Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Complexity of Computing Sparse Equilibria and Lower Bounds for No-Regret Learning in Games

Characterizing the performance of no-regret dynamics in multi-player games is a foundational problem at the interface of online learning and game theory. Recent results have revealed that when all players adopt specific learning algorithms, it is possible to improve exponentially over what is predicted by the overly pessimistic no-regret framework in the traditional adversarial regime, thereby leading to faster convergence to the set of coarse correlated equilibria (CCE). Yet, despite considerable recent progress, the fundamental complexity barriers for learning in normal- and extensive-form games are poorly understood. In this paper, we make a step towards closing this gap by first showing that -- barring major complexity breakthroughs -- any polynomial-time learning algorithms in extensive-form games need at least $2^{\log^{1/2 - o(1)} |\mathcal{T}|}$ iterations for the average regret to reach below even an absolute constant, where $|\mathcal{T}|$ is the number of nodes in the game. This establishes a superpolynomial separation between no-regret learning in normal- and extensive-form games, as in the former class a logarithmic number of iterations suffices to achieve constant average regret. Furthermore, our results imply that algorithms such as multiplicative weights update, as well as its \emph{optimistic} counterpart, require at least $2^{(\log \log m)^{1/2 - o(1)}}$ iterations to attain an $O(1)$-CCE in $m$-action normal-form games. These are the first non-trivial -- and dimension-dependent -- lower bounds in that setting for the most well-studied algorithms in the literature. From a technical standpoint, we follow a beautiful connection recently made by Foster, Golowich, and Kakade (ICML '23) between sparse CCE and Nash equilibria in the context of Markov games. Consequently, our lower bounds rule out polynomial-time algorithms well beyond the traditional online learning framework.
InfoPattern: Unveiling Information Propagation Patterns in Social Media

Social media play a significant role in shaping public opinion and influencing ideological communities through information propagation. Our demo InfoPattern centers on the interplay between language and human ideology. The demo (Code: https://github.com/blender-nlp/InfoPattern ) is capable of: (1) red teaming to simulate adversary responses from opposite ideology communities; (2) stance detection to identify the underlying political sentiments in each message; (3) information propagation graph discovery to reveal the evolution of claims across various communities over time. (Live Demo: https://incas.csl.illinois.edu/blender/About )
Kernel-based measures of association between inputs and outputs based on ANOVA

ANOVA decomposition of function with random input variables provides ANOVA functionals (AFs), which contain information about the contributions of the input variables on the output variable(s). By embedding AFs into an appropriate reproducing kernel Hilbert space regarding their distributions, we propose an efficient statistical test of independence between the input variables and output variable(s). The resulting test statistic leads to new dependent measures of association between inputs and outputs that allow for i) dealing with any distribution of AFs, including the Cauchy distribution, ii) accounting for the necessary or desirable moments of AFs and the interactions among the input variables. In uncertainty quantification for mathematical models, a number of existing measures are special cases of this framework. We then provide unified and general global sensitivity indices and their consistent estimators, including asymptotic distributions. For Gaussian-distributed AFs, we obtain Sobol' indices and dependent generalized sensitivity indices using quadratic kernels.
Reinforcement Learning from Statistical Feedback: the Journey from AB Testing to ANT Testing

Reinforcement Learning from Human Feedback (RLHF) has played a crucial role in the success of large models such as ChatGPT. RLHF is a reinforcement learning framework which combines human feedback to improve learning effectiveness and performance. However, obtaining preferences feedback manually is quite expensive in commercial applications. Some statistical commercial indicators are usually more valuable and always ignored in RLHF. There exists a gap between commercial target and model training. In our research, we will attempt to fill this gap with statistical business feedback instead of human feedback, using AB testing which is a well-established statistical method. Reinforcement Learning from Statistical Feedback (RLSF) based on AB testing is proposed. Statistical inference methods are used to obtain preferences for training the reward network, which fine-tunes the pre-trained model in reinforcement learning framework, achieving greater business value. Furthermore, we extend AB testing with double selections at a single time-point to ANT testing with multiple selections at different feedback time points. Moreover, we design numerical experiences to validate the effectiveness of our algorithm framework.
Learning Multi-Frequency Partial Correlation Graphs

Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlations but fail to discriminate across distinct frequency bands. Motivated by many applications in which this differentiation is pivotal, we overcome this limitation by learning a block-sparse, frequency-dependent, partial correlation graph, in which layers correspond to different frequency bands, and partial correlations can occur over just a few layers. To this aim, we formulate and solve two nonconvex learning problems: the first has a closed-form solution and is suitable when there is prior knowledge about the number of partial correlations; the second hinges on an iterative solution based on successive convex approximation, and is effective for the general case where no prior knowledge is available. Numerical results on synthetic data show that the proposed methods outperform the current state of the art. Finally, the analysis of financial time series confirms that partial correlations exist only within a few frequency bands, underscoring how our methods enable the gaining of valuable insights that would be undetected without discriminating along the frequency domain.
Decoding Social Sentiment in DAO: A Comparative Analysis of Blockchain Governance Communities

Blockchain technology is leading a revolutionary transformation across diverse industries, with effective governance standing as a critical determinant for the success and sustainability of blockchain projects. Community forums, pivotal in engaging decentralized autonomous organizations (DAOs), wield a substantial impact on blockchain governance decisions. Concurrently, Natural Language Processing (NLP), particularly sentiment analysis, provides powerful insights from textual data. While prior research has explored the potential of NLP tools in social media sentiment analysis, a gap persists in understanding the sentiment landscape of blockchain governance communities. The evolving discourse and sentiment dynamics on the forums of top DAOs remain largely unknown. This paper delves deep into the evolving discourse and sentiment dynamics on the public forums of leading DeFi projects -- Aave, Uniswap, Curve Dao, Aragon, Yearn.finance, Merit Circle, and Balancer -- placing a primary focus on discussions related to governance issues. Despite differing activity patterns, participants across these decentralized communities consistently express positive sentiments in their Discord discussions, indicating optimism towards governance decisions. Additionally, our research suggests a potential interplay between discussion intensity and sentiment dynamics, indicating that higher discussion volumes may contribute to more stable and positive emotions. The insights gained from this study are valuable for decision-makers in blockchain governance, underscoring the pivotal role of sentiment analysis in interpreting community emotions and its evolving impact on the landscape of blockchain governance. This research significantly contributes to the interdisciplinary exploration of the intersection of blockchain and society, with a specific emphasis on the decentralized blockchain governance ecosystem.
Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement

Humans use abstract concepts for understanding instead of hard features. Recent interpretability research has focused on human-centered concept explanations of neural networks. Concept Activation Vectors (CAVs) estimate a model's sensitivity and possible biases to a given concept. In this paper, we extend CAVs from post-hoc analysis to ante-hoc training in order to reduce model bias through fine-tuning using an additional Concept Loss. Concepts were defined on the final layer of the network in the past. We generalize it to intermediate layers using class prototypes. This facilitates class learning in the last convolution layer, which is known to be most informative. We also introduce Concept Distillation to create richer concepts using a pre-trained knowledgeable model as the teacher. Our method can sensitize or desensitize a model towards concepts. We show applications of concept-sensitive training to debias several classification problems. We also use concepts to induce prior knowledge into IID, a reconstruction problem. Concept-sensitive training can improve model interpretability, reduce biases, and induce prior knowledge. Please visit https://avani17101.github. io/Concept-Distilllation/ for code and more details.
Quantum simulation of entanglement dynamics in a quantum processor

We implement a five-qubit protocol in IBM quantum processors to get entanglement dynamics in a two qubit system in the presence of an environment. Specifically, two qubits represent the main system, another two qubits the environment, and an additional qubit is used as an auxiliary qubit to perform the quantum entanglement estimation. We focus on measuring, in this superconducting quantum processor, the sudden death and sudden birth of entanglement. We obtain the quantum entanglement evolution of the main system qubits and the environment qubits as the average of $N=10$ independent experiments in the same quantum device, observing that the noisy nature of current quantum processors produce a shift on times signaling sudden death o sudden birth of entanglement. This work takes relevance showing the usefulness of current noisy quantum devices to test fundamental concepts in quantum information.
GloNets: Globally Connected Neural Networks

