Miracle-dz/UniFAR-FTUs huggingface dataset:
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA

Ambiguous questions persist in open-domain question answering, because formulating a precise question with a unique answer is often challenging. Previously, Min et al. (2020) have tackled this issue by generating disambiguated questions for all possible interpretations of the ambiguous question. This can be effective, but not ideal for providing an answer to the user. Instead, we propose to ask a clarification question, where the user's response will help identify the interpretation that best aligns with the user's intention. We first present CAMBIGNQ, a dataset consisting of 5,654 ambiguous questions, each with relevant passages, possible answers, and a clarification question. The clarification questions were efficiently created by generating them using InstructGPT and manually revising them as necessary. We then define a pipeline of tasks and design appropriate evaluation metrics. Lastly, we achieve 61.3 F1 on ambiguity detection and 40.5 F1 on clarification-based QA, providing strong baselines for future work.
ConvBoost

Human activity recognition (HAR) is one of the core research themes in ubiquitous and wearable computing. With the shift to deep learning (DL) based analysis approaches, it has become possible to extract high-level features and perform classification in an end-to-end manner. Despite their promising overall capabilities, DL-based HAR may suffer from overfitting due to the notoriously small, often inadequate, amounts of labeled sample data that are available for typical HAR applications. In response to such challenges, we propose ConvBoost -- a novel, three-layer, structured model architecture and boosting framework for convolutional network based HAR. Our framework generates additional training data from three different perspectives for improved HAR, aiming to alleviate the shortness of labeled training data in the field. Specifically, with the introduction of three conceptual layers--Sampling Layer, Data Augmentation Layer, and Resilient Layer--we develop three "boosters"--R-Frame, Mix-up, and C-Drop--to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively. These new conceptual layers and boosters, that are universally applicable for any kind of convolutional network, have been designed based on the characteristics of the sensor data and the concept of frame-wise HAR. In our experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, GOTOV) we demonstrate the effectiveness of our ConvBoost framework for HAR applications based on variants of convolutional networks: vanilla CNN, ConvLSTM, and Attention Models. We achieved substantial performance gains for all of them, which suggests that the proposed approach is generic and can serve as a practical solution for boosting the performance of existing ConvNet-based HAR models. This is an open-source project, and the code can be found at https://github.com/sshao2013/ConvBoost
MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra

This paper proposes MP-SENet, a novel Speech Enhancement Network which directly denoises Magnitude and Phase spectra in parallel. The proposed MP-SENet adopts a codec architecture in which the encoder and decoder are bridged by convolution-augmented transformers. The encoder aims to encode time-frequency representations from the input noisy magnitude and phase spectra. The decoder is composed of parallel magnitude mask decoder and phase decoder, directly recovering clean magnitude spectra and clean-wrapped phase spectra by incorporating learnable sigmoid activation and parallel phase estimation architecture, respectively. Multi-level losses defined on magnitude spectra, phase spectra, short-time complex spectra, and time-domain waveforms are used to train the MP-SENet model jointly. Experimental results show that our proposed MP-SENet achieves a PESQ of 3.50 on the public VoiceBank+DEMAND dataset and outperforms existing advanced speech enhancement methods.
Subspace-Configurable Networks

While the deployment of deep learning models on edge devices is increasing, these models often lack robustness when faced with dynamic changes in sensed data. This can be attributed to sensor drift, or variations in the data compared to what was used during offline training due to factors such as specific sensor placement or naturally changing sensing conditions. Hence, achieving the desired robustness necessitates the utilization of either an invariant architecture or specialized training approaches, like data augmentation. Alternatively, input transformations can be treated as a domain shift problem, and solved by post-deployment model adaptation. In this paper, we train a parameterized subspace of configurable networks, where an optimal network for a particular parameter setting is part of this subspace. The obtained subspace is low-dimensional and has a surprisingly simple structure even for complex, non-invertible transformations of the input, leading to an exceptionally high efficiency of subspace-configurable networks (SCNs) when limited storage and computing resources are at stake. We evaluate SCNs on a wide range of standard datasets, architectures, and transformations, and demonstrate their power on resource-constrained IoT devices, where they can take up to 2.4 times less RAM and be 7.6 times faster at inference time than a model that achieves the same test set accuracy, yet is trained with data augmentations to cover the desired range of input transformations.
Do Physics-informed Neural Networks Satisfy Local and Global Mass Balance?

