Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Convergence of No-Regret Learning Dynamics in Time-Varying Games

Most of the literature on learning in games has focused on the restrictive setting where the underlying repeated game does not change over time. Much less is known about the convergence of no-regret learning algorithms in dynamic multiagent settings. In this paper, we characterize the convergence of optimistic gradient descent (OGD) in time-varying games. Our framework yields sharp convergence bounds for the equilibrium gap of OGD in zero-sum games parameterized on natural variation measures of the sequence of games, subsuming known results for static games. Furthermore, we establish improved second-order variation bounds under strong convexity-concavity, as long as each game is repeated multiple times. Our results also apply to time-varying general-sum multi-player games via a bilinear formulation of correlated equilibria, which has novel implications for meta-learning and for obtaining refined variation-dependent regret bounds, addressing questions left open in prior papers. Finally, we leverage our framework to also provide new insights on dynamic regret guarantees in static games.
On the Global Convergence of Risk-Averse Policy Gradient Methods with Dynamic Time-Consistent Risk Measures

Risk-sensitive reinforcement learning (RL) has become a popular tool to control the risk of uncertain outcomes and ensure reliable performance in various sequential decision-making problems. While policy gradient methods have been developed for risk-sensitive RL, it remains unclear if these methods enjoy the same global convergence guarantees as in the risk-neutral case (Bhandari & Russo, 2019; Mei et al., 2020; Agarwal et al., 2021; Cen et al., 2022). In this paper, we consider a class of dynamic time-consistent risk measures, called Expected Conditional Risk Measures (ECRMs), and derive policy gradient updates for ECRM-based objective functions. Under both constrained direct parameterization and unconstrained softmax parameterization, we provide global convergence of the corresponding risk-averse policy gradient algorithms. We further test a risk-averse variant of REINFORCE algorithm (Williams, 1992) on a stochastic Cliﬀwalk environment to demonstrate the eﬃcacy of our algorithm and the importance of risk control.
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge

A directed acyclic graph (DAG) provides valuable prior knowledge that is often discarded in regression tasks in machine learning. We show that the independences arising from the presence of collider structures in DAGs provide meaningful inductive biases, which constrain the regression hypothesis space and improve predictive performance. We introduce collider regression, a framework to incorporate probabilistic causal knowledge from a collider in a regression problem. When the hypothesis space is a reproducing kernel Hilbert space, we prove a strictly positive generalisation benefit under mild assumptions and provide closed-form estimators of the empirical risk minimiser. Experiments on synthetic and climate model data demonstrate performance gains of the proposed methodology.
Chemical Power for Swarms of Microscopic Robots in Blood Vessels

Microscopic robots in the bloodstream could obtain power from fuel cells using glucose and oxygen. Previous studies of small numbers of such robots operating near each other showed how robots compete with their neighbors for oxygen. However, proposed applications involve billions of such robots operating throughout the body. With such large numbers, the robots can have systemic effects on oxygen concentration. This paper evaluates these effects and their consequences for robot power generation, oxygen available to tissue and heating as such robots move with the blood. When robots consume oxygen as fast as it diffuses to their surfaces, available power decreases significantly as robots move from the lungs, through arteries to capillaries and veins. Tens of billions of robots can obtain hundreds of picowatts throughout the circuit, while a trillion robots significantly deplete oxygen in the veins. Robots can mitigate this depletion by limiting their oxygen consumption, either overall or in specific locations or situations.
Unravelling physics beyond the standard model with classical and quantum anomaly detection

Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC). However, current experiments do not indicate clear signs of new physics that could guide the development of additional Beyond Standard Model (BSM) theories. Identifying signatures of new physics out of the enormous amount of data produced at the LHC falls into the class of anomaly detection and constitutes one of the greatest computational challenges. In this article, we propose a novel strategy to perform anomaly detection in a supervised learning setting, based on the artificial creation of anomalies through a random process. For the resulting supervised learning problem, we successfully apply classical and quantum support vector classifiers (CSVC and QSVC respectively) to identify the artificial anomalies among the SM events. Even more promising, we find that employing an SVC trained to identify the artificial anomalies, it is possible to identify realistic BSM events with high accuracy. In parallel, we also explore the potential of quantum algorithms for improving the classification accuracy and provide plausible conditions for the best exploitation of this novel computational paradigm.
Joint data rate and EMF exposure analysis in user-centric cell-free massive MIMO networks

