Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Estimation and Inference in Panels with Interactive Fixed Effects

We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure). We show that previously developed estimators and confidence intervals (CIs) might be heavily biased and size-distorted when some of the factors are weak. We propose estimators with improved rates of convergence and bias-aware CIs that are uniformly valid regardless of whether the factors are strong or not. Our approach applies the theory of minimax linear estimation to form a debiased estimate using a nuclear norm bound on the error of an initial estimate of the interactive fixed effects. We use the obtained estimate to construct a bias-aware CI taking into account the remaining bias due to weak factors. In Monte Carlo experiments, we find a substantial improvement over conventional approaches when factors are weak, with little cost to estimation error when factors are strong.
DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Diffusion Models

Diffusion models emerge to establish the new state of the art in the visual generation. In particular, text-to-image diffusion models that generate images based on caption descriptions have attracted increasing attention, impressed by their user controllability. Despite encouraging performance, they exaggerate concerns of fake image misuse and cast new pressures on fake image detection. In this work, we pioneer a systematic study of the authenticity of fake images generated by text-to-image diffusion models. In particular, we conduct comprehensive studies from two perspectives unique to the text-to-image model, namely, visual modality and linguistic modality. For visual modality, we propose universal detection that demonstrates fake images of these text-to-image diffusion models share common cues, which enable us to distinguish them apart from real images. We then propose source attribution that reveals the uniqueness of the ﬁnger-prints held by each diffusion model, which can be used to attribute each fake image to its model source. A variety of ablation and analysis studies further interpret the improvements from each of our proposed methods. For linguistic modality, we delve deeper to comprehensively analyze the impacts of text captions (called prompt analysis ) on the image authenticity of text-to-image diffusion models, and reason the impacts to the detection and attribution performance of fake images. All ﬁndings contribute to the community’s insight into the natural properties of text-to-image diffusion models, and we appeal to our community’s consideration on the counterpart solutions, like ours, against the rapidly-evolving fake image generators.
First results of a large-area cryogenic gaseous photomultiplier coupled to a dual-phase liquid xenon TPC

We discuss recent advances in the development of cryogenic gaseous photomultipliers (GPM), for possible use in dark matter and other rare-event searches using noble-liquid targets. We present results from a 10 cm diameter GPM coupled to a dual-phase liquid xenon (LXe) TPC, demonstrating—for the first time—the feasibility of recording both primary (``S1'') and secondary (``S2'') scintillation signals. The detector comprised a triple Thick Gas Electron Multiplier (THGEM) structure with cesium iodide photocathode on the first element; it was shown to operate stably at 180 K with gains above 105, providing high single-photon detection efficiency even in the presence of large α particle-induced S2 signals comprising thousands of photoelectrons. S1 scintillation signals were recorded with a time resolution of 1.2 ns (RMS). The energy resolution (σ/E) for S2 electroluminescence of 5.5 MeV α particles was ∼ 9%, which is comparable to that obtained in the XENON100 TPC with PMTs. The results are discussed within the context of potential GPM deployment in future multi-ton noble-liquid detectors.
AN EXPLANATION FOR THE DIFFERENT X-RAY TO OPTICAL COLUMN DENSITIES IN THE ENVIRONMENTS OF GAMMA RAY BURSTS: A PROGENITOR EMBEDDED IN A DENSE MEDIUM

We study the ≳ 10 ratios in the X-ray to optical column densities inferred from afterglow spectra of gamma ray bursts (GRBs) due to gas surrounding their progenitors. We present time-evolving photoionization calculations for these afterglows and explore different conditions of their environment. We find that homogenous models of the environment (constant density) predict X-ray columns similar to those found in the optical spectra, with the bulk of the opacity being produced by neutral material at large distances from the burst. This result is independent of gas density or metallicity. Only models assuming a progenitor immersed in a dense (∼102–4 cm−3) cloud of gas (with radius ∼10 pc), with a strong, declining gradient of density for the surrounding interstellar medium (ISM) are able to account for the large X-ray to optical column density ratios. However, to avoid an unphysical correlation between the size of this cloud and the size of the ionization front produced by the GRB, the models also require that the circumburst medium is already ionized prior to the burst. The inferred cloud masses are ≲ 106 M☉, even if low metallicities in the medium are assumed (Z ∼ 0.1 Z☉). These cloud properties are consistent with those found in giant molecular clouds and our results support a scenario in which the progenitors reside within intense star formation regions of galaxies. Finally, we show that modeling over large samples of GRB afterglows may offer strong constraints on the range of properties in these clouds, and the host galaxy ISM.
Sentence Ambiguity, Grammaticality and Complexity Probes

