Miracle-dz/UniFAR-FTUs huggingface dataset:
Code Smells in Elixir: Early Results from a Grey Literature Review

Elixir is a new functional programming language whose popularity is rising in the industry. However, there are few works in the literature focused on studying the internal quality of systems implemented in this language. Particularly, to the best of our knowledge, there is currently no catalog of code smells for Elixir. Therefore, in this paper, through a grey literature review, we investigate whether Elixir developers discuss code smells. Our preliminary results indicate that 11 of the 22 traditional code smells cataloged by Fowler and Beck are discussed by Elixir developers. We also propose a list of 18 new smells specific for Elixir systems and investigate whether these smells are currently identified by Credo, a well-known static code analysis tool for Elixir. We conclude that only two traditional code smells and one Elixir-specific code smell are automatically detected by this tool. Thus, these early results represent an opportunity for extending tools such as Credo to detect code smells and then contribute to improving the internal quality of Elixir systems.
An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Models

We propose a framework for training non-autoregressive sequence-to-sequence models for editing tasks, where the original input sequence is iteratively edited to produce the output. We show that the imitation learning algorithms designed to train such models for machine translation introduces mismatches between training and inference that lead to undertraining and poor generalization in editing scenarios. We address this issue with two complementary strategies: 1) a roll-in policy that exposes the model to intermediate training sequences that it is more likely to encounter during inference, 2) a curriculum that presents easy-to-learn edit operations first, gradually increasing the difficulty of training samples as the model becomes competent. We show the efficacy of these strategies on two challenging English editing tasks: controllable text simplification and abstractive summarization. Our approach significantly improves output quality on both tasks and controls output complexity better on the simplification task.
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation

Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improvements by learning object fragments as segmentation. In this work, we present a discrete descriptor, which can represent the object surface densely. By incorporating a hierarchical binary grouping, we can encode the object surface very efficiently. Moreover, we propose a coarse to fine training strategy, which enables fine-grained correspondence prediction. Finally, by matching predicted codes with object surface and using a PnP solver, we estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show major improvement over the state of the art w.r. t. ADD(-S) metric, even surpassing RGB-D based methods in some cases.
ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images

In recent years, convolutional neural network-based single image adverse weather removal methods have achieved significant performance improvements on many benchmark datasets. However, these methods require large amounts of clean-weather degraded image pairs for training, which is often difficult to obtain in practice. Although various weather degradation synthesis methods exist in the literature, the use of synthetically generated weather degraded images often results in sub-optimal performance on the real weather degraded images due to the domain gap between synthetic and real-world images. To deal with this problem, various semi-supervised restoration (SSR) methods have been proposed for deraining or dehazing which learn to restore the clean image using synthetically generated datasets while generalizing better using unlabeled real-world images. The performance of a semi-supervised method is essentially based on the quality of the unlabeled data. In particular, if the unlabeled data characteristics are very different from that of the labeled data, then the performance of a semi-supervised method degrades significantly. We theoretically study the effect of unlabeled data on the performance of an SSR method and develop a technique that rejects the unlabeled images that degrade the performance. Extensive experiments and ablation study show that the proposed sample rejection method increases the performance of existing SSR deraining and dehazing methods significantly. Code is available at :https://github.com/rajeevyasarla/ART-SS
Spatially Resolved Observations of Europa’s Surface with Subaru/IRCS at 1.0–1.8 μm: Upper Limits to the Abundances of Hydrated Cl-bearing Salts

