Miracle-dz/UniFAR-FTUs huggingface dataset:
Renovation of Seoul Radio Astronomy Observatory and Its First Millimeter VLBI Observations

: The Seoul Radio Astronomy Observatory (SRAO) operates a 6.1-meter radio telescope on the Gwanak campus of Seoul National University. We present the eﬀorts to reform SRAO to a Very Long Baseline Interferometry (VLBI) station, motivated by recent achievements by millimeter interferometer networks such as Event Horizon Telescope, East Asia VLBI Network, and Korean VLBI Network (KVN). For this goal, we installed a receiver that had been used in the Combined Array for Research in Millimeter-wave Astronomy and a digital backend, including an H-maser clock. The existing hardware and software were also revised, which had been dedicated only to single-dish operations. After several years of prepara-tions and test observations in 1 and 3-millimeter bands, a fringe was successfully detected toward 3C 84 in 86 GHz in June 2022 for a baseline between SRAO and KVN Ulsan station separated by 300 km. Thanks to the dual frequency operation of the receiver, the VLBI observations will soon be extended to the 1 mm band and verify the frequency phase referencing technique between 1 and 3-millimeter bands.
Large low background kTon-scale liquid argon time projection chambers

We find that it is possible to increase sensitivity to low energy physics in a third or fourth DUNE-like module with careful controls over radiopurity and targeted modifications to a detector similar to the DUNE Far Detector design. In particular, sensitivity to supernova and solar neutrinos can be enhanced with improved MeV-scale reach. A neutrinoless double beta decay search with $^{136}$Xe loading appears feasible. Furthermore, sensitivity to Weakly-Interacting Massive Particle (WIMP) Dark Matter (DM) becomes competitive with the planned world program in such a detector, offering a unique seasonal variation detection that is characteristic for the nature of WIMPs.
Is embodied interaction beneficial? A study on navigating network visualizations

Network visualizations are commonly used to analyze relationships in various contexts, such as social, biological, and geographical interactions. To efficiently explore a network visualization, the user needs to quickly navigate to different parts of the network and analyze local details. Recent advancements in display and interaction technologies inspire new visions for improved visualization and interaction design. Past research into network design has identified some key benefits to visualizing networks in 3D versus 2D. However, little work has been done to study the impact of varying levels of embodied interaction on network analysis. We present a controlled user study that compared four network visualization environments featuring conditions and hardware that leveraged different amounts of embodiment and visual perception ranging from a 2D visualization desktop environment with a standard mouse to a 3D visualization virtual reality environment. We measured the accuracy, speed, perceived workload, and preferences of 20 participants as they completed three network analytic tasks, each of which required unique navigation and substantial effort to complete. For the task that required participants to iterate over the entire visualization rather than focus on a specific area, we found that participants were more accurate using a VR HMD and a trackball mouse than conventional desktop settings. From a workload perspective, VR was generally considered the least mentally demanding and least frustrating to use in two of our three tasks. It was also preferred and ranked as the most effective and visually appealing condition overall. However, using VR to compare two side-by-side networks was difficult, and it was similar to or slower than other conditions in two of the three tasks. Overall, the accuracy and workload advantages of conditions with greater embodiment in specific tasks suggest promising opportunities to create more effective environments in which to analyze network visualizations.
Error estimates for a Gaussian rule involving Bessel functions

This paper deals with the estimation of the quadrature error of a Gaussian formula for weight functions involving fractional powers, exponentials and Bessel functions of the first kind. For this purpose, in this work the averaged and generalized averaged Gaussian rules are employed, together with a tentative a priori approximation of the error. The numerical examples confirm the reliability of these approaches.
Candidate Soups: Fusing Candidate Results Improves Translation Quality for Non-Autoregressive Translation

Non-autoregressive translation (NAT) model achieves a much faster inference speed than the autoregressive translation (AT) model because it can simultaneously predict all tokens during inference. However, its translation quality suffers from degradation compared to AT. And existing NAT methods only focus on improving the NAT model’s performance but do not fully utilize it. In this paper, we propose a simple but effective method called “Candidate Soups,” which can obtain high-quality translations while maintaining the inference speed of NAT models. Unlike previous approaches that pick the individual result and discard the remainders, Candidate Soups (CDS) can fully use the valuable information in the different candidate translations through model uncertainty. Extensive experiments on two benchmarks (WMT’14 EN–DE and WMT’16 EN–RO) demonstrate the effectiveness and generality of our proposed method, which can significantly improve the translation quality of various base models. More notably, our best variant outperforms the AT model on three translation tasks with 7.6× speedup.
Quantized Deep Path-following Control on a Microcontroller

