﬁcant for the heating of the IGM, and consistent with recent constraints from the 21-cm cosmic signal. Based on the dependence on the adopted compact-object (CO) mass and accretion model, we encourage e ﬀ orts in modeling ULX spectra via simulations, and their combination with detailed binary population synthesis models.
Research on DNA Sequence Homology Based on Second Order Markov Model

DNA sequence homology is a critical and fundamental problem in bioinformatics. In this paper, we solve this problem by use of the second order Markov modal instead of traditional sequence alignment because DNA character sequence meets the Markov properties. Hence, the characteristics of DNA sequences are represented by using their two-step transition probabilities matrices. The similarity degree measurement between two different DNA sequences is defined. Our DSHM algorithm is put forward which is implemented by MyEclipse. The contrast experiments are done between DSHM and other two methods. The experimental results show that DSHM algorithm can determine DNA sequence homology correctly in the more effective way.