Miracle-dz/UniFAR-FTUs huggingface dataset:
A Temporal Logic for Strategic Hyperproperties

Hyperproperties are commonly used in computer security to define information-flow policies and other requirements that reason about the relationship between multiple computations. In this paper, we study a novel class of hyperproperties where the individual computation paths are chosen by the strategic choices of a coalition of agents in a multi-agent system. We introduce HyperATL*, an extension of computation tree logic with path variables and strategy quantifiers. HyperATL* can express strategic hyperproperties, such as that the scheduler in a concurrent system has a strategy to avoid information leakage. HyperATL* is particularly useful to specify asynchronous hyperproperties, i.e., hyperproperties where the speed of the execution on the different computation paths depends on the choices of the scheduler. Unlike other recent logics for the specification of asynchronous hyperproperties, our logic is the first to admit decidable model checking for the full logic. We present a model checking algorithm for HyperATL* based on alternating word automata and show that our algorithm is asymptotically optimal by providing a matching lower bound. We have implemented a prototype model checker for a fragment of HyperATL*, able to check various security properties on small programs.
Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks

Cooperative beamforming across access points (APs) and fronthaul quantization strategies are essential for cloud radio access network (C-RAN) systems. The nonconvexity of the C-RAN optimization problems, which is stemmed from per-AP power and fronthaul capacity constraints, requires high computational complexity for executing iterative algorithms. To resolve this issue, we investigate a deep learning approach where the optimization module is replaced with a well-trained deep neural network (DNN). An efficient learning solution is proposed which constructs a DNN to produce a low-dimensional representation of optimal beamforming and quantization strategies. Numerical results validate the advantages of the proposed learning solution.
Comparison of maximum-likelihood mapping methods for gravitational-wave backgrounds

Detection of a stochastic background of gravitational waves is likely to occur in the next few years. Beyond searches for the isotropic component of a stochastic gravitational-wave background, there have been various mapping methods proposed to target anisotropic backgrounds. Some of these methods have been applied to data taken by the Laser Interferometer Gravitational-wave Observatory (LIGO) and Virgo. Specifically, these directional searches have focused on mapping the intensity of the signal on the sky via maximum-likelihood solutions. We compare this intensity mapping approach to a previously proposed, but never employed, amplitude-phase mapping method to understand whether this latter approach may be employed in future searches. We build up our understanding of the differences between these two approaches by analyzing simple toy models of time-stream data, and run mock-data mapping tests for the two methods. We find that the amplitude-phase method is only applicable to the case of a background which is phase coherent on large scales or, at the very least, has an intrinsic coherence scale that is larger than the resolution of the detector. Otherwise, the amplitude-phase mapping method leads to an overall loss of information, with respect to both phase and amplitude. Since we do not expect these phase-coherent properties to hold for any of the gravitational-wave background signals we hope to detect in the near future, we conclude that intensity mapping is the preferred method for such backgrounds.
DTGAN: Differential Private Training for Tabular GANs

Tabular generative adversarial networks (TGAN) have recently emerged to cater to the need of synthesizing tabular data -- the most widely used data format. While synthetic tabular data offers the advantage of complying with privacy regulations, there still exists a risk of privacy leakage via inference attacks due to interpolating the properties of real data during training. Differential private (DP) training algorithms provide theoretical guarantees for training machine learning models by injecting statistical noise to prevent privacy leaks. However, the challenges of applying DP on TGAN are to determine the most optimal framework (i.e., PATE/DP-SGD) and neural network (i.e., Generator/Discriminator)to inject noise such that the data utility is well maintained under a given privacy guarantee. In this paper, we propose DTGAN, a novel conditional Wasserstein tabular GAN that comes in two variants DTGAN_G and DTGAN_D, for providing a detailed comparison of tabular GANs trained using DP-SGD for the generator vs discriminator, respectively. We elicit the privacy analysis associated with training the generator with complex loss functions (i.e., classification and information losses) needed for high quality tabular data synthesis. Additionally, we rigorously evaluate the theoretical privacy guarantees offered by DP empirically against membership and attribute inference attacks. Our results on 3 datasets show that the DP-SGD framework is superior to PATE and that a DP discriminator is more optimal for training convergence. Thus, we find (i) DTGAN_D is capable of maintaining the highest data utility across 4 ML models by up to 18% in terms of the average precision score for a strict privacy budget, epsilon = 1, as compared to the prior studies and (ii) DP effectively prevents privacy loss against inference attacks by restricting the success probability of membership attacks to be close to 50%.
A Unified Off-Policy Evaluation Approach for General Value Function

General Value Function (GVF) is a powerful tool to represent both the {\em predictive} and {\em retrospective} knowledge in reinforcement learning (RL). In practice, often multiple interrelated GVFs need to be evaluated jointly with pre-collected off-policy samples. In the literature, the gradient temporal difference (GTD) learning method has been adopted to evaluate GVFs in the off-policy setting, but such an approach may suffer from a large estimation error even if the function approximation class is sufficiently expressive. Moreover, none of the previous work have formally established the convergence guarantee to the ground truth GVFs under the function approximation settings. In this paper, we address both issues through the lens of a class of GVFs with causal filtering, which cover a wide range of RL applications such as reward variance, value gradient, cost in anomaly detection, stationary distribution gradient, etc. We propose a new algorithm called GenTD for off-policy GVFs evaluation and show that GenTD learns multiple interrelated multi-dimensional GVFs as efficiently as a single canonical scalar value function. We further show that unlike GTD, the learned GVFs by GenTD are guaranteed to converge to the ground truth GVFs as long as the function approximation power is sufficiently large. To our best knowledge, GenTD is the first off-policy GVF evaluation algorithm that has global optimality guarantee.
From mean field games to Navier-Stokes equations

<p style='text-indent:20px;'>This work establishes the equivalence between Mean Field Game and a class of PDE systems closely related to compressible Navier-Stokes equations. The solvability of the PDE system via the existence of the Nash Equilibrium of the Mean Field Game is provided under a set of conditions. </p>
Physical interaction as communication: Learning robot objectives online from human corrections

When a robot performs a task next to a human, physical interaction is inevitable: the human might push, pull, twist, or guide the robot. The state of the art treats these interactions as disturbances that the robot should reject or avoid. At best, these robots respond safely while the human interacts; but after the human lets go, these robots simply return to their original behavior. We recognize that physical human–robot interaction (pHRI) is often intentional: the human intervenes on purpose because the robot is not doing the task correctly. In this article, we argue that when pHRI is intentional it is also informative: the robot can leverage interactions to learn how it should complete the rest of its current task even after the person lets go. We formalize pHRI as a dynamical system, where the human has in mind an objective function they want the robot to optimize, but the robot does not get direct access to the parameters of this objective: they are internal to the human. Within our proposed framework human interactions become observations about the true objective. We introduce approximations to learn from and respond to pHRI in real-time. We recognize that not all human corrections are perfect: often users interact with the robot noisily, and so we improve the efficiency of robot learning from pHRI by reducing unintended learning. Finally, we conduct simulations and user studies on a robotic manipulator to compare our proposed approach with the state of the art. Our results indicate that learning from pHRI leads to better task performance and improved human satisfaction.
Asymptotic Analysis of Max-Min Weighted SINR for IRS-Assisted MISO Systems With Hardware Impairments

We focus on the realistic maximization of the uplink minimum signal-to-interference-plus-noise ratio (SINR) of a general multiple-input single-output (MISO) system assisted by an intelligent reflecting surface (IRS) in the large system limit accounting for HIs hardware impairments (HIs). In particular, we introduce the HIs at both the IRS (IRS-HIs) and the additive transceiver HIs (AT-HIs), usually neglected despite their inevitable impact. Specifically, the deterministic equivalent analysis enables the derivation of the asymptotic weighted maximum-minimum SINR with HIs by jointly optimizing the HIs-aware receiver, the transmit power, and the reflecting beamforming matrix (RBM). Notably, we obtain the optimal power allocation and reflecting beamforming matrix with low overhead instead of their frequent necessary computation in IRS-assisted MIMO systems based on the instantaneous channel information. Monte Carlo simulations verify the analytical results which show the insightful interplay among the key parameters and the degradation of the performance due to HIs.
Evaluating subgroup disparity using epistemic uncertainty in mammography

As machine learning (ML) continue to be integrated into healthcare systems that affect clinical decision making, new strategies will need to be incorporated in order to effectively detect and evaluate subgroup disparities to ensure accountability and generalizability in clinical workflows. In this paper, we explore how epistemic uncertainty can be used to evaluate disparity in patient demographics (race) and data acquisition (scanner) subgroups for breast density assessment on a dataset of 108,190 mammograms collected from 33 clinical sites. Our results show that even if aggregate performance is comparable, the choice of uncertainty quantification metric can significantly the subgroup level. We hope this analysis can promote further work on how uncertainty can be leveraged to increase transparency of machine learning applications for clinical deployment.
The Observed Cosmic Star Formation Rate Density Has an Evolution that Resembles a Γ(a, bt) Distribution and Can Be Described Successfully by Only Two Parameters

A debate is emerging regarding the recent inconsistent results of different studies for the cosmic star formation rate density (CSFRD) at high-z. We employ UV and IR data sets to investigate the SFR function (SFRF) at z ∼ 0–9. We find that the SFRFs derived from the dust-corrected UV (UVcorr) data contradict those from IR on some key issues because they are described by different distributions (Schechter versus double power law), imply different physics for galaxy formation (UVcorr data suggest an SFR limit/strong mechanism that diminish the number density of highly star-forming systems with respect to IR) and compare differently with the stellar mass density evolution obtained from spectral energy distribution fitting (UVcorr is in agreement, while IR differs up to 0.5 dex). However, both tracers agree on a constant CSFRD evolution at z ∼ 1–4 and point to a plateau instead of a peak. In addition, using both indicators, we demonstrate that the evolution of the observed CSFRD can be described by only two parameters and a function that has the form of a Gamma distribution (Γ(a, bt)). In contrast to previous parameterizations used in the literature, our framework connects the parameters to physical properties such as the SFR depletion time and cosmic baryonic gas density. The build-up of stellar mass occurs in Γ(a, bt) distributed steps and is the result of gas consumption up to the limit at which no eligible gas for SF at t = ∞ remains, resulting in a final cosmic stellar mass density of ∼ 0.5×109M⊙Mpc3 .
Normal and stable approximation to subgraph counts in superpositions of Bernoulli random graphs

Real networks often exhibit clustering, the tendency to form relatively small groups of nodes with high edge densities. This clustering property can cause large numbers of small and dense subgraphs to emerge in otherwise sparse networks. Subgraph counts are an important and commonly used source of information about the network structure and function. We study probability distributions of subgraph counts in a community affiliation graph. This is a random graph generated as an overlay of m partly overlapping independent Bernoulli random graphs (layers) $G_1,\dots,G_m$ with variable sizes and densities. The model is parameterised by a joint distribution of layer sizes and densities. When m grows linearly in the number of nodes n, the model generates sparse random graphs with a rich statistical structure, admitting a nonvanishing clustering coefficient and a power-law limiting degree distribution. In this paper we establish the normal and $\alpha$ -stable approximations to the numbers of small cliques, cycles, and more general 2-connected subgraphs of a community affiliation graph.
Collaborative Insurance Sustainability and Network Structure

The peer-to-peer (P2P) economy has been growing with the advent of the Internet, with well known brands such as Uber , Airbnb or Prosper being examples thereof. Whereas the P2P lending sector has already received considerable attention in both practice and literature, the insurance is still in its infancy. Companies such as Lemonade in the U.S. or Inspeer in France have recently started to explore P2P-based collaborative insurance products and the academic literature has also begun to study such products from a mathematical perspective. Previous research in P2P lending motivates us to consider P2P insurance not merely as a diﬀerent form of traditional insurance, but instead as a complement thereof. Accordingly, we propose a novel toolkit to analyse P2P insurance products based on the explicit structure of (P2P) networks rather than individual characteristics. We study the general case of risk sharing via network connections using convex order and extend the framework to incorporate characteristics typically encountered in P2P settings via simulations.
Preparation, Structure and Photochromic Properties of Dilong-chain Alkyl Group Dawson Type Molybdophosphoric Acid Multicomposite Films

A multicomposite P 2Mo 18 /DODA self assembled multilayer film was fabricated by casting the chloroform solutions of ionic complex between a dimethyldidecylammomium and a phosphomolybdate anion onto the borosilicate glass and calcium fluoride substrates. Infrared spectra revealed that the Dawson strcucture characteristic of the P 2Mo 18 O 6- 62 anion was presented in the composite film. A well ordered superlattice structure, d spacing 3.59 nm, was identified by X ray diffraction. The composite film shows good photochromic properties. Under the irradiation with UV light, the transparent film changed from yellow to blue. Then, bleaching occurred when the film was in contact with ambient air or O 2 in the dark. If the UV irradiated films were stored in nitrogen, helium, argon or vacuum conditions, it could retain blue coloration for a long time. It suggests that oxygen plays an important role during the bleaching process. The photochromic mechanism of the composite film was studied by UV Vis spectra, FTIR spectra and ESR. The appearance of IVCT bands shows that electron transfer occurs between the organic substrate and heteropolyanion, converting heteropolyanions to heteropolyblues with simultaneous oxidation of the organic substrate. Observation of UV induced ESR signal suggests that electron transfer takes place in the lattice and P 2Mo 18 O 6- 62 acting as an acceptor gets electrons during UV irradiation. The changes of IR spectra for the composite film after UV irradiation are another powerful proof to identify the DODA was oxidized and P 2Mo 18 O 6- 62 was reduced in the photochronic process.
FloorLevel-Net: Recognizing Floor-Level Lines With Height-Attention-Guided Multi-Task Learning

The ability to recognize the position and order of the floor-level lines that divide adjacent building floors can benefit many applications, for example, urban augmented reality (AR). This work tackles the problem of locating floor-level lines in street-view images, using a supervised deep learning approach. Unfortunately, very little data is available for training such a network – current street-view datasets contain either semantic annotations that lack geometric attributes, or rectified facades without perspective priors. To address this issue, we first compile a new dataset and develop a new data augmentation scheme to synthesize training samples by harassing (i) the rich semantics of existing rectified facades and (ii) perspective priors of buildings in diverse street views. Next, we design FloorLevel-Net, a multi-task learning network that associates explicit features of building facades and implicit floor-level lines, along with a height-attention mechanism to help enforce a vertical ordering of floor-level lines. The generated segmentations are then passed to a second-stage geometry post-processing to exploit self-constrained geometric priors for plausible and consistent reconstruction of floor-level lines. Quantitative and qualitative evaluations conducted on assorted facades in existing datasets and street views from Google demonstrate the effectiveness of our approach. Also, we present context-aware image overlay results and show the potentials of our approach in enriching AR-related applications. Project website: https://wumengyangok.github. io/Project/FloorLevelNet.
The QR decomposition for radial neural networks

A bstract . We provide a theoretical framework for neural networks in terms of the representation theory of quivers, thus revealing symmetries of the parameter space of neural networks. An exploitation of these symmetries leads to a model compression algorithm for radial neural networks based on an analogue of the QR decomposition. A projected version of backpropogation on the original model matches usual backpropogation on the compressed model.
A Multi-Objective Approach for Sustainable Generative Audio Models

In recent years, the deep learning community has largely focused on the accuracy of deep generative models, resulting in impressive improvements in several research fields. However, this scientific race for quality comes at a tremendous computational cost, which incurs vast energy consumption and greenhouse gas emissions. If the current exponential growth of computational consumption persists, Artificial Intelligence (AI) will sadly become a considerable contributor to global warming. At the heart of this problem are the measures that we use as a scientific community to evaluate our work. Currently, researchers in the field of AI judge scientific works mostly based on the improvement in accuracy, log-likelihood, reconstruction or opinion scores, all of which entirely obliterates the actual computational cost of generative models. In this paper, we introduce the idea of relying on a multi-objective measure based on Pareto optimality, which simultaneously integrates the models accuracy, as well as the environmental impact of their training. By applying this measure on the current state-of-the-art in generative audio models, we show that this measure drastically changes the perceived significance of the results in the field, encouraging optimal training techniques and resource allocation. We hope that this type of measure will be widely adopted, in order to help the community to better evaluate the significance of their work, while bringing computational cost – and in fine carbon emissions – in the spotlight of AI research.
SAGE: Intrusion Alert-driven Attack Graph Extractor

Attack graphs (AG) are used to assess pathways availed by cyber adversaries to penetrate a network. State-of-the-art approaches for AG generation focus mostly on deriving dependencies between system vulnerabilities based on network scans and expert knowledge. In real-world operations however, it is costly and ineffective to rely on constant vulnerability scanning and expert-crafted AGs. We propose to automatically learn AGs based on actions observed through intrusion alerts, without prior expert knowledge. Specifically, we develop an unsupervised sequence learning system, SAGE, that leverages the temporal and probabilistic dependence between alerts in a suffix-based probabilistic deterministic finite automaton (S-PDFA) – a model that accentuates infrequent severe alerts and summarizes paths leading to them. AGs are then derived from the S-PDFA on a per-objective, per-victim basis. Tested with intrusion alerts collected through Collegiate Penetration Testing Competition, SAGE compresses over 330k alerts into 93 AGs. These AGs reflect the strategies used by the participating teams. The AGs are succinct, interpretable, and capture behavioral dynamics, e.g., that attackers will often follow shorter paths to re-exploit objectives.
Near-optimal inference in adaptive linear regression

When data is collected in an adaptive manner, even simple methods like ordinary least squares can exhibit non-normal asymptotic behavior. As an undesirable consequence, hypothesis tests and confidence intervals based on asymptotic normality can lead to erroneous results. We propose a family of online debiasing estimators to correct these distributional anomalies in least squares estimation. Our proposed methods take advantage of the covariance structure present in the dataset and provide sharper estimates in directions for which more information has accrued. We establish an asymptotic normality property for our proposed online debiasing estimators under mild conditions on the data collection process and provide asymptotically exact confidence intervals. We additionally prove a minimax lower bound for the adaptive linear regression problem, thereby providing a baseline by which to compare estimators. There are various conditions under which our proposed estimators achieve the minimax lower bound. We demonstrate the usefulness of our theory via applications to multi-armed bandit, autoregressive time series estimation, and active learning with exploration.