Model predictive Path-Following Control (MPFC) is a viable option for motion systems in many application domains. However, despite considerable progress on tailored numerical methods for predictive control, the real-time implementation of predictive control and MPFC on small-scale autonomous platforms with low-cost embedded hardware remains challenging. While usual stabilizing MPC formulations lead to static feedback laws, the MPFC feedback turns out to be dynamic as the path parameter acts as an internal controller variable. In this paper, we leverage deep learning to implement predictive path-following control on microcontrollers. We show that deep neural networks can approximate the dynamic MPFC feedback law accurately. Moreover, we illustrate and tackle the challenges that arise if the target platform employs limited precision arithmetic. Specifically, we draw upon a post-stabilization with an additional feedback law to attenuate undesired quantization effects. Simulation examples underpin the efficacy of the proposed approach.
Convolutional neural networks for valid and efficient causal inference

Convolutional neural networks (CNN) have been successful in machine learning applications. Their success relies on their ability to consider space invariant local features. We consider the use of CNN to fit nuisance models in semiparametric estimation of the average causal effect of a treatment. In this setting, nuisance models are functions of pre-treatment covariates that need to be controlled for. In an application where we want to estimate the effect of early retirement on a health outcome, we propose to use CNN to control for time-structured covariates. Thus, CNN is used when fitting nuisance models explaining the treatment and the outcome. These fits are then combined into an augmented inverse probability weighting estimator yielding efficient and uniformly valid inference. Theoretically, we contribute by providing rates of convergence for CNN equipped with the rectified linear unit activation function and compare it to an existing result for feedforward neural networks. We also show when those rates guarantee uniformly valid inference. A Monte Carlo study is provided where the performance of the proposed estimator is evaluated and compared with other strategies. Finally, we give results on a study of the effect of early retirement on hospitalization using data covering the whole Swedish population.
Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?

In this article, we use probing to investigate phenomena that occur during fine-tuning and knowledge distillation of a BERT-based natural language understanding (NLU) model. Our ultimate purpose was to use probing to better understand practical production problems and consequently to build better NLU models. We designed experiments to see how fine-tuning changes the linguistic capabilities of BERT, what the optimal size of the fine-tuning dataset is, and what amount of information is contained in a distilled NLU based on a tiny Transformer. The results of the experiments show that the probing paradigm in its current form is not well suited to answer such questions. Structural, Edge and Conditional probes do not take into account how easy it is to decode probed information. Consequently, we conclude that quantification of information decodability is critical for many practical applications of the probing paradigm.
Causal Bandits without Graph Learning

We study the causal bandit problem when the causal graph is unknown and develop an efficient algorithm for finding the parent node of the reward node using atomic interventions. We derive the exact equation for the expected number of interventions performed by the algorithm and show that under certain graphical conditions it could perform either logarithmically fast or, under more general assumptions, slower but still sublinearly in the number of variables. We formally show that our algorithm is optimal as it meets the universal lower bound we establish for any algorithm that performs atomic interventions. Finally, we extend our algorithm to the case when the reward node has multiple parents. Using this algorithm together with a standard algorithm from bandit literature leads to improved regret bounds.
Fast optical measurement of intraocular straylight

Light scattering in the human eye can deteriorate image quality and limit visual performance especially at the presence of a glare source. Optical measurement of straylight in the human eye is a challenging task where issues related to various inherent artifacts must be addressed. We report on a novel instrument based on the principle of double-pass optical integration that has been adapted for fast measurements suitable for a clinical setting. The instrument utilizes a light source formed by an array of green light emitting diodes that is projected onto the ocular fundus. The source has two concentric parts, a disk (field angle 0-3 degrees) and an annulus (3 - 8 degrees) that are modulated at different frequencies. A silicon photomultiplier receives the light reflected from the central part of the fundus and the Fourier transform of the signal reveals the contribution of each part of the source. Their relative amplitude is used to quantify light scattering by means of the straylight parameter. The instrument was initially validated using known diffusers. Straylight in a cohort of cataract patients (N=39) was measured. The optically measured straylight parameter was correlated to the clinical cataract grade as well to the psychophysically estimated value. The measurement method, utilizing rotational symmetry and coding filed angles with different frequencies eliminates the need for a highperformance camera and allows fast measurements. This approach can be further advanced with multiple wavelengths and field angles to perform other measurements such as that of the macular pigment density.
Electron spin secluded inside a bottom-up assembled standing metal-molecule nanostructure

