Reviewerly/exHarmony huggingface dataset:
Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation

The future of automated driving (AD) is rooted in the development of robust, fair and explainable artificial intelligence methods. Upon request, automated vehicles must be able to explain their decisions to the driver and the car passengers, to the pedestrians and other vulnerable road users and potentially to external auditors in case of accidents. However, nowadays, most explainable methods still rely on quantitative analysis of the AD scene representations captured by multiple sensors. This paper proposes a novel representation of AD scenes, called Qualitative eXplainable Graph (QXG), dedicated to qualitative spatiotemporal reasoning of long-term scenes. The construction of this graph exploits the recent Qualitative Constraint Acquisition paradigm. Our experimental results on NuScenes, an open real-world multi-modal dataset, show that the qualitative eXplainable graph of an AD scene composed of 40 frames can be computed in real-time and light in space storage which makes it a potentially interesting tool for improved and more trustworthy perception and control processes in AD.
On a question of Matt Baker regarding the dollar game

In an introductory paper on dollar game played on a graph, Matt Baker wrote the following: ``The total number of borrowing moves required to win the game when playing the 'borrowing binge strategy' is independent of which borrowing moves you do in which order! Note, however, that it is usually possible to win in fewer moves by employing lending moves in combination with borrowing moves. The optimal strategy when one uses both kinds of moves is not yet understood.'' In this article, we give a lower bound on the minimum number $ M_{\text{min}} $ of such moves of an optimal algorithm in terms of the number of moves $ M_0 $ of the borrowing binge strategy. Concretely, we have: $ M_{\text{min}} \geq \frac{M_0}{n-1} $ where $ n $ is the number of vertices of the graph. This bound is tight.
Fast Adversarial Training with Smooth Convergence

Fast adversarial training (FAT) is beneficial for improving the adversarial robustness of neural networks. However, previous FAT work has encountered a significant issue known as catastrophic overfitting when dealing with large perturbation budgets, \ie the adversarial robustness of models declines to near zero during training. To address this, we analyze the training process of prior FAT work and observe that catastrophic overfitting is accompanied by the appearance of loss convergence outliers. Therefore, we argue a moderately smooth loss convergence process will be a stable FAT process that solves catastrophic overfitting. To obtain a smooth loss convergence process, we propose a novel oscillatory constraint (dubbed ConvergeSmooth) to limit the loss difference between adjacent epochs. The convergence stride of ConvergeSmooth is introduced to balance convergence and smoothing. Likewise, we design weight centralization without introducing additional hyperparameters other than the loss balance coefficient. Our proposed methods are attack-agnostic and thus can improve the training stability of various FAT techniques. Extensive experiments on popular datasets show that the proposed methods efficiently avoid catastrophic overfitting and outperform all previous FAT methods. Code is available at \url{https://github.com/FAT-CS/ConvergeSmooth}.
Astronomical interferometry using continuous variable quantum teleportation

We propose a method to build an astronomical interferometer using continuous variable quantum teleportation to overcome the transmission loss between distant telescopes. The scheme relies on two-mode squeezed states shared by distant telescopes as entanglement resources, which are distributed using continuous variable quantum repeaters. We find the optimal measurement on the teleported states, which uses beam-splitters and photon-number-resolved detection. Compared to prior proposals relying on discrete states, our scheme has the advantages of using linear optics to implement the scheme without wasting stellar photons and making use of multiphoton events, which are regarded as noise in previous discrete schemes.
Beyond Document Page Classification: Design, Datasets, and Challenges

This paper highlights the need to bring document classification benchmarking closer to real-world applications, both in the nature of data tested ($X$: multi-channel, multi-paged, multi-industry; $Y$: class distributions and label set variety) and in classification tasks considered ($f$: multi-page document, page stream, and document bundle classification, ...). We identify the lack of public multi-page document classification datasets, formalize different classification tasks arising in application scenarios, and motivate the value of targeting efficient multi-page document representations. An experimental study on proposed multi-page document classification datasets demonstrates that current benchmarks have become irrelevant and need to be updated to evaluate complete documents, as they naturally occur in practice. This reality check also calls for more mature evaluation methodologies, covering calibration evaluation, inference complexity (time-memory), and a range of realistic distribution shifts (e.g., born-digital vs. scanning noise, shifting page order). Our study ends on a hopeful note by recommending concrete avenues for future improvements.}
Panoptic-Depth Color Map for Combination of Depth and Image Segmentation

Image segmentation and depth estimation are crucial tasks in computer vision, especially in autonomous driving scenarios. Although these tasks are typically addressed separately, we propose an innovative approach to combine them in our novel deep learning network, Panoptic-DepthLab. By incorporating an additional depth estimation branch into the segmentation network, it can predict the depth of each instance segment. Evaluating on Cityscape dataset, we demonstrate the effectiveness of our method in achieving high-quality segmentation results with depth and visualize it with a color map. Our proposed method demonstrates a new possibility of combining different tasks and networks to generate a more comprehensive image recognition result to facilitate the safety of autonomous driving vehicles.
Parallel-in-time quantum simulation via Page and Wootters quantum time

