Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Boosting Robustness of DNN-Based Image Watermarking via Dbmark

Image watermarking is a technique for hiding information into images that can withstand distortions while requiring the encoded image to be perceptually identical to the original image. Recent work based on deep neural networks (DNN) has achieved impressive progression in digital watermarking. Higher robustness under various distortions is the eternal pursuit of digital image watermarking approaches. In this paper, we propose DB MARK, a novel end-to-end digital image watermarking framework to deep boost the robustness of DNN-based image watermarking. The key novelty is the synergy of invertible neural networks (INN) and effective watermark features generation. The framework generates watermark features with redundancy and error correction ability through the effective neural network based message processor, synergized with the powerful information embedding and extraction abilities of INN to achieve higher robustness and invisibility. The powerful learning ability of neural networks enables the message processor to adapt to various distortions. In addition, we propose to embed the watermark information in the discrete wavelet transform (DWT) domain and design low-low $(LL)$ sub-band loss to enhance invisibility. Extensive experiment results demonstrate the superiority of the proposed framework compared with the state-of-the-art ones under various distortions such as dropout, cropout, crop, Gaussian filter, and JPEG compression.
Model Constraints Based on the IceCube Neutrino Nondetection of GRB 221009A

GRB 221009A is a bright gamma-ray burst (GRB) with isotropic energy larger than 1054 erg. Its fairly low redshift makes it a promising candidate for high-energy neutrino detection. However, a neutrino search for this GRB reported by the IceCube collaboration yielded a null result. In this paper, we utilize the upper limit from the IceCube observation to test different GRB prompt emission models. We find that, at least for this specific burst, the dissipative photosphere model could be ruled out in a large parameter space. The internal-shock model can survive only with a large bulk motion Lorentz factor Γ, where the most stringent and conservative constraints are Γ > ∼ 450 and Γ > ∼ 200, respectively. Also, the ratio of the total dissipated energy that goes into the protons and electrons (ϵ p /ϵ e ) can be constrained with a given Γ. For Γ < 400, ϵ p /ϵ e < 10 is required. For the Internal-collision-induced Magnetic Reconnection and Turbulence (ICMART) model, the constraint from GRB 221009A is modest. Under the ICMART model, only for extreme situations when most dissipated energy deposit into protons and all accelerated protons are suitable for producing neutrinos, a slightly large bulk motion (Γ > ∼ 250) is required.
Understanding Inconsistency in Azure Cosmos DB with TLA+

Beyond implementation correctness of a distributed system, it is equally important to understand exactly what users should expect to see from that system. Even if the system itself works as designed, insufficient understanding of its user-observable semantics can cause bugs in its dependencies. By focusing a formal specification effort on precisely defining the expected user-observable behaviors of the Azure Cosmos DB service at Microsoft, we were able to write a formal specification of the database that was significantly smaller and conceptually simpler than any other specification of Cosmos DB, while representing a wider range of valid user-observable behaviors than existing more detailed specifications. Many of the additional behaviors we documented were previously poorly understood outside of the Cosmos DB development team, even informally, leading to data consistency errors in Microsoft products that depend on it. Using this specification, we were able to raise two key issues in Cosmos DB’s public-facing documentation, which have since been addressed. We were also able to offer a fundamental solution to a previous high-impact outage within another Azure service that depends on Cosmos DB.
A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models

High-throughput screening of large hypothetical databases of metal-organic frameworks (MOFs) can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning (ML) models to identify MOFs that are thermally stable and stable upon activation. We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50,000 structures that samples orders of magnitude more connectivity nets and inorganic building blocks than prior databases. This database shows an order of magnitude enrichment of ultrastable MOF structures that are stable upon activation and more than one standard deviation more thermally stable than the average experimentally characterized MOF. For the nearly 10,000 ultrastable MOFs, we compute bulk elastic moduli to confirm these materials have good mechanical stability, and we report methane deliverable capacities. Our work identifies privileged metal nodes in ultrastable MOFs that optimize gas storage and mechanical stability simultaneously.
Prediction intervals for economic fixed-event forecasts

