Miracle-dz/UniFAR-FTUs huggingface dataset:
Correction of Conservative Euler Solvers for Gas Mixtures

Conservative Euler solvers for gas mixtures produce numerical errors near contact discontinuities, if the temperature and the ratio of specific heats are not constant there. For mixtures of perfect gases, a simple correction of the total energy per unit volume is proposed to avoid these errors. This is done in a physical way and only the total energy looses some of its conservativity. Numerical simulations of contact discontinuity convection, a shock tube problem, and shock-interface interactions in 1D and 2D yield much more accurate solutions, if the correction is applied. The straightforward extension to 3D is outlined.
Dual-layer Image Compression via Adaptive Downsampling and Spatially Varying Upconversion

Ultra high resolution (UHR) images are almost always downsampled to fit small displays of mobile end devices and upsampled to its original resolution when exhibited on very high-resolution displays. This observation motivates us on jointly optimizing operation pairs of downsampling and upsampling that are spatially adaptive to image contents for maximal rate-distortion performance. In this paper, we propose an adaptive downsampled dual-layer (ADDL) image compression system. In the ADDL compression system, an image is reduced in resolution by learned content-adaptive downsampling kernels and compressed to form a coded base layer. For decompression the base layer is decoded and upconverted to the original resolution using a deep upsampling neural network, aided by the prior knowledge of the learned adaptive downsampling kernels. We restrict the downsampling kernels to the form of Gabor filters in order to reduce the complexity of filter optimization and also reduce the amount of side information needed by the decoder for adaptive upsampling. Extensive experiments demonstrate that the proposed ADDL compression approach of jointly optimized, spatially adaptive downsampling and upconversion outperforms the state of the art image compression methods.
A note on strongly and totally chain intersecting families

Bern\'ath and Gerbner in 2007 introduced $(p,q)$-chain intersecting families of subsets of an $n$-element underlying set. Those have the property that for any $p$-chain $A_1\subsetneq A_2\subsetneq \dots \subsetneq A_p$ and $q$-chain $B_1\subsetneq B_2\subsetneq \dots \subsetneq B_q$, we have $A_p\cap B_q\neq \emptyset$. Bern\'ath and Gerbner determined the largest cardinality of such families. They also introduced strongly $(p,q)$-chain intersecting families, where $A_p\cap B_1\neq \emptyset$ and totally $(p,q)$-chain intersecting families, where $A_1\cap B_1\neq \emptyset$. They obtained some partial results on the maximum cardinality of such families. We extend those results by determining the largest cardinality of strongly $(p,q)$-chain intersecting families if $n$ is sufficiently large, and by determining the largest cardinality of totally $(2,2)$-chain intersecting families.
Certification of genuine time-bin and energy-time entanglement with integrated photonics

Time-bin (TB) and energy-time (ET) entanglement are crucial resources for long-distance quantum information processing. Recently, major efforts have been made to produce compact high-quality sources of TB/ET entangled photons based on solid-state integrated technologies. However, these attempts failed to close the so-called"post-selection loophole". Here, we present an integrated photonic general Bell-test chip for genuine (i.e., free of the post-selection loophole) TB and ET entanglement certification. We report a violation of a Bell inequality by more than 10 standard deviations using our device based on the"hug"interferometric scheme. The experiment also demonstrates that the hug scheme, previously exploited for ET entanglement, can also be used for genuine TB entanglement.
Universal Online Optimization in Dynamic Environments via Uniclass Prediction

Recently, several universal methods have been proposed for online convex optimization which can handle convex, strongly convex and exponentially concave cost functions simultaneously. However, most of these algorithms have been designed with static regret minimization in mind, but this notion of regret may not be suitable for changing environments. To address this shortcoming, we propose a novel and intuitive framework for universal online optimization in dynamic environments. Unlike existing universal algorithms, our strategy does not rely on the construction of a set of experts and an accompanying meta-algorithm. Instead, we show that the problem of dynamic online optimization can be reduced to a uniclass prediction problem. By leaving the choice of uniclass loss function in the user's hands, they are able to control and optimize dynamic regret bounds, which in turn carry over into the original problem. To the best of our knowledge, this is the first paper proposing a universal approach with state-of-the-art dynamic regret guarantees even for general convex cost functions.
Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation

Semantic segmentation is a crucial step to extract quantitative information from medical (and, specifically, radiological) images to aid the diagnostic process, clinical follow-up. and to generate biomarkers for clinical research. In recent years, machine learning algorithms have become the primary tool for this task. However, its real-world performance is heavily reliant on the comprehensiveness of training data. Dafne is the first decentralized, collaborative solution that implements continuously evolving deep learning models exploiting the collective knowledge of the users of the system. In the Dafne workflow, the result of each automated segmentation is refined by the user through an integrated interface, so that the new information is used to continuously expand the training pool via federated incremental learning. The models deployed through Dafne are able to improve their performance over time and to generalize to data types not seen in the training sets, thus becoming a viable and practical solution for real-life medical segmentation tasks.
User-Driven Support for Rapid Visualization Prototyping in D3

Templates have emerged as an effective approach to simplifying the visualization design and programming process. For example, they enable users to quickly generate multiple visualization designs even when using complex toolkits like D3. However, these templates are often treated as rigid artifacts that respond poorly to changes made outside of the template’s established parameters, limiting user creativity. Preserving the user’s creative flow requires a more dynamic approach to template-based visualization design, where tools can respond gracefully to users’ edits when they modify templates in unexpected ways. In this paper, we leverage the structural similarities revealed by templates to design resilient support features for prototyping D3 visualizations: recommendations to suggest complementary interactions for a users’ D3 program; and code augmentation to implement recommended interactions with a single click, even when users deviate from pre-defined templates. We demonstrate the utility of these features in Mirny, a design-focused prototyping environment for D3. In a user study with 20 D3 users, we find that these automated features enable participants to prototype their design ideas with significantly fewer programming iterations. We also characterize key modification strategies used by participants to customize D3 templates. Informed by our findings and participants’ feedback, we discuss the key implications of the use of templates for interleaving visualization programming and design.
An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis

Fault detection and diagnosis is significant for reducing maintenance costs and improving health and safety in chemical processes. Convolution neural network (CNN) is a popular deep learning algorithm with many successful applications in chemical fault detection and diagnosis tasks. However, convolution layers in CNN are very sensitive to the order of features, which can lead to instability in the processing of tabular data. Optimal order of features result in better performance of CNN models but it is expensive to seek such optimal order. In addition, because of the encapsulation mechanism of feature extraction, most CNN models are opaque and have poor interpretability, thus failing to identify root-cause features without human supervision. These difficulties inevitably limit the performance and credibility of CNN methods. In this paper, we propose an order-invariant and interpretable hierarchical dilated convolution neural network (HDLCNN), which is composed by feature clustering, dilated convolution and the shapley additive explanations (SHAP) method. The novelty of HDLCNN lies in its capability of processing tabular data with features of arbitrary order without seeking the optimal order, due to the ability to agglomerate correlated features of feature clustering and the large receptive field of dilated convolution. Then, the proposed method provides interpretability by including the SHAP values to quantify feature contribution. Therefore, the root-cause features can be identified as the features with the highest contribution. Computational experiments are conducted on the Tennessee Eastman chemical process benchmark dataset. Compared with the other methods, the proposed HDLCNN-SHAP method achieves better performance on processing tabular data with features of arbitrary order, detecting faults, and identifying the root-cause features.
Iterative methods in geophysical diffraction tomography

The problem of determining the acoustic properties of geological formations from cross-well tomographic experiments is addressed. An iterative reconstruction procedure is developed which constitutes a generalization of the algebraic reconstruction technique of X-ray and geophysical traveltime tomography to cases where wave effects in the interaction of the medium with the incident wavefield must be taken into account. The method is shown to generate a minimum normal solution to the inverse geophysical problem and is applicable to situations where data from only a small number of tomographic experiments are available. The inversion procedure is illustrated with a computer simulated example where its performance is compared to that of the filtered backpropagation algorithm.
Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task

RNA, whose functionality is largely determined by its structure, plays an important role in many biological activities. The prediction of pairwise structural proximity between each nucleotide of an RNA sequence can characterize the structural information of the RNA. Historically, this problem has been tackled by machine learning models using expert-engineered features and trained on scarce labeled datasets. Here, we find that the knowledge learned by a protein-coevolution Transformer-based deep neural network can be transferred to the RNA contact prediction task. As protein datasets are orders of magnitude larger than those for RNA contact prediction, our findings and the subsequent framework greatly reduce the data scarcity bottleneck. Experiments confirm that RNA contact prediction through transfer learning using a publicly available protein model is greatly improved. Our findings indicate that the learned structural patterns of proteins can be transferred to RNAs, opening up potential new avenues for research.
“Push-Grasp-Suction” Integrated Network for Ambidextrous Robot Picking