The objective of this study is to analyze the statistics of the data rate and of the incident power density (IPD) in user-centric cell-free networks (UCCFNs). To this purpose, our analysis proposes a number of performance metrics derived using stochastic geometry (SG). On the one hand, the first moments and the marginal distribution of the IPD are calculated. On the other hand, bounds on the joint distributions of rate and IPD are provided for two scenarios: when it is relevant to obtain IPD values above a given threshold (for energy harvesting purposes), and when these values should instead remain below the threshold (for public health reasons). In addition to deriving these metrics, this work incorporates features related to UCCFNs which are new in SG models: a power allocation based on collective channel statistics, as well as the presence of potential overlaps between adjacent clusters. Our numerical results illustrate the achievable trade-offs between the rate and IPD performance. For the considered system, these results also highlight the existence of an optimal node density maximizing the joint distributions. (This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.)
Transformation and symmetries for the Andrews-Garvan crank function

A BSTRACT . Let R ( z, q ) be the two-variable generating function of Dyson’s rank function. In a recent joint work with Frank Garvan, we investigated the transformation of the elements of the p -dissection of R ( ζ p , q ) , where ζ p is a primitive p -th root of unity, under special congruence subgroups of SL 2 ( Z ) , leading us to interesting symmetry observations. In this work, we derive analogous transformation results for the two-variable crank generating function C ( z, q ) in terms of generalized eta products. We consider the action of the group Γ 0 ( p ) on the elements of the p -dissection of C ( z, q ) , leading us to new symmetries for the crank function. As an application, we give a new proof of the crank theorem predicted by Dyson in 1944 and resolved by Andrews and Garvan in 1988. Furthermore, we present identities ex-pressing the elements of the crank dissection in terms of generalized eta products for primes p = 11 , 13 , 17 and 19 .
Understanding Finetuning for Factual Knowledge Extraction from Language Models

Language models (LMs) pretrained on large corpora of text from the web have been observed to contain large amounts of various types of knowledge about the world. This observation has led to a new and exciting paradigm in knowledge graph construction where, instead of manual curation or text mining, one extracts knowledge from the parameters of an LM. Recently, it has been shown that finetuning LMs on a set of factual knowledge makes them produce better answers to queries from a different set, thus making finetuned LMs a good candidate for knowledge extraction and, consequently, knowledge graph construction. In this paper, we analyze finetuned LMs for factual knowledge extraction. We show that along with its previously known positive effects, finetuning also leads to a (potentially harmful) phenomenon which we call Frequency Shock, where at the test time the model over-predicts rare entities that appear in the training set and under-predicts common entities that do not appear in the training set enough times. We show that Frequency Shock leads to a degradation in the predictions of the model and beyond a point, the harm from Frequency Shock can even outweigh the positive effects of finetuning, making finetuning harmful overall. We then consider two solutions to remedy the identified negative effect: 1- model mixing and 2- mixture finetuning with the LM's pre-training task. The two solutions combined lead to significant improvements compared to vanilla finetuning.
A Cloud-Edge Continuum Experimental Methodology Applied to a 5G Core Study

There is an increasing interest in extending traditional cloud-native technologies, such as Kubernetes, outside the data center to build a continuum towards the edge and between. However, traditional resource orchestration algorithms do not work well in this case, and it is also difficult to test applications for a heterogeneous cloud infrastructure without actually building it. To address these challenges, we propose a new methodology to aid in deploying, testing, and analyzing the effects of microservice placement and scheduling in a heterogeneous Cloud environment. With this methodology, we can investigate any combination of deployment scenarios and monitor metrics in accordance with the placement of microservices in the cloud-edge continuum. Edge devices may be simulated, but as we use Kubernetes, any device which can be attached to a Kubernetes cluster could be used. In order to demonstrate our methodology, we have applied it to the problem of network function placement of an open-source 5G core implementation.
Causal Reasoning of Entities and Events in Procedural Texts

Entities and events are crucial to natural language reasoning and common in procedural texts. Existing work has focused either exclusively on entity state tracking (e.g., whether a pan is hot) or on event reasoning (e.g., whether one would burn themselves by touching the pan), while these two tasks are often causally related. We propose CREPE, the first benchmark on causal reasoning of event plausibility and entity states. We show that most language models, including GPT-3, perform close to chance at . 35 F1, lagging far behind human at . 87 F1. We boost model performance to . 59 F1 by creatively representing events as programming languages while prompting language models pretrained on code. By injecting the causal relations between entities and events as intermediate reasoning steps in our representation, we further boost the performance to . 67 F1. Our findings indicate not only the challenge that CREPE brings for language models, but also the efficacy of code-like prompting combined with chain-of-thought prompting for multihop event reasoning.
Flexagon: A Multi-dataflow Sparse-Sparse Matrix Multiplication Accelerator for Efficient DNN Processing

