Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Approximate Cross-Validation

Cross-validation (CV) is one of the most popular tools for assessing and selecting predictive models. However, standard CV suffers from high computational cost when the number of folds is large. Recently, under the empirical risk minimization (ERM) framework, a line of works proposed efficient methods to approximate CV based on the solution of the ERM problem trained on the full dataset. However, in large-scale problems, it can be hard to obtain the exact solution of the ERM problem, either due to limited computational resources or due to early stopping as a way of preventing overfitting. In this paper, we propose a new paradigm to efficiently approximate CV when the ERM problem is solved via an iterative first-order algorithm, without running until convergence. Our new method extends existing guarantees for CV approximation to hold along the whole trajectory of the algorithm, including at convergence, thus generalizing existing CV approximation methods. Finally, we illustrate the accuracy and computational efficiency of our method through a range of empirical studies.
Emergent competition shapes the ecological properties of multi-trophic ecosystemss

Ecosystems are commonly organized into trophic levels – organisms that occupy the same level in a food chain (e.g., plants, herbivores, carnivores). A fundamental question in theoretical ecology is how the interplay between trophic structure, diversity, and competition shapes the properties of ecosystems. To address this problem, we analyze a generalized Consumer Resource Model with three trophic levels using the zero-temperature cavity method and numerical simulations. We find that intra-trophic diversity gives rise to “emergent competition” between species within a trophic level due to feedbacks mediated by other trophic levels. This emergent competition gives rise to a crossover from a regime of top-down control (populations are limited by predators) to a regime of bottom-up control (populations are limited by primary producers) and is captured by a simple order parameter related to the ratio of surviving species in different trophic levels. We show that our theoretical results agree with empirical observations, suggesting that the theoretical approach outlined here can be used to understand complex ecosystems with multiple trophic levels.
A Review of Deep Learning-Powered Mesh Reconstruction Methods

With the recent advances in hardware and rendering techniques, 3D models have emerged everywhere in our life. Yet creating 3D shapes is arduous and requires significant professional knowledge. Meanwhile, Deep learning has enabled high-quality 3D shape reconstruction from various sources, making it a viable approach to acquiring 3D shapes with minimal effort. Importantly, to be used in common 3D applications, the reconstructed shapes need to be represented as polygonal meshes, which is a challenge for neural networks due to the irregularity of mesh tessellations. In this survey, we provide a comprehensive review of mesh reconstruction methods that are powered by machine learning. We first describe various representations for 3D shapes in the deep learning context. Then we review the development of 3D mesh reconstruction methods from voxels, point clouds, single images, and multi-view images. Finally, we identify several challenges in this field and propose potential future directions.
Know Your Cybercriminal: Evaluating Attacker Preferences by Measuring Profile Sales on an Active, Leading Criminal Market for User Impersonation at Scale

In this paper we exploit market features proper of a leading Russian cybercrime market for user impersonation at scale to evaluate attacker preferences when purchasing stolen user profiles, and the overall economic activity of the market. We run our data collection over a period of $161$ days and collect data on a sample of $1'193$ sold user profiles out of $11'357$ advertised products in that period and their characteristics. We estimate a market trade volume of up to approximately $700$ profiles per day, corresponding to estimated daily sales of up to $4'000$ USD and an overall market revenue within the observation period between $540k$ and $715k$ USD. We find profile provision to be rather stable over time and mainly focused on European profiles, whereas actual profile acquisition varies significantly depending on other profile characteristics. Attackers' interests focus disproportionally on profiles of certain types, including those originating in North America and featuring $crypto$ resources. We model and evaluate the relative importance of different profile characteristics in the final decision of an attacker to purchase a profile, and discuss implications for defenses and risk evaluation.
On Spatial Behavior of the Harmonic Vibrations in Thermoviscoelastic Mixtures

This paper concerns the study of time–harmonic vibrations for homogeneous and anisotropic thermoviscoelastic mixtures. The dissipative effects are used to introduce an appropriate measure associated with the amplitude of the steady–state vibrations and to establish an exponential decay estimate of Saint–Venant type, which holds for every value of the frequency of vibrations and for arbitrary values of the elastic coefficients.
ONE PIECE: One Patchwork In Effectively Combined Extraction for grasp