The fixed-event forecasting setup is common in economic policy. It involves a sequence of forecasts of the same (`fixed') predictand, so that the difficulty of the forecasting problem decreases over time. Fixed-event point forecasts are typically published without a quantitative measure of uncertainty. To construct such a measure, we consider forecast postprocessing techniques tailored to the fixed-event case. We develop regression methods that impose constraints motivated by the problem at hand, and use these methods to construct prediction intervals for gross domestic product (GDP) growth in Germany and the US.
Time-dependent Steklov-Poincaré operators and space-time Robin-Robin decomposition for the heat equation

Domain decomposition methods are a set of widely used tools for parallelization of partial differential equation solvers. Convergence is well studied for elliptic equations, but in the case of parabolic equations there are hardly any results for general Lipschitz domains in two or more dimensions. The aim of this work is therefore to construct a new framework for analyzing nonoverlapping domain decomposition methods for the heat equation in a space-time Lipschitz cylinder. The framework is based on a variational formulation, inspired by recent studies of space-time ﬁnite elements using Sobolev spaces with fractional time regularity. In this framework, the time-dependent Steklov–Poincar ´ e operators are introduced and their essential properties are proven. We then derive the interface interpretations of the Dirichlet–Neumann, Neumann–Neumann and Robin–Robin methods and show that these methods are well deﬁned. Finally, we prove convergence of the Robin–Robin method and introduce a modiﬁed method with stronger convergence properties.
Technical Report: Implementation of Single Packet Number Space in Multi-Path QUIC

Over the past few of years, we have witnessed increasing interests in the use cases of multi-path QUIC from both industry and academia. For example, Alibaba deployed XLINK, a QoE-driven multi-path QUIC solution, in Taobao short video and showed benefits in both reduced tail latency and video re-buffering. For the time being, the multi-path QUIC protocol is in the process of standardization at the IETF QUIC working group, with the draft recently updated to version 02. The focus of the draft is to provide basic guidance on the implementation so that we can encourage more exploration, testing, and finally, an accelerated adoption of this technology. However, draft-02 has brought up an open issue on whether the multi-path QUIC should be implemented using single packet number space (SPNS) or multiple packet number space (MPNS), as in the current draft, both options co-exist. Knowing that one cannot draw a solid conclusion without experiments, we implement both SPNS and MPNS at Alibaba and measured their performance. The goal is to help the community better understand the implication, and we hope this report can be a useful resource for engineers and researchers who are interested in deploying multi-path QUIC.
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources

Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate cheaper, lower-fidelity approximations of the objective function into the optimization process, with the goal of converging towards the global optimum at a reduced cost. This task is generally referred to as multi-fidelity Bayesian optimization (MFBO). However, MFBO algorithms can lead to higher optimization costs than their vanilla BO counterparts, especially when the low-fidelity sources are poor approximations of the objective function, therefore defeating their purpose. To address this issue, we propose rMFBO (robust MFBO), a methodology to make any GP-based MFBO scheme robust to the addition of unreliable information sources. rMFBO comes with a theoretical guarantee that its performance can be bound to its vanilla BO analog, with high controllable probability. We demonstrate the effectiveness of the proposed methodology on a number of numerical benchmarks, outperforming earlier MFBO methods on unreliable sources. We expect rMFBO to be particularly useful to reliably include human experts with varying knowledge within BO processes.
Datavoidant: An AI System for Addressing Political Data Voids on Social Media

The limited information (data voids) on political topics relevant to underrepresented communities has facilitated the spread of disinformation. Independent journalists who combat disinformation in underrepresented communities have reported feeling overwhelmed because they lack the tools necessary to make sense of the information they monitor and address the data voids. In this paper, we present a system to identify and address political data voids within underrepresented communities. Armed with an interview study, indicating that the independent news media has the potential to address them, we designed an intelligent collaborative system, called Datavoidant. Datavoidant uses state-of-the-art machine learning models and introduces a novel design space to provide independent journalists with a collective understanding of data voids to facilitate generating content to cover the voids. We performed a user interface evaluation with independent news media journalists (N=22). These journalists reported that Datavoidant's features allowed them to more rapidly while easily having a sense of what was taking place in the information ecosystem to address the data voids. They also reported feeling more confident about the content they created and the unique perspectives they had proposed to cover the voids. We conclude by discussing how Datavoidant enables a new design space wherein individuals can collaborate to make sense of their information ecosystem and actively devise strategies to prevent disinformation.
Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data

Several approaches have been developed to mitigate algorithmic bias stemming from health data poverty, where minority groups are underrepresented in training datasets. Augmenting the minority class using resampling (such as SMOTE) is a widely used approach due to the simplicity of the algorithms. However, these algorithms decrease data variability and may introduce correlations between samples, giving rise to the use of generative approaches based on GAN. Generation of high-dimensional, time-series, authentic data that provides a wide distribution coverage of the real data, remains a challenging task for both resampling and GAN-based approaches. In this work we propose CA-GAN architecture that addresses some of the shortcomings of the current approaches, where we provide a detailed comparison with both SMOTE and WGAN-GP*, using a high-dimensional, time-series, real dataset of 3343 hypotensive Caucasian and Black patients. We show that our approach is better at both generating authentic data of the minority class and remaining within the original distribution of the real data.
ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs

Knowledge Graph Question Answering (KGQA) involves retrieving entities as answers from a Knowledge Graph (KG) using natural language queries. The challenge is to learn to reason over question-relevant KG facts that traverse KG entities and lead to the question answers. To facilitate reasoning, the question is decoded into instructions, which are dense question representations used to guide the KG traversals. However, if the derived instructions do not exactly match the underlying KG information, they may lead to reasoning under irrelevant context. Our method, termed ReaRev, introduces a new way to KGQA reasoning with respect to both instruction decoding and execution. To improve instruction decoding, we perform reasoning in an adaptive manner, where KG-aware information is used to iteratively update the initial instructions. To improve instruction execution, we emulate breadth-first search (BFS) with graph neural networks (GNNs). The BFS strategy treats the instructions as a set and allows our method to decide on their execution order on the fly. Experimental results on three KGQA benchmarks demonstrate the ReaRev's effectiveness compared with previous state-of-the-art, especially when the KG is incomplete or when we tackle complex questions. Our code is publicly available at https://github.com/cmavro/ReaRev_KGQA.
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning

Policies produced by deep reinforcement learning are typically characterised by their learning curves, but they remain poorly understood in many other respects. ReLU-based policies result in a partitioning of the input space into piecewise linear regions. We seek to understand how observed region counts and their densities evolve during deep reinforcement learning using empirical results that span a range of continuous control tasks and policy network dimensions. Intuitively, we may expect that during training, the region density increases in the areas that are frequently visited by the policy, thereby affording fine-grained control. We use recent theoretical and empirical results for the linear regions induced by neural networks in supervised learning settings for grounding and comparison of our results. Empirically, we find that the region density increases only moderately throughout training, as measured along fixed trajectories coming from the final policy. However, the trajectories themselves also increase in length during training, and thus the region densities decrease as seen from the perspective of the current trajectory. Our findings suggest that the complexity of deep reinforcement learning policies does not principally emerge from a significant growth in the complexity of functions observed on-and-around trajectories of the policy.
Adapters for Enhanced Modeling of Multilingual Knowledge and Text

Large language models appear to learn facts from the large text corpora they are trained on. Such facts are encoded implicitly within their many parameters, making it difficult to verify or manipulate what knowledge has been learned. Language models have recently been extended to multilingual language models (MLLMs), enabling knowledge to be learned across hundreds of languages. Meanwhile, knowledge graphs contain facts in an explicit triple format, which require careful and costly curation and are only available in a few high-resource languages, restricting their research and application. To address these issues, we propose to enhance MLLMs with knowledge from multilingual knowledge graphs (MLKGs) so as to tackle language and knowledge graph tasks across many languages, including low-resource ones. Specifically, we introduce a lightweight adapter set to enhance MLLMs with cross-lingual entity alignment and facts from MLKGs for many languages. Experiments on common benchmarks show that such enhancement benefits both MLLMs and MLKGs, achieving: (1) comparable or improved performance for knowledge graph completion and entity alignment relative to baselines, especially for low-resource languages (for which knowledge graphs are unavailable); and (2) improved MLLM performance on language understanding tasks that require multilingual factual knowledge; all while maintaining performance on other general language tasks.
Enhanced Fuzzy Decomposition of Sound Into Sines, Transients, and Noise

The decomposition of sounds into sines, transients, and noise is a long-standing research problem in audio processing. The current solutions for this three-way separation detect either horizontal and vertical structures or anisotropy and orientations in the spectrogram to identify the properties of each spectral bin and classify it as sinusoidal, transient, or noise. This paper proposes an enhanced three-way decomposition method based on fuzzy logic, enabling soft masking while preserving the perfect reconstruction property. The proposed method allows each spectral bin to simultaneously belong to two classes, sine and noise or transient and noise. Results of a subjective listening test against three other techniques are reported, showing that the proposed decomposition yields a better or comparable quality. The main improvement appears in transient separation, which enjoys little or no loss of energy or leakage from the other components and performs well for test signals presenting strong transients. The audio quality of the separation is shown to depend on the complexity of the input signal for all tested methods. The proposed method helps improve the quality of various audio processing applications. A successful implementation over a state-of-the-art time-scale modification method is reported as an example.
Practical learning method for multi-scale entangled states

We describe two related methods for reconstructing multi-scale entangled states from a small number of efficiently-implementable measurements and fast post-processing. Both methods only require single-particle measurements and the total number of measurements is polynomial in the number of particles. Data post-processing for state reconstruction uses standard tools, namely matrix diagonalization and conjugate gradient method, and scales polynomially with the number of particles. Both methods prevent the build-up of errors from both numerical and experimental imperfections. The first method is conceptually simpler but requires unitary control. The second method circumvents the need for unitary control but requires more measurements and produces an estimated state of lower fidelity.
Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media

NLP research on public opinion manipulation campaigns has primarily focused on detecting overt strategies such as fake news and disinformation. However, information manipulation in the ongoing Russia-Ukraine war exemplifies how governments and media also employ more nuanced strategies. We release a new dataset, VoynaSlov, containing 38M+ posts from Russian media outlets on Twitter and VKontakte, as well as public activity and responses, immediately preceding and during the 2022 Russia-Ukraine war. We apply standard and recently-developed NLP models on VoynaSlov to examine agenda setting, framing, and priming, several strategies underlying information manipulation, and reveal variation across media outlet control, social media platform, and time. Our examination of these media effects and extensive discussion of current approaches' limitations encourage further development of NLP models for understanding information manipulation in emerging crises, as well as other real-world and interdisciplinary tasks.
Dipole radio-frequency power from laser plasmas with no dipole moment

The radio-frequency power radiated from laser-target plasmas in a vacuum can be orders of magnitude greater than expected from such sources that have a negligible electric dipole moment. A model combining the Tidman–Stamper circuit model of a laser-target plasma with the theory of radiation from currents immersed in plasmas, however, predicts scaling of electric-dipole power radiated from laser plasmas in agreement with experiments.
Evaluation and Management of Convergence Insufficiency in Adults

ABSTRACT A search was performed of the literature published in peer-reviewed journals from disciplines related to eye care and rehabilitation between 2000 and 2022. The purpose was to discover new thinking on the topic of the evaluation and management of convergence insufficiency in adults. Ninety-one papers were reviewed and summarized. The etiology, identification, evaluation, and management of convergence insufficiency in this population, as reported in the literature, is presented in this paper. Many publications addressing rehabilitation after traumatic head injury addressed the management of convergence insufficiency. There was consensus in the importance of complete patient examination, to include a complete sensorimotor examination and careful refraction, prior to initiating any therapy. A chronic issue continues in the paucity of large, placebo-controlled studies to provide clear best practice for providers. Additionally, healthcare professionals from eye care and other professions are working with patients who have convergence insufficiency without significant interprofessional collaboration.
Selective triggering in-plane and out-of-plane dipolar modes of hexagonal Au nanoplate with the polarization of excitation beam

The dipolar responses of a single hexagonal Au nanoplate are investigated under the illuminations of linearly polarized beam and tightly focused radially polarized beam (RPB). It is found from the scattering spectra that the in-plane and out-of-plane electric dipole modes can be selectively triggered with a linearly polarized beam and tightly focused RPB, respectively. The features of these two dipolar modes are further confirmed in terms of electrical field and charge maps by the finite-difference time-domain simulation. Additionally, using the multipole expansion method, the existence of the out-of-plane dipole mode is further verified by the fact that the z-component of electric dipole response has a dominant contribution to the scattered power. Moreover, by combining the back focal plane imaging technique with the simulation, the appearance of in-plane and out-of-plane dipoles in the scattering pattern are clearly discerned. Our results provide an efficient method for selectively exciting the in-plane and out-of-plane dipolar modes of the nanoplate. We envision that the ease of tuning the dipolar momentum may facilitate the enhancement of the interaction between the plasmon and emitters at single-particle level.