It is unclear whether, how and where large pre-trained language models capture subtle linguistic traits like ambiguity, grammaticality and sentence complexity. We present results of automatic classification of these traits and compare their viability and patterns across representation types. We demonstrate that template-based datasets with surface-level artifacts should not be used for probing, careful comparisons with baselines should be done and that t-SNE plots should not be used to determine the presence of a feature among dense vectors representations. We also show how features might be highly localized in the layers for these models and get lost in the upper layers.
On the Utility of Self-Supervised Models for Prosody-Related Tasks

Self-Supervised Learning (SSL) from speech data has produced models that have achieved remarkable performance in many tasks, and that are known to implicitly represent many aspects of information latently present in speech signals. However, relatively little is known about the suitability of such models for prosody-related tasks or the extent to which they encode prosodic information. We present a new evaluation framework, “SUPERB-prosody,” consisting of three prosody-related downstream tasks and two pseudo tasks. We find that 13 of the 15 SSL models outperformed the baseline on all the prosody-related tasks. We also show good performance on two pseudo tasks: prosody reconstruction and future prosody prediction. We further analyze the layerwise contributions of the SSL models. Overall we conclude that SSL speech models are highly effective for prosody-related tasks. We release our code11https://github.com/JSALT-2022-SSL/superb-prosody for the community to support further investigation of SSL models' utility for prosody.
Neuro-Symbolic Explainable Artificial Intelligence Twin for Zero-Touch IoE in Wireless Network

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. Thus, a reliable XAI twin system becomes essential to discretizing the physical behavior of the Internet of Everything (IoE) and identifying the reasons behind that behavior for enabling ZSM. To address the challenges of extensible, modular, and stateless management functions in ZSM, a novel neuro-symbolic XAI twin framework is proposed that to enable trustworthy ZSM for a wireless IoE. The proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. The physical space of the XAI twin executes a neural-network-driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation, such as uplink, downlink, and service provisioning. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multiarm bandit-based learning problem is proposed for reducing the gap between the expected explained score and the current obtained score of the proposed neuro-symbolic XAI twin. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation.
Near-infrared proper motions and spectroscopy of infrared excess sources at the Galactic center

Context. There are a number of faint compact infrared excess sources in the central stellar cluster of the Milky Way. Their nature a nd origin is unclear. In addition to several isolated objects o f this kind there is a small but dense cluster of comoving sources (IRS13N) located∼3” west of SgrA* just 0.5” north of the bright IRS13E cluster of Wolf-Rayet and O-type stars. Based on the analysis of their color and brightness, there are two main possibilities: (1) they may be dust-embedded stars older than a few Myr, or (2) very young, dusty stars with ages younger than 1 Myr. Aims. We present a first K s-band identification and proper motions of the IRS13N member s, the high-velocity dusty S-cluster object (DSO, also referred to as G2), and other infrared excess sour ces in the central field. Goal is to constrain the nature of the se source. Methods. The L ′ - (3.8� m) Ks- (2.2� m) and H-band (1.65� m) observations were carried out using the NACO adaptive optics system at the ESO VLT. Proper motions were obtained by linear fitting of the stellar positions extracted by StarFinder as a functi on of time, weighted by positional uncertainties, and by Gaussian fitti ng from high-pass filtered and deconvolved images. We also pr esent results of near-infrared (NIR) H- and Ks-band ESO-SINFONI integral field spectroscopy of the Galact ic Center cluster ISR13N. Results. We show that within the uncertainties, the positions and proper motions of the IRS13N sources in Ks- and L ′ -band are identical. The HK−sL’ colors then indicate that the bright L ′ -band IRS13N sources are indeed dust-enshrouded stars rather than core-less dust clouds. The proper motions also show that the IRS13N sources are not strongly gravitationally bound to each other. Combined with their NIR colors, this implies that they have been formed recently. For the DSO we obtain proper motions and a Ks-L ′ -color. Conclusions. Most of the compact L ′ -band excess emission sources have a compact H- or Ks-band counterpart and therefore are likely stars with dust shells or disks. Our new results and or bital analysis from our previous work favor the hypothesis t hat the infrared excess IRS13N members and other dusty sources close to SgrA* are young dusty stars and that star formation at the Galactic Center (GC) is a continuously ongoing process. For the DSO the color information indicates that it may be a dust cloud or a dust-embedded star.
Identification of Exciton Complexes in Charge-Tunable Janus WSeS Monolayers