—Object picking in cluttered scenes is a widely investigated ﬁeld of robot manipulation, however, ambidextrous robot picking is still an important and challenging issue. We found the fusion of different prehensile actions (grasp and suction) can expand the range of objects that can be picked by robot, and the fusion of prehensile action and nonprehensile action (push) can expand the picking space of ambidextrous robot. In this paper, we propose a “Push-Grasp-Suction” (PGS) integrated network for ambidextrous robot picking through the fusion of different prehensile actions and the fusion of prehen-sile action and nonprehensile aciton. The prehensile branch of PGS takes point clouds as input, and the 6-DoF picking conﬁguration of grasp and suction in cluttered scenes are generated by multi-task point cloud learning. The nonprehensile branch with depth image input generates instance segmentation map and push conﬁguration, cooperating with the prehensile actions to complete the picking of objects out of single-arm space. PGS generalizes well in real scene and achieves state-of-the-art picking performance. More information is at https://sites.google. com/view/push-grasp-suction/
Zero-Knowledge Mechanisms

A powerful feature in mechanism design is the ability to irrevocably commit to the rules of a mechanism. Commitment is achieved by public declaration, which enables players to verify incentive properties in advance and the outcome in retrospect. However, public declaration can reveal superfluous information that the mechanism designer might prefer not to disclose, such as her target function or private costs. Avoiding this may be possible via a trusted mediator; however, the availability of a trusted mediator, especially if mechanism secrecy must be maintained for years, might be unrealistic. We propose a new approach to commitment, and show how to commit to, and run, any given mechanism without disclosing it, while enabling the verification of incentive properties and the outcome -- all without the need for any mediators. Our framework is based on zero-knowledge proofs -- a cornerstone of modern cryptographic theory. Applications include non-mediated bargaining with hidden yet binding offers.
The Impact of Expertise in the Loop for Exploring Machine Rationality

Human-in-the-loop optimization utilizes human expertise to guide machine optimizers iteratively and search for an optimal solution in a solution space. While prior empirical studies mainly investigated novices, we analyzed the impact of the levels of expertise on the outcome quality and corresponding subjective satisfaction. We conducted a study (N=60) in text, photo, and 3D mesh optimization contexts. We found that novices can achieve an expert level of quality performance, but participants with higher expertise led to more optimization iteration with more explicit preference while keeping satisfaction low. In contrast, novices were more easily satisfied and terminated faster. Therefore, we identified that experts seek more diverse outcomes while the machine reaches optimal results, and the observed behavior can be used as a performance indicator for human-in-the-loop system designers to improve underlying models. We inform future research to be cautious about the impact of user expertise when designing human-in-the-loop systems.
EFFICIENCY OF LINEAR ESTIMATORS UNDER HEAVY-TAILEDNESS: CONVOLUTIONS OF α-SYMMETRIC DISTRIBUTIONS

This paper focuses on the analysis of efficiency, peakedness, and majorization properties of linear estimators under heavy-tailedness assumptions. We demonstrate that peakedness and majorization properties of log-concavely distributed random samples continue to hold for convolutions of α-symmetric distributions with α > 1. However, these properties are reversed in the case of convolutions of α-symmetric distributions with α < 1. We show that the sample mean is the best linear unbiased estimator of the population mean for not extremely heavy-tailed populations in the sense of its peakedness. In such a case, the sample mean exhibits monotone consistency, and an increase in the sample size always improves its performance. However, efficiency of the sample mean in the sense of peakedness decreases with the sample size if it is used to estimate the location parameter under extreme heavy-tailedness. We also present applications of the results in the study of concentration inequalities for linear estimators.The results in this paper constitute a part of the author's dissertation “New Majorization Theory in Economics and Martingale Convergence Results in Econometrics” presented to the faculty of the Graduate School of Yale University in candidacy for the degree of Doctor of Philosophy in Economics in March 2005. Some of the results were originally contained in the work circulated in 2003–2005 under the titles “Shifting Paradigms: On the Robustness of Economic Models to Heavy-Tailedness Assumptions” and “On the Robustness of Economic Models to Heavy-Tailedness Assumptions. ” I am indebted to my advisers, Donald Andrews, Peter Phillips, and Herbert Scarf, for all their support and guidance in all stages of the current project. I also thank the associate editor, two anonymous referees, Donald Brown, Aydin Cecen, Gary Chamberlain, Brian Dineen, Darrell Duffie, Xavier Gabaix, Tilmann Gneiting, Philip Haile, Wolfgang Härdle, Boyan Jovanovic, Samuel Karlin, Benoît Mandelbrot, Alex Maynard, Marcelo Morreira, Ingram Olkin, Ben Polak, Gustavo Soares, Kevin Song, and the participants at seminars at the Departments of Economics at Yale University, University of British Columbia, the University of California at San Diego, Harvard University, the London School of Economics and Political Science, Massachusetts Institute of Technology, the Université de Montréal, McGill University, and New York University, the Division of the Humanities and Social Sciences at California Institute of Technology, Nuffield College, University of Oxford, and the Department of Statistics at Columbia University, and the participants at the 18th New England Statistics Symposium at Harvard University, April 2004, the International Conference on Stochastic Finance, Lisbon, Portugal, September 2004, and the Conference “Heavy Tails and Stable Paretian Distributions in Finance and Macroeconomics” in celebration of the 80th birthday of Benoît Mandelbrot, Deutsche Bundesbank, Eltville, Germany, November 2005, for many helpful comments and discussions.
SemanticAC: Semantics-Assisted Framework for Audio Classification