Sparsity is a growing trend in modern DNN models. Existing Sparse-Sparse Matrix Multiplication (SpMSpM) accelerators are tailored to a particular SpMSpM dataflow (i.e., Inner Product, Outer Product or Gustavson's), which determines their overall efficiency. We demonstrate that this static decision inherently results in a suboptimal dynamic solution. This is because different SpMSpM kernels show varying features (i.e., dimensions, sparsity pattern, sparsity degree), which makes each dataflow better suited to different data sets. In this work we present Flexagon, the first SpMSpM reconfigurable accelerator that is capable of performing SpMSpM computation by using the particular dataflow that best matches each case. Flexagon accelerator is based on a novel Merger-Reduction Network (MRN) that unifies the concept of reducing and merging in the same substrate, increasing efficiency. Additionally, Flexagon also includes a new L1 on-chip memory organization, specifically tailored to the different access characteristics of the input and output compressed matrices. Using detailed cycle-level simulation of contemporary DNN models from a variety of application domains, we show that Flexagon achieves average performance benefits of 4.59x, 1.71x, and 1.35x with respect to the state-of-the-art SIGMA-like, SpArch-like and GAMMA-like accelerators (265%, 67%, and 18%, respectively, in terms of average performance/area efficiency).
Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)

Introduction Data imbalance is one of the crucial issues in big data analysis with fewer labels. For example, in real-world healthcare data, spam detection labels, and financial fraud detection datasets. Many data balance methods were introduced to improve machine learning algorithms' performance. Research claims SMOTE and SMOTE-based data-augmentation (generate new data points) methods could improve algorithm performance. However, we found in many online tutorials, the valuation methods were applied based on synthesized datasets that introduced bias into the evaluation, and the performance got a false improvement. In this study, we proposed, a new evaluation framework for imbalanced data learning methods. We have experimented on five data balance methods and whether the algorithms’ performance will improve or not.
Bayesian Detection of Mesoscale Structures in Pathway Data on Graphs

Mesoscale structures are an integral part of the abstraction and analysis of complex systems. They reveal a node's function in the network, and facilitate our understanding of the network dynamics. For example, they can represent communities in social or citation networks, roles in corporate interactions, or core-periphery structures in transportation networks. We usually detect mesoscale structures under the assumption of independence of interactions. Still, in many cases, the interactions invalidate this assumption by occurring in a specific order. Such patterns emerge in pathway data; to capture them, we have to model the dependencies between interactions using higher-order network models. However, the detection of mesoscale structures in higher-order networks is still under-researched. In this work, we derive a Bayesian approach that simultaneously models the optimal partitioning of nodes in groups and the optimal higher-order network dynamics between the groups. In synthetic data we demonstrate that our method can recover both standard proximity-based communities and role-based groupings of nodes. In synthetic and real world data we show that it can compete with baseline techniques, while additionally providing interpretable abstractions of network dynamics.
Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning

We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-quality intrinsic rewards to enhance exploration in reinforcement learning (RL). More specifically, AIRS selects shaping function from a predefined set based on the estimated task return in real-time, providing reliable exploration incentives and alleviating the biased objective problem. Moreover, we develop an intrinsic reward toolkit to provide efficient and reliable implementations of diverse intrinsic reward approaches. We test AIRS on various tasks of MiniGrid, Procgen, and DeepMind Control Suite. Extensive simulation demonstrates that AIRS can outperform the benchmarking schemes and achieve superior performance with simple architecture.
Photophysics of Intrinsic Single‐Photon Emitters in Silicon Nitride at Low Temperatures

A robust process for fabricating intrinsic single‐photon emitters in silicon nitride is recently established. These emitters show promise for quantum applications due to room‐temperature operation and monolithic integration with technologically mature silicon nitride photonics platforms. Here, the fundamental photophysical properties of these emitters are probed through measurements of optical transition wavelengths, linewidths, and photon antibunching as a function of temperature from 4.2 to 300 K. Important insight into the potential for lifetime‐limited linewidths is provided through measurements of inhomogeneous and temperature‐dependent broadening of the zero‐phonon lines. At 4.2 K, spectral diffusion is found to be the main broadening mechanism, while spectroscopy time series reveal zero‐phonon lines with instrument‐limited linewidths.
NASCTY: Neuroevolution to Attack Side-channel Leakages Yielding Convolutional Neural Networks