For grasp network algorithms, generating grasp datasets for a large number of 3D objects is a crucial task. However, generating grasp datasets for hundreds of objects can be very slow and consume a lot of storage resources, which hinders algorithm iteration and promotion. For point cloud grasp network algorithms, the network input is essentially the internal point cloud of the grasp area that intersects with the object in the gripper coordinate system. Due to the existence of a large number of completely consistent gripper area point clouds based on the gripper coordinate system in the grasp dataset generated for hundreds of objects, it is possible to remove the consistent gripper area point clouds from many objects and assemble them into a single object to generate the grasp dataset, thus replacing the enormous workload of generating grasp datasets for hundreds of objects. We propose a new approach to map the repetitive features of a large number of objects onto a finite set.To this end, we propose a method for extracting the gripper area point cloud that intersects with the object from the simulator and design a gripper feature filter to remove the shape-repeated gripper space area point clouds, and then assemble them into a single object. The experimental results show that the time required to generate the new object grasp dataset is greatly reduced compared to generating the grasp dataset for hundreds of objects, and it performs well in real machine grasping experiments. We will release the data and tools after the paper is accepted.
Data-Driven Distributionally Robust Optimal Control with State-Dependent Noise

Distributionally Robust Optimal Control (DROC) is a technique that enables robust control in a stochastic setting when the true distribution is not known. Traditional DROC approaches require given ambiguity sets or a KL divergence bound to represent the distributional uncertainty. These may not be known a priori and may require hand-crafting. In this paper, we lift this assumption by introducing a data-driven technique for estimating the uncertainty and a bound for the KL divergence. We call this technique D3ROC. To evaluate the effectiveness of our approach, we consider a navigation problem for a car-like robot with unknown noise distributions. The results demonstrate that D3ROC provides robust and efficient control policies that outperform the iterative Linear Quadratic Gaussian (iLQG) control. The results also show the effectiveness of our proposed approach in handling different noise distributions.
Learning k-Level Sparse Neural Networks Using a New Generalized Group Sparse Envelope Regularization

We propose an efficient method to learn both unstructured and structured sparse neural networks during training, utilizing a novel generalization of the sparse envelope function (SEF) used as a regularizer, termed {\itshape{weighted group sparse envelope function}} (WGSEF). The WGSEF acts as a neuron group selector, which is leveraged to induce structured sparsity. The method ensures a hardware-friendly structured sparsity of a deep neural network (DNN) to efficiently accelerate the DNN's evaluation. Notably, the method is adaptable, letting any hardware specify group definitions, such as filters, channels, filter shapes, layer depths, a single parameter (unstructured), etc. Owing to the WGSEF's properties, the proposed method allows to a pre-define sparsity level that would be achieved at the training convergence, while maintaining negligible network accuracy degradation or even improvement in the case of redundant parameters. We introduce an efficient technique to calculate the exact value of the WGSEF along with its proximal operator in a worst-case complexity of $O(n)$, where $n$ is the total number of group variables. In addition, we propose a proximal-gradient-based optimization method to train the model, that is, the non-convex minimization of the sum of the neural network loss and the WGSEF. Finally, we conduct an experiment and illustrate the efficiency of our proposed technique in terms of the completion ratio, accuracy, and inference latency.
Scalable Object Detection on Embedded Devices Using Weight Pruning and Singular Value Decomposition

This paper presents a method for optimizing object detection models by combining weight pruning and singular value decomposition (SVD). The proposed method was evaluated on a custom dataset of street work images obtained from https://universe.roboflow. com/roboflow-100/street-work. The dataset consists of 611 training images, 175 validation images, and 87 test images with 7 classes. We compared the performance of the optimized models with the original unoptimized model in terms of frame rate, mean average precision (mAP@50), and weight size. The results show that the weight pruning + SVD model achieved a 0.724 mAP@50 with a frame rate of 1.48 FPS and a weight size of 12.1 MB, outperforming the original model (0.717 mAP@50, 1.50 FPS, and 12.3 MB). Precision-recall curves were also plotted for all models. Our work demonstrates that the proposed method can effectively optimize object detection models while balancing accuracy, speed, and model size.
Quantum Multi-Solution Bernoulli Search with Applications to Bitcoin's Post-Quantum Security

A proof of work (PoW) is an important cryptographic construct enabling a party to convince others that they invested some effort in solving a computational task. Arguably, its main impact has been in the setting of cryptocurrencies such as Bitcoin and its underlying blockchain protocol, which received significant attention in recent years due to its potential for various applications as well as for solving fundamental distributed computing questions in novel threat models. PoWs enable the linking of blocks in the blockchain data structure and thus the problem of interest is the feasibility of obtaining a sequence (chain) of such proofs. In this work, we examine the hardness of finding such chain of PoWs against quantum strategies. We prove that the chain of PoWs problem reduces to a problem we call multi-solution Bernoulli search, for which we establish its quantum query complexity. Effectively, this is an extension of a threshold direct product theorem to an average-case unstructured search problem. Our proof, adding to active recent efforts, simplifies and generalizes the recording technique of Zhandry (Crypto'19). As an application, we revisit the formal treatment of security of the core of the Bitcoin consensus protocol, the Bitcoin backbone (Eurocrypt'15), against quantum adversaries, while honest parties are classical and show that protocol's security holds under a quantum analogue of the classical “honest majority'' assumption. Our analysis indicates that the security of Bitcoin backbone is guaranteed provided the number of adversarial quantum queries is bounded so that each quantum query is worth O(p−1/2) classical ones, where p is the success probability of a single classical query to the protocol's underlying hash function. Somewhat surprisingly, the wait time for safe settlement in the case of quantum adversaries matches the safe settlement time in the classical case.
DPA-P2PNet: Deformable Proposal-Aware P2PNet for Accurate Point-Based Cell Detection

