Miracle-dz/UniFAR-FTUs huggingface dataset:
Factual Consistency Oriented Speech Recognition

This paper presents a novel optimization framework for automatic speech recognition (ASR) with the aim of reducing hallucinations produced by an ASR model. The proposed framework optimizes the ASR model to maximize an expected factual consistency score between ASR hypotheses and ground-truth transcriptions, where the factual consistency score is computed by a separately trained estimator. Experimental results using the AMI meeting corpus and the VoxPopuli corpus show that the ASR model trained with the proposed framework generates ASR hypotheses that have significantly higher consistency scores with ground-truth transcriptions while maintaining the word error rates close to those of cross entropy-trained ASR models. Furthermore, it is shown that training the ASR models with the proposed framework improves the speech summarization quality as measured by the factual consistency of meeting conversation summaries generated by a large language model.
Heterogeneous source model for magnetoenecephalography

In this paper, we propose a novel source model for a magnetoencephalography (MEG) inverse problem that combines a conventional extended parametric approach and an imaging approach.Our aim is to separately identify a focal current source and background activities spread over the brain.The new source model consists of two terms to represent different spatial characteristics: one is a localized patch source represented with a few parameters based on a mapping from a sphere to the cortex surface,and the other is a distributed source expressed using elemental dipoles on grid points on the cortical surface. We call it a heterogeneous source model, because these two models have not been used simultaneously.Effectiveness of the proposed method is shown via numerical simulations.
STA: Self-controlled Text Augmentation for Improving Text Classifications

Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving natural language problems difficult. Recently, a number of text augmentation techniques have emerged in the field of Natural Language Processing (NLP) which can enrich the training data with new examples, though they are not without their caveats. For instance, simple rule-based heuristic methods are effective, but lack variation in semantic content and syntactic structure with respect to the original text. On the other hand, more complex deep learning approaches can cause extreme shifts in the intrinsic meaning of the text and introduce unwanted noise into the training data. To more reliably control the quality of the augmented examples, we introduce a state-of-the-art approach for Self-Controlled Text Augmentation (STA). Our approach tightly controls the generation process by introducing a self-checking procedure to ensure that generated examples retain the semantic content of the original text. Experimental results on multiple benchmarking datasets demonstrate that STA substantially outperforms existing state-of-the-art techniques, whilst qualitative analysis reveals that the generated examples are both lexically diverse and semantically reliable.
Detection of anomalously emitting ships through deviations from predicted TROPOMI NO2 retrievals

Starting from 2021, more demanding NO x emission restrictions were introduced for ships operating in the North and Baltic Sea waters. Since all methods currently used for ship compliance monitoring are financially and time demanding, it is important to pri-oritize the inspection of ships that have high chances of being non-compliant. The current state-of-the-art approach for a large-scale ship NO 2 estimation is a supervised machine learning-based segmentation of ship plumes on TROPOMI images. However, challenging data annotation and insufficiently complex ship emission proxy used for the validation limit the applicability of the model for ship compliance monitoring. In this study, we present a method for the automated selection of potentially non-compliant ships using a combination of machine learning models on TROPOMI/S5P satellite data. It is based on a proposed regression model predicting the amount of NO 2 that is expected to be produced by a ship with certain properties operating in the given atmospheric conditions. The model does not require manual labeling and is validated with TROPOMI data directly. The differences between the predicted and actual amount of produced NO 2 are integrated over different observations of the same ship in time and are used as a measure of the inspection worthiness of a ship. To assure the robustness of the results, we compare the obtained results with the results of the previously developed segmentation-based method. Ships that are also highly deviating in accordance with the segmentation method require further attention. If no other explanations can be found by checking the TROPOMI data, the respective ships are advised to be the candidates for inspection.
A Machine Learning Approach for Hierarchical Classification of Software Requirements

Context: Classification of software requirements into different categories is a critically important task in requirements engineering (RE). Developing machine learning (ML) approaches for requirements classification has attracted great interest in the RE community since the 2000s. Objective: This paper aims to address two related problems that have been challenging real-world applications of ML approaches: the problems of class imbalance and high dimensionality with low sample size data (HDLSS). These problems can greatly degrade the classification performance of ML methods. Method: The paper proposes HC4RC, a novel ML approach for multiclass classification of requirements. HC4RC solves the aforementioned problems through semantic-role-based feature selection, dataset decomposition and hierarchical classification. We experimentally compare the effectiveness of HC4RC with three closely related approaches - two of which are based on a traditional statistical classification model whereas one uses an advanced deep learning model. Results: Our experiment shows: 1) The class imbalance and HDLSS problems present a challenge to both traditional and advanced ML approaches. 2) The HC4RC approach is simple to use and can effectively address the class imbalance and HDLSS problems compared to similar approaches. Conclusion: This paper makes an important practical contribution to addressing the class imbalance and HDLSS problems in multiclass classification of software requirements.
DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference

To accelerate the inference of deep neural networks (DNNs), quantization with low-bitwidth numbers is actively researched. A prominent challenge is to quantize the DNN models into low-bitwidth numbers without significant accuracy degradation, especially at very low bitwidths (<8 bits). This work targets an adaptive data representation with variable-length encoding called DyBit. DyBit can dynamically adjust the precision and range of separate bit-field to be adapted to the DNN weights/activations distribution. We also propose a hardware-aware quantization framework with a mixed-precision accelerator to trade-off the inference accuracy and speedup. Experimental results demonstrate that the inference accuracy via DyBit is 1.997% higher than the state-of-the-art at 4-bit quantization, and the proposed framework can achieve up to 8.1x speedup compared with the original model.
Practical Knowledge Distillation: Using DNNs to Beat DNNs

For tabular data sets, we explore data and model distillation, as well as data denoising. These techniques improve both gradient-boosting models and a specialized DNN architecture. While gradient boosting is known to outperform DNNs on tabular data, we close the gap for datasets with 100K+ rows and give DNNs an advantage on small data sets. We extend these results with input-data distillation and optimized ensembling to help DNN performance match or exceed that of gradient boosting. As a theoretical justification of our practical method, we prove its equivalence to classical cross-entropy knowledge distillation. We also qualitatively explain the superiority of DNN ensembles over XGBoost on small data sets. For an industry end-to-end real-time ML platform with 4M production inferences per second, we develop a model-training workflow based on data sampling that distills ensembles of models into a single gradient-boosting model favored for high-performance real-time inference, without performance loss. Empirical evaluation shows that the proposed combination of methods consistently improves model accuracy over prior best models across several production applications deployed worldwide.
Retrospective Uncertainties for Deep Models using Vine Copulas

Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge. Existing solutions rely on modified loss functions or architectural changes. We propose to compensate for the lack of built-in uncertainty estimates by supplementing any network, retrospectively, with a subsequent vine copula model, in an overall compound we call Vine-Copula Neural Network (VCNN). Through synthetic and real-data experiments, we show that VCNNs could be task (regression/classification) and architecture (recurrent, fully connected) agnostic while providing reliable and better-calibrated uncertainty estimates, comparable to state-of-the-art built-in uncertainty solutions.
A model-agnostic analysis of hybrid stars with reactive interfaces

We study hybrid stars considering the effects on stellar stability of the hadron-quark conversion speed at the sharp interface. The equation of state is constructed by combining a model-agnostic hadronic description with a constant speed of sound model for quark matter. We show that current LIGO/Virgo, NICER, low-density nuclear and high-density perturbative QCD constraints can be satisfied in two scenarios, with low and high transition pressures. If the conversion speed at the interface is slow, a new class of dynamically stable hybrid objects is possible and very stiff hadronic equations of state cannot be discarded. Densities tens of times larger than the nuclear saturation density are possible at the center of these objects. We discuss possible formation mechanisms for the new class of hybrid stars and smoking guns for their observational identification.
A generalization of Bondy’s pancyclicity theorem

The \emph{bipartite independence number} of a graph $G$, denoted as $\tilde\alpha(G)$, is the minimal number $k$ such that there exist positive integers $a$ and $b$ with $a+b=k+1$ with the property that for any two sets $A,B\subseteq V(G)$ with $A=a$ and $B=b$, there is an edge between $A$ and $B$. McDiarmid and Yolov showed that if $\delta(G)\geq\tilde \alpha(G)$ then $G$ is Hamiltonian, extending the famous theorem of Dirac which states that if $\delta(G)\geq G/2$ then $G$ is Hamiltonian. In 1973, Bondy showed that, unless $G$ is a complete bipartite graph, Dirac‘s Hamiltonicity condition also implies pancyclicity, i.e., existence of cycles of all the lengths from $3$ up to $n$. In this paper we show that $\delta(G)\geq\tilde \alpha(G)$ implies that $G$ is pancyclic or that $G=K_{\frac{n}{2},\frac{n}{2}}$, thus extending the result of McDiarmid and Yolov, and generalizing the classic theorem of Bondy.
Fiber-optic detection of snow avalanches using telecommunication infrastructure

We demonstrate the detectability of snow avalanches using Distributed Acoustic Sensing (DAS) with existing fiber-optic telecommunication cables. For this, during winter 2021/2022, we interrogated a 10 km long cable closely following the avalanche prone Fluelapass road in the Swiss Alps. In addition to other signals like traffic and earthquakes, the DAS data contain clear recordings of numerous snow avalanches, even though most of them do not reach the cable. Here we present two examples of snow avalanche recordings that could be verified photographically. Our results open new perspectives for cost-effective, near-real-time avalanche monitoring over long distances using pre-installed fiber-optic infrastructure.
Feedback control of multiinput nonlinear systems by extended linearization

Control of a nonlinear system is addressed by considering its family of linearizations. Given that a family of linear state-feedback controllers has been designed to meet some (linear) control objective, it is shown how these controllers can be pieced together into an overall, nonlinear, state-feedback controller. The resulting closed-loop system has the property that its linearizations are equal to the desired linearly controlled systems. A similar approach can be used to design nonlinear state estimators. The approach is illustrated by applying it to the control of a planar two-link manipulator. >
Multi-dimensional Backward Stochastic Differential Equations of Diagonally Quadratic Generators with a Special Structure

The present paper is devoted to the well-posedness of a type of multi-dimensional backward stochastic differential equations (BSDEs) with a diagonally quadratic generator. We give a new priori estimate, and prove that the BSDE admits a unique global solution when the generator has a logarithmic growth of the off-diagonal elements (i.e., for each $i$, the $i$-th component of the generator has a logarithmic growth of the $j$-th row $z^j$ of the variable $z$ for each $j \neq i$). Finally, we give a solvability result when the diagonally quadratic generator is triangular.
Beyond Moments: Robustly Learning Affine Transformations with Asymptotically Optimal Error

We present a polynomial-time algorithm for robustly learning an unknown affine transformation of the standard hypercube from samples, an important and well-studied setting for independent component analysis (ICA). Specifically, given an $\varepsilon$-corrupted sample from a distribution D obtained by applying an unknown affine transformation $x \rightarrow A x+b$ to the uniform distribution on a d-dimensional hypercube $[-1,1]^{d}$, our algorithm constructs $\widehat{A}, \hat{b}$ such that the total variation distance of the distribution $\widehat{D}$ from D is $O(\varepsilon)$ using poly $(d)$ time and samples. Total variation distance is the information-theoretically strongest possible notion of distance in our setting and our recovery guarantees in this distance are optimal up to the absolute constant factor multiplying $\varepsilon$. In particular, if the rows of A are normalized to be unit length, our total variation distance guarantee implies a bound on the sum of the $\ell_{2}$ distances between the row vectors of A and $A^{\prime}, \sum_{i=1}^{d}\left\|a_{(i)}-\hat{a}_{(i)}\right\|_{2}=O(\varepsilon)$. In contrast, the strongest known prior results only yield an $\varepsilon^{O(1)}$ (relative) bound on the distance between individual $a_{i}$’s and their estimates and translate into an $O\left(d \varepsilon^{O(1)}\right)$ bound on the total variation distance.Prior algorithms for this problem rely on implementing standard approaches [12] for ICA based on the classical method of moments [18], [32] combined with robust moment estimators. We prove that any approach that relies on method of moments must provably fail to obtain a dimension independent bound on the total error $\sum_{i}\left\|a_{(i)}-\hat{a}_{(i)}\right\|_{2}$ (and consequently, also in total variation distance). Our key innovation is a new approach to ICA (even to outlier-free ICA) that circumvents the difficulties in the classical method of moments and instead relies on a new geometric certificate of correctness of an affine transformation. Our algorithm, Robust Gradient Descent, is based on a new method that iteratively improves its estimate of the unknown affine transformation whenever the requirements of the certificate are not met.
Bender-Knuth involutions on linear extensions of posets

We study the permutation group $\mathcal{BK}_P$ generated by Bender-Knuth moves on linear extensions of a poset $P$, an analog of the Berenstein-Kirillov group on column-strict tableaux. We explore the group relations, with an emphasis on identifying posets $P$ for which the cactus relations hold in $\mathcal{BK}_P$. We also examine $\mathcal{BK}_P$ as a subgroup of the symmetric group $\mathfrak{S}_{\mathcal{L}(P)}$ on the set of linear extensions of $P$ with the focus on analyzing posets $P$ for which $\mathcal{BK}_P = \mathfrak{S}_{\mathcal{L}(P)}$.
A Note on Bootstrapping Generalized Method of Moments Estimators

Recently, Arcones and Giné (1992, pp. 13–47, in R. LePage & L. Billard [eds.], Exploring the Limits of Bootstrap, New York: Wiley) established that the bootstrap distribution of the M-estimator converges weakly to the limit distribution of the estimator in probability. In contrast, Brown and Newey (1992, Bootstrapping for GMM, Seminar note) discovered that the bootstrap distribution of the GMM overidentification test statistic does not converge weakly to the x2 distribution. In this paper, it is shown that the bootstrap distribution of the GMM estimator converges weakly to the limit distribution of the estimator in probability. Asymptotic coverage probabilities of the confidence intervals based on the bootstrap percentile method are thus equal to their nominal coverage probability.
Characterizing Novelty in the Military Domain

A critical factor in utilizing agents with Artificial Intelligence (AI) is their robustness to novelty. AI agents include models that are either engineered or trained. Engineered models include knowledge of those aspects of the environment that are known and considered important by the engineers. Learned models form embeddings of aspects of the environment based on connections made through the training data. In operation, however, a rich environment is likely to present challenges not seen in training sets or accounted for in engineered models. Worse still, adversarial environments are subject to change by opponents. A program at the Defense Advanced Research Project Agency (DARPA) seeks to develop the science necessary to develop and evaluate agents that are robust to novelty. This capability will be required, before AI has the role envisioned within mission critical environments. As part of the Science of AI and Learning for Open-world Novelty (SAIL-ON), we are mapping possible military domain novelty types to a domain-independent ontology developed as part of a theory of novelty. Characterizing the possible space of novelty mathematically and ontologically will allow us to experiment with agent designs that are coming from the DARPA SAIL-ON program in relevant military environments. Utilizing the same techniques as being used in laboratory experiments, we will be able to measure agent ability to detect, characterize, and accommodate novelty.
GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations

Counterfactual explanations are a common tool to explain artificial intelligence models. For Reinforcement Learning (RL) agents, they answer"Why not? "or"What if? "questions by illustrating what minimal change to a state is needed such that an agent chooses a different action. Generating counterfactual explanations for RL agents with visual input is especially challenging because of their large state spaces and because their decisions are part of an overarching policy, which includes long-term decision-making. However, research focusing on counterfactual explanations, specifically for RL agents with visual input, is scarce and does not go beyond identifying defective agents. It is unclear whether counterfactual explanations are still helpful for more complex tasks like analyzing the learned strategies of different agents or choosing a fitting agent for a specific task. We propose a novel but simple method to generate counterfactual explanations for RL agents by formulating the problem as a domain transfer problem which allows the use of adversarial learning techniques like StarGAN. Our method is fully model-agnostic and we demonstrate that it outperforms the only previous method in several computational metrics. Furthermore, we show in a user study that our method performs best when analyzing which strategies different agents pursue.
Constraints on the Cosmological Coupling of Black Holes from the Globular Cluster NGC 3201

Globular clusters are among the oldest stellar populations in the Milky Way; consequently, they also host some of the oldest known stellar-mass black holes, providing insight into black hole formation and evolution in the early (z ≳ 2) universe. Recent observations of supermassive black holes in elliptical galaxies have been invoked to suggest the possibility of a cosmological coupling between astrophysical black holes and the surrounding expanding universe, offering a mechanism for black holes to grow over cosmic time and potentially explaining the origin of dark energy. In this paper, I show that the mass functions of the two radial velocity black hole candidates in NGC 3201 place strong constraints on the cosmologically coupled growth of black holes. In particular, the amount of coupling required to explain the origin of dark energy would either require both NGC 3201 black holes to be nearly face on (a configuration with probability of at most 10−4) or one of the BHs would need to have formed with a mass below that of the most massive neutron stars (2.2 M ⊙). This emphasizes that these and other detached black hole–star binaries can serve not only as laboratories for compact object and binary astrophysics but as constraints on the long-term evolution of astrophysical black holes.
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning

We study differentially private (DP) machine learning algorithms as instances of noisy fixed-point iterations, in order to derive privacy and utility results from this well-studied framework. We show that this new perspective recovers popular private gradient-based methods like DP-SGD and provides a principled way to design and analyze new private optimization algorithms in a flexible manner. Focusing on the widely-used Alternating Directions Method of Multipliers (ADMM) method, we use our general framework to derive novel private ADMM algorithms for centralized, federated and fully decentralized learning. For these three algorithms, we establish strong privacy guarantees leveraging privacy amplification by iteration and by subsampling. Finally, we provide utility guarantees using a unified analysis that exploits a recent linear convergence result for noisy fixed-point iterations.
Uncertainty Injection: A Deep Learning Method for Robust Optimization

This paper proposes a paradigm of uncertainty injection for training deep learning model to solve robust optimization problems. The majority of existing studies on deep learning focus on the model learning capability, while assuming the quality and accuracy of the inputs data can be guaranteed. However, in realistic applications of deep learning for solving optimization problems, the accuracy of inputs, which are the problem parameters in this case, plays a large role. This is because, in many situations, it is often costly or sometime impossible to obtain the problem parameters accurately, and correspondingly, it is highly desirable to develop learning algorithms that can account for the uncertainties in the input and produce solutions that are robust against these uncertainties. This paper presents a novel uncertainty injection scheme for training machine learning models that are capable of implicitly accounting for the uncertainties and producing statistically robust solutions. We further identify the wireless communications as an application field where uncertainties are prevalent in problem parameters such as the channel coefficients. We show the effectiveness of the proposed training scheme in two applications: the robust power loading for multiuser multiple-input-multiple-output (MIMO) downlink transmissions; and the robust power control for device-to-device (D2D) networks.