Side-channel analysis (SCA) is a class of attacks on the physical implementation of a cipher, which enables the extraction of confidential key information by exploiting unintended leaks generated by a device. In recent years, researchers have observed that neural networks (NNs) can be utilized to perform highly effective SCA profiling, even against countermeasure-hardened targets. This study investigates a new approach to designing NNs for SCA, called neuroevolution to attack side-channel traces yielding convolutional neural networks (NASCTY-CNNs). This method is based on a genetic algorithm (GA) that evolves the architectural hyperparameters to automatically create CNNs for side-channel analysis. The findings of this research demonstrate that we can achieve performance results comparable to state-of-the-art methods when dealing with desynchronized leakages protected by masking techniques. This indicates that employing similar neuroevolutionary techniques could serve as a promising avenue for further exploration. Moreover, the similarities observed among the constructed neural networks shed light on how NASCTY effectively constructs architectures and addresses the implemented countermeasures.
A Context-based Multi-task Hierarchical Inverse Reinforcement Learning Algorithm

—Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks, which is essential for general-purpose robots, based on multi-task expert demonstrations. Existing MIL algorithms suffer from low data efﬁciency and poor performance on complex long-horizontal tasks. We develop Multi-task Hierarchical Adversarial Inverse Reinforcement Learning (MH-AIRL) to learn hierarchically-structured multi-task policies, which is more beneﬁcial for compositional tasks with long horizons and has higher expert data efﬁciency through identifying and transferring reusable basic skills across tasks. To realize this, MH-AIRL effectively synthesizes context-based multi-task learning, AIRL (an IL approach), and hierarchical policy learning. Further, MH-AIRL can be adopted to demonstrations without the task or skill annotations (i.e., state-action pairs only) which are more accessible in practice. Theoretical justiﬁcations are provided for each module of MH-AIRL, and evaluations on challenging multi-task settings demonstrate superior performance and transferability of the multi-task policies learned with MH-AIRL as compared to SOTA MIL baselines.
Distilling Cognitive Backdoor Patterns within an Image

This paper proposes a simple method to distill and detect backdoor patterns within an image: \emph{Cognitive Distillation} (CD). The idea is to extract the"minimal essence"from an input image responsible for the model's prediction. CD optimizes an input mask to extract a small pattern from the input image that can lead to the same model output (i.e., logits or deep features). The extracted pattern can help understand the cognitive mechanism of a model on clean vs. backdoor images and is thus called a \emph{Cognitive Pattern} (CP). Using CD and the distilled CPs, we uncover an interesting phenomenon of backdoor attacks: despite the various forms and sizes of trigger patterns used by different attacks, the CPs of backdoor samples are all surprisingly and suspiciously small. One thus can leverage the learned mask to detect and remove backdoor examples from poisoned training datasets. We conduct extensive experiments to show that CD can robustly detect a wide range of advanced backdoor attacks. We also show that CD can potentially be applied to help detect potential biases from face datasets. Code is available at \url{https://github.com/HanxunH/CognitiveDistillation}.
Graph Contrastive Learning for Skeleton-based Action Recognition

In the field of skeleton-based action recognition, current top-performing graph convolutional networks (GCNs) exploit intra-sequence context to construct adaptive graphs for feature aggregation. However, we argue that such context is still local since the rich cross-sequence relations have not been explicitly investigated. In this paper, we propose a graph contrastive learning framework for skeletonbased action recognition (SkeletonGCL) to explore the global context across all sequences. In specific, SkeletonGCL associates graph learning across sequences by enforcing graphs to be class-discriminative, i.e., intra-class compact and interclass dispersed, which improves the GCN capacity to distinguish various action patterns. Besides, two memory banks are designed to enrich cross-sequence context from two complementary levels, i.e., instance and semantic levels, enabling graph contrastive learning in multiple context scales. Consequently, SkeletonGCL establishes a new training paradigm, and it can be seamlessly incorporated into current GCNs. Without loss of generality, we combine SkeletonGCL with three GCNs (2S-ACGN, CTR-GCN, and InfoGCN), and achieve consistent improvements on NTU60, NTU120, and NW-UCLA benchmarks. The source code will be available at https://github.com/OliverHxh/SkeletonGCL.
API Integrator: A UI Design and Code Automation Application Supporting API-First Design

Microservice Architecture (MSA) is a standard for solving the technical challenges of distributed software systems. Design methodologies assist in making decisions about the functionality encapsulated by each microservice in a system. Despite the documentation supporting microservice design, challenges remain in defining microservice boundaries and concise APIs exposing business capacities of the modern distributed, data-intensive, microservice-based system. API-First Design is an emerging methodology for enterprise system design. Its primary principle suggests that software design focus on well-defined APIs as its foundation. In its infancy, research, documentation, and tools that assist in adopting an API-First Design approach are minimal. The project discussed in this paper provides the research community with a set of practitioner-defined features and a prototype for an application intended to help promote the adoption of API-First Design via an API-Driven UI design and code generation application.