Point-based cell detection (PCD), which pursues high-performance cell sensing under low-cost data annotation, has garnered increased attention in computational pathology community. Unlike mainstream PCD methods that rely on intermediate density map representations, the Point-to-Point network (P2PNet) has recently emerged as an end-to-end solution for PCD, demonstrating impressive cell detection accuracy and efficiency. Nevertheless, P2PNet is limited to decoding from a single-level feature map due to the scale-agnostic property of point proposals, which is insufficient to leverage multi-scale information. Moreover, the spatial distribution of pre-set point proposals is biased from that of cells, leading to inaccurate cell localization. To lift these limitations, we present DPA-P2PNet in this work. The proposed method directly extracts multi-scale features for decoding according to the coordinates of point proposals on hierarchical feature maps. On this basis, we further devise deformable point proposals to mitigate the positional bias between proposals and potential cells to promote cell localization. Inspired by practical pathological diagnosis that usually combines high-level tissue structure and low-level cell morphology for accurate cell classification, we propose a multi-field-of-view (mFoV) variant of DPA-P2PNet to accommodate additional large FoV images with tissue information as model input. Finally, we execute the first self-supervised pre-training on immunohistochemistry histopathology image data and evaluate the suitability of four representative self-supervised methods on the PCD task. Experimental results on three benchmarks and a large-scale and real-world interval dataset demonstrate the superiority of our proposed models over the state-of-the-art counterparts. Codes and pre-trained weights are available at https://github.com/windygoo/DPA-P2PNet.
HARNACK INEQUALITIES AND CONTINUITY OF SOLUTIONS UNDER EXPONENTIAL LOGARITHMIC ORLICZ-SOBOLEV INEQUALITIES

In dimensions n ≥ 2 we implement the Moser iteration in Rn endowed with a certain metric d which is topologically equivalent to the Euclidean metric, but for which Lebesgue’s measure is not doubling. In this metric space we prove Harnack’s inequality for weak solutions to infinitely degenerate quasilinear elliptic equations − divA (x, u)∇u = φ0 − divA ~ φ1. The degeneracy is provided by one of the eigenvalues of the elliptic matrix vanishing to infinite order as x approaches the hyperplane x1 = 0. The right hand sides φ0 and ~ φ1 belong to appropriate admissible spaces of functions. We consider Young functions Φ with growth at infinity of the order t exp ( C (log t) m−1 m ) with 1 < m < 2 and show that if the nonhomogeneous Sobolev estimate
Logarithmic motivic homotopy theory

This work is dedicated to the construction of a new motivic homotopy theory for (log) schemes, generalizing Morel-Voevodsky's (un)stable $\mathbb{A}^1$-homotopy category. Our setting can be used to represent log topological Hochschild and cyclic homology, as well as algebraic $K$-theory of regular schemes, and to realize the cyclotomic trace as a morphism between motivic spectra. Among the applications, a generalized framework of oriented cohomology theories allows us to produce new residue sequences for (topological) Hochschild, periodic and cyclic homology of classical schemes, to compute $\mathrm{THH}$ and variants of Grassmannians, and to define a new version of algebraic cobordism. Finally, we give a construction of a log \'etale stable realization functor, as well as a Kato-Nakayama realization functor, which are of independent interest for applications in log geometry.
PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling

Masked Image Modeling (MIM) has achieved promising progress with the advent of Masked Autoencoders (MAE) and BEiT. However, subsequent works have complicated the framework with new auxiliary tasks or extra pre-trained models, inevitably increasing computational overhead. This paper undertakes a fundamental analysis of MIM from the perspective of pixel reconstruction, which examines the input image patches and reconstruction target, and highlights two critical but previously overlooked bottlenecks. Based on this analysis, we propose a remarkably simple and effective method, {\ourmethod}, that entails two strategies: 1) filtering the high-frequency components from the reconstruction target to de-emphasize the network's focus on texture-rich details and 2) adopting a conservative data transform strategy to alleviate the problem of missing foreground in MIM training. {\ourmethod} can be easily integrated into most existing pixel-based MIM approaches (\ie, using raw images as reconstruction target) with negligible additional computation. Without bells and whistles, our method consistently improves three MIM approaches, MAE, ConvMAE, and LSMAE, across various downstream tasks. We believe this effective plug-and-play method will serve as a strong baseline for self-supervised learning and provide insights for future improvements of the MIM framework. Code and models are available at \url{https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/pixmim}.
Electrical Flows for Polylogarithmic Competitive Oblivious Routing