Recent infrared observations at 1.5–4.0 μm using large ground-based telescopes have suggested that Cl-bearing salts are likely present on Europa’s surface as non-ice materials. The chemical compositions of those Cl-bearing salts are key to understanding Europa’s ocean chemistry and habitability. Here we report the results of ground-based telescope observations of Europa across two wavelength ranges, 1.0–1.5 μm and 1.5–1.8 μm, of which the former range includes absorption features due to some hydrated Cl-bearing salts. We obtained spatially resolved reflectance spectra using the Subaru telescope/IRCS and the adaptive optics system AO188 with high wavelength resolutions (δλ ∼ 2 nm for 1.0–1.5 μm and δλ ∼ 0.9 nm for 1.5–1.8 μm) and low noise levels (1σ ∼ (1–2) × 10−3). We found no clear absorption features at ∼1.2 μm caused by hydrated Cl-bearing salts. We estimated that conservative upper limits to the abundances of MgCl2 · nH2O, NaClO4 · 2H2O, Mg(ClO3)2 · 6H2O, and Mg(ClO4)2 · 6H2O on Europa are 17% (<10% for most) at the 3σ noise level. These values are lower than the proposed abundance of some hydrated Cl-bearing salts (>∼20%) on Europa based on previous observations. This supports the idea that Cl-bearing salts on Europa are likely anhydrous Na salts of NaCl and/or NaClO4, or hydrated NaCl · 2H2O. The presence of Na salts suggests that Na+ could be the major cation in Europa’s ocean, which would be possible if the oceanic pH is circumneutral or alkaline.
Further Evidence of Modified Spin-down in Sun-like Stars: Pileups in the Temperature–Period Distribution

We combine stellar surface rotation periods determined from NASA’s Kepler mission with spectroscopic temperatures to demonstrate the existence of pileups at the long-period and short-period edges of the temperature–period distribution for main-sequence stars with temperatures exceeding ∼5500 K. The long-period pileup is well described by a curve of constant Rossby number, with a critical value of Rocrit ≲ Ro⊙. The long-period pileup was predicted by van Saders et al. as a consequence of weakened magnetic braking, in which wind-driven angular momentum losses cease once stars reach a critical Rossby number. Stars in the long-period pileup are found to have a wide range of ages (∼2–6 Gyr), meaning that, along the pileup, rotation period is strongly predictive of a star’s surface temperature but weakly predictive of its age. The short-period pileup, which is also well described by a curve of constant Rossby number, is not a prediction of the weakened magnetic braking hypothesis but may instead be related to a phase of slowed surface spin-down due to core-envelope coupling. The same mechanism was proposed by Curtis et al. to explain the overlapping rotation sequences of low-mass members of differently aged open clusters. The relative dearth of stars with intermediate rotation periods between the short- and long-period pileups is also well described by a curve of constant Rossby number, which aligns with the period gap initially discovered by McQuillan et al. in M-type stars. These observations provide further support for the hypothesis that the period gap is due to stellar astrophysics, rather than a nonuniform star formation history in the Kepler field.
Finding Structural Knowledge in Multimodal-BERT

In this work, we investigate the knowledge learned in the embeddings of multimodal-BERT models. More specifically, we probe their capabilities of storing the grammatical structure of linguistic data and the structure learned over objects in visual data. To reach that goal, we first make the inherent structure of language and visuals explicit by a dependency parse of the sentences that describe the image and by the dependencies between the object regions in the image, respectively. We call this explicit visual structure the scene tree, that is based on the dependency tree of the language description. Extensive probing experiments show that the multimodal-BERT models do not encode these scene trees.
CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Data

Vision-based segmentation of the robotic tool during robot-assisted surgery enables downstream applications, such as augmented reality feedback, while allowing for inaccuracies in robot kinematics. With the introduction of deep learning, many methods were presented to solve instrument segmentation directly and solely from images. While these approaches made remarkable progress on benchmark datasets, fundamental challenges pertaining to their robustness remain. We present CaRTS, a causality-driven robot tool segmentation algorithm, that is designed based on a complementary causal model of the robot tool segmentation task. Rather than directly inferring segmentation masks from observed images, CaRTS iteratively aligns tool models with image observations by updating the initially incorrect robot kinematic parameters through forward kinematics and differentiable rendering to optimize image feature similarity end-to-end. We benchmark CaRTS with competing techniques on both synthetic as well as real data from the dVRK, generated in precisely controlled scenarios to allow for counterfactual synthesis. On training-domain test data, CaRTS achieves a Dice score of 93.4 that is preserved well (Dice score of 91.8) when tested on counterfactually altered test data, exhibiting low brightness, smoke, blood, and altered background patterns. This compares favorably to Dice scores of 95.0 and 86.7, respectively, of the SOTA image-based method. Future work will involve accelerating CaRTS to achieve video framerate and estimating the impact occlusion has in practice. Despite these limitations, our results are promising: In addition to achieving high segmentation accuracy, CaRTS provides estimates of the true robot kinematics, which may benefit applications such as force estimation. Code is available at: https://github.com/hding2455/CaRTS
Feature-informed Latent Space Regularization for Music Source Separation

