Miracle-dz/UniFAR-FTUs huggingface dataset:
PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations

Considering a probability distribution over parameters is known as an efficient strategy to learn a neural network with non-differentiable activation functions. We study the expectation of a probabilistic neural network as a predictor by itself, focusing on the aggregation of binary activated neural networks with normal distributions over real-valued weights. Our work leverages a recent analysis derived from the PAC-Bayesian framework that derives tight generalization bounds and learning procedures for the expected output value of such an aggregation, which is given by an analytical expression. While the combinatorial nature of the latter has been circumvented by approximations in previous works, we show that the exact computation remains tractable for deep but narrow neural networks, thanks to a dynamic programming approach. This leads us to a peculiar bound minimization learning algorithm for binary activated neural networks, where the forward pass propagates probabilities over representations instead of activation values. A stochastic counterpart that scales to wide architectures is proposed.
Neutrino signals from neutron star implosions to black holes

We calculate the neutrino luminosity in an astrophysical scenario where dark matter is captured by a neutron star which eventually implodes to form a low mass black hole. The Trojan horse scenario involves the collapse of a neutron star (NS) due to the accumulation of a critical amount of dark matter (DM) during its lifetime. As a result, a central disk forms out of the ejected material with a finite radial extension, density, temperature, and lepton fraction, producing fainter neutrino luminosities and colder associated spectra than found in a regular core-collapse supernova. The emitted gravitational wave (GW) signal from the imploding NS should be detectable at ultra-high $\gtrsim 0.1$ GHz frequencies.
A Multi-Battery Model for the Aggregate Flexibility of Heterogeneous Electric Vehicles

The increasing prevalence of electric vehicles (EVs) in the transportation sector will introduce a large number of highly flexible electric loads that EV aggregators can pool and control to provide energy and ancillary services to the wholesale electricity market. To integrate large populations of EVs into electricity market operations, aggregators must express the aggregate flexibility of the EVs under their control in the form of a small number of energy storage (battery) resources that accurately capture the supply/demand capabilities of the individual EVs as a collective. To this end, we propose a novel multi-battery flexibility model defined as a linear combination of a small number of base sets (termed batteries) that reflect the differing geometric shapes of the individual EV flexibility sets, and suggest a clustering approach to identify these base sets. We study the problem of computing a multi-battery flexibility set that has minimum Hausdorff distance to the aggregate flexibility set, subject to the constraint that the multi-battery flexibility set be a subset of the aggregate flexibility set. We show how to conservatively approximate this problem with a tractable convex program, and illustrate the performance achievable by our method with several numerical experiments.
Ranking from Pairwise Comparisons in General Graphs and Graphs with Locality

This technical report studies the problem of ranking from pairwise comparisons in the classical Bradley-Terry-Luce (BTL) model, with a focus on score estimation. For general graphs, we show that, with sufficiently many samples, maximum likelihood estimation (MLE) achieves an entrywise estimation error matching the Cram\'er-Rao lower bound, which can be stated in terms of effective resistances; the key to our analysis is a connection between statistical estimation and iterative optimization by preconditioned gradient descent. We are also particularly interested in graphs with locality, where only nearby items can be connected by edges; our analysis identifies conditions under which locality does not hurt, i.e. comparing the scores between a pair of items that are far apart in the graph is nearly as easy as comparing a pair of nearby items. We further explore divide-and-conquer algorithms that can provably achieve similar guarantees even in the regime with the sparsest samples, while enjoying certain computational advantages. Numerical results validate our theory and confirm the efficacy of the proposed algorithms.
AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks

Sparse training is emerging as a promising avenue for reducing the computational cost of training neural networks. Several recent studies have proposed pruning methods using learnable thresholds to efficiently explore the non-uniform distribution of sparsity inherent within the models. In this paper, we propose Gradient Annealing (GA), where gradients of masked weights are scaled down in a non-linear manner. GA provides an elegant trade-off between sparsity and accuracy without the need for additional sparsity-inducing regularization. We integrated GA with the latest learnable pruning methods to create an automated sparse training algorithm called AutoSparse, which achieves better accuracy and/or training/inference FLOPS reduction than existing learnable pruning methods for sparse ResNet50 and MobileNetV1 on ImageNet-1K: AutoSparse achieves (2x, 7x) reduction in (training,inference) FLOPS for ResNet50 on ImageNet at 80% sparsity. Finally, AutoSparse outperforms sparse-to-sparse SotA method MEST (uniform sparsity) for 80% sparse ResNet50 with similar accuracy, where MEST uses 12% more training FLOPS and 50% more inference FLOPS.
CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery

We tackle the issue of generalized category discovery (GCD). GCD considers the open-world problem of automatically clustering a partially labelled dataset, in which the unlabelled data may contain instances from both novel categories and labelled classes. In this paper, we address the GCD problem with an unknown category number for the unlabelled data. We propose a framework, named CiPR, to bootstrap the representation by exploiting Cross-instance Positive Relations in the partially labelled data for contrastive learning, which have been neglected in existing methods. To obtain reliable cross-instance relations to facilitate representation learning, we introduce a semi-supervised hierarchical clustering algorithm, named selective neighbor clustering (SNC), which can produce a clustering hierarchy directly from the connected components of a graph constructed from selective neighbors. We further present a method to estimate the unknown class number using SNC with a joint reference score that considers clustering indexes of both labelled and unlabelled data, and extend SNC to allow label assignment for the unlabelled instances with a given class number. We thoroughly evaluate our framework on public generic image recognition datasets and challenging fine-grained datasets, and establish a new state-of-the-art. Code: https://github.com/haoosz/CiPR
The sufficient conditions for $k$-leaf-connected graphs in terms of several topological indices

Let $G=(V(G), E(G))$ be a graph with vertex set $V(G)$ and edge set $E(G)$. For $k\geq2$ and given any subset $S\subseteq|V(G)|$ with $|S|=k$, if a graph $G$ of order $|V(G)|\geq k+1$ always has a spanning tree $T$ such that $S$ is precisely the set of leaves of $T$, then the graph $G$ is a $k$-leaf-connected graph. A graph $G$ is called Hamilton-connected if any two vertices of $G$ are connected by a Hamilton path. Based on the definitions of $k$-leaf-connected and Hamilton-connected, we known that a graph is $2$-leaf-connected if and only if it is Hamilton-connected. During the past decades, there have been many results of sufficient conditions for Hamilton-connected with respect to topological indices. In this paper, we present sufficient conditions for a graph to be $k$-leaf-connected in terms of the Zagreb index, the reciprocal degree distance or the hyper-Zagreb index. Furthermore, we use the first (hyper-)Zagreb index of the complement graph to give sufficient conditions for a graph to be $k$-leaf-connected.
OPI at SemEval-2023 Task 1: Image-Text Embeddings and Multimodal Information Retrieval for Visual Word Sense Disambiguation

The goal of visual word sense disambiguation is to find the image that best matches the provided description of the word’s meaning. It is a challenging problem, requiring approaches that combine language and image understanding. In this paper, we present our submission to SemEval 2023 visual word sense disambiguation shared task. The proposed system integrates multimodal embeddings, learning to rank methods, and knowledge-based approaches. We build a classifier based on the CLIP model, whose results are enriched with additional information retrieved from Wikipedia and lexical databases. Our solution was ranked third in the multilingual task and won in the Persian track, one of the three language subtasks.
Structured Pruning for Multi-Task Deep Neural Networks

Although multi-task deep neural network (DNN) models have computation and storage benefits over individual single-task DNN models, they can be further optimized via model compression. Numerous structured pruning methods are already developed that can readily achieve speedups in single-task models, but the pruning of multi-task networks has not yet been extensively studied. In this work, we investigate the effectiveness of structured pruning on multi-task models. We use an existing single-task filter pruning criterion and also introduce an MTL-based filter pruning criterion for estimating the filter importance scores. We prune the model using an iterative pruning strategy with both pruning methods. We show that, with careful hyper-parameter tuning, architectures obtained from different pruning methods do not have significant differences in their performances across tasks when the number of parameters is similar. We also show that iterative structure pruning may not be the best way to achieve a well-performing pruned model because, at extreme pruning levels, there is a high drop in performance across all tasks. But when the same models are randomly initialized and re-trained, they show better results.
Contingency Analyses with Warm Starter using Probabilistic Graphical Model

Cyberthreats are an increasingly common risk to the power grid and can thwart secure grid operations. We propose to extend contingency analysis to include cyberthreat evaluations. However, unlike the traditional N-1 or N-2 contingencies, cyberthreats (e.g., MadIoT) require simulating hard-to-solve N-k (with k>>2) contingencies in a practical amount of time. Purely physics-based power flow solvers, while being accurate, are slow and may not solve N-k contingencies in a timely manner, whereas the emerging data-driven alternatives are fast but not sufficiently generalizable, interpretable, and scalable. To address these challenges, we propose a novel conditional Gaussian Random Field-based data-driven method that performs fast and accurate evaluation of cyberthreats. It achieves speedup of contingency analysis by warm-starting simulations, i.e., improving starting points, for the physical solvers. To improve the physical interpretability and generalizability, the proposed method incorporates domain knowledge by considering the graphical nature of the grid topology. To improve scalability, the method applies physics-informed regularization that reduces model complexity. Experiments validate that simulating MadIoT-induced attacks with our warm starter becomes approximately 5x faster on a realistic 2000-bus system.
Beyond Planarity: A Spring-Based Approach

Planar drawings of graphs tend to be favored over non-planar drawings. Testing planarity and creating a planar layout of a planar graph can be done in linear time. However, creating readable drawings of nearly planar graphs remains a challenge. We therefore seek to answer which edges of nearly planar graphs create clutter in their drawings generated by the mainstream graph drawing algorithms. We present a heuristic to identify problematic edges in nearly planar graphs and adjust their weights, in order to produce higher quality layouts with spring-based drawing algorithms. Our experiments show that the heuristic produces signiﬁcantly higher quality drawings for augmented grid graphs
Corner states of two-dimensional second-order topological insulators with a chiral symmetry and broken time reversal and charge conjugation

Two-dimensional second-order topological insulators are characterized by the presence of topologically protected zero-energy bound states localized at the corners of a flake. In this paper we theoretically study the occurrence and features of such corner states inside flakes in the shape of a convex polygon. We consider two different models, both in Cartan class IIIA, the first obeying inversion symmetry and the other obeying a combined $\pi/4$ rotation symmetry and time-reversal symmetry ($\hat{C}_4^z\hat{T}$). By using an analytical effective model of an edge corresponding to a massive Dirac fermion, we determine the presence of a corner state between two given edges by studying the sign of their induced masses and derive general rules for flakes in the shape of a convex polygon. In particular, we find that the number of corner states in a flake is always two in the first model, while in the second model there are either 0, 2 or 4. To corroborate our findings, we focus on flakes of specific shapes (a triangle and a square) and use a numerical finite-difference approach to determine the features of the corner states in terms of their probability density. In the case of a triangular flake, we can change the position of corner states by rotating the flake in the first model, while in the second model we can also change their number. Remarkably, when the induced mass of an edge is zero the corresponding corner state becomes delocalized along the edge. In the case of a square flake and the model with $\hat{C}_4^z\hat{T}$ symmetry, there is an orientation of the flake with respect to the crystal axes, for which the corner states extend along the whole perimeter of the square.
Beyond Inclusion-and-Exclusion: Natural Identities for P(exactly t events) and P(at least t events) and Resulting Inequalities

Summary We present universal identities for the probability of occurrence of at least t, or exactly t, out of n arbitrary events. Well-known, as well as new, bounds can be derived simply from these identities. The approach is probabilistic and provides a natural and unified approach to these inequalities. It also has the advantage of showing how these relationships are consequences of two elementary results: P(AB) = P(A) - P(AB); and P(E) > P(F) if E D F.
High‐Efficiency Electro‐Optic Modulator on Thin‐Film Lithium Niobate with High‐Permittivity Cladding

Thin‐film lithium niobate is a promising platform owing to its large electro‐optic (EO) coefficients and low propagation loss. However, the large footprints of devices limit their application in large‐scale integrated optical systems. A crucial challenge is how to maintain the performance advantage given the design space restrictions in this situation. This work proposes and demonstrates a high‐efficiency lithium niobate EO modulator with high‐permittivity cladding to improve the electric field strength in waveguides and its overlap with optical fields while maintaining low optical loss and broad bandwidth. The proposed modulator exhibits considerable improvement, featuring a low half‐wave voltage–length product of 1.41 V∙cm, a low excess loss of ≈0.5 dB, and a broad 3 dB EO bandwidth of >67 GHz. Among all the Mach‐Zehnder interferometer modulators reported thus far, the proposed modulator demonstrates notably high modulation efficiency while maintaining excellent high‐frequency performance. The design scheme of using high‐permittivity cladding may provide a promising solution for improving the integration of photonic devices on the thin‐film lithium niobate platform, and these devices may serve as fundamental components in large‐scale photonic integrated circuits in the future.
UVA: Towards Unified Volumetric Avatar for View Synthesis, Pose rendering, Geometry and Texture Editing