Oblivious routing is a well-studied paradigm that uses static precomputed routing tables for selecting routing paths within a network. Existing oblivious routing schemes with polylogarithmic competitive ratio for general networks are tree-based, in the sense that routing is performed according to a convex combination of trees. However, this restriction to trees leads to a construction that has time quadratic in the size of the network and does not parallelize well. In this paper we study oblivious routing schemes based on electrical routing. In particular, we show that general networks with $n$ vertices and $m$ edges admit a routing scheme that has competitive ratio $O(\log^2 n)$ and consists of a convex combination of only $O(\sqrt{m})$ electrical routings. This immediately leads to an improved construction algorithm with time $\tilde{O}(m^{3/2})$ that can also be implemented in parallel with $\tilde{O}(\sqrt{m})$ depth.
The Signature of Γ+ 0(n)

Abstract The signature of Γ+ 0(eN2), where e is square free, is completely determined if e = 3 or N is odd or e ∈ Ψ, where Ψ is the set of all square free integers e ∈ N such that • (i) if is odd, then admits no divisors of the form 8 + 3, • (ii) if is even, then admits no divisors of the form 8 + 7. In particular, v2 (number of elliptic classes of period 2 of Γ+ 0(n)) is expressed as a linear combination of multiplicative functions.
Decentralized Caching Under Nonuniform File Popularity and Size: Memory-Rate Tradeoff Characterization

This paper aims to characterize the memory-rate tradeoff for decentralized caching under nonuniform file popularity and size. We consider a recently proposed decentralized modified coded caching scheme (D-MCCS) and formulate the cache placement optimization problem to minimize the average rate for the D-MCCS. To solve this challenging non-convex optimization problem, we first propose a successive Geometric Programming (GP) approximation algorithm, which guarantees convergence to a stationary point but has high computational complexity. Next, we develop a low-complexity file-group-based approach, where we propose a popularity-first and size-aware (PF-SA) cache placement strategy to partition files into two groups, taking into account the nonuniformity in file popularity and size. Both algorithms do not require the knowledge of active users beforehand for cache placement. Numerical results show that they perform very closely to each other. We further develop a lower bound for decentralized caching under nonuniform file popularity and size as a non-convex optimization problem and solved it using a similar successive GP approximation algorithm. We show that the D-MCCS with the optimized cache placement attains this lower bound when no more than two active users request files at a time. The same is true for files with uniform size but nonuniform popularity and the optimal cache placement being symmetric among files. In these cases, the optimized D-MCCS characterizes the exact memory-rate tradeoff for decentralized caching. For general cases, our numerical results show that the average rate achieved by the optimized D-MCCS is very close to the lower bound.
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance

Natural language understanding(NLU) is challenging for finance due to the lack of annotated data and the specialized language in that domain. As a result, researchers have proposed to use pre-trained language model and multi-task learning to learn robust representations. However, aggressive fine-tuning often causes over-fitting and multi-task learning may favor tasks with significantly larger amounts data, etc. To address these problems, in this paper, we investigate model-agnostic meta-learning algorithm(MAML) in low-resource financial NLU tasks. Our contribution includes: 1. we explore the performance of MAML method with multiple types of tasks: GLUE datasets, SNLI, Sci-Tail and Financial PhraseBank; 2. we study the performance of MAML method with multiple single-type tasks: a real scenario stock price prediction problem with twitter text data. Our models achieve the state-of-the-art performance according to the experimental results, which demonstrate that our method can adapt fast and well to low-resource situations.
Optimization of dye Q-switched lasers

Analytical expressions for all key parameters of the optimally coupled Q-switched laser are derived. These parameters include the optimal reflectivity of the output mirror, the optimal initial transmission of the dye, and the maximum attainable output energy and corresponding peak power and pulse width for a given pump level. Meanwhile, according to the optimization theory of dye Q-switched lasers, a NAB miniature dye Q-switched laser is studied experimentally and experiment shows a good agreement with the theory. >
Monetary Policy and Economic Growth in Developing Countries: A Literature Review

This article conducts a literature review on the topic of monetary policy in developing countries and focuses on the effectiveness of monetary policy in promoting economic growth and the relationship between monetary policy and economic growth. The literature review finds that the activities of central banks in developing countries are often overlooked by economic models, but recent studies have shown that there are many factors that can affect the effectiveness of monetary policy in these countries. These factors include the profitability of central banks and monetary unions, the independence of central banks in their operations, and lags, rigidities, and disequilibrium analysis. The literature review also finds that studies on the topic have produced mixed results, with some studies finding that monetary policy has a limited or non-existent impact on economic growth and others finding that it plays a crucial role. The article aims to provide a comprehensive understanding of the current state of research in this field and to identify areas for future study.