Physics-informed neural networks (PINN) have recently become attractive to solve partial differential equations (PDEs) that describe physics laws. By including PDE-based loss functions, physics laws such as mass balance are enforced softly. In this paper, we examine how well mass balance constraints are satisfied when PINNs are used to solve the resulting PDEs. We investigate PINN's ability to solve the 1D saturated groundwater flow equations for homogeneous and heterogeneous media and evaluate the local and global mass balance errors. We compare the obtained PINN's numerical solution and associated mass balance errors against a two-point finite volume numerical method and the corresponding analytical solution. We also evaluate the accuracy of PINN to solve the 1D saturated groundwater flow equation with and without incorporating hydraulic head as training data. Our results showed that one needs to provide hydraulic head data to obtain an accurate solution for the head for the heterogeneous case. That is, only after adding data at some collocation points the PINN methodology was able to give a solution close to the finite volume and analytical solutions. In addition, we demonstrate that even after adding hydraulic head data at some collocation points, the resulting local mass balance errors are still significant at the other collocation points compared to the finite volume approach. Tuning the PINN's hyperparameters such as the number of collocation points, epochs, and learning rate did not improve the solution accuracy or the mass balance errors compared to the finite volume solution. Mass balance errors may pose a significant challenge to PINN's utility for applications where satisfying physical and mathematical properties is crucial.
Indirect Detection of Decaying Dark Matter with High Angular Resolution: Case for axion search by IRCS at Subaru Telescope

Recent advances in cosmic-ray detectors have provided exceptional sensitivities of dark matter with high angular resolution. Motivated by this, we present a comprehensive study of cosmic-ray flux from dark matter decay in dwarf spheroidal galaxies (dSphs), with a focus on detectors possessing arcsecond-level field of view and/or angular resolution. We propose to use differential $D$-factors, which are estimated for various dSphs since such detectors are sensitive to their dark matter distributions. Our findings reveal that the resulting signal flux can experience a more than $O$(1-10) enhancement with different theoretical uncertainty compared to traditional estimations. Based on this analysis, we find that the Infrared Camera and Spectrograph (IRCS) installed on the 8.2m Subaru telescope can be a good dark matter detector for the mass in the eV range, particularly axion-like particles (ALPs). Observing the Draco or Ursa Major II galaxies with the IRCS for just a few nights will be sufficient to surpass the stellar cooling bounds for ALP dark matter with a mass in the range of $1\,{\rm eV} \lesssim m_a \lesssim 2\,\rm eV$.
From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving

Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions. Even though simulations come with a series of benefits compared to real-world experiments, various challenges still prevent virtual testing from entirely replacing physical test-drives. Our work provides an overview of these challenges with regard to different aspects and types of simulation and subsumes current trends to overcome them. We cover aspects around perception-, behavior- and content-realism as well as general hurdles in the domain of simulation. Among others, we observe a trend of data-driven, generative approaches and high-fidelity data synthesis to increasingly replace model-based simulation.
Conditional Online Learning for Keyword Spotting

Modern approaches for keyword spotting rely on training deep neural networks on large static datasets with i.i. d. distributions. However, the resulting models tend to underperform when presented with changing data regimes in real-life applications. This work investigates a simple but effective online continual learning method that updates a keyword spotter on-device via SGD as new data becomes available. Contrary to previous research, this work focuses on learning the same KWS task, which covers most commercial applications. During experiments with dynamic audio streams in different scenarios, that method improves the performance of a pre-trained small-footprint model by 34%. Moreover, experiments demonstrate that, compared to a naive online learning implementation, conditional model updates based on its performance in a small hold-out set drawn from the training distribution mitigate catastrophic forgetting.
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at github.com/Aldrich2y/MIANet.
Balancing Explainability-Accuracy of Complex Models

