Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability of Decentralized Queueing Networks Beyond Complete Bipartite Cases

Gaitonde and Tardos recently studied a model of queueing networks where queues compete for servers and re-send returned packets in future rounds. They quantify the amount of additional processing power that guarantees a decentralized system's stability, both when the queues adapt their strategies from round to round using no-regret learning algorithms, and when they are patient and evaluate the utility of a strategy over long periods of time. In this paper, we generalize Gaitonde and Tardos's model and consider scenarios where not all servers can serve all queues (i.e., the underlying graph is an incomplete bipartite graphs) and, further, when packets need to go through more than one layer of servers before their completions (i.e., when the underlying graph is a DAG). For the bipartite case, we obtain bounds comparable to those by Gaitonde and Tardos, with the factor slightly worse in the patient queueing model. For the more general multi-layer systems, we show that straightforward generalizations of the utility function and servers' priority rules in Gaitonde and Tardos's model may lead to unbounded gaps between centralized and decentralized systems when the queues use no regret strategies. We define a new utility and a service priority rule that are aware of the queue lengths, and show that these suffice to restore the bounded gap between centralized and decentralized systems observed in bipartite graphs.
Physical properties of the layered $f$-electron van der Waals magnet Ce$_2$Te$_5$

We report a detailed study of the magnetic, transport, and thermodynamic properties of Ce$_2$Te$_5$ single crystals, a layered $f$-electron van der Waals magnet. Four consecutive transitions at $\sim$ 5.2, 2.1, 0.9, and 0.4 K were observed in the $ac$-plane electrical resistivity $\rho$(T), which were further confirmed in specific heat $C_\textrm{p}$(T) measurements. Analysis of the magnetic susceptibility $\chi$(T), the magnetic-field variation of $\rho$(T), and the increase of the first transition temperature ($T_\textrm{c} \sim$ 5.2 K) with applied magnetic field indicates ferromagnetic order, while the decrease of the other transitions with field suggests different states with dominant antiferromagnetic interactions below $T_2 \sim$ 2.1 K, $T_3 \sim$ 0.9 K, and $T_4$ = 0.4 K. Critical behavior analysis around $T_\textrm{c}$ that gives critical exponents $\beta = 0.31(2)$, $\gamma = 0.99(2)$, $\delta = 4.46(1)$, $T_\textrm{c} = 5.32(1)$ K indicates that Ce$_2$Te$_5$ shows a three-dimensional magnetic critical behavior. Moreover, the Hall resistivity $\rho_{\textrm{xy}}$ indicates that Ce$_2$Te$_5$ is a multi-band system with a relatively high electron mobility $\sim 2900$ cm$^2$ V$^{-1}$ s$^{-1}$ near $T_\textrm{c}$, providing further opportunities for future device applications.
Understanding Underground Incentivized Review Services

While human factors in fraud have been studied by the HCI and security communities, most research has been directed to understanding either the victims' perspectives or prevention strategies, and not on fraudsters, their motivations and operation techniques. Additionally, the focus has been on a narrow set of problems: phishing, spam and bullying. In this work, we seek to understand review fraud on e-commerce platforms through an HCI lens. Through surveys with real fraudsters (N=36 agents and N=38 reviewers), we uncover sophisticated recruitment, execution, and reporting mechanisms fraudsters use to scale their operation while resisting takedown attempts, including the use of AI tools like ChatGPT. We find that countermeasures that crack down on communication channels through which these services operate are effective in combating incentivized reviews. This research sheds light on the complex landscape of incentivized reviews, providing insights into the mechanics of underground services and their resilience to removal efforts.
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization

Decentralized optimization is effective to save communication in large-scale machine learning. Although numerous algorithms have been proposed with theoretical guarantees and empirical successes, the performance limits in decentralized optimization, especially the influence of network topology and its associated weight matrix on the optimal convergence rate, have not been fully understood. While (Lu and Sa, 2021) have recently provided an optimal rate for non-convex stochastic decentralized optimization with weight matrices defined over linear graphs, the optimal rate with general weight matrices remains unclear. This paper revisits non-convex stochastic decentralized optimization and establishes an optimal convergence rate with general weight matrices. In addition, we also establish the optimal rate when non-convex loss functions further satisfy the Polyak-Lojasiewicz (PL) condition. Following existing lines of analysis in literature cannot achieve these results. Instead, we leverage the Ring-Lattice graph to admit general weight matrices while maintaining the optimal relation between the graph diameter and weight matrix connectivity. Lastly, we develop a new decentralized algorithm to nearly attain the above two optimal rates under additional mild conditions.
Threshold solutions for cubic Schr\"odinger systems