In the past few decades, researchers have created a veritable zoo of quantum algorithm by drawing inspiration from classical computing, information theory, and even from physical phenomena. Here we present quantum algorithms for parallel-in-time simulations that are inspired by the Page and Wooters formalism. In this framework, and thus in our algorithms, the classical time-variable of quantum mechanics is promoted to the quantum realm by introducing a Hilbert space of "clock" qubits which are then entangled with the "system" qubits. We show that our algorithms can compute temporal properties over $N$ different times of many-body systems by only using $\log(N)$ clock qubits. As such, we achieve an exponential trade-off between time and spatial complexities. In addition, we rigorously prove that the entanglement created between the system qubits and the clock qubits has operational meaning, as it encodes valuable information about the system's dynamics. We also provide a circuit depth estimation of all the protocols, showing an exponential advantage in computation times over traditional sequential in time algorithms. In particular, for the case when the dynamics are determined by the Aubry-Andre model, we present a hybrid method for which our algorithms have a depth that only scales as $\mathcal{O}(\log(N)n)$. As a by product we can relate the previous schemes to the problem of equilibration of an isolated quantum system, thus indicating that our framework enable a new dimension for studying dynamical properties of many-body systems.
Counting Distinct Elements Under Person-Level Differential Privacy

We study the problem of counting the number of distinct elements in a dataset subject to the constraint of differential privacy. We consider the challenging setting of person-level DP (a.k.a. user-level DP) where each person may contribute an unbounded number of items and hence the sensitivity is unbounded. Our approach is to compute a bounded-sensitivity version of this query, which reduces to solving a max-flow problem. The sensitivity bound is optimized to balance the noise we must add to privatize the answer against the error of the approximation of the bounded-sensitivity query to the true number of unique elements.
MapPrior: Bird's-Eye View Map Layout Estimation with Generative Models

Despite tremendous advancements in bird's-eye view (BEV) perception, existing models fall short in generating realistic and coherent semantic map layouts, and they fail to account for uncertainties arising from partial sensor information (such as occlusion or limited coverage). In this work, we introduce MapPrior, a novel BEV perception framework that combines a traditional discriminative BEV perception model with a learned generative model for semantic map layouts. Our MapPrior delivers predictions with better accuracy, realism, and uncertainty awareness. We evaluate our model on the large-scale nuScenes benchmark. At the time of submission, MapPrior outperforms the strongest competing method, with significantly improved MMD and ECE scores in camera- and LiDAR-based BEV perception.
POCO: 3D Pose and Shape Estimation with Confidence

The regression of 3D Human Pose and Shape (HPS) from an image is becoming increasingly accurate. This makes the results useful for downstream tasks like human action recognition or 3D graphics. Yet, no regressor is perfect, and accuracy can be affected by ambiguous image evidence or by poses and appearance that are unseen during training. Most current HPS regressors, however, do not report the confidence of their outputs, meaning that downstream tasks cannot differentiate accurate estimates from inaccurate ones. To address this, we develop POCO, a novel framework for training HPS regressors to estimate not only a 3D human body, but also their confidence, in a single feed-forward pass. Specifically, POCO estimates both the 3D body pose and a per-sample variance. The key idea is to introduce a Dual Conditioning Strategy (DCS) for regressing uncertainty that is highly correlated to pose reconstruction quality. The POCO framework can be applied to any HPS regressor and here we evaluate it by modifying HMR, PARE, and CLIFF. In all cases, training the network to reason about uncertainty helps it learn to more accurately estimate 3D pose. While this was not our goal, the improvement is modest but consistent. Our main motivation is to provide uncertainty estimates for downstream tasks; we demonstrate this in two ways: (1) We use the confidence estimates to bootstrap HPS training. Given unlabelled image data, we take the confident estimates of a POCO-trained regressor as pseudo ground truth. Retraining with this automatically-curated data improves accuracy. (2) We exploit uncertainty in video pose estimation by automatically identifying uncertain frames (e.g. due to occlusion) and inpainting these from confident frames. Code and models will be available for research at https://poco.is.tue.mpg.de.
Motion-Guided Masking for Spatiotemporal Representation Learning