Explainability of AI models is an important topic that can have a significant impact in all domains and applications from autonomous driving to healthcare. The existing approaches to explainable AI (XAI) are mainly limited to simple machine learning algorithms, and the research regarding the explainability-accuracy tradeoff is still in its infancy especially when we are concerned about complex machine learning techniques like neural networks and deep learning (DL). In this work, we introduce a new approach for complex models based on the co-relation impact which enhances the explainability considerably while also ensuring the accuracy at a high level. We propose approaches for both scenarios of independent features and dependent features. In addition, we study the uncertainty associated with features and output. Furthermore, we provide an upper bound of the computation complexity of our proposed approach for the dependent features. The complexity bound depends on the order of logarithmic of the number of observations which provides a reliable result considering the higher dimension of dependent feature space with a smaller number of observations.
Continual Learning with Strong Experience Replay

Continual Learning (CL) aims at incrementally learning new tasks without forgetting the knowledge acquired from old ones. Experience Replay (ER) is a simple and effective rehearsal-based strategy, which optimizes the model with current training data and a subset of old samples stored in a memory buffer. To further reduce forgetting, recent approaches extend ER with various techniques, such as model regularization and memory sampling. However, the prediction consistency between the new model and the old one on current training data has been seldom explored, resulting in less knowledge preserved when few previous samples are available. To address this issue, we propose a CL method with Strong Experience Replay (SER), which additionally utilizes future experiences mimicked on the current training data, besides distilling past experience from the memory buffer. In our method, the updated model will produce approximate outputs as its original ones, which can effectively preserve the acquired knowledge. Experimental results on multiple image classification datasets show that our SER method surpasses the state-of-the-art methods by a noticeable margin.
Counterfactual Augmentation for Multimodal Learning Under Presentation Bias

In real-world machine learning systems, labels are often derived from user behaviors that the system wishes to encourage. Over time, new models must be trained as new training examples and features become available. However, feedback loops between users and models can bias future user behavior, inducing a presentation bias in the labels that compromises the ability to train new models. In this paper, we propose counterfactual augmentation, a novel causal method for correcting presentation bias using generated counterfactual labels. Our empirical evaluations demonstrate that counterfactual augmentation yields better downstream performance compared to both uncorrected models and existing bias-correction methods. Model analyses further indicate that the generated counterfactuals align closely with true counterfactuals in an oracle setting.
Liouville Space Neural Network Representation of Density Matrices

Neural network quantum states as ansatz wavefunctions have shown a lot of promise for finding the ground state of spin models. Recently, work has been focused on extending this idea to mixed states for simulating the dynamics of open systems. Most approaches so far have used a purification ansatz where a copy of the system Hilbert space is added which when traced out gives the correct density matrix. Here, we instead present an extension of the Restricted Boltzmann Machine which directly represents the density matrix in Liouville space. This allows the compact representation of states which appear in mean-field theory. We benchmark our approach on two different version of the dissipative transverse field Ising model which show our ansatz is able to compete with other state-of-the-art approaches.
Intersection of Two 5D Submanifolds of the Displacement 6D Lie Group: X(u)X(v)X(s)X(t)

The product of two Schoenflies motion subgroups of the group of general displacements characterizes a noteworthy type of 5-dimensional (5D) displacement set called double Schoenflies or XX motion. It includes any spatial translation and any two sequential rotations whose axes are parallel to two given independent vectors. The irreducible factorizations of the 5D set of XX motions and their primitive generators are briefly recalled; various intersection sets of two XX motions are emphasized. Starting with a canonical factorization of XX product, the general case of the intersection of two XX motion sets is disclosed. Three special cases: 4-DoF Schoenflies motion, bifurcation of 4-DoF X motion and 5-DoF XX motion are obtained. Meanwhile, two general overconstrained 6H chains with one-dof finite mobility that is not paradoxical but exceptional are unveiled. The looseness of the concept of “3T1R” (“three translations and one rotation”) motion is also confirmed with an example.
Abstractive Text Summarization Using the BRIO Training Paradigm

Summary sentences produced by abstractive summarization models may be coherent and comprehensive, but they lack control and rely heavily on reference summaries. The BRIO training paradigm assumes a non-deterministic distribution to reduce the model's dependence on reference summaries, and improve model performance during inference. This paper presents a straightforward but effective technique to improve abstractive summaries by fine-tuning pre-trained language models, and training them with the BRIO paradigm. We build a text summarization dataset for Vietnamese, called VieSum. We perform experiments with abstractive summarization models trained with the BRIO paradigm on the CNNDM and the VieSum datasets. The results show that the models, trained on basic hardware, outperform all existing abstractive summarization models, especially for Vietnamese.
Ion optical clocks with three electronic states

Optical clocks are the apotheosis of precision measurement, but they require frequent maintenance by scientists. The supporting laser systems are a particularly demanding component of these instruments. To reduce complexity and increase robustness we propose an optical clock with trapped alkali-like ions that use the $S_{1/2}\rightarrow D_{3/2}$ electric quadrupole transition. Compared to traditional group-II ion clocks this reduces the number of laser wavelengths required, and uses hyperfine state preparation and readout techniques enabled by the nuclear spin $I=1/2$. We consider $^{225}$Ra$^{+}$ as a candidate system for a clock with three electronic states, and discuss the potential to help realize a transportable optical clock.
Comment on “Weak values and the past of a quantum particle”

In a recent paper, Hance, Rarity and Ladyman [Phys. Rev. Res. {\bf 5}, 023048 (2023)] criticized recent proposals connecting weak values and the past of a quantum particle. I argue that their conclusion follows from a conceptual error in understanding the approach to the past of the particle they discuss.
DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection

The increasingly pervasive facial recognition (FR) systems raise serious concerns about personal privacy, especially for billions of users who have publicly shared their photos on social media. Several attempts have been made to protect individuals from being identified by unauthorized FR systems utilizing adversarial attacks to generate encrypted face images. However, existing methods suffer from poor visual quality or low attack success rates, which limit their utility. Recently, diffusion models have achieved tremendous success in image generation. In this work, we ask: can diffusion models be used to generate adversarial examples to improve both visual quality and attack performance? We propose DiffProtect, which utilizes a diffusion autoencoder to generate semantically meaningful perturbations on FR systems. Extensive experiments demonstrate that DiffProtect produces more natural-looking encrypted images than state-of-the-art methods while achieving significantly higher attack success rates, e.g., 24.5% and 25.1% absolute improvements on the CelebA-HQ and FFHQ datasets.
Consequences of Slow Neural Dynamics for Incremental Learning

In the human brain, internal states are often correlated over time (due to local recurrence and other intrinsic circuit properties), punctuated by abrupt transitions. At first glance, temporal smoothness of internal states presents a problem for learning input-output mappings (e.g. category labels for images), because the internal representation of the input will contain a mixture of current input and prior inputs. However, when training with naturalistic data (e.g. movies) there is also temporal autocorrelation in the input. How does the temporal"smoothness"of internal states affect the efficiency of learning when the training data are also temporally smooth? How does it affect the kinds of representations that are learned? We found that, when trained with temporally smooth data,"slow"neural networks (equipped with linear recurrence and gating mechanisms) learned to categorize more efficiently than feedforward networks. Furthermore, networks with linear recurrence and multi-timescale gating could learn internal representations that"un-mixed"quickly-varying and slowly-varying data sources. Together, these findings demonstrate how a fundamental property of cortical dynamics (their temporal autocorrelation) can serve as an inductive bias, leading to more efficient category learning and to the representational separation of fast and slow sources in the environment.
Waves in inhomogeneous isotropic media

For the case of a lossless medium containing no free charges and possessing a continuous and sufficiently differentiable spatially dependent permeability and permittivity, two vectorial differential wave equations, one for the electric and one for the magnetic f i e l d , are derived through the use of Maxwell's equations. From these two equations necessary conditions,for E-. ahd H-modes . , to exist in a waveguide are established,. The f i e l d equations for the case of constant permeability and z-dependent permittivity as well as the interchanged case are investigated,, A test is developed which, i f met, assures that the solutions are oscillatory for the ordinary differential equations containing the z-dependent part of the wave function. For the dielectric loaded periodic structure the theory for inhomogeneous isotropic media is used to determine the restrictions on the f i e l d components which are necessary before E—modes can exist and to find the Emode wave solutions for the solid disc case when the dielectric regions are matched into the air regions. An investigation i s carried out into the behaviour of plane waves in a medium with the permeability constant and the permittivity varying in the direction of propagation.