Common and almost common knowledge of credible assignments in a coordination game

We build on Van Huyck, Gillette and Battalio (1992) and examine the efficacy of credible assignments in a stag-hunt type coordination game with two Pareto-ranked equilibria, one payoff dominant and the other risk dominant. The majority of our subjects fail to coordinate to the payoff dominant outcome when no assignment is made. However, the majority of them always coordinate to the payoff dominant outcome when an assignment is made. This happens regardless of whether the assignment is “almost common knowledge†or “common knowledgeâ€.Coordination

Strange metal phase of disordered magic-angle twisted bilayer graphene: from flatbands to weakly coupled Sachdev-Ye-Kitaev bundles

We use stochastic expansion and exact diagonalization to study the magic-angle twisted bilayer graphene (TBG) on a disordered substrate. We show that the substrate-induced strong Coulomb disorder in TBG with the chemical potential in the center of the flatbands drives the system to a network of weakly coupled Sachdev-Ye-Kitaev (SYK) bundles, stabilizing an emergent quantum chaotic strange metal (SM) phase of TBG that exhibits the absence of quasiparticles. The Gaussian orthogonal ensemble dominates TBG's long-time chaotic dynamics at strong disorder, whereas fast quantum scrambling appears in the short-time dynamics. In weak disorder, TBG exhibits exponentially decaying specific heat capacity and exponential decay in out-of-time-ordered correlators. The latter follows the Larkin-Ovchinnikov behavior of the correlator signaling the onset of the formation of a superconducting state. The result suggests the superconducting transition upon doping the system above the charge neutrality and weakening the disorder strength. We propose a finite-temperature phase diagram for Coulomb disordered TBG and discuss the experimental consequences of the emergent SM phase.Comment: 8 pages, 11 figure

Chiral spin liquid state of strongly interacting bosons with a moat dispersion: a Monte Carlo simulation

We consider a system of strongly interacting Bosons in two dimensions with moat band dispersion which supports an infinitely degenerate energy minimum along a closed contour in the Brillouin zone. The system has been theoretically predicted to stabilize a chiral spin liquid (CSL) ground state. In the thermodynamic limit and vanishing densities, $n\rightarrow 0$, chemical potential, $\mu$, was shown to scale with $n$ as $\mu\sim n^2\log ^2n$. Here we perform a Monte Carlo simulation to find the parametric window for particle density, $n \lesssim \frac{k^2_0}{82 \pi}$, where $k_0$ is the linear size of the moat (the radius for a circular moat), for which the scaling $\sim n^2\log ^2n$ in the equation of state of the CSL is preserved. We variationally show that the CSL state is favorable in a interval beyond the obtained scale and present a schematic phase diagram for the system. Our results offer some density estimates for observing the low-density behavior of CSL in time of flight experiments with a recently Floquet-engineered moat band system of ultracold atoms in Phys. Rev. Lett. 128, 213401 (2022), and for the recent experiments on emergent excitonic topological order in imbalanced electron-hole bilayers.Comment: 18 pages, 3 figures. Contribution to the Annals of Physics volume dedicated to the memory of Konstantin B. Efeto

Shared sex hormone metabolism-related gene prognostic index between breast and endometrial cancers

AimsAs sex hormone-dependent tumors, it remains to be clarified whether there is a common genetic signature and its value between breast and endometrial cancers. The aim of this study was to establish the shared sex hormone metabolism-related gene prognostic index (SHMRGPI) between breast and endometrial cancers and to analyze its potential role in the therapeutic and prognostic assessment of endometrial cancers.MethodsUsing transcriptome data from TCGA, tumor-associated gene modules were identified by weighted gene co-expression network analysis, and the intersection of module genes with female sex hormone synthesis and metabolism genes was defined as sex hormone metabolism-related gene. SHMRGPI was established by the least absolute shrinkage and selection operator and Cox regression. Its prognostic value of patients with endometrial cancer was validated, and a nomogram was constructed. We further investigated the relationship between SHMRGPI groups and clinicopathological features, immune infiltration, tumor mutation burden, and drug sensitivity.ResultsA total of 8 sex hormone metabolism-related gene were identified as key genes for the construction of prognostic models. Based on SHMRGPI, endometrial cancer patients were divided into high and low SHMRGPI groups. Patients in the low SHMRGPI group had longer overall survival (OS) compared with the high group (P< 0.05). Furthermore, we revealed significant differences between SHMRGPI groups as regards tumor immune cell infiltration, somatic mutation, microsatellite instability and drug sensitivity. Patients with low SHMRGPI may be the beneficiaries of immunotherapy and targeted therapy.ConclusionsThe SHMRGPI established in this study has prognostic power and may be used to screen patients with endometrial cancer who may benefit from immunotherapy or targeted therapy

Mid-Infrared Supercontinuum Laser System and its Biomedical Applications.