Several recent works have directly extended the image masked autoencoder (MAE) with random masking into video domain, achieving promising results. However, unlike images, both spatial and temporal information are important for video understanding. This suggests that the random masking strategy that is inherited from the image MAE is less effective for video MAE. This motivates the design of a novel masking algorithm that can more efficiently make use of video saliency. Specifically, we propose a motion-guided masking algorithm (MGM) which leverages motion vectors to guide the position of each mask over time. Crucially, these motion-based correspondences can be directly obtained from information stored in the compressed format of the video, which makes our method efficient and scalable. On two challenging large-scale video benchmarks (Kinetics-400 and Something-Something V2), we equip video MAE with our MGM and achieve up to +$1.3\%$ improvement compared to previous state-of-the-art methods. Additionally, our MGM achieves equivalent performance to previous video MAE using up to $66\%$ fewer training epochs. Lastly, we show that MGM generalizes better to downstream transfer learning and domain adaptation tasks on the UCF101, HMDB51, and Diving48 datasets, achieving up to +$4.9\%$ improvement compared to baseline methods.
SONAR: Sentence-Level Multimodal and Language-Agnostic Representations

We introduce SONAR, a new multilingual and multimodal fixed-size sentence embedding space. Our single text encoder, covering 200 languages, substantially outperforms existing sentence embeddings such as LASER3 and LabSE on the xsim and xsim++ multilingual similarity search tasks. Speech segments can be embedded in the same SONAR embedding space using language-specific speech encoders trained in a teacher-student setting on speech transcription data. Our encoders outperform existing speech encoders on similarity search tasks. We also provide a text decoder for 200 languages, which allows us to perform text-to-text and speech-to-text machine translation, including for zero-shot language and modality combinations. Our text-to-text results are competitive compared to the state-of-the-art NLLB~1B model, despite the fixed-size bottleneck representation. Our zero-shot speech-to-text translation results compare favorably with strong supervised baselines such as Whisper.
Spectral Variability in Fixed Windows using Fractional Fourier Transform: Application in Power Spectral Density Estimation

In statistical signal processing, power spectral density estimation is a frequency domain analysis in which power contents of a signal are measured with respect to frequency components of that signal. The power estimation of a signal can be carried out more precisely by using a window with a narrower 3-dB bandwidth and higher side-lobe attenuation. Theoretically, these two spectral parameters show trade-off in variable windows and remain constant in fixed windows. In this work, spectral behavior of fixed windows has been elaborated using Fractional Fourier Transform (FRFT) keeping their inherent time domain behavior intact. The FRFT is an extension of conventional Fourier transform with an additional variable parameter, known as rotation angle, which makes it more flexible and useful in various signal processing applications viz. power estimation and designing of tunable transition band FIR filters. In this article, variability in 3-dB bandwidth and sidelobe attenuation of fixed windows has been achieved by exploiting the available flexibility in FRFT and obtained variability has been applied in the estimation of signal power. Simulation results demonstrate that both of these two spectral parameters are improved and hence, trade-off problem between resolution and spectral leakage in the power spectral density estimation is overcome upto an extent.
Perceived organizational support, psychological empowerment and employees’ job performance: Inputs for business management improvement model in internet enterprises

This paper mainly studies the relationship between organizational support, psychological empowerment, employees' job performance of Internet enterprises in China.On the basis of theoretical research on organizational support, psychological empowerment, employees' work performance, combined with self-determination theory, the theoretical conception of this paper is proposed, and the structure and framework of organizational support, psychological empowerment and employees' work performance are set up as the basis for improving employee management in Internet enterprises.This paper uses descriptive research methods and questionnaires as the sources of data collection.Correlation analysis methods are used to make data statistics, aiming to study the degree of organizational support of Internet enterprises in terms of work support, value recognition and concern for employees' interests.Further, we examined the significant differences of organizational support, psychological empowerment and work performance among employees grouped by personal data.Finally, the study examines the significant relationship between work support, employees' work performance and psychological empowerment.Through empirical research, it is found that the three variables of organizational support, psychological empowerment and work performance of employees in Chinese Internet enterprises can influence each other, which can effectively mobilize the enthusiasm of employees and play a positive role in enterprise management.Through the above research, it can be further clarified how Internet enterprises should improve Perceived organizational support and psychological empowerment to achieve the improvement of employees' work performance and the overall economic benefits of enterprises.
Designing an English Coursebook for Senior Executive Administrative Assistants for English Department Students Based on Indonesian National Work Competency Standards (SKKNI)

Designing a comprehensive course book for English for Business is a challenging endeavor that requires a deep understanding of the unique linguistic and professional needs of learners. In the rapidly evolving landscape of international business communication, educators and instructional designers face a lot of obstacles that must be addressed in order to create a truly effective and relevant course book. This study aims to create a coursebook for an English for Business class based on SKKNI in the scheme of executive administrative assistants. The instructor must construct their own coursebook because SKKNI does not give references. Most studies, however, claimed that generating ESP content is challenging due to a lack of sources, assistance, and a national curriculum. Theoretically, this research is based on Borg and Gall's RnD steps, and just 5 steps have been chosen to conduct this research. The researchers conducted expert validation to evaluate the coursebook and administered a questionnaire to students. The coursebook was judged good (73%) by expert validators with some revisions and 79% by students with several features needing improvement in various categories such as typeface, illustration, and mistyped words.
Forging Better Axes: Evaluating and Improving the Measurement of Semantic Dimensions in Word Embeddings