Janus transition-metal dichalcogenide monolayers are artificial materials, where one plane of chalcogen atoms is replaced by chalcogen atoms of a different type. Theory predicts an in-built out-of-plane electric field, giving rise to long-lived, dipolar excitons, while preserving direct-bandgap optical transitions in a uniform potential landscape. Previous Janus studies had broad photoluminescence (>18 meV) spectra obfuscating their specific excitonic origin. Here, we identify the neutral and the negatively charged inter- and intravalley exciton transitions in Janus WSeS monolayers with ∼6 meV optical line widths. We integrate Janus monolayers into vertical heterostructures, allowing doping control. Magneto-optic measurements indicate that monolayer WSeS has a direct bandgap at the K points. Our results pave the way for applications such as nanoscale sensing, which relies on resolving excitonic energy shifts, and the development of Janus-based optoelectronic devices, which requires charge-state control and integration into vertical heterostructures.
Further On the Fountain Effect in Superfluid Helium

In the previous paper (Attard 2022d) on the fountain pressure in superﬂuid helium, it was shown that the experimentally conﬁrmed expression of H. London (1939) was thermodynamically equivalent to equality of chemical potential. However this theoretical equivalence was not reﬂected in the experimental data. The problem has now been traced to errors in the enthalpy and entropy derived from the measured heat capacity by Donnelly and Barenghi (1998). In this paper the corrected thermodynamic data yields almost exact agreement between the two expressions and the measured fountain pressure. A physical explanation is given for energy minimization as the principle that drives the fountain eﬀect and superﬂuid ﬂow more generally.
Real World Offline Reinforcement Learning with Realistic Data Source

Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability to learn from arbitrary pre-generated experience. However, current ORL benchmarks are almost entirely in simulation and utilize contrived datasets like replay buffers of online RL agents or sub-optimal trajectories, and thus hold limited relevance for real-world robotics. In this work (Real-ORL), we posit that data collected from safe operations of closely related tasks are more practical data sources for real-world robot learning. Under these settings, we perform an extensive (6500+ trajectories collected over 800+ robot hours and 270+ human labor hour) empirical study evaluating generalization and transfer capabilities of representative ORL methods on four real-world tabletop manipulation tasks. Our study finds that ORL and imitation learning prefer different action spaces, and that ORL algorithms can generalize from leveraging offline heterogeneous data sources and outperform imitation learning. We release our dataset and implementations at URL: https://sites.google. com/view/real-orl.
Improving the Reliability for Confidence Estimation

Confidence estimation, a task that aims to evaluate the trustworthiness of the model's prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models. Previous works have outlined two important qualities that a reliable confidence estimation model should possess, i.e., the ability to perform well under label imbalance and the ability to handle various out-of-distribution data inputs. In this work, we propose a meta-learning framework that can simultaneously improve upon both qualities in a confidence estimation model. Specifically, we first construct virtual training and testing sets with some intentionally designed distribution differences between them. Our framework then uses the constructed sets to train the confidence estimation model through a virtual training and testing scheme leading it to learn knowledge that generalizes to diverse distributions. We show the effectiveness of our framework on both monocular depth estimation and image classification.
Estimating Excited-States Contamination of $B \to \pi$ Form Factors Using Heavy Meson Chiral Perturbation Theory

Using Heavy Meson Chiral Perturbation Theory (HMChPT), the $B^* \pi$ excited states contamination of the $B \to \pi$ vector form factors is computed to NLO in the chiral expansion and in the static limit. The results suggest that the excited states for $h_\parallel$ are of the order of a few percent whereas $h_\perp$ receives large negative contributions and thus might be significantly underestimated in lattice simulations.
Effects of Galaxy Intrinsic Alignment on Weak Lensing Peak Statistics

The galaxy intrinsic alignment (IA) is a dominant source of systematics in weak lensing (WL) studies. In this paper, by employing large simulations with semianalytical galaxy formation, we investigate the IA effects on WL peak statistics. Different simulated source galaxy samples of different redshift distributions are constructed, where both WL shear and IA signals are included. Convergence reconstruction and peak statistics are then performed for these samples. Our results show that the IA effects on peak abundances mainly consist of two aspects. One is the additional contribution from IA to the shape noise. The other is from the satellite IA that can affect the peak signals from their host clusters significantly. The latter depends on the level of inclusion in a shear sample of the satellite galaxies of the clusters that contribute to WL peaks and thus is sensitive to the redshift distribution of source galaxies. We pay particular attention to satellite IA and adjust it artificially in the simulations to analyze the dependence of the satellite IA impacts on its strength. This information can potentially be incorporated into the modeling of WL peak abundances, especially for high peaks physically originated from massive clusters of galaxies, and thus mitigate the IA systematics on the cosmological constraints derived from WL peaks.
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer

Recently, transformer-based networks have shown impressive results in semantic segmentation. Yet for real-time semantic segmentation, pure CNN-based approaches still dominate in this field, due to the time-consuming computation mechanism of transformer. We propose RTFormer, an efficient dual-resolution transformer for real-time semantic segmenation, which achieves better trade-off between performance and efficiency than CNN-based models. To achieve high inference efficiency on GPU-like devices, our RTFormer leverages GPU-Friendly Attention with linear complexity and discards the multi-head mechanism. Besides, we find that cross-resolution attention is more efficient to gather global context information for high-resolution branch by spreading the high level knowledge learned from low-resolution branch. Extensive experiments on mainstream benchmarks demonstrate the effectiveness of our proposed RTFormer, it achieves state-of-the-art on Cityscapes, CamVid and COCOStuff, and shows promising results on ADE20K. Code is available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.
Understand-Before-Talk (UBT): A Semantic Communication Approach to 6G Networks

In Shannon theory, semantic aspects of communication were identified but considered irrelevant to the technical communication problems. Semantic communication (SC) techniques have recently attracted renewed research interests in $\rm 6^{th}$ generation (6G) wireless, because they have the capability to support an efficient interpretation of the significance and meaning intended by a sender (or accomplishment of the goal) when dealing with multi-modal data such as videos, images, audio, text messages, and so on, which would be the case for various applications such as intelligent transportation systems where each autonomous vehicle needs to deal with real-time videos and data from a number of sensors including radars. To this end, most of the emerging SC works focus on specific data types and employ sophisticated machine learning models including deep learning and neural networks. However, they could be impractical for multi-modal data possibly within a real-time constraint, relative to the purpose of the communication. A notable difficulty of existing SC frameworks lies in handling the discrete constraints imposed on the pursued semantic coding and its interaction with the independent knowledge-base, which makes reliable semantic extraction extremely challenging. Therefore, we develop a new hashing-based semantic extraction approach to SC framework, where our learning objective is to generate one time signatures (hash codes) using supervised learning for low latency, secure and efficient management of the SC dynamics. We first evaluate the proposed semantic extraction framework over large image data sets, extend it with domain adaptive hashing and then demonstrate the effectiveness of “semantics signature” in bulk transmission and multi-modal data.
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations

We study a game between liquidity provider (LP) and liquidity taker agents interacting in an over‐the‐counter market, for which the typical example is foreign exchange. We show how a suitable design of parameterized families of reward functions coupled with shared policy learning constitutes an efficient solution to this problem. By playing against each other, our deep‐reinforcement‐learning‐driven agents learn emergent behaviors relative to a wide spectrum of objectives encompassing profit‐and‐loss, optimal execution, and market share. In particular, we find that LPs naturally learn to balance hedging and skewing, where skewing refers to setting their buy and sell prices asymmetrically as a function of their inventory. We further introduce a novel RL‐based calibration algorithm, which we found performed well at imposing constraints on the game equilibrium. On the theoretical side, we are able to show convergence rates for our multi‐agent policy gradient algorithm under a transitivity assumption, closely related to generalized ordinal potential games.
BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning

Despite their theoretical appealingness, Bayesian neural networks (BNNs) are left behind in real-world adoption, mainly due to persistent concerns on their scalability, accessibility, and reliability. In this work, we develop the BayesAdapter framework to relieve these concerns. In particular, we propose to adapt pre-trained deterministic NNs to be variational BNNs via cost-effective Bayesian fine-tuning. Technically, we develop a modularized implementation for the learning of variational BNNs, and refurbish the generally applicable exemplar reparameterization trick through exemplar parallelization to efficiently reduce the gradient variance in stochastic variational inference. Based on the lightweight Bayesian learning paradigm, we conduct extensive experiments on a variety of benchmarks, and show that our method can consistently induce posteriors with higher quality than competitive baselines, yet significantly reducing training overheads. Code is available at https://github.com/thudzj/ScalableBDL.
On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity

The recent advent of powerful generative models has triggered the renewed development of quantitative measures to assess the proximity of two probability distributions. As the scalar Frechet inception distance remains popular, several methods have explored computing entire curves, which reveal the trade-off between the fidelity and variability of the first distribution with respect to the second one. Several of such variants have been proposed independently and while intuitively similar, their relationship has not yet been made explicit. In an effort to make the emerging picture of generative evaluation more clear, we propose a unification of four curves known respectively as: the precision-recall (PR) curve, the Lorenz curve, the receiver operating characteristic (ROC) curve and a special case of R\'enyi divergence frontiers. In addition, we discuss possible links between PR / Lorenz curves with the derivation of domain adaptation bounds.