Artificial nanostructures, fabricated by placing building blocks such as atoms or molecules in well-defined positions, are a powerful platform in which quantum effects can be studied and exploited on the atomic scale. Here, we report a strategy to significantly reduce the electron-electron coupling between a large planar aromatic molecule and the underlying metallic substrate. To this end, we use the manipulation capabilities of a scanning tunneling microscope (STM) and lift the molecule into a metastable upright geometry on a pedestal of two metal atoms. Measurements at millikelvin temperatures and magnetic fields reveal that the bottom-up assembled standing metal-molecule nanostructure has an $S = \frac{1}{2}$ spin which is screened by the substrate electrons, resulting in a Kondo temperature of only $291 \pm 13$ mK. We extract the Land\'e $g$-factor of the molecule and the exchange coupling $J\rho$ to the substrate by modeling the differential conductance spectra using a third-order perturbation theory in the weak coupling and high-field regimes. Furthermore, we show that the interaction between the STM tip and the molecule can tune the exchange coupling to the substrate, which suggests that the bond between the standing metal-molecule nanostructure and the surface is mechanically soft.
Certified Invertibility in Neural Networks via Mixed-Integer Programming

Neural networks are known to be vulnerable to adversarial attacks, which are small, imperceptible perturbations that can significantly alter the network's output. Conversely, there may exist large, meaningful perturbations that do not affect the network's decision (excessive invariance). In our research, we investigate this latter phenomenon in two contexts: (a) discrete-time dynamical system identification, and (b) the calibration of a neural network's output to that of another network. We examine noninvertibility through the lens of mathematical optimization, where the global solution measures the ``safety"of the network predictions by their distance from the non-invertibility boundary. We formulate mixed-integer programs (MIPs) for ReLU networks and $L_p$ norms ($p=1,2,\infty$) that apply to neural network approximators of dynamical systems. We also discuss how our findings can be useful for invertibility certification in transformations between neural networks, e.g. between different levels of network pruning.
Revisiting Discriminative Entropy Clustering and its relation to K-means

Maximization of mutual information between the model’s input and output is formally related to “decisiveness” and “fairness” of the softmax predictions (Bridle et al., 1991), motivating such un-supervised entropy-based losses for discriminative neural networks. Recent self-labeling meth-ods based on such losses represent the state of the art in deep clustering. However, some important properties of entropy clustering are not well-known or even misunderstood. For example, we provide a counterexample to prior claims about equivalence to variance clustering (K-means) and point out technical mistakes in such theories. We discuss the fundamental differences between these discriminative and generative clustering approaches. Moreover, we show the susceptibility of standard entropy clustering to narrow margins and motivate an explicit margin maximization term. We also propose an improved self-labeling loss; it is robust to pseudo-labeling errors and enforces stronger fairness. We develop an EM algorithm for our loss that is significantly faster than the standard alternatives. Our results improve the state-of-the-art on standard benchmarks.
Graph Attention with Hierarchies for Multi-hop Question Answering

Multi-hop QA (Question Answering) is the task of finding the answer to a question across multiple documents. In recent years, a number of Deep Learning-based approaches have been proposed to tackle this complex task, as well as a few standard benchmarks to assess models Multi-hop QA capabilities. In this paper, we focus on the well-established HotpotQA benchmark dataset, which requires models to perform answer span extraction as well as support sentence prediction. We present two extensions to the SOTA Graph Neural Network (GNN) based model for HotpotQA, Hierarchical Graph Network (HGN): (i) we complete the original hierarchical structure by introducing new edges between the query and context sentence nodes; (ii) in the graph propagation step, we propose a novel extension to Hierarchical Graph Attention Network GATH (Graph ATtention with Hierarchies) that makes use of the graph hierarchy to update the node representations in a sequential fashion. Experiments on HotpotQA demonstrate the efficiency of the proposed modifications and support our assumptions about the effects of model related variables.
Image Restoration with Mean-Reverting Stochastic Differential Equations

This paper presents a stochastic differential equation (SDE) approach for general-purpose image restoration. The key construction consists in a mean-reverting SDE that transforms a high-quality image into a degraded counterpart as a mean state with fixed Gaussian noise. Then, by simulating the corresponding reverse-time SDE, we are able to restore the origin of the low-quality image without relying on any task-specific prior knowledge. Crucially, the proposed mean-reverting SDE has a closed-form solution, allowing us to compute the ground truth time-dependent score and learn it with a neural network. Moreover, we propose a maximum likelihood objective to learn an optimal reverse trajectory that stabilizes the training and improves the restoration results. The experiments show that our proposed method achieves highly competitive performance in quantitative comparisons on image deraining, deblurring, and denoising, setting a new state-of-the-art on two deraining datasets. Finally, the general applicability of our approach is further demonstrated via qualitative results on image super-resolution, inpainting, and dehazing. Code is available at https://github.com/Algolzw/image-restoration-sde.
Feature space exploration as an alternative for design space exploration beyond the parametric space