We consider the following Scrödinger system with initial data ( u , v ) ∈ H ( R ) × H 1 ( R 3 ) at the so-called mass-energy threshold , i.e., such that M ( u 0 , v 0 ) E ( u 0 , v 0 ) = M ( ϕ, ψ ) E ( ϕ, ψ ), where ( ϕ, ψ ) is a ground state. For a suitable range of values of β > 0, we show the existence of special solutions to this system, which converge to a standing wave solution in one time direction, and either blows up or scatters in the opposite direction. Moreover, we classify general solutions at the ground state, showing a rigidity result regarding the possible long-time behaviors that might occur. Our results do not rely on the uniqueness of the corresponding ground state: indeed, the main results hold even in the case where the Weinstein functional is known to have more than one optimizer.
Sequential Learning Of Neural Networks for Prequential MDL

Minimum Description Length (MDL) provides a framework and an objective for principled model evaluation. It formalizes Occam's Razor and can be applied to data from non-stationary sources. In the prequential formulation of MDL, the objective is to minimize the cumulative next-step log-loss when sequentially going through the data and using previous observations for parameter estimation. It thus closely resembles a continual- or online-learning problem. In this study, we evaluate approaches for computing prequential description lengths for image classification datasets with neural networks. Considering the computational cost, we find that online-learning with rehearsal has favorable performance compared to the previously widely used block-wise estimation. We propose forward-calibration to better align the models predictions with the empirical observations and introduce replay-streams, a minibatch incremental training technique to efficiently implement approximate random replay while avoiding large in-memory replay buffers. As a result, we present description lengths for a suite of image classification datasets that improve upon previously reported results by large margins.
SQA3D: Situated Question Answering in 3D Scenes

We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D). Given a scene context (e.g., 3D scan), SQA3D requires the tested agent to first understand its situation (position, orientation, etc.) in the 3D scene as described by text, then reason about its surrounding environment and answer a question under that situation. Based upon 650 scenes from ScanNet, we provide a dataset centered around 6.8k unique situations, along with 20.4k descriptions and 33.4k diverse reasoning questions for these situations. These questions examine a wide spectrum of reasoning capabilities for an intelligent agent, ranging from spatial relation comprehension to commonsense understanding, navigation, and multi-hop reasoning. SQA3D imposes a significant challenge to current multi-modal especially 3D reasoning models. We evaluate various state-of-the-art approaches and find that the best one only achieves an overall score of 47.20%, while amateur human participants can reach 90.06%. We believe SQA3D could facilitate future embodied AI research with stronger situation understanding and reasoning capability.
Some new examples of nonlinear quotients with nonnegative sectional curvature

In this note, new examples of manifolds with Riemannian metrics of nonnegative sectional curvature are constructed. These manifolds are orbit spaces of free nonlinear orientation-preserving and orientation-reversing involutions on products of spheres, their fundamental group is of order two, and they realize homeomorphism classes that were not previously known to contain nonnegatively curved smooth manifolds. We remark that our examples are obtained as a cut-and-paste construction along spheres and real projective spaces on sphere bundles.
Continuous-Time Active Filter Design

Fundamentals Introduction Filter Characterization Types of Filters Steps in Filter Design Analysis Continuous-Time Filter Functions Stability Passivity for One- and Two-Port Networks Reciprocity The Approximation Problem Introduction Filter Specifications and Permitted Functions Formulation of the Approximation Problem Approximation to the Ideal Lowpass Filter Filters with Linear Phase: Delays Bessel-Thomson Delay Approximation Delay Equalization Frequency Transformations Design Tables of Passive LC Ladder Filters Impedance Scaling Predistortion Active Elements Introduction Ideal Controlled Sources Impedance Transformation (Generalized Impedance Converters and Inverters) Negative Resistance Ideal Operational Amplifier The Ideal Operational Transconductance Amplifier (OTA) Realization of 1st- and 2nd-Order Functions Using Opamps Introduction Realization of 1st-Order Functions The General 2nd-Order Filter Function Sensitivity of 2nd-Order Filters Realization of Biquadratic Functions Using SABs Realization of a Quadratic with a Positive Real Zero Biquads Obtained Using the Twin-Tee RC Network Two Opamp Biquads Three Opamp Biquads Realization of High-Order Functions Using Opamps Introduction Selection Criteria for High-Order Function Realizations Mutliparameter Sensitivity High-Order Function Realization Methods Cascade Connection of 2nd-Order Sections Mutli-Loop Feedback Filters Cascade of Biquartics Simulation of LC Ladder Filters Using Opamps Introduction Resistively Terminated Lossless LC ladder Filters Methods of LC Ladder Filter Simulation The Gyrator Generalized Impedance Converter FDNRs Complex Impedance Scaling Functional Simulation Wave Active Filters Introduction Wave Active Filters Wave Active Equivalents (WAE) Economical Wave Active Filters Sensitivity of WAFs Operation of WAFs at Higher Frequencies Complementary Transfer Functions Wave Simulation of Inductance Linear Transformation Active Filters (LTA Filters)
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation

Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i.e., uniform). To tackle the consequent cross-distribution generalization concerns, we bring the knowledge distillation to this field and propose an Adaptive Multi-Distribution Knowledge Distillation (AMDKD) scheme for learning more generalizable deep models. Particularly, our AMDKD leverages various knowledge from multiple teachers trained on exemplar distributions to yield a light-weight yet generalist student model. Meanwhile, we equip AMDKD with an adaptive strategy that allows the student to concentrate on difficult distributions, so as to absorb hard-to-master knowledge more effectively. Extensive experimental results show that, compared with the baseline neural methods, our AMDKD is able to achieve competitive results on both unseen in-distribution and out-of-distribution instances, which are either randomly synthesized or adopted from benchmark datasets (i.e., TSPLIB and CVRPLIB). Notably, our AMDKD is generic, and consumes less computational resources for inference.
Q-TOD: A Query-driven Task-oriented Dialogue System

Existing pipelined task-oriented dialogue systems usually have difficulties adapting to unseen domains, whereas end-to-end systems are plagued by large-scale knowledge bases in practice. In this paper, we introduce a novel query-driven task-oriented dialogue system, namely Q-TOD. The essential information from the dialogue context is extracted into a query, which is further employed to retrieve relevant knowledge records for response generation. Firstly, as the query is in the form of natural language and not confined to the schema of the knowledge base, the issue of domain adaption is alleviated remarkably in Q-TOD. Secondly, as the query enables the decoupling of knowledge retrieval from the generation, Q-TOD gets rid of the issue of knowledge base scalability. To evaluate the effectiveness of the proposed Q-TOD, we collect query annotations for three publicly available task-oriented dialogue datasets. Comprehensive experiments verify that Q-TOD outperforms strong baselines and establishes a new state-of-the-art performance on these datasets.
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture

Vision Transformers (ViTs) have recently achieved competitive performance in broad vision tasks. Unfortunately, on popular threat models, naturally trained ViTs are shown to provide no more adversarial robustness than convolutional neural networks (CNNs). Adversarial training is still required for ViTs to defend against such adversarial attacks. In this paper, we provide the first and comprehensive study on the adversarial training recipe of ViTs via extensive evaluation of various training techniques across benchmark datasets. We find that pre-training and SGD optimizer are necessary for ViTs' adversarial training. Further considering ViT as a new type of model architecture, we investigate its adversarial robustness from the perspective of its unique architectural components. We find, when randomly masking gradients from some attention blocks or masking perturbations on some patches during adversarial training, the adversarial robustness of ViTs can be remarkably improved, which may potentially open up a line of work to explore the architectural information inside the newly designed models like ViTs. Our code is available at https://github.com/mo666666/When-Adversarial-Training-Meets-Vision-Transformers.
Learning to Autonomously Reach Objects with NICO and Grow-When-Required Networks

The act of reaching for an object is a fundamental yet complex skill for a robotic agent, requiring a high degree of visuomotor control and coordination. In consideration of dynamic environments, a robot capable of autonomously adapting to novel situations is desired. In this paper, a developmental robotics approach is used to autonomously learn visuomotor coordination on the NICO (Neuro-Inspired COmpanion) platform, for the task of object reaching. The robot interacts with its environment and learns associations between motor commands and temporally correlated sensory perceptions based on Hebbian learning. Multiple Grow-When-Required (GWR) networks are used to learn increasingly more complex motoric behaviors, by first learning how to direct the gaze towards a visual stimulus, followed by learning motor control of the arm, and finally learning how to reach for an object using eye-hand coordination. We demonstrate that the model is able to deal with an unforeseen mechanical change in the NICO's body, showing the adaptability of the proposed approach. In evaluations of our approach, we show that the humanoid robot NICO is able to reach objects with a 76% success rate.
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning

Aiming at selecting a small subset of nodes with maximum influence on networks, the Influence Maximization (IM) problem has been extensively studied. Since it is #P-hard to compute the influence spread given a seed set, the state-of-the-art methods, including heuristic and approximation algorithms, faced with great difficulties such as theoretical guarantee, time efficiency, generalization, etc. This makes it unable to adapt to large-scale networks and more complex applications. On the other side, with the latest achievements of Deep Reinforcement Learning (DRL) in artificial intelligence and other fields, lots of works have been focused on exploiting DRL to solve combinatorial optimization problems. Inspired by this, we propose a novel end-to-end DRL framework, ToupleGDD, to address the IM problem in this paper, which incorporates three coupled graph neural networks for network embedding and double deep Q-networks for parameters learning. Previous efforts to solve IM problem with DRL trained their models on subgraphs of the whole network, and then tested on the whole graph, which makes the performance of their models unstable among different networks. However, our model is trained on several small randomly generated graphs with a small budget, and tested on completely different networks under various large budgets, which can obtain results very close to IMM and better results than OPIM-C on several datasets, and shows strong generalization ability. Finally, we conduct a large number of experiments on synthetic and realistic datasets, and experimental results prove the effectiveness and superiority of our model.
A descent algorithm for the optimal control of ReLU neural network informed PDEs based on approximate directional derivatives