Word embeddings are a powerful tool for measuring cultural meaning using large text corpora. In sociology, some of their most common applications estimate relationships between concepts like intelligence or disability and latent semantic axes like status or stigma. These techniques, however, are underdeveloped, with no metrics to capture axis quality and no clear understanding of what properties this quality entails. We tackle these issues by linking this task to the better-studied problem of solving analogies. This lets us identify properties relevant to axis reliability--parallelism, antonymy, and synonymy--and construct metrics to measure them. We then employ existing survey data with ratings of several hundred target terms along 23 semantic dimensions to calculate the accuracy of 23,000 axes that operationalize these 23 dimensions in embedding space. We use these accuracy ratings to evaluate our metrics. Finally, to demonstrate how our metrics can be applied in empirical research, we use them to improve a semantic axis studied in prior work.
Mobile Learning Research Trends in Indonesia: A Systematic Literature Review

The existence of mobile devices and advances in technology has made mobile learning one of the learning trends in schools ranging from elementary to high school and even college in Indonesia. This content analysis research aims to systematically analyze trends in the development of mobile learning in Indonesia based on analysis of articles found in international and national journals. Based on the search results obtained 61 articles. Article analysis uses the Paper Classification Form (PCF) instrument which has been tested for validity and reliability. Based on the results of the analysis, the most widely used research method for mobile learning is Quasi-Experiment (44%) by testing the results of Research and Development (82%) of mobile learning products. Most mobile learning is for high school students (57%). Android Mobile Learning Apps (52%) are the most widely used mobile learning products in research. Learning outcomes (19%) are the most dominant bond variables related to mobile learning. This result can be used as a reference for research on the development of mobile learning media using the Android platform in science learning in schools.
Development of Class V Science Learning Modules with the SQ3R Methode to Improve Student’s Reading Comprehension Ability at SDN Inpres Kala

This study aims to Produce Class V Science learning modules using the SQ3R method as learning media, Describe the feasibility of Class V Science learning modules, and Analyze the effectiveness of implementing Science learning modules prepared using the SQ3R method in improving reading skills understanding of fifth grade students at SDN Inpres Kala, Bima Regency. This research is an ADDIE model development research. The results of this research resulted in even semester Class V Science learning modules compiled using the SQ3R method (survey, question, read, recite, and review). The module has been validated by experts and has the criteria of being very suitable for use as a learning medium with the eligibility percentage of media experts 98%, material experts 95%, linguists 94%, practitioners 96%, and peer review 92%, and a very good response from teachers by 85%, and 90% students. Science learning modules prepared using the SQ3R method can improve students reading comprehension skills, this is obtained based on the results of hypothesis testing paired sample t-test with result themselves. 2 tailed of 0.000 &lt;0.05, and with a mean-value comparison pretest 42.64 and posttes 55.27. From these results it can be concluded that students reading skills experienced a significant increase.
Rapid Source Characterization of the Maule Earthquake Using Prompt Elasto‐Gravity Signals

Abstract The recently identified Prompt Elasto‐Gravity Signals (PEGS), generated by large earthquakes, propagate at the speed of light and are sensitive to the earthquake magnitude and focal mechanism. These characteristics make PEGS potentially very advantageous for tsunami early warning, which relies on fast and accurate estimation of the magnitude of large offshore earthquakes. PEGS‐based early warning does not suffer from the problem of magnitude estimation saturation, that P‐wave based early warning algorithms have, and could be faster than Global Navigation Satellite Systems (GNSS)‐based systems while not requiring a priori assumptions on slip distribution. We use a deep learning model called PEGSNet to evaluate the possibility to estimate in real time the evolution of the magnitude of big earthquakes in the tsunamigenic zone of Chile. The model is a Convolutional Neural Network (CNN) trained on a database of synthetic PEGS—simulated for an exhaustive set of possible earthquakes distributed along the Chilean subduction zone—augmented with empirical noise. The approach is multi‐station and leverages the information recorded by the seismic network to estimate as fast as possible the magnitude and location of an ongoing earthquake. Our results indicate that PEGSNet could have estimated that the magnitude of the 2010 M w 8.8 Maule earthquake was above 8.7, 90 s after origin time. Our offline simulations using real data and noise recordings further support the instantaneous tracking of the source time function of the earthquake and show that deploying seismic stations in optimal locations could improve the performance of the algorithm.