The integration of additional side information to improve music source separation has been investigated numerous times, e.g., by adding features to the input or by adding learning targets in a multi-task learning scenario. These approaches, however, require additional annotations such as musical scores, instrument labels, etc. in training and possibly during inference. The available datasets for source separation do not usually provide these additional annotations. In this work, we explore transfer learning strategies to incorporate VGGish features with a state-of-the-art source separation model; VGGish features are known to be a very condensed representation of audio content and have been successfully used in many MIR tasks. We introduce three approaches to incorporate the features, including two latent space regularization methods and one naive concatenation method. Experimental results show that our proposed approaches improve several evaluation metrics for music source separation.
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction

Despite the importance of relation extraction in building and representing knowledge, less research is focused on generalizing to unseen relations types. We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods. Given an input sentence, each extracted triplet consists of the head entity, relation label, and tail entity where the relation label is not seen at the training stage. To solve ZeroRTE, we propose to synthesize relation examples by prompting language models to generate structured texts. Concretely, we unify language model prompts and structured text approaches to design a structured prompt template for generating synthetic relation samples when conditioning on relation label prompts (RelationPrompt). To overcome the limitation for extracting multiple relation triplets in a sentence, we design a novel Triplet Search Decoding method. Experiments on FewRel and Wiki-ZSL datasets show the efficacy of RelationPrompt for the ZeroRTE task and zero-shot relation classification. Our code and data are available at github.com/declare-lab/RelationPrompt.
Computer Vision Algorithm for Predicting the Welding Efficiency of Friction Stir Welded Copper Joints from its Microstructures

This research paper presents a study of the prediction of Friction Stir Welded (FSW) joint effectiveness using microstructure images with the aid of Convolutional Neural Networks (CNNs). A total of 3000 microstructure pictures were used for training the CNN, and 300 new microstructure photographs were used to test the accuracy of the model. The results showed that the CNN was able to accurately predict the effectiveness of FSW joints with an accuracy of 81 percent. The current work highlights the potential of using microstructure images and CNNs for improving the quality control and assessment of FSW joints in the materials and manufacturing industries. The findings of this study have important implications for the development of new techniques for improving the performance of FSW joints and for the wider application of computer vision and machine learning in the materials and manufacturing industries.
Multimodal Learning on Graphs for Disease Relation Extraction

OBJECTIVE Disease knowledge graphs have emerged as a powerful tool for AI, enabling the connection, organization, and access to diverse information about diseases. However, the relations between disease concepts are often distributed across multiple data formats, including plain language and incomplete disease knowledge graphs. As a result, extracting disease relations from multimodal data sources is crucial for constructing accurate and comprehensive disease knowledge graphs. METHODS We introduce REMAP, a multimodal approach for disease relation extraction. The REMAP machine learning approach jointly embeds a partial, incomplete knowledge graph and a medical language dataset into a compact latent vector space, aligning the multimodal embeddings for optimal disease relation extraction. Additionally, REMAP utilizes a decoupled model structure to enable inference in single-modal data, which can be applied under missing modality scenarios. RESULTS We apply the REMAP approach to a disease knowledge graph with 96,913 relations and a text dataset of 1.24 million sentences. On a dataset annotated by human experts, REMAP improves language-based disease relation extraction by 10.0% (accuracy) and 17.2% (F1-score) by fusing disease knowledge graphs with language information. Furthermore, REMAP leverages text information to recommend new relationships in the knowledge graph, outperforming graph-based methods by 8.4% (accuracy) and 10.4% (F1-score). CONCLUSION In summary, REMAP is a flexible multimodal approach for extracting disease relations by fusing structured knowledge and language information. This approach provides a powerful model to easily find, access, and evaluate relations between disease concepts.
A Survey of Multi-Tenant Deep Learning Inference on GPU