We propose and analyze a numerical algorithm for solving a class of optimal control problems for learning-informed semilinear partial differential equations. The latter is a class of PDEs with constituents that are in principle unknown and are approximated by nonsmooth ReLU neural networks. We first show that a direct smoothing of the ReLU network with the aim to make use of classical numerical solvers can have certain disadvantages, namely potentially introducing multiple solutions for the corresponding state equation. This motivates us to devise a numerical algorithm that treats directly the nonsmooth optimal control problem, by employing a descent algorithm inspired by a bundle-free method. Several numerical examples are provided and the efficiency of the algorithm is shown.
Caption supervision enables robust learners

Vision language (VL) models like CLIP are robust to natural distribution shifts, in part because CLIP learns on unstructured data using a technique called caption supervision; the model inteprets image-linked texts as ground-truth labels. In a carefully controlled comparison study, we show that caption-supervised CNNs trained on a standard cross-entropy loss (with image labels assigned by scanning captions for class names) can exhibit greater distributional robustness than VL models trained on the same data. To facilitate future experiments with high-accuracy caption-supervised models, we introduce CaptionNet (https://github.com/penfever/CaptionNet/), which includes a class-balanced, fully supervised dataset with over 50,000 new human-labeled ImageNet-compliant samples which includes web-scraped captions. In a series of experiments on CaptionNet, we show how the choice of loss function, data filtration and supervision strategy enable robust computer vision. We also provide the codebase necessary to reproduce our experiments at VL Hub (https://github.com/penfever/vlhub/).
GriT-DBSCAN: A Spatial Clustering Algorithm for Very Large Databases

DBSCAN is a fundamental spatial clustering algorithm with numerous practical applications. However, a bottleneck of the algorithm is in the worst case, the run time complexity is $O(n^2)$. To address this limitation, we propose a new grid-based algorithm for exact DBSCAN in Euclidean space called GriT-DBSCAN, which is based on the following two techniques. First, we introduce a grid tree to organize the non-empty grids for the purpose of efficient non-empty neighboring grids queries. Second, by utilising the spatial relationships among points, we propose a technique that iteratively prunes unnecessary distance calculations when determining whether the minimum distance between two sets is less than or equal to a certain threshold. We theoretically prove that the complexity of GriT-DBSCAN is linear to the data set size. In addition, we obtain two variants of GriT-DBSCAN by incorporating heuristics, or by combining the second technique with an existing algorithm. Experiments are conducted on both synthetic and real-world data sets to evaluate the efficiency of GriT-DBSCAN and its variants. The results of our analyses show that our algorithms outperform existing algorithms.
Extracting Cultural Commonsense Knowledge at Scale

Structured knowledge is important for many AI applications. Commonsense knowledge, which is crucial for robust human-centric AI, is covered by a small number of structured knowledge projects. However, they lack knowledge about human traits and behaviors conditioned on socio-cultural contexts, which is crucial for situative AI. This paper presents Candle, an end-to-end methodology for extracting high-quality cultural commonsense knowledge (CCSK) at scale. Candle extracts CCSK assertions from a huge web corpus and organizes them into coherent clusters, for 3 domains of subjects (geography, religion, occupation) and several cultural facets (food, drinks, clothing, traditions, rituals, behaviors). Candle includes judicious techniques for classification-based filtering and scoring of interestingness. Experimental evaluations show the superiority of the Candle CCSK collection over prior works, and an extrinsic use case demonstrates the benefits of CCSK for the GPT-3 language model. Code and data can be accessed at https://candle.mpi-inf.mpg. de/.
Secure Multiparty Computation for Synthetic Data Generation from Distributed Data

Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, ﬁnance, and education. Synthetic data generation algorithms with privacy guarantees are emerging as a paradigm to break this data logjam. Existing approaches, however, assume that the data holders supply their raw data to a trusted curator, who uses it as fuel for synthetic data generation. This severely limits the applicability, as much of the valuable data in the world is locked up in silos, controlled by entities who cannot show their data to each other or a central aggregator without raising privacy concerns. To overcome this roadblock, we propose the ﬁrst solution in which data holders only share encrypted data for differentially private synthetic data generation. Data holders send shares to servers who perform Secure Multiparty Computation (MPC) computations while the original data stays encrypted. We instantiate this idea in an MPC protocol for the Multiplicative Weights with Exponential Mechanism (MWEM) algorithm to generate synthetic data based on real data originating from many data holders without reliance on a single point of failure.