A mid-infrared supercontinuum (SC) laser system is developed, which provides a continuous spectrum from ~0.8 to ~4.5 μm and is pumped by amplified nanosecond laser diode pulses. The SC laser uses ZBLAN (ZrF4-BaF2-LaF3-AlF3-NaF) fluoride fibers. The SC light source is all-fiber-integrated with no moving parts, operates at room temperature, and eliminates the need of mode-locked lasers. The time-averaged power of the SC is scalable up to 10.5 W by amplifying the pump pulses using cladding-pumped erbium/ytterbium co-doped fiber power amplifiers. SC has also been generated in silica fibers with spectrum extending to ~3 μm and an average power up to 5.3 W. The SC laser system comprises an all-fiber-spliced high power pump laser system followed by nonlinear optical generation fibers, i.e. ZBLAN and silica fibers. The SC generation is initiated by breaking up the nanosecond diode pulses into femtosecond pulses through modulation instability, and the spectrum is then broadened through the interplay of self-phase modulation, parametric four-wave mixing, and stimulated Raman scattering. Theoretical simulations have been carried out to study the SC generation mechanism by numerically solving the generalized nonlinear Schrödinger equation. The SC long wavelength edge is limited by the intrinsic fiber material absorption, i.e. ~3 μm in silica fibers and ~4.5 μm in ZBLAN fibers, respectively. Mid-infrared absorption spectroscopy of the constituents of normal artery, e.g. endothelial cells and smooth muscle cells, and atherosclerotic plaques, e.g. adipose tissue, macrophages and foam cells, and selective ablation of lipid-rich tissues have also been demonstrated using the SC laser system.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/62250/1/caxia_1.pd

Semantic Adversarial Attacks via Diffusion Models

Traditional adversarial attacks concentrate on manipulating clean examples in the pixel space by adding adversarial perturbations. By contrast, semantic adversarial attacks focus on changing semantic attributes of clean examples, such as color, context, and features, which are more feasible in the real world. In this paper, we propose a framework to quickly generate a semantic adversarial attack by leveraging recent diffusion models since semantic information is included in the latent space of well-trained diffusion models. Then there are two variants of this framework: 1) the Semantic Transformation (ST) approach fine-tunes the latent space of the generated image and/or the diffusion model itself; 2) the Latent Masking (LM) approach masks the latent space with another target image and local backpropagation-based interpretation methods. Additionally, the ST approach can be applied in either white-box or black-box settings. Extensive experiments are conducted on CelebA-HQ and AFHQ datasets, and our framework demonstrates great fidelity, generalizability, and transferability compared to other baselines. Our approaches achieve approximately 100% attack success rate in multiple settings with the best FID as 36.61. Code is available at https://github.com/steven202/semantic_adv_via_dm.Comment: To appear in BMVC 202

Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models

Although Large Language Models (LLMs) have shown great potential in Natural Language Generation, it is still challenging to characterize the uncertainty of model generations, i.e., when users could trust model outputs. Our research is derived from the heuristic facts that tokens are created unequally in reflecting the meaning of generations by auto-regressive LLMs, i.e., some tokens are more relevant (or representative) than others, yet all the tokens are equally valued when estimating uncertainty. It is because of the linguistic redundancy where mostly a few keywords are sufficient to convey the meaning of a long sentence. We name these inequalities as generative inequalities and investigate how they affect uncertainty estimation. Our results reveal that considerable tokens and sentences containing limited semantics are weighted equally or even heavily when estimating uncertainty. To tackle these biases posed by generative inequalities, we propose to jointly Shifting Attention to more Relevant (SAR) components from both the token level and the sentence level while estimating uncertainty. We conduct experiments over popular "off-the-shelf" LLMs (e.g., OPT, LLaMA) with model sizes up to 30B and powerful commercial LLMs (e.g., Davinci from OpenAI), across various free-form question-answering tasks. Experimental results and detailed demographic analysis indicate the superior performance of SAR. Code is available at https://github.com/jinhaoduan/shifting-attention-to-relevance

Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

Diffusion models have demonstrated remarkable performance in image generation tasks, paving the way for powerful AIGC applications. However, these widely-used generative models can also raise security and privacy concerns, such as copyright infringement, and sensitive data leakage. To tackle these issues, we propose a method, Unlearnable Diffusion Perturbation, to safeguard images from unauthorized exploitation. Our approach involves designing an algorithm to generate sample-wise perturbation noise for each image to be protected. This imperceptible protective noise makes the data almost unlearnable for diffusion models, i.e., diffusion models trained or fine-tuned on the protected data cannot generate high-quality and diverse images related to the protected training data. Theoretically, we frame this as a max-min optimization problem and introduce EUDP, a noise scheduler-based method to enhance the effectiveness of the protective noise. We evaluate our methods on both Denoising Diffusion Probabilistic Model and Latent Diffusion Models, demonstrating that training diffusion models on the protected data lead to a significant reduction in the quality of the generated images. Especially, the experimental results on Stable Diffusion demonstrate that our method effectively safeguards images from being used to train Diffusion Models in various tasks, such as training specific objects and styles. This achievement holds significant importance in real-world scenarios, as it contributes to the protection of privacy and copyright against AI-generated content

Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study

Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10−15), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10−6). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk