Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributionally Robust Linear Quadratic Control

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations, subject to additive noise, with the goal of minimizing a quadratic cost function for the state and control variables. In this work, we consider a generalization of the discrete-time, finite-horizon LQG problem, where the noise distributions are unknown and belong to Wasserstein ambiguity sets centered at nominal (Gaussian) distributions. The objective is to minimize a worst-case cost across all distributions in the ambiguity set, including non-Gaussian distributions. Despite the added complexity, we prove that a control policy that is linear in the observations is optimal for this problem, as in the classic LQG problem. We propose a numerical solution method that efficiently characterizes this optimal control policy. Our method uses the Frank-Wolfe algorithm to identify the least-favorable distributions within the Wasserstein ambiguity sets and computes the controller's optimal policy using Kalman filter estimation under these distributions.
Insight from JWST/NIRCam into galaxy overdensities around bright Ly α emitters during reionization: implications for ionized bubbles at z ∼ 9

Several studies have detected Lyman-alpha (Ly α) from bright (MUV ≲ −21.5) galaxies during the early stages of reionization despite the significantly neutral intergalactic medium. To explain these detections, it has been suggested that z > 7 Ly α emitters (LAEs) inhabit physical Mpc (pMpc)-scale ionized regions powered by overdensities of faint galaxies, but systematic searches for these overdensities near LAEs have been challenging. Here, we use CEERS JWST/NIRCam imaging to search for large-scale galaxy overdensities near two very UV-bright, z = 8.7 LAEs in the EGS field. We colour select 27 z = 8.4 − 9.1 candidates, including the one LAE in the footprint (EGSY8p7). From SED models, we infer moderately faint UV luminosities (MUV ≲ −19.1) and stellar masses of M* ≈ 107.5 − 8.8 M⊙. All are efficient ionizing agents ($\xi _{\text{ion}}^{*}\approx 10^{25.5-26.0}$ Hz erg−1) and are generally morphologically simple with only one compact (re ≲ 140 to ∼650 pc) star-forming component. 13 candidates lie within 5 arcmin of EGSY8p7, leading to a factor-of-four galaxy overdensity at ≲ 5 arcmin (∼1.4 projected pMpc at z ∼ 8.7) separations from EGSY8p7. Separations of 10 − 15 arcmin (∼2.7 − 4.1 projected pMpc) are consistent with an average field. The spatial distribution of our sample may qualitatively suggest an R ≥ 2 pMpc ionized bubble encompassing both LAEs in EGS, which is theoretically unexpected but may be possible for a galaxy population 4 × more numerous than the average to create with moderate escape fractions (fesc ≳ 0.15) over long times (≳ 200 Myr). Upcoming spectroscopic follow-up will characterize the size of any ionized bubble that may exist and the properties of the galaxies powering such a bubble.
Filtering losslessly a single spatial mode of bright squeezed vacuum

Lossless filtering of a single coherent (Schmidt) mode from spatially multimode radiation is a problem crucial for optics in general and for quantum optics in particular. It becomes especially important in the case of nonclassical light that is fragile to optical losses. An example is bright squeezed vacuum generated via high-gain parametric down conversion or four-wave mixing. Its highly multiphoton and multimode structure offers a huge increase in the information capacity provided that each mode can be addressed separately. However, the nonclassical signature of bright squeezed vacuum, photon-number correlations, are highly susceptible to losses. Here we demonstrate lossless filtering of a single spatial Schmidt mode by projecting the spatial spectrum of bright squeezed vacuum on the eigenmode of a single-mode fiber. Moreover, we show that the first Schmidt mode can be captured by simply maximizing the fiber-coupled intensity. Importantly, the projection operation does not affect the targeted mode and leaves it usable for further applications.
Credit Valuation Adjustment in Financial Networks

Credit Valuation Adjustment captures the difference in the value of derivative contracts when the counterparty default probability is taken into account. However, in the context of a network of contracts, the default probability of a direct counterparty can depend substantially on the default probabilities of indirect counterparties. We develop a model to clarify when and how these network effects matter for CVA, in particular in the presence of correlation among counterparties defaults. We provide an approximate analytical solution for the default probabilities. This solution allows for identifying conditions on key parameters such as network degree, leverage and correlation, where network effects yield large differences in CVA (e.g. above 50%), and thus relevant for practical applications. Moreover, we find evidence that network effects induce a multi-modal distribution of CVA values.
Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues

User Satisfaction Modeling (USM) is one of the popular choices for task-oriented dialogue systems evaluation, where user satisfaction typically depends on whether the user’s task goals were fulfilled by the system. Task-oriented dialogue systems use task schema, which is a set of task attributes, to encode the user’s task goals. Existing studies on USM neglect explicitly modeling the user’s task goals fulfillment using the task schema. In this paper, we propose SG-USM, a novel schema-guided user satisfaction modeling framework. It explicitly models the degree to which the user’s preferences regarding the task attributes are fulfilled by the system for predicting the user’s satisfaction level. SG-USM employs a pre-trained language model for encoding dialogue context and task attributes. Further, it employs a fulfillment representation layer for learning how many task attributes have been fulfilled in the dialogue, an importance predictor component for calculating the importance of task attributes. Finally, it predicts the user satisfaction based on task attribute fulfillment and task attribute importance. Experimental results on benchmark datasets (i.e. MWOZ, SGD, ReDial, and JDDC) show that SG-USM consistently outperforms competitive existing methods. Our extensive analysis demonstrates that SG-USM can improve the interpretability of user satisfaction modeling, has good scalability as it can effectively deal with unseen tasks and can also effectively work in low-resource settings by leveraging unlabeled data.Code is available at https://github.com/amzn/user-satisfaction-modeling.
A Tale of Two Approximations: Tightening Over-Approximation for DNN Robustness Verification via Under-Approximation

The robustness of deep neural networks (DNNs) is crucial to the hosting system’s reliability and security. Formal verification has been demonstrated to be effective in providing provable robustness guarantees. To improve its scalability, over-approximating the non-linear activation functions in DNNs by linear constraints has been widely adopted, which transforms the verification problem into an efficiently solvable linear programming problem. Many efforts have been dedicated to defining the so-called tightest approximations to reduce overestimation imposed by over-approximation. In this paper, we study existing approaches and identify a dominant factor in defining tight approximation, namely the approximation domain of the activation function. We find out that tight approximations defined on approximation domains may not be as tight as the ones on their actual domains, yet existing approaches all rely only on approximation domains. Based on this observation, we propose a novel dual-approximation approach to tighten overapproximations, leveraging an activation function’s underestimated domain to define tight approximation bounds. We implement our approach with two complementary algorithms based respectively on Monte Carlo simulation and gradient descent into a tool called DualApp. We assess it on a comprehensive benchmark of DNNs with different architectures. Our experimental results show that DualApp significantly outperforms the state-of-the-art approaches with 100% − 1000% improvement on the verified robustness ratio and 10.64% on average (up to 66.53%) on the certified lower bound.
Physical Deep Reinforcement Learning: Safety and Unknown Unknowns

In this paper, we propose the Phy-DRL: a physics-model-regulated deep reinforcement learning framework for safety-critical autonomous systems. The Phy-DRL is unique in three innovations: i) proactive unknown-unknowns training, ii) conjunctive residual control (i.e., integration of data-driven control and physics-model-based control) and safety- \&stability-sensitive reward, and iii) physics-model-based neural network editing, including link editing and activation editing. Thanks to the concurrent designs, the Phy-DRL is able to 1) tolerate unknown-unknowns disturbances, 2) guarantee mathematically provable safety and stability, and 3) strictly comply with physical knowledge pertaining to Bellman equation and reward. The effectiveness of the Phy-DRL is finally validated by an inverted pendulum and a quadruped robot. The experimental results demonstrate that compared with purely data-driven DRL, Phy-DRL features remarkably fewer learning parameters, accelerated training and enlarged reward, while offering enhanced model robustness and safety assurance.
Evaluation of Question Generation Needs More References

Question generation (QG) is the task of generating a valid and fluent question based on a given context and the target answer. According to various purposes, even given the same context, instructors can ask questions about different concepts, and even the same concept can be written in different ways. However, the evaluation for QG usually depends on single reference-based similarity metrics, such as n-gram-based metric or learned metric, which is not sufficient to fully evaluate the potential of QG methods. To this end, we propose to paraphrase the reference question for a more robust QG evaluation. Using large language models such as GPT-3, we created semantically and syntactically diverse questions, then adopt the simple aggregation of the popular evaluation metrics as the final scores. Through our experiments, we found that using multiple (pseudo) references is more effective for QG evaluation while showing a higher correlation with human evaluations than evaluation with a single reference.
Towards More Realistic Evaluation for Neural Test Oracle Generation

Unit testing has become an essential practice during software development and maintenance. Effective unit tests can help guard and improve software quality but require a substantial amount of time and effort to write and maintain. A unit test consists of a test prefix and a test oracle. Synthesizing test oracles, especially functional oracles, is a well-known challenging problem. Recent studies proposed to leverage neural models to generate test oracles, i.e., neural test oracle generation (NTOG), and obtained promising results. However, after a systematic inspection, we find there are some inappropriate settings in existing evaluation methods for NTOG. These settings could mislead the understanding of existing NTOG approaches’ performance. We summarize them as 1) generating test prefixes from bug-fixed program versions, 2) evaluating with an unrealistic metric, and 3) lacking a straightforward baseline. In this paper, we first investigate the impacts of these settings on evaluating and understanding the performance of NTOG approaches. We find that 1) unrealistically generating test prefixes from bug-fixed program versions inflates the number of bugs found by the state-of-the-art NTOG approach TOGA by 61.8%, 2) FPR (False Positive Rate) is not a realistic evaluation metric and the Precision of TOGA is only 0.38%, and 3) a straightforward baseline NoException, which simply expects no exception should be raised, can find 61% of the bugs found by TOGA with twice the Precision. Furthermore, we introduce an additional ranking step to existing evaluation methods and propose an evaluation metric named Found@K to better measure the cost-effectiveness of NTOG approaches in terms of bug-finding. We propose a novel unsupervised ranking method to instantiate this ranking step, significantly improving the cost-effectiveness of TOGA. Eventually, based on our experimental results and observations, we propose a more realistic evaluation method TEval+ for NTOG and summarize seven rules of thumb to boost NTOG approaches into their practical usages.
Forecasting intraday financial time series with sieve bootstrapping and dynamic updating

Intraday financial data often take the form of a collection of curves that can be observed sequentially over time, such as intraday stock price curves. These curves can be viewed as a time series of functions observed on equally spaced and dense grids. Due to the curse of dimensionality, high-dimensional data poses challenges from a statistical aspect; however, it also provides opportunities to analyze a rich source of information so that the dynamic changes within short-time intervals can be better understood. We consider a sieve bootstrap method of Paparoditis and Shang (2022) to construct one-day-ahead point and interval forecasts in a model-free way. As we sequentially observe new data, we also implement two dynamic updating methods to update point and interval forecasts for achieving improved accuracy. The forecasting methods are validated through an empirical study of 5-minute cumulative intraday returns of the S&P/ASX All Ordinaries Index.
DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction

Conversational speech often consists of deviations from the speech plan, producing disfluent utterances that affect downstream NLP tasks. Removing these disfluencies is necessary to create fluent and coherent speech. This paper presents DisfluencyFixer, a tool that performs speech-to-speech disfluency correction in English and Hindi using a pipeline of Automatic Speech Recognition (ASR), Disfluency Correction (DC) and Text-To-Speech (TTS) models. Our proposed system removes disfluencies from input speech and returns fluent speech as output along with its transcript, disfluency type and total disfluency count in source utterance, providing a one-stop destination for language learners to improve the fluency of their speech. We evaluate the performance of our tool subjectively and receive scores of 4.26, 4.29 and 4.42 out of 5 in ASR performance, DC performance and ease-of-use of the system. Our tool can be accessed openly at the following link.
A Search for Extraterrestrial Technosignatures in Archival FAST Survey Data Using a New Procedure

The search for extraterrestrial intelligence (SETI) commensal surveys aim to scan the sky to find possible technosignatures from an extraterrestrial intelligence (ETI). The mitigation of radio frequency interference (RFI) is an important step, especially for the most sensitive Five-hundred-meter Aperture Spherical radio Telescope (FAST), which can detect more weak RFI. In this paper, we propose several new techniques for RFI mitigation and use our procedure to search for ETI signals from the archival data of FAST’s first SETI commensal survey. We detect the persistent narrowband RFI by setting a threshold of the signals’ sky separation and detect the drifting RFI (and potentially other types of RFI) using the Hough transform. We also use the clustering algorithms to remove more RFI and select candidates. The results of our procedure are compared to the earlier work on the same FAST data. We find that our methods, though relatively simpler in computation, remove more RFI (99.9912% compared to 99.9063% in the earlier work) but preserve the simulated ETI signals, except for those (5.1%) severely affected by the RFI. We also report more interesting candidate signals, about a dozen of which are new candidates that were not previously reported. In addition, we find that the proposed Hough transform method, with suitable parameters, also has the potential to remove the broadband RFI. We conclude that our methods can effectively remove the vast majority of the RFI while preserving and finding the candidate signals that we are interested in.
Diverse influences of hyperfine interactions on strongly correlated electron states

The motivation to develop materials for quantum technologies has put exploration of novel quantum states of matter at the focus of several research fields, with particular efforts towards understanding and controlling the behaviour of quantum entangled and other strongly interacting electronic states. Experimental investigation is of primary importance, but requires measurements at ultra-low temperatures where the quantum states of interest have long lifetimes. Under these conditions, low energy interactions, such as hyperfine or nuclear exchange interactions, become relevant, and can modify electronic ground states and their associated excitations in multiple ways that are not well understood or characterised. In this work, we use a recently developed magnetic susceptibility technique, compatible with ultra-low temperatures and high magnetic fields, to probe the influence of nuclear interactions on superconducting and multipole ordered ground states in the strongly correlated electron system PrOs4Sb12. We find that the multipole order develops a novel, entangled nuclear-electronic character at the lowest temperatures, which significantly modifies the phase boundary and leads to a nuclear quantum critical point. In the superconducting phase, we find that hyperfine interactions suppress superconductivity in a manner that provides evidence for superconducting pairing mediated by crystal field excitations. Our results on PrOs4Sb12 experimentally establish a new type of non-magnetic, nuclear quantum critical point, and give revealing insight into a highly unusual superconducting state. They also demonstrate more generally the feasibility of exploiting hyperfine interactions as a tuning parameter for experimental creation and investigation of a variety of quantum states and phenomena in correlated electron materials.
Large Field‐of‐View and Multi‐Color Imaging with GaP Quadratic Metalenses

Metalenses, in order to compete with conventional bulk optics in commercial imaging systems, often require large field of view (FOV) and broadband operation simultaneously. However, the strong chromatic and coma aberrations present in common metalens designs have so far limited their widespread use. Stacking of metalenses as one of the possible solutions increases the overall complexity of the optical system and hinders the main benefits of reduced thickness and light weight. To tackle both issues, this study proposes a single‐layer imaging system utilizing a recently developed class of metalenses providing large FOV. Using it, full‐color imaging with a FOV of is demonstrated. This approach, empowered by computational imaging techniques, produces high‐quality images, both in terms of color reproduction and sharpness. Suitable for real‐time unpolarized light operation with the standard color filters present in prevalent camera systems, these results might enable a pathway for consumer electronics applications of this emerging technology.
Emotion Experiencer Recognition as a Prerequisite for Experiencer-Specific Emotion Analysis

Emotion role labeling aims at extracting who is described in text to experience an emotion, why, and towards whom. This is often a challenging modeling task which might be overly sophisticated if the main question to answer is who feels which emotion. Recently, Troiano et al. (2022) proposed a data set that focuses on assigning emotion labels and appraisal labels to individual entities in text and Wegge et al. (2022) presented the first modeling experiments. Their experiencer-specific emotion prediction model has, however, only been evaluated on gold-annotated experiencers, due to the unavailability of an automatic experiencer detection approach. We fill this gap with the first experiments to automatically detect emotion experiencers in text and, subsequently, assign them emotions. We show that experiencer detection in text is a challenging task, with a precision of . 82 and a recall of . 56 (F 1 =.66). Consequently, the performance of the experiencer-specific emotion detection pipeline drops with these predictions in comparison to using gold experiencer annotations. This motivates future work of jointly modeling emotion experiencer detection and emotion/appraisal recognition.
Diable: Efficient Dialogue State Tracking as Operations on Tables

Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn. This approach is inefficient, especially when the number of slots is large and the conversation is long. We propose Diable, a new task formalisation that simplifies the design and implementation of efficient DST systems and allows one to easily plug and play large language models. We represent the dialogue state as a table and formalise DST as a table manipulation task. At each turn, the system updates the previous state by generating table operations based on the dialogue context. Extensive experimentation on the MultiWoz datasets demonstrates that Diable (i) outperforms strong efficient DST baselines, (ii) is 2.4x more time efficient than current state-of-the-art methods while retaining competitive Joint Goal Accuracy, and (iii) is robust to noisy data annotations due to the table operations approach.
On efficient covariate adjustment selection in causal effect estimation

In order to achieve unbiased and efficient estimators of causal effects from observational data, covariate selection for confounding adjustment becomes an important task in causal inference. Despite recent advancements in graphical criterion for constructing valid and efficient adjustment sets, these methods often rely on assumptions that may not hold in practice. We examine the properties of existing graph-free covariate selection methods with respect to both validity and efficiency, highlighting the potential dangers of producing invalid adjustment sets when hidden variables are present. To address this issue, we propose a novel graph-free method, referred to as CMIO, adapted from Mixed Integer Optimization (MIO) with a set of causal constraints. Our results demonstrate that CMIO outperforms existing state-of-the-art methods and provides theoretically sound outputs. Furthermore, we present a revised version of CMIO capable of handling the scenario in the absence of causal sufficiency and graphical information, offering efficient and valid covariate adjustments for causal inference.