Miracle-dz/UniFAR-FTUs huggingface dataset:
Plug-and-Play Deep Energy Model for Inverse problems

We introduce a novel energy formulation for Plug- and-Play (PnP) image recovery. Traditional PnP methods that use a convolutional neural network (CNN) do not have an energy based formulation. The primary focus of this work is to introduce an energy-based PnP formulation, which relies on a CNN that learns the log of the image prior from training data. The score function is evaluated as the gradient of the energy model, which resembles a UNET with shared encoder and decoder weights. The proposed score function is thus constrained to a conservative vector field, which is the key difference with classical PnP models. The energy-based formulation offers algorithms with convergence guarantees, even when the learned score model is not a contraction. The relaxation of the contraction constraint allows the proposed model to learn more complex priors, thus offering improved performance over traditional PnP schemes. Our experiments in magnetic resonance image reconstruction demonstrates the improved performance offered by the proposed energy model over traditional PnP methods.
Decentralized core-periphery structure in social networks accelerates cultural innovation in agent-based model

Previous investigations into creative and innovation networks have suggested that innovations often occurs at the boundary between the network's core and periphery. In this work, we investigate the effect of global core-periphery network structure on the speed and quality of cultural innovation. Drawing on differing notions of core-periphery structure from [arXiv:1808.07801] and [doi:10.1016/S0378-8733(99)00019-2], we distinguish decentralized core-periphery, centralized core-periphery, and affinity network structure. We generate networks of these three classes from stochastic block models (SBMs), and use them to run an agent-based model (ABM) of collective cultural innovation, in which agents can only directly interact with their network neighbors. In order to discover the highest-scoring innovation, agents must discover and combine the highest innovations from two completely parallel technology trees. We find that decentralized core-periphery networks outperform the others by finding the final crossover innovation more quickly on average. We hypothesize that decentralized core-periphery network structure accelerates collective problem-solving by shielding peripheral nodes from the local optima known by the core community at any given time. We then build upon the"Two Truths"hypothesis regarding community structure in spectral graph embeddings, first articulated in [arXiv:1808.07801], which suggests that the adjacency spectral embedding (ASE) captures core-periphery structure, while the Laplacian spectral embedding (LSE) captures affinity. We find that, for core-periphery networks, ASE-based resampling best recreates networks with similar performance on the innovation SBM, compared to LSE-based resampling. Since the Two Truths hypothesis suggests that ASE captures core-periphery structure, this result further supports our hypothesis.
Intermittently Observable Markov Decision Processes

This paper investigates MDPs with intermittent state information. We consider a scenario where the controller perceives the state information of the process via an unreliable communication channel. The transmissions of state information over the whole time horizon are modeled as a Bernoulli lossy process. Hence, the problem is finding an optimal policy for selecting actions in the presence of state information losses. We first formulate the problem as a belief MDP to establish structural results. The effect of state information losses on the expected total discounted reward is studied systematically. Then, we reformulate the problem as a tree MDP whose state space is organized in a tree structure. Two finite-state approximations to the tree MDP are developed to find near-optimal policies efficiently. Finally, we put forth a nested value iteration algorithm for the finite-state approximations, which is proved to be faster than standard value iteration. Numerical results demonstrate the effectiveness of our methods.
Shield Model Predictive Path Integral: A Computationally Efficient Robust MPC Method Using Control Barrier Functions

Model Predictive Path Integral (MPPI) control is a type of sampling-based model predictive control that simulates thousands of trajectories and uses these trajectories to synthesize optimal controls on-the-fly. In practice, however, MPPI encounters problems limiting its application. For instance, it has been observed that MPPI tends to make poor decisions if unmodeled dynamics or environmental disturbances exist, preventing its use in safety-critical applications. Moreover, the multi-threaded simulations used by MPPI require significant onboard computational resources, making the algorithm inaccessible to robots without modern GPUs. To alleviate these issues, we propose a novel (Shield-MPPI) algorithm that provides robustness against unpredicted disturbances and achieves real-time planning using a much smaller number of parallel simulations on regular CPUs. The novel Shield-MPPI algorithm is tested on an aggressive autonomous racing platform both in simulation and in hardware. The results show that the proposed controller greatly reduces the number of constraint violations compared to state-of-the-art robust MPPI variants and stochastic Model Predictive Control methods.
Cross-City Traffic Prediction via Semantic-Fused Hierarchical Graph Transfer Learning