Deep learning architectures suffer from depth-related performance degradation, limiting the effective depth of neural networks. Approaches like ResNet are able to mitigate this, but they do not completely eliminate the problem. We introduce Globally Connected Neural Networks (GloNet), a novel architecture overcoming depth-related issues, designed to be superimposed on any model, enhancing its depth without increasing complexity or reducing performance. With GloNet, the network's head uniformly receives information from all parts of the network, regardless of their level of abstraction. This enables GloNet to self-regulate information flow during training, reducing the influence of less effective deeper layers, and allowing for stable training irrespective of network depth. This paper details GloNet's design, its theoretical basis, and a comparison with existing similar architectures. Experiments show GloNet's self-regulation ability and resilience to depth-related learning challenges, like performance degradation. Our findings suggest GloNet as a strong alternative to traditional architectures like ResNets.
Comparison between Tensor Networks and Variational Quantum Classifier

The primary objective of this paper is to conduct a comparative analysis between two Machine Learning approaches: Tensor Networks (TN) and Variational Quantum Classifiers (VQC). While both approaches share similarities in their representation of the Hilbert space using a logarithmic number of parameters, they diverge in the manifolds they cover. Thus, the aim is to evaluate and compare the expressibility and trainability of these approaches. By conducting this comparison, we can gain insights into potential areas where quantum advantage may be found. Our findings indicate that VQC exhibits advantages in terms of speed and accuracy when dealing with data, characterized by a small number of features. However, for high-dimensional data, TN surpasses VQC in overall classification accuracy. We believe that this disparity is primarily attributed to challenges encountered during the training of quantum circuits. We want to stress that in this article, we focus on only one particular task and do not conduct thorough averaging of the results. Consequently, we recommend considering the results of this article as a unique case without excessive generalization.
Distributed Consensus of Heterogeneous Multi-Agent Systems Based on Feedforward Control

This paper studies the consensus problem of heterogeneous multi-agent systems by the feedforward control and linear quadratic (LQ) optimal control theory. Different from the existing consensus control algorithms, which require to design an additional distributed observer for estimating the leader's information and to solve a set of regulator equations. In this paper, by designing a distributed feedforward controller, a non-standard neighbor error system is transformed into a standard linear system, and then an optimal consensus controller is designed by minimizing a combined state error with neighbour agents. The proposed optimal controller is obtained by solving Riccati equations, and it is shown that the corresponding cost function under the proposed distributed controllers is asymptotically optimal. The proposed consensus algorithm can be directly applicable to solve the consensus problem of homogeneous systems. Simulation example indicates the effectiveness of the proposed scheme and a much faster convergence speed than the existing algorithm.
Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context

Lexical ambiguity is a challenging and pervasive problem in machine translation (\mt). We introduce a simple and scalable approach to resolve translation ambiguity by incorporating a small amount of extra-sentential context in neural \mt. Our approach requires no sense annotation and no change to standard model architectures. Since actual document context is not available for the vast majority of \mt training data, we collect related sentences for each input to construct pseudo-documents. Salient words from pseudo-documents are then encoded as a prefix to each source sentence to condition the generation of the translation. To evaluate, we release \docmucow, a challenge set for translation disambiguation based on the English-German \mucow \cite{raganato-etal-2020-evaluation} augmented with document IDs. Extensive experiments show that our method translates ambiguous source words better than strong sentence-level baselines and comparable document-level baselines while reducing training costs.
Speed of sound exceeding the conformal bound in dense 2-color QCD

We review recent works on the Monte Carlo simulations of dense two-color QCD (QC$_2$D) by focusing on the phase diagram, the equation of state, and the sound velocity at nonzero quark chemical potential. A possible upper bound of the sound velocity is known as the conformal bound, namely, $c_s^2/c^2 \leq 1/3$. The sound velocity is below the bound at least in the case of finite-temperature QCD. However, our recent work~\cite{Iida:2022hyy} shows the breaking of this bound in dense QC$_2$D. This phenomenon was previously unknown from any lattice calculations. We also discuss recent related works including lattice studies on QCD at nonzero isospin chemical potential, some effective model analyses, and an analysis based on recent neutron star observations. These works also suggest the breaking of the conformal bound.
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation

Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural networks as function approximators is typically considered a black box with little explainability and often suffers from suboptimal performance, especially for autonomous navigation in highly interactive multi-agent environments. To address these issues, we propose three auxiliary tasks with spatio-temporal relational reasoning and integrate them into the standard DRL framework, which improves the decision making performance and provides explainable intermediate indicators. We propose to explicitly infer the internal states (i.e., traits and intentions) of surrounding agents (e.g., human drivers) as well as to predict their future trajectories in the situations with and without the ego agent through counterfactual reasoning. These auxiliary tasks provide additional supervision signals to infer the behavior patterns of other interactive agents. Multiple variants of framework integration strategies are compared. We also employ a spatio-temporal graph neural network to encode relations between dynamic entities, which enhances both internal state inference and decision making of the ego agent. Moreover, we propose an interactivity estimation mechanism based on the difference between predicted trajectories in these two situations, which indicates the degree of influence of the ego agent on other agents. To validate the proposed method, we design an intersection driving simulator based on the Intelligent Intersection Driver Model (IIDM) that simulates vehicles and pedestrians. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics and provides explainable intermediate indicators (i.e., internal states, and interactivity scores) for decision making.
A composite ansatz for the calculation of dynamical structure factor

We propose an ansatz without adjustable parameters for the calculation of dynamical structure factor. The ansatz combines quasi-particle Green’s function, especially the contribution from the renormalization factor, and the exchange-correlation kernel from time-dependent density functional theory together, verified for typical metals and semiconductors from plasmon excitation regime to Compton scattering regime. It has the capability to reconcile both small-angle and large-angle inelastic x-ray scattering (IXS) signals with much improved accuracy, which can be used, as the theoretical base model, in inversely inferring electronic structures of condensed matter from IXS experimental signals directly. It may also be used to diagnose thermal parameters, such as temperature and density, of dense plasmas in x-ray Thomson scattering experiments.
Learning to Skip for Language Modeling

Overparameterized large-scale language models have impressive generalization performance of in-context few-shot learning. However, most language models allocate the same amount of parameters or computation to each token, disregarding the complexity or importance of the input data. We argue that in language model pretraining, a variable amount of computation should be assigned to different tokens, and this can be efficiently achieved via a simple routing mechanism. Different from conventional early stopping techniques where tokens can early exit at only early layers, we propose a more general method that dynamically skips the execution of a layer (or module) for any input token with a binary router. In our extensive evaluation across 24 NLP tasks, we demonstrate that the proposed method can significantly improve the 1-shot performance compared to other competitive baselines only at mild extra cost for inference.
The invasion potential of the non-native Chilean oyster (Ostrea chilensis Philippi 1845) in the Menai Strait (North Wales, UK) : present observations and future predictions

Crynodeb. List of Figures. List of Tables. Glossary. Chapter 1 – General Introduction. 1.1 Biological invasions – what, where, when and why? 1.2 The invasion process. 1.3 Why should we care? 1.3. 1 Socio-economic impacts. 1.3. 2 Invasion lags. 1.3. 3 Changes in the rate of invasions. 1.4 Transfers of non-native oysters around the world. 1.5 The ecological importance of oysters. 1.5. 1 Oysters as habitat modifiers. 1.5. 2 Oysters as translocators of energy from the water column to the benthos. 1.5. 3 Oysters as prey items for keystone predators. 1.6 Case Study – the non-native Chilean oyster, Ostrea chilensis Philippi 1845, in the Menai Strait (North Wales, UK). 1.7 Conclusions and questions addressed. Appendix I Assessing oyster reef biodiversity and its relationship with biodiversity. Chapter 2 – Capricious bioinvasions versus uncoordinated management strategies: how the current UK legislation framework allows for the most unlikely invaders. 2.1 Abstract. 2.2 Introduction. 2.3 Methods. 2.2.1 Study site. 1 3
High-resolution 3T to 7T MRI Synthesis with a Hybrid CNN-Transformer Model

7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multi-modal 3T MRI. The Vision CNN-Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in the Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided: 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). The results are as follows: PSNR: 27.0+-0.9 dB, SSIM: 0.945+-0.010, and MSE: 2.0+-0.4E-3. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high-quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, especially when 7T MRI scanners are unavailable.
A connection between the $A_\alpha$-spectrum and the Lov\'asz theta number

We show that the smallest $\alpha$ so that $\alpha D + (1-\alpha)A \succcurlyeq 0$ is at least $1/\vartheta(\overline{G})$, significantly improving upon a result due to Nikiforov and Rojo (2017). In fact, we display an even stronger connection: if the nonzero entries of $A$ are allowed to vary and those of $D$ vary accordingly, then we show that this smallest $\alpha$ is in fact equal to $1/\vartheta(\overline{G})$. We also show other results obtained as an application of this optimization framework, including a connection to the well-known quadratic formulation for $\omega(G)$ due to Motzkin and Straus (1964).