Neural radiance field (NeRF) has become a popular 3D representation method for human avatar reconstruction due to its high-quality rendering capabilities, e.g., regarding novel views and poses. However, previous methods for editing the geometry and appearance of the avatar only allow for global editing through body shape parameters and 2D texture maps. In this paper, we propose a new approach named \textbf{U}nified \textbf{V}olumetric \textbf{A}vatar (\textbf{UVA}) that enables local and independent editing of both geometry and texture, while retaining the ability to render novel views and poses. UVA transforms each observation point to a canonical space using a skinning motion field and represents geometry and texture in separate neural fields. Each field is composed of a set of structured latent codes that are attached to anchor nodes on a deformable mesh in canonical space and diffused into the entire space via interpolation, allowing for local editing. To address spatial ambiguity in code interpolation, we use a local signed height indicator. We also replace the view-dependent radiance color with a pose-dependent shading factor to better represent surface illumination in different poses. Experiments on multiple human avatars demonstrate that our UVA achieves competitive results in novel view synthesis and novel pose rendering while enabling local and independent editing of geometry and appearance. The source code will be released.
Unsupervised Learning Optical Flow in Multi-frame Dynamic Environment Using Temporal Dynamic Modeling

For visual estimation of optical flow, a crucial function for many vision tasks, unsupervised learning, using the supervision of view synthesis has emerged as a promising alternative to supervised methods, since ground-truth flow is not readily available in many cases. However, unsupervised learning is likely to be unstable when pixel tracking is lost due to occlusion and motion blur, or the pixel matching is impaired due to variation in image content and spatial structure over time. In natural environments, dynamic occlusion or object variation is a relatively slow temporal process spanning several frames. We, therefore, explore the optical flow estimation from multiple-frame sequences of dynamic scenes, whereas most of the existing unsupervised approaches are based on temporal static models. We handle the unsupervised optical flow estimation with a temporal dynamic model by introducing a spatial-temporal dual recurrent block based on the predictive coding structure, which feeds the previous high-level motion prior to the current optical flow estimator. Assuming temporal smoothness of optical flow, we use motion priors of the adjacent frames to provide more reliable supervision of the occluded regions. To grasp the essence of challenging scenes, we simulate various scenarios across long sequences, including dynamic occlusion, content variation, and spatial variation, and adopt self-supervised distillation to make the model understand the object's motion patterns in a prolonged dynamic environment. Experiments on KITTI 2012, KITTI 2015, Sintel Clean, and Sintel Final datasets demonstrate the effectiveness of our methods on unsupervised optical flow estimation. The proposal achieves state-of-the-art performance with advantages in memory overhead.
Spectral Transfer Guided Active Domain Adaptation For Thermal Imagery

The exploitation of visible spectrum datasets has led deep networks to show remarkable success. However, real-world tasks include low-lighting conditions which arise performance bottlenecks for models trained on large-scale RGB image datasets. Thermal IR cameras are more robust against such conditions. Therefore, the usage of thermal imagery in real-world applications can be useful. Unsupervised domain adaptation (UDA) allows transferring information from a source domain to a fully unlabeled target domain. Despite substantial improvements in UDA, the performance gap between UDA and its supervised learning counterpart remains significant. By picking a small number of target samples to annotate and using them in training, active domain adaptation tries to mitigate this gap with minimum annotation expense. We propose an active domain adaptation method in order to examine the efficiency of combining the visible spectrum and thermal imagery modalities. When the domain gap is considerably large as in the visible-to-thermal task, we may conclude that the methods without explicit domain alignment cannot achieve their full potential. To this end, we propose a spectral transfer guided active domain adaptation method to select the most informative unlabeled target samples while aligning source and target domains. We used the large-scale visible spectrum dataset MS-COCO as the source domain and the thermal dataset FLIR ADAS as the target domain to present the results of our method. Extensive experimental evaluation demonstrates that our proposed method outperforms the state-of-the-art active domain adaptation methods. The code and models are publicly available. 1
Stabilisation of a three-dimensional boundary layer by base-flow manipulation using plasma actuators

The applicability of dielectric barrier discharge plasma actuators for controlling the crossflow-vortex-induced laminar breakdown in a three-dimensional swept-wing-type boundary-layer flow is investigated using direct numerical simulation. Similar to the classical application of suction at the wall the aim is to modify the quasi two-dimensional base flow and to weaken primary crossflow (CF) instability, mainly due to a reduction of the basic CF. Not only localised volumetric forcing by plasma actuators but also CF counter-blowing and spots with a moving wall are investigated to identify effective fundamental mechanisms. It is found that counter blowing always results in partial blockage of the flow and eventually increased CF velocity, whereas moving-wall spots can slightly reduce the CF and the amplitude of crossflow vortices. Using discrete volumetric forcing a significant attenuation even of finite-amplitude crossflow vortices and thus a distinct transition delay is achieved.