Accurate traffic prediction benefits urban management and improves transportation efficiency. Recently, data-driven methods have been widely applied in traffic prediction and outperformed traditional methods. However, data-driven methods normally require massive data for training, while data scarcity is ubiquitous in low-developmental or newly constructed regions. To tackle this problem, we can extract meta knowledge from data-rich cities to data-scarce cities via transfer learning. Besides, relations among urban regions can be organized into various semantic graphs, e.g. proximity and POI similarity, which is barely considered in previous studies. In this paper, we propose Semantic-Fused Hierarchical Graph Transfer Learning (SF-HGTL) model to achieve knowledge transfer across cities with fused semantics. In detail, we employ hierarchical graph transformation followed by meta-knowledge retrieval to achieve knowledge transfer in various granularity. In addition, we introduce meta semantic nodes to reduce the number of parameters as well as share information across semantics. Afterwards, the parameters of the base model are generated by fused semantic embeddings to predict traffic status in terms of task heterogeneity. We implement experiments on five real-world datasets and verify the effectiveness of our SF-HGTL model by comparing it with other baselines.
Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference

We present counterfactual situation testing (CST), a causal data mining framework for detecting individual discrimination in a dataset of classifier decisions. CST answers the question “what would have been the model outcome had the individual, or complainant, been of a different protected status? ” in an actionable and meaningful way. It extends the legally-grounded situation testing of Thanh et al. [62] by operationalizing the notion of fairness given the difference of Kohler-Hausmann [38] using counterfactual reasoning. In standard situation testing we find for each complainant similar protected and non-protected instances in the dataset; construct respectively a control and test group; and compare the groups such that a difference in decision outcomes implies a case of potential individual discrimination. In CST we avoid this idealized comparison by establishing the test group on the complainant’s counterfactual generated via the steps of abduction, action, and prediction. The counterfactual reflects how the protected attribute, when changed, affects the other seemingly neutral attributes of the complainant. Under CST we, thus, test for discrimination by comparing similar individuals within each group but dissimilar individuals across both groups for each complainant. Evaluating it on two classification scenarios, CST uncovers a greater number of cases than ST, even when the classifier is counterfactually fair.
Vacuum misalignment in presence of four-Fermi operators

We consider the issue of vacuum misalignment induced by four-Fermi couplings in a generic strongly coupled four-dimensional gauge theory. After briefly reviewing the general formalism, we focus on the case of partial compositness-like operators at leading order, which is relevant in applications to phenomenology. We show that the interactions between an elementary fermion and composite spin-1/2 operators in various representations contribute to the effective potential with relative sign differences. Thus the correct sign required to misalign the vacuum is guaranteed to occur for some representations but not all of them. The overall sign dictating the specific representations responsible for misalignment can in principle be determined on the lattice. We also comment on the likely sign for some simple cases.
Improving the generalization via coupled tensor norm regularization

In this paper, we propose a coupled tensor norm regularization that could enable the model output feature and the data input to lie in a low-dimensional manifold, which helps us to reduce overfitting. We show this regularization term is convex, differentiable, and gradient Lipschitz continuous for logistic regression, while nonconvex and nonsmooth for deep neural networks. We further analyze the convergence of the first-order method for solving this model. The numerical experiments demonstrate that our method is efficient.
Star-formation rates and forbidden O II emission in blue galaxies

Observations of forbidden O II 3727 A and H-beta emission-line strengths are presented from large-aperture spectrophotometric observations of nearby (z less than 0.1) optically blue galaxies covering a wide range in surface brightness and luminosity. The forbidden O II emission-line equivalent widths in these galaxies primarily lie between 30 and 100 A. Emission-line strengths in nearby blue galaxies thus resemble those found in faint galaxies at moderate-to-high redshifts. The most actively star-forming nearby galaxies are characterized by surface brightness in the forbidden O II line of about 10 to the -15th to 10 to the -14th ergs/s per sq cm per sq arcsec over spatial distances of more than 1 kpc. The fluxes of the forbidden O II and H-beta emission lines are well correlated, and thus forbidden O II luminosities can be used to derive approximate star-formation rates in blue galaxies.
Sequential Counterfactual Risk Minimization

Counterfactual Risk Minimization (CRM) is a framework for dealing with the logged bandit feedback problem, where the goal is to improve a logging policy using offline data. In this paper, we explore the case where it is possible to deploy learned policies multiple times and acquire new data. We extend the CRM principle and its theory to this scenario, which we call"Sequential Counterfactual Risk Minimization (SCRM). "We introduce a novel counterfactual estimator and identify conditions that can improve the performance of CRM in terms of excess risk and regret rates, by using an analysis similar to restart strategies in accelerated optimization methods. We also provide an empirical evaluation of our method in both discrete and continuous action settings, and demonstrate the benefits of multiple deployments of CRM.
What makes a language easy to deep-learn?

Neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to produce forms for new meanings systematically. However, unlike humans, neural networks notoriously struggle with systematic generalization, and do not necessarily benefit from compositional structure in emergent communication simulations. This poses a problem for using neural networks to simulate human language learning and evolution, and suggests crucial differences in the biases of the different learning systems. Here, we directly test how neural networks compare to humans in learning and generalizing different input languages that vary in their degree of structure. We evaluate the memorization and generalization capabilities of a pre-trained language model GPT-3.5 (analagous to an adult second language learner) and recurrent neural networks trained from scratch (analaogous to a child first language learner). Our results show striking similarities between deep neural networks and adult human learners, with more structured linguistic input leading to more systematic generalization and to better convergence between neural networks and humans. These findings suggest that all the learning systems are sensitive to the structure of languages in similar ways with compositionality being advantageous for learning. Our findings draw a clear prediction regarding children's learning biases, as well as highlight the challenges of automated processing of languages spoken by small communities. Notably, the similarity between humans and machines opens new avenues for research on language learning and evolution.
A Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization Approach for Community Detection

Community is a fundamental and critical characteristic of an undirected social network, making community detection be a vital yet thorny issue in network representation learning. A symmetric and non-negative matrix factorization (SNMF) model is frequently adopted to address this issue owing to its great interpretability and scalability. However, it adopts a single latent factor matrix to represent an undirected network for precisely representing its symmetry, which leads to loss of representation learning ability due to the reduced latent space. Motivated by this discovery, this paper proposes a novel Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization (CFS) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent factor matrices, thus preserving its representation learning capacity; b) Incorporating a symmetry-regularizer that preserves the symmetry of the learnt low-rank approximation to the adjacency matrix into the loss function, thus making the resultant detector well-aware of the target network's symmetry; and c) Introducing a graph-regularizer that preserves local invariance of the network's intrinsic geometry, thus making the achieved detector well-aware of community structure within the target network. Extensively empirical studies on eight real-world social networks from industrial applications demonstrate that the proposed CFS model significantly outperforms state-of-the-art models in achieving highly-accurate community detection results.
Quantum spherical codes

We introduce a framework for constructing quantum codes defined on spheres by recasting such codes as quantum analogues of the classical spherical codes. We apply this framework to bosonic coding, obtaining multimode extensions of the cat codes that can outperform previous constructions while requiring a similar type of overhead. Our polytope-based cat codes consist of sets of points with large separation that at the same time form averaging sets known as spherical designs. We also recast concatenations of CSS codes with cat codes as quantum spherical codes, revealing a new way to autonomously protect against dephasing noise.
Integrating Data Structures and Algorithms in K-12 Education using Block-based Programming

This paper describes the design and evaluation of DSAScratch, an extension to Scratch, a widely used block-based programming language. The DSAScratch framework implements advanced data structures such as arrays, sets, dictionaries, and searching and sorting algorithms. By presenting these concepts in an intuitive block-based interface, these blocks abstract away technical details and simplify data structures and algorithms concepts for K-12 students to grasp and apply to programming problems more readily. A preliminary evaluation of the tools' usability and learning outcomes is presented in this paper. Given the information we have gathered about DSAScratch, we show that the extension is beneficial for students to develop a deeper understanding of programming and an intuitive understanding of these concepts in high school. We present the methodology and preliminary results of a user study conducted with ten high school students. During the user study, 70% of the participants understood the key ideas behind DSAScratch implemented data structures and algorithms through a mixture of lectures and hands-on activities. We show that DSAScratch was also an important part of the workshop for 90% of the students who participated, as it enhanced their understanding of algorithms and data structures. Furthermore, they indicated that they would recommend DSAScratch to their peers.
D2Former: A Fully Complex Dual-Path Dual-Decoder Conformer Network Using Joint Complex Masking and Complex Spectral Mapping for Monaural Speech Enhancement

Monaural speech enhancement has been widely studied using real networks in the time-frequency (TF) domain. However, the input and the target are naturally complex-valued in the TF domain, a fully complex network is highly desirable for effectively learning the feature representation and modelling the sequence in the complex domain. Moreover, phase, an important factor for perceptual quality of speech, has been proved learnable together with magnitude from noisy speech using complex masking or complex spectral mapping. Many recent studies focus on either complex masking or complex spectral mapping, ignoring their performance boundaries. To address above issues, we propose a fully complex dual-path dual-decoder conformer network (D2Former) using joint complex masking and complex spectral mapping for monaural speech enhancement. In D2Former, we extend the conformer network into the complex domain and form a dual-path complex TF self-attention architecture for effectively modelling the complex-valued TF sequence. We further boost the TF feature representation in the encoder and the decoders using a dual-path learning structure by exploiting complex dilated convolutions on time dependency and complex feedforward sequential memory networks (CFSMN) for frequency recurrence. In addition, we improve the performance boundaries of complex masking and complex spectral mapping by combining the strengths of the two training targets into a joint-learning framework. As a consequence, D2Former takes fully advantages of the complex-valued operations, the dual-path processing, and the joint-training targets. Compared to the previous models, D2Former achieves state-of-the-art results on the VoiceBank+Demand benchmark with the smallest model size of 0.87M parameters.
FingerMapper: Mapping Finger Motions onto Virtual Arms to Enable Safe Virtual Reality Interaction in Confined Spaces

Whole-body movements enhance the presence and enjoyment of Virtual Reality (VR) experiences. However, using large gestures is often uncomfortable and impossible in confined spaces (e.g., public transport). We introduce FingerMapper, mapping small-scale finger motions onto virtual arms and hands to enable whole-body virtual movements in VR. In a first target selection study (n=13) comparing FingerMapper to hand tracking and ray-casting, we found that FingerMapper can significantly reduce physical motions and fatigue while having a similar degree of precision. In a consecutive study (n=13), we compared FingerMapper to hand tracking inside a confined space (the front passenger seat of a car). The results showed participants had significantly higher perceived safety and fewer collisions with FingerMapper while preserving a similar degree of presence and enjoyment as hand tracking. Finally, we present three example applications demonstrating how FingerMapper could be applied for locomotion and interaction for VR in confined spaces.
Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs

We advance a recently flourishing line of work at the intersection of learning theory and computational economics by studying the learnability of two classes of mechanisms prominent in economics, namely menus of lotteries and two-part tariffs. The former is a family of randomized mechanisms designed for selling multiple items, known to achieve revenue beyond deterministic mechanisms, while the latter is designed for selling multiple units (copies) of a single item with applications in real-world scenarios such as car or bike-sharing services. We focus on learning high-revenue mechanisms of this form from buyer valuation data in both distributional settings, where we have access to buyers' valuation samples up-front, and the more challenging and less-studied online settings, where buyers arrive one-at-a-time and no distributional assumption is made about their values. Our main contribution is proposing the first online learning algorithms for menus of lotteries and two-part tariffs with strong regret-bound guarantees. In the general case, we provide a reduction to a finite number of experts, and in the limited buyer type case, we show a reduction to online linear optimization, which allows us to obtain no-regret guarantees by presenting buyers with menus that correspond to a barycentric spanner. In addition, we provide algorithms with improved running times over prior work for the distributional settings. The key difficulty when deriving learning algorithms for these settings is that the relevant revenue functions have sharp transition boundaries. In stark contrast with the recent literature on learning such unstructured functions, we show that simple discretization-based techniques are sufficient for learning in these settings.
A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots

Building advanced machine learning (ML) models requires expert knowledge and many trials to discover the best architecture and hyperparameter settings. Previous work demonstrates that model information can be leveraged to assist other attacks, such as membership inference, generating adversarial examples. Therefore, such information, e.g., hyperparameters, should be kept confidential. It is well known that an adversary can leverage a target ML model's output to steal the model's information. In this paper, we discover a new side channel for model information stealing attacks, i.e., models' scientific plots which are extensively used to demonstrate model performance and are easily accessible. Our attack is simple and straightforward. We leverage the shadow model training techniques to generate training data for the attack model which is essentially an image classifier. Extensive evaluation on three benchmark datasets shows that our proposed attack can effectively infer the architecture/hyperparameters of image classifiers based on convolutional neural network (CNN) given the scientific plot generated from it. We also reveal that the attack's success is mainly caused by the shape of the scientific plots, and further demonstrate that the attacks are robust in various scenarios. Given the simplicity and effectiveness of the attack method, our study indicates scientific plots indeed constitute a valid side channel for model information stealing attacks. To mitigate the attacks, we propose several defense mechanisms that can reduce the original attacks' accuracy while maintaining the plot utility. However, such defenses can still be bypassed by adaptive attacks.