In this paper, we propose SemanticAC, a semantics-assisted framework for Audio Classification to better leverage the semantic information. Unlike conventional audio classification methods that treat class labels as discrete vectors, we employ a language model to extract abundant semantics from labels and optimize the semantic consistency between audio signals and their labels. We verify that simple textual information from labels and advanced pretraining models enable more abundant semantic supervision for better performance. Specifically, we design a text encoder to capture the semantic information from the text extension of labels. Then we map the audio signals to align with the semantics of corresponding class labels via an audio encoder and a similarity calculation module so as to enforce the semantic consistency. Extensive experiments on two audio datasets, ESC-50 and US8K demonstrate that our proposed method consistently outperforms the compared audio classification methods.
An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation

Unit tests play a key role in ensuring the correctness of software. However, manually creating unit tests is a laborious task, motivating the need for automation. Large Language Models (LLMs) have recently been applied to various aspects of software development, including their suggested use for automated generation of unit tests, but while requiring additional training or few-shot learning on examples of existing tests. This paper presents a large-scale empirical evaluation on the effectiveness of LLMs for automated unit test generation without requiring additional training or manual effort. Concretely, we consider an approach where the LLM is provided with prompts that include the signature and implementation of a function under test, along with usage examples extracted from documentation. Furthermore, if a generated test fails, our approach attempts to generate a new test that fixes the problem by re-prompting the model with the failing test and error message. We implement our approach in <sc>TestPilot</sc>, an adaptive LLM-based test generation tool for JavaScript that automatically generates unit tests for the methods in a given project's API. We evaluate <sc>TestPilot</sc> using OpenAI's <italic>gpt3.5-turbo</italic> LLM on 25 npm packages with a total of 1,684 API functions. The generated tests achieve a median statement coverage of 70.2% and branch coverage of 52.8%. In contrast, the state-of-the feedback-directed JavaScript test generation technique, Nessie, achieves only 51.3% statement coverage and 25.6% branch coverage. Furthermore, experiments with excluding parts of the information included in the prompts show that all components contribute towards the generation of effective test suites. We also find that 92.8% of <sc>TestPilot</sc>'s generated tests have <inline-formula><tex-math notation="LaTeX">$\leq$</tex-math><alternatives><mml:math display="inline"><mml:mo>≤</mml:mo></mml:math><inline-graphic xlink:href="schaefer-ieq1-3334955.gif"/></alternatives></inline-formula> 50% similarity with existing tests (as measured by normalized edit distance), with none of them being exact copies. Finally, we run <sc>TestPilot</sc> with two additional LLMs, OpenAI's older <italic>code-cushman-002</italic> LLM and <italic>StarCoder</italic>, an LLM for which the training process is publicly documented. Overall, we observed similar results with the former (68.2% median statement coverage), and somewhat worse results with the latter (54.0% median statement coverage), suggesting that the effectiveness of the approach is influenced by the size and training set of the LLM, but does not fundamentally depend on the specific model.
Vowel spacing in four‐vowel systems

Languages with fewer than average vowel quality contrasts provide a testbed for proposed universals of vowel systems. This paper examines the positioning in two‐ and three‐formant acoustic spaces of the vowels of several languages with four contrastive vowel qualities. All have vowels like /i/ and /a/, with /i/ seeming the most similar across the languages. Most have a back rounded vowel with a higher F1 than that of /i/, resulting in the transcription /o/ rather than /u/. The position of the fourth vowel is highly variable. In San Carlos Apache it is a front mid vowel, in Shipibo a high central‐back vowel, and in Paiwan a mid central vowel. The overall maximal separation of vowels in the acoustic space is therefore not the major organizing principle of these vowel systems. Rather, their construction is anchored by the polarity of /i/ vs /a/ (dispersed spectral energy versus compact mid‐frequency energy). A high back vowel with close rounding is the next most distinct vowel in terms of spectral energy dis.