Deep Learning (DL) models have achieved superior performance. Meanwhile, computing hardware like NVIDIA GPUs also demonstrated strong computing scaling trends with 2x throughput and memory bandwidth for each generation. With such strong computing scaling of GPUs, multi-tenant deep learning inference by co-locating multiple DL models onto the same GPU becomes widely deployed to improve resource utilization, enhance serving throughput, reduce energy cost, etc. However, achieving efficient multi-tenant DL inference is challenging which requires thorough full-stack system optimization. This survey aims to summarize and categorize the emerging challenges and optimization opportunities for multi-tenant DL inference on GPU. By overviewing the entire optimization stack, summarizing the multi-tenant computing innovations, and elaborating the recent technological advances, we hope that this survey could shed light on new optimization perspectives and motivate novel works in future large-scale DL system optimization.
Beauty and the Beast: A Case Study on Performance Prototyping of Data-Intensive Containerized Cloud Applications

Data-intensive container-based cloud applications have become popular with the increased use cases in the Internet of Things domain. Challenges arise when engineering such applications to meet quality requirements, both classical ones like performance and emerging ones like resilience. There is a lack of reference use cases, applications, and experiences when prototyping such applications that could benefit the research community. Moreover, it is hard to generate realistic and reliable workloads that exercise the resources according to a specification. Hence, designing reference applications that would exhibit similar performance behavior in such environments is hard. In this paper, we present a work in progress towards a reference use case and application for data-intensive containerized cloud applications having an industrial motivation. Moreover, to generate reliable CPU workloads we make use of ProtoCom, a well-known library for the generation of resource demands, and report the performance under various quality requirements in a Kubernetes cluster of moderate size. Finally, we present the scalability of the current solution assuming a particular autoscaling policy. Results of the calibration show high variability of the ProtoCom library when executed in a cloud environment. We observe a moderate association between the occupancy of node and the relative variability of execution time.
Decision Trees with Hypotheses for Recognition of Monotone Boolean Functions and for Sorting

In this paper, we consider decision trees that use both queries based on one attribute each and queries based on hypotheses about values of all attributes. Such decision trees are similar to ones studied in exact learning, where not only membership but also equivalence queries are allowed. We investigate the problem of recognition of monotone Boolean functions with $n$ variables, $n=2, \ldots, 4$, and the problem of sorting $n$ pairwise different elements from linearly ordered set, $n=3, \ldots, 6$. For each of these problems, we compare the complexity of different types of optimal (relative to the depth or the number of realizable nodes) decision trees with hypotheses. We also study the complexity of decision trees constructed by entropy-based greedy algorithm and analyze the length of decision rules derived from these trees.
Understanding training in superconducting accelerator magnets using acoustic emission diagnostics

Among various long-standing standing problems in superconducting accelerator magnet development, training is one of the oldest and well-known issues affecting magnet performance. Understanding physics behind the training process is crucial for proper mitigation and elimination of this phenomenon in new magnet designs. This paper discusses application of acoustic emission (AE) diagnostics to localization and classification of transient mechanical events in superconducting magnets and characterization of transient energy releases responsible for premature quenching, memory effects and, ultimately, training. Prospective of future AE diagnostics development are outlined in connection to the goals of US Magnet Development Program.
The Compact Structures of Massive z ∼ 0.7 Post-starburst Galaxies in the SQuIGGL⃗E Sample

We present structural measurements of 145 spectroscopically selected intermediate-redshift (z ∼ 0.7), massive (M ⋆ ∼ 1011 M ⊙) post-starburst galaxies from the SQuIGGL⃗E sample measured using wide-depth Hyper Suprime-Cam i-band imaging. This deep imaging allows us to probe the sizes and structures of these galaxies, which we compare to a control sample of star-forming and quiescent galaxies drawn from the LEGA-C Survey. We find that post-starburst galaxies systematically lie ∼0.1 dex below the quiescent mass–size (half-light radius) relation, with a scatter of ∼0.2 dex. This finding is bolstered by nonparametric measures, such as the Gini coefficient and the concentration, which also reveal these galaxies to have more compact light profiles than both quiescent and star-forming populations at similar mass and redshift. The sizes of post-starburst galaxies show either negative or no correlation with the time since quenching, such that more recently quenched galaxies are larger or similarly sized. This empirical finding disfavors the formation of post-starburst galaxies via a purely central burst of star formation that simultaneously shrinks the galaxy and shuts off star formation. We show that the central densities of post-starburst and quiescent galaxies at this epoch are very similar, in contrast with their effective radii. The structural properties of z ∼ 0.7 post-starburst galaxies match those of quiescent galaxies that formