Search for: All records

Generative models have achieved remarkable success in a wide range of applications. Training such models using proprietary data from multiple parties has been studied in the realm of federated learning. Yet recent studies showed that reconstruction of authentic training data can be achieved in such settings. On the other hand, multiparty computation (MPC) guarantees standard data privacy, yet scales poorly for training generative models. In this paper, we focus on improving reconstruction hardness during Generative Adversarial Network (GAN) training while keeping the training cost tractable. To this end, we explore two training protocols that use a public generator and an MPC discriminator: Protocol 1 (P1) uses a fully private discriminator, while Protocol 2 (P2) privatizes the first three discriminator layers. We prove reconstruction hardness for P1 and P2 by showing that (1) a public generator does not allow recovery of authentic training data, as long as the first two layers of the discriminator are private; and through an existing approximation hardness result on ReLU networks, (2) a discriminator with at least three private layers does not allow authentic data reconstruction with algorithms polynomial in network depth and size. We show empirically that compared with fully MPC training, P1 reduces the training time by 2× and P2 further by 4 − 16×. Our implementation can be found at https://github.com/asu-crypto/ppgan 

We study zero-sum differential games with state constraints and one-sided information, where the informed player (Player 1) has a categorical payoff type unknown to the uninformed player (Player 2). The goal of Player 1 is to minimize his payoff without violating the constraints, while that of Player 2 is to either violate the state constraints, or otherwise, to maximize the payoff. One example of the game is a man-to-man matchup in football. Without state constraints, Cardaliaguet (2007) showed that the value of such a game exists and is convex to the common belief of players. Our theoretical contribution is an extension of this result to differential games with state constraints and the derivation of the primal and dual subdynamic principles necessary for computing the behavioral strategies. Compared with existing works on imperfect-information dynamic games that focus on scalability and generalization, our focus is instead on revealing the mechanism of belief manipulation behaviors resulted from information asymmetry and state constraints. We use a simplified football game to demonstrate the utility of this work, where we reveal player positions and belief states in which the attacker should (or should not) play specific random fake moves to take advantage of information asymmetry, and compute how the defender should respond. 

The values of two-player general-sum differential games are viscosity solutions to Hamilton-Jacobi-Isaacs (HJI) equations. Value and policy approximations for such games suffer from the curse of dimensionality (CoD). Alleviating CoD through physics-informed neural networks (PINN) encounters convergence issues when value discontinuity is present due to state constraints. On top of these challenges, it is often necessary to learn generalizable values and policies across a parametric space of games, eg, for game parameter inference when information is incomplete. To address these challenges, we propose in this paper a Pontryagin-mode neural operator that outperforms existing state-of-the-art (SOTA) on safety performance across games with parametric state constraints. Our key contribution is the introduction of a costate loss defined on the discrepancy between forward and backward costate rollouts, which are computationally cheap. We show that the discontinuity of costate dynamics (in the presence of state constraints) effectively enables the learning of discontinuous values, without requiring manually supervised data as suggested by the current SOTA. More importantly, we show that the close relationship between costates and policies makes the former critical in learning feedback control policies with generalizable safety performance. 

Abstract We present near-infraredJHKphotometry for the resolved stellar populations in 13 nearby galaxies: NGC 6822, IC 1613, NGC 3109, Sextans B, Sextans A, NGC 300, NGC 55, NGC 7793, NGC 247, NGC 5253, Cen A, NGC 1313, and M83, acquired from the 6.5 m Baade–Magellan telescope. We measure distances to each galaxy using the J-region asymptotic giant branch (JAGB) method, a new standard candle that leverages the constant luminosities of color-selected, carbon-rich AGB stars. While only single-epoch, random-phase photometry is necessary to derive JAGB distances, our photometry is time-averaged over multiple epochs, thereby decreasing the contribution of the JAGB stars’ intrinsic variability to the measured dispersions in their observed luminosity functions. To cross-validate these distances, we also measure near-infrared tip of the red giant branch (TRGB) distances to these galaxies. The residuals obtained from subtracting the distance moduli from the two methods yield an rms scatter ofσ_JAGB−TRGB= ±0.07 mag. Therefore, all systematics in the JAGB method and TRGB method (e.g., crowding, differential reddening, star formation histories) must be contained within these ±0.07 mag bounds for this sample of galaxies because the JAGB and TRGB distance indicators are drawn from entirely distinct stellar populations and are thus affected by these systematics independently. Finally, the composite JAGB star luminosity function formed from this diverse sample of galaxies is well described by a Gaussian function with a modal value ofM_J= –6.20 ± 0.003 mag (stat), indicating that the underlying JAGB star luminosity function of a well-sampled full star formation history is highly symmetric and Gaussian based on over 6700 JAGB stars in the composite sample. 

Generative models have enabled the creation of contents that are indistinguishable from those taken from nature. Open-source development of such models raised concerns about the risks of their misuse for malicious purposes. One potential risk mitigation strategy is to attribute generative models via fingerprinting. Current fingerprinting methods exhibit a significant tradeoff between robust attribution accuracy and generation quality while lacking design principles to improve this tradeoff. This paper investigates the use of latent semantic dimensions as fingerprints, from where we can analyze the effects of design variables, including the choice of fingerprinting dimensions, strength, and capacity, on the accuracy-quality tradeoff. Compared with previous SOTA, our method requires minimum computation and is more applicable to large-scale models. We use StyleGAN2 and the latent diffusion model to demonstrate the efficacy of our method. 

Finding Nash equilibrial policies for two-player differential games requires solving Hamilton-Jacobi-Isaacs (HJI) PDEs. Self-supervised learning has been used to approximate solutions of such PDEs while circumventing the curse of dimensionality. However, this method fails to learn discontinuous PDE solutions due to its sampling nature, leading to poor safety performance of the resulting controllers in robotics applications when player rewards are discontinuous. This paper investigates two potential solutions to this problem: a hybrid method that leverages both supervised Nash equilibria and the HJI PDE, and a value-hardening method where a sequence of HJIs are solved with a gradually hardening reward. We compare these solutions using the resulting generalization and safety performance in two vehicle interaction simulation studies with 5D and 9D state spaces, respectively. Results show that with informative supervision (e.g., collision and near-collision demonstrations) and the low cost of self-supervised learning, the hybrid method achieves better safety performance than the supervised, self-supervised, and value hardening approaches on equal computational budget. Value hardening fails to generalize in the higher-dimensional case without informative supervision. Lastly, we show that the neural activation function needs to be continuously differentiable for learning PDEs and its choice can be case dependent. 

Macrophages can be characterized as a very multifunctional cell type with a spectrum of phenotypes and functions being observed spatially and temporally in various disease states. Ample studies have now demonstrated a possible causal link between macrophage activation and the development of autoimmune disorders. How these cells may be contributing to the adaptive immune response and potentially perpetuating the progression of neurodegenerative diseases and neural injuries is not fully understood. Within this review, we hope to illustrate the role that macrophages and microglia play as initiators of adaptive immune response in various CNS diseases by offering evidence of: (1) the types of immune responses and the processes of antigen presentation in each disease, (2) receptors involved in macrophage/microglial phagocytosis of disease-related cell debris or molecules, and, finally, (3) the implications of macrophages/microglia on the pathogenesis of the diseases.