Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact Spectrum of Anomalous Dimensions of Planar N = 4 Supersymmetric Yang-Mills Theory

We present a set of functional equations defining the anomalous dimensions of arbitrary local single trace operators in planar $\mathcal{N}=4$ supersymmetric Yang-Mills theory. It takes the form of a $Y$ system based on the integrability of the dual superstring $\ensuremath{\sigma}$ model on the five-dimensional anti\char21{}de Sitter space (${\mathrm{AdS}}_{5}\ifmmode\times\else\texttimes\fi{}{\mathrm{S}}^{5}$) background. This $Y$ system passes some very important tests: it incorporates the full asymptotic Bethe ansatz at large length of operator $L$, including the dressing factor, and it confirms all recently found wrapping corrections. The recently proposed ${\mathrm{AdS}}_{4}/\mathrm{\text{three-dimensional}}$ conformal field theory duality is also treated in a similar fashion.
He ion radiation effects in high electron mobility transistors

The effects of He ion irradiation have been investigated in high electron mobility transistors. Heterojunctions have been also used to monitor the two‐dimensional electron‐gas degradation. Devices with different layer structures have been employed for the better understanding of failure mechanism sources. Finally, introduction of a charge control model allowed the determination of buffer layer degradation.
Partial regularity for minimizers of splitting-type variational integrals under general growth conditions. - Part 1

We consider variational problems of splitting-type, i.e. the density F:\mathbb{R}^{n}\supset\Omega\rightarrow\mathbb{R}^{N} has an additive decomposition into two functions f and g. Assuming power growth conditions with exponents p and q for these functions, Bildhauer and Fuchs [BF2,3] show partial regularity in the general vector case and full regularity for n = 2 in the superquadratic situation. If the functions f and g depend on the modulus, i.e. f(·) = a(|·|) and g(·) = b(|·|), we generalize the statements for splitting-type variational integrals with power growth conditions to the case of N-functions a and b.
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin

In supervised learning - for instance in image classification - modern massive datasets are commonly labeled by a crowd of workers. The obtained labels in this crowdsourcing setting are then aggregated for training, generally leveraging a per-worker trust score. Yet, such workers oriented approaches discard the tasks' ambiguity. Ambiguous tasks might fool expert workers, which is often harmful for the learning step. In standard supervised learning settings - with one label per task - the Area Under the Margin (AUM) was tailored to identify mislabeled data. We adapt the AUM to identify ambiguous tasks in crowdsourced learning scenarios, introducing the Weighted Areas Under the Margin (WAUM). The WAUM is an average of AUMs weighted according to task-dependent scores. We show that the WAUM can help discarding ambiguous tasks from the training set, leading to better generalization performance. We report improvements over existing strategies for learning with a crowd, both on simulated settings, and on real datasets such as CIFAR-10H (a crowdsourced dataset with a high number of answered labels),LabelMe and Music (two datasets with few answered votes).
Psychophysical-Score: A Behavioral Measure for Assessing the Biological Plausibility of Visual Recognition Models

For the last decade, convolutional neural networks (CNNs) have vastly superseded their predecessors in nearly all vision tasks in artificial intelligence, including object recognition. However, despite abundant advancements, they continue to pale in comparison to biological vision. This chasm has prompted the development of biologically-inspired models that have attempted to mimic the human visual system, primarily at a neural level, which is evaluated using standard dataset benchmarks. However, more work is needed to understand how these models perceive the visual world. This article proposes a state-of-the-art procedure that generates a new metric, Psychophysical-Score, which is grounded in visual psychophysics and is capable of reliably estimating perceptual responses across numerous models -- representing a large range in complexity and biological inspiration. We perform the procedure on twelve models that vary in degree of biological inspiration and complexity, we compare the results against the aggregated results of 2,390 Amazon Mechanical Turk workers who together provided ~2.7 million perceptual responses. Each model's Psychophysical-Score is compared against the state-of-the-art neural activity-based metric, Brain-Score. Our study indicates that models with a high correlation to human perceptual behavior also have a high correlation with the corresponding neural activity.
Shifting Consensus in a Biased Compromise Model

We investigate the effect of bias on the formation and dynamics of political parties in the bounded confidence model. For weak bias, we quantify the change in average opinion and potential dispersion and decrease in party size. For nonlinear bias modeling self-incitement, we establish coherent drifting motion of parties on a background of uniform opinion distribution for biases below a critical threshold where parties dissolve. Technically, we use geometric singular perturbation theory to derive drift speeds, we rely on a nonlocal center manifold analysis to construct drifting parties near threshold, and we implement numerical continuation in a forward-backward delay equation to connect asymptotic regimes.
Taming Local Effects in Graph-based Spatiotemporal Forecasting

Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings. These architectures take advantage of a graph structure and relational inductive biases to learn a single (global) inductive model to predict any number of the input time series, each associated with a graph node. Despite the gain achieved in computational and data efficiency w.r. t. fitting a set of local models, relying on a single global model can be a limitation whenever some of the time series are generated by a different spatiotemporal stochastic process. The main objective of this paper is to understand the interplay between globality and locality in graph-based spatiotemporal forecasting, while contextually proposing a methodological framework to rationalize the practice of including trainable node embeddings in such architectures. We ascribe to trainable node embeddings the role of amortizing the learning of specialized components. Moreover, embeddings allow for 1) effectively combining the advantages of shared message-passing layers with node-specific parameters and 2) efficiently transferring the learned model to new node sets. Supported by strong empirical evidence, we provide insights and guidelines for specializing graph-based models to the dynamics of each time series and show how this aspect plays a crucial role in obtaining accurate predictions.
Impact of Zr substitution on the electronic structure of ferroelectric hafnia

HfO2-based dielectrics are promising for nanoscale ferroelectric applications, and the most favorable material within the family is Zr-substituted hafnia, i.e., Hf1−xZrxO2 (HZO). The extent of Zr substitution can be great, and x is commonly set to 0.5. However, the bandgap of ZrO2 is lower than HfO2, thus it is uncertain how the Zr content should influence the electronic band structure of HZO. A reduced bandgap is detrimental to the cycling endurance as charge injection and dielectric breakdown would become easier. Another issue is regarding the comparison on the bandgaps between HfO2/ZrO2 superlattices and HZO solid-state solutions. In this work, we systematically investigated the electronic structures of HfO2, ZrO2, and HZO using self-energy corrected density functional theory. In particular, the conduction band minimum of Pca21-HfO2 is found to lie at an ordinary k-point on the Brillouin zone border, not related to any interlines between high-symmetry k-points. Moreover, the rule of HZO bandgap variation with respect to x has been extracted. The physical mechanisms for the exponential reduction regime and linear decay regime have been revealed. The bandgaps of HfO2/ZrO2 ferroelectric superlattices are investigated in a systematic manner, and the reason why the superlattice could possess a bandgap lower than that of ZrO2 is revealed through comprehensive analysis.
SLaM: Student-Label Mixing for Semi-Supervised Knowledge Distillation

Semi-supervised knowledge distillation is a powerful training paradigm for generating compact and lightweight student models in settings where the amount of labeled data is limited but one has access to a large pool of unlabeled data. The idea is that a large teacher model is utilized to generate “smoothed” pseudo-labels for the unlabeled dataset which are then used for training the student model. Despite its success in a wide variety of applications, a shortcoming of this approach is that the teacher’s pseudo-labels are often noisy, leading to impaired student performance. In this paper, we present a principled method for semi-supervised knowledge distillation that we call Student-Label Mixing (SLaM) and we show that it consistently improves over prior approaches by evaluating it on several standard benchmarks. Finally, we show that SLaM comes with theoretical guarantees; along the way we give an al-gorithm improving the best-known sample complexity for learning halfspaces with margin under random classiﬁcation noise, and provide the ﬁrst convergence analysis for so-called “forward loss-adjustment” methods.
Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning

Prompt-based or in-context learning has achieved high zero-shot performance on many natural language generation (NLG) tasks. Here we explore the performance of prompt-based learning for simultaneously controlling the personality and the semantic accuracy of an NLG for task-oriented dialogue. We experiment with prompt-based learning on the PERSONAGE restaurant recommendation corpus to generate semantically and stylistically-controlled text for 5 different Big-5 personality types: agreeable, disagreeable, conscientious, unconscientious, and extravert. We test two different classes of discrete prompts to generate utterances for a particular personality style: (1) prompts that demonstrate generating directly from a meaning representation that includes a personality specification; and (2) prompts that rely on first converting the meaning representation to a textual pseudo-reference, and then using the pseudo-reference in a textual style transfer (TST) prompt. In each case, we show that we can vastly improve performance by over-generating outputs and ranking them, testing several ranking functions based on automatic metrics for semantic accuracy, personality-match, and fluency. We also test whether NLG personality demonstrations from the restaurant domain can be used with meaning representations for the video game domain to generate personality stylized utterances about video games. Our findings show that the TST prompts produces the highest semantic accuracy (78.46% for restaurants and 87.6% for video games) and personality accuracy (100% for restaurants and 97% for video games). Our results on transferring personality style to video game utterances are surprisingly good. To our knowledge, there is no previous work testing the application of prompt-based learning to simultaneously controlling both style and semantic accuracy in NLG.
GLAZE: Protecting Artists from Style Mimicry by Text-to-Image Models

Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to displace many in the professional artist community. In particular, models can learn to mimic the artistic style of specific artists after"fine-tuning"on samples of their art. In this paper, we describe the design, implementation and evaluation of Glaze, a tool that enables artists to apply"style cloaks"to their art before sharing online. These cloaks apply barely perceptible perturbations to images, and when used as training data, mislead generative models that try to mimic a specific artist. In coordination with the professional artist community, we deploy user studies to more than 1000 artists, assessing their views of AI art, as well as the efficacy of our tool, its usability and tolerability of perturbations, and robustness across different scenarios and against adaptive countermeasures. Both surveyed artists and empirical CLIP-based scores show that even at low perturbation levels (p=0.05), Glaze is highly successful at disrupting mimicry under normal conditions (>92%) and against adaptive countermeasures (>85%).
Information-Theoretic Diffusion

Denoising diffusion models have spurred significant gains in density modeling and image generation, precipitating an industrial revolution in text-guided AI art generation. We introduce a new mathematical foundation for diffusion models inspired by classic results in information theory that connect Information with Minimum Mean Square Error regression, the so-called I-MMSE relations. We generalize the I-MMSE relations to exactly relate the data distribution to an optimal denoising regression problem, leading to an elegant refinement of existing diffusion bounds. This new insight leads to several improvements for probability distribution estimation, including theoretical justification for diffusion model ensembling. Remarkably, our framework shows how continuous and discrete probabilities can be learned with the same regression objective, avoiding domain-specific generative models used in variational methods. Code to reproduce experiments is provided at http://github.com/kxh001/ITdiffusion and simplified demonstration code is at http://github.com/gregversteeg/InfoDiffusionSimple.
Dynamic Programming for Pure-Strategy Subgame Perfection in an Arbitrary Game

This paper uses value functions to characterize the pure-strategy subgame-perfect equilibria of an arbitrary, possibly infinite-horizon game. It specifies the game's extensive form as a pentaform (Streufert 2023p, arXiv:2107.10801v4), which is a set of quintuples formalizing the abstract relationships between nodes, actions, players, and situations (situations generalize information sets). Because a pentaform is a set, this paper can explicitly partition the game form into piece forms, each of which starts at a (Selten) subroot and contains all subsequent nodes except those that follow a subsequent subroot. Then the set of subroots becomes the domain of a value function, and the piece-form partition becomes the framework for a value recursion which generalizes the Bellman equation from dynamic programming. The main results connect the value recursion with the subgame-perfect equilibria of the original game, under the assumptions of upper- and lower-convergence. Finally, a corollary characterizes subgame perfection as the absence of an improving one-piece deviation.
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks

Irregularly-sampled time series (ITS) are native to high-impact domains like healthcare, where measurements are collected over time at uneven intervals. However, for many classification problems, only small portions of long time series are often relevant to the class label. In this case, existing ITS models often fail to classify long series since they rely on careful imputation, which easily over- or under-samples the relevant regions. Using this insight, we then propose CAT, a model that classifies multivariate ITS by explicitly seeking highly-relevant portions of an input series' timeline. CAT achieves this by integrating three components: (1) A Moment Network learns to seek relevant moments in an ITS's continuous timeline using reinforcement learning. (2) A Receptor Network models the temporal dynamics of both observations and their timing localized around predicted moments. (3) A recurrent Transition Model models the sequence of transitions between these moments, cultivating a representation with which the series is classified. Using synthetic and real data, we find that CAT outperforms ten state-of-the-art methods by finding short signals in long irregular time series.
Effect of SiO2 encapsulation on the nitrogen reorganization in GaNAs/GaAs single quantum well

Effects of SiO2 encapsulation and rapid thermal annealing (RTA) on the optical properties of GaNAs/GaAs single quantum well (SQW) were studied by low temperature photoluminescence (PL). A blueshift of the PL peak energy for both the SiO2-capped region and the bare region was observed. The results were attributed to the nitrogen reorganization in the GaNAs/GaAs SQW. It was also shown that the nitrogen reorganization was obviously enhanced by SiO2 cap-layer. A simple model [1] was used to describe the SiO2-enhanced blueshift of the low temperature PL peak energy.
“STILL AROUND”: Experiences and Survival Strategies of Veteran Women Software Developers

The intersection of ageism and sexism can create a hostile environment for veteran software developers belonging to marginalized genders. In this study, we conducted 14 interviews to examine the experiences of people at this intersection, primar-ily women, in order to discover the strategies they employed in order to successfully remain in the field. We identified 283 codes, which fell into three main categories: Strategies, Experiences, and Perception. Several strategies we identified, such as (Deliberately) Not Trying to Look Younger, were not previously described in the software engineering literature. We found that, in some compa-nies, older women developers are recognized as having particular value, further strengthening the known benefits of diversity in the workforce. Based on the experiences and strategies, we suggest organizations employing software developers to consider the benefits of hiring veteran women software developers. For example, companies can draw upon the life experiences of older women developers in order to better understand the needs of customers from a similar demographic. While we recognize that many of the strategies employed by our study participants are a response to systemic issues, we still consider that, in the short-term, there is benefit in describing these strategies for developers who are experiencing such issues today.
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution

Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros. We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing. The key idea is to transform each user's ratings on the items to a function (signal) on the vertices of an item-item graph, then learn structural graph properties to recover the function from its values on certain vertices -- the problem of graph signal sampling. We propose a class of regularization functionals that takes into account discrete random label noise in the graph vertex domain, then develop the GS-IMC approach which biases the reconstruction towards functions that vary little between adjacent vertices for noise reduction. Theoretical result shows that accurate reconstructions can be achieved under mild conditions. For the online setting, we develop a Bayesian extension, i.e., BGS-IMC which considers continuous random Gaussian noise in the graph Fourier domain and builds upon a prediction-correction update algorithm to obtain the unbiased and minimum-variance reconstruction. Both GS-IMC and BGS-IMC have closed-form solutions and thus are highly scalable in large data. Experiments show that our methods achieve state-of-the-art performance on public benchmarks.
EDEN Survey: Small Transiting Planet Detection Limits and Constraints on the Occurrence Rates of Planets around Late-M Dwarfs within 15 pc

Earth-sized exoplanets that transit nearby, late-spectral-type red dwarfs will be prime targets for atmospheric characterization in the coming decade. Such systems, however, are difficult to find via widefield transit surveys like Kepler or TESS. Consequently, the presence of such transiting planets is unexplored and the occurrence rates of short-period Earth-sized planets around late-M dwarfs remain poorly constrained. Here, we present the deepest photometric monitoring campaign of 22 nearby late-M dwarf stars, using data from over 500 nights on seven 1–2 m class telescopes. Our survey includes all known single quiescent northern late-M dwarfs within 15 pc. We use transit injection-and-recovery tests to quantify the completeness of our survey, successfully identify most (>80%) transiting short-period (0.5–1 days) super-Earths (R >1.9 R ⊕), and are sensitive (∼50%) to transiting Earth-sized planets (1.0–1.2 R ⊕). Our high sensitivity to transits with a near-zero false-positive rate demonstrates an efficient survey strategy. Our survey does not yield a transiting planet detection, yet it provides the most sensitive upper limits on transiting planets orbiting our target stars. Finally, we explore multiple hypotheses about the occurrence rates of short-period planets (from Earth-sized planets to giant planets) around late-M dwarfs. We show, for example, that giant planets with short periods (<1 day) are uncommon around our target stars. Our data set provides some insight into the occurrence rates of short-period planets around TRAPPIST-1-like stars, and our results can help test planetary formation and system evolution models, as well as guide future observations of nearby late-M dwarfs.
Optical Intelligent Reflecting Surface Assisted MIMO VLC: Channel Modeling and Capacity Characterization

Although the multi-antenna or so-called multiple-input multiple-output (MIMO) transmission is an enabling technology for past generations of wireless communication systems, its application to visible light communication (VLC) still faces a critical challenge due to the strong spatial correlation of VLC channels, which makes it difficult to achieve sufficient spatial multiplexing gain. This paper proposes to use optical intelligent reflecting surfaces (OIRS) to tackle this challenge. Firstly, we characterize the extremely near-field channel condition in the optical frequency range and reveal a peculiar “inter-element interference (IEI) free” property of the OIRS-reflected channel, where the OIRS reflecting elements can be individually configured to align with one pair of transmitter and receiver antennas without causing interference to each other. Next, we characterize the OIRS-assisted MIMO VLC capacities under different power constraints at the transmitter antennas, and then proceed to maximize them by jointly optimizing the OIRS element alignment and transmitter emission power. In particular, we propose two algorithms for the OIRS optimization, namely, location-aided interior-point algorithm and log-det-based alternating optimization algorithm, to balance the performance versus complexity trade-off; while the optimal transmitter emission power is derived in closed form. Numerical results are provided to validate the capacity improvement of OIRS-assisted MIMO VLC against the VLC without OIRS and demonstrate the superior performance of the proposed algorithms compared to baseline schemes.