This paper compares the parametric design space with a feature space generated by the extraction of design features using deep learning (DL) as an alternative way for design space exploration. In this comparison, the parametric design space is constructed by creating a synthetic dataset of 15.000 elements using a parametric algorithm and reducing its dimensions for visualization. The feature space - reduced-dimensionality vector space of embedded data features - is constructed by training a DL model on the same dataset. We analyze and compare the extracted design features by reducing their dimension and visualizing the results. We demonstrate that parametric design space is narrow in how it describes the design solutions because it is based on the combination of individual parameters. In comparison, we observed that the feature design space can intuitively represent design solutions according to complex parameter relationships. Based on our results, we discuss the potential of translating the features learned by DL models to provide a mechanism for intuitive design exploration space and visualization of possible design solutions.
Method and apparatus for monitoring swimming pools

A swimming pool alarm system is provided for actuating an alarm in response to the movement of a person in a swimming pool. The system includes a transmitter mounted below the surface of a swimming pool to continuously transmit ultrasonic sound waves through the body of water, and a similarly positioned receiver detects sound waves in the water and generates an electrical signal in response thereto. The electrical signal is continuously monitored, and the movement of a person in the pool alters the received ultrasonic sound waves and the corresponding electrical signal to produce an alarm signal for actuating an alarm indicator.
Neural Abstractions

We present a novel method for the safety verification of nonlinear dynamical models that uses neural networks to represent abstractions of their dynamics. Neural networks have extensively been used before as approximators; in this work, we make a step further and use them for the first time as abstractions. For a given dynamical model, our method synthesises a neural network that overapproximates its dynamics by ensuring an arbitrarily tight, formally certified bound on the approximation error. For this purpose, we employ a counterexample-guided inductive synthesis procedure. We show that this produces a neural ODE with non-deterministic disturbances that constitutes a formal abstraction of the concrete model under analysis. This guarantees a fundamental property: if the abstract model is safe, i.e., free from any initialised trajectory that reaches an undesirable state, then the concrete model is also safe. By using neural ODEs with ReLU activation functions as abstractions, we cast the safety verification problem for nonlinear dynamical models into that of hybrid automata with affine dynamics, which we verify using SpaceEx. We demonstrate that our approach performs comparably to the mature tool Flow* on existing benchmark nonlinear models. We additionally demonstrate and that it is effective on models that do not exhibit local Lipschitz continuity, which are out of reach to the existing technologies.
Intrinsically Motivated Compositional Language Emergence

Recently, there has been a great deal of research in emergent communication on artificial agents interacting in simulated environments. Recent studies have revealed that, in general, emergent languages do not follow the compositionality patterns of natural language. To deal with this, existing works have proposed a limited channel capacity as an important constraint for learning highly compositional languages. In this paper, we show that this is not a sufficient condition and propose an intrinsic reward framework for improving compositionality in emergent communication. We use a reinforcement learning setting with two agents -- a \textit{task-aware} Speaker and a \textit{state-aware} Listener that are required to communicate to perform a set of tasks. Through our experiments on three different referential game setups, including a novel environment gComm, we show intrinsic rewards improve compositionality scores by $\approx \mathbf{1.5-2}$ times that of existing frameworks that use limited channel capacity.
The Svalbard treaty and identity of place: Impacts and implications for Longyearbyen, Svalbard

Abstract The Svalbard Treaty established Norway’s full and absolute sovereignty over the Arctic archipelago of Svalbard. At the same time, it also established special territorial status for the archipelago, providing nationals of all signatory nations equal access to its resources. During fieldwork in Longyearbyen, conducted as part of a bottom-up exploration of place in 2018, several recurring issues came up in the analysis of interviews with residents using grounded theory methodology. Two of these issues, sometimes linked and sometimes seen as separate, were a questioning of the legitimacy of the community and a sense of geopolitical vulnerability. These emerging categories led to a series of focus groups, conducted between December 2018 and November 2019, that was designed to explore the impacts and implications of the Svalbard Treaty through the articulation of residents’ lived experiences in Longyearbyen. This paper examines the findings that emerged within an Identity of Place framing that point to an inherent conflict between the Svalbard Treaty’s special territorial status and the possibility of establishing a fully functioning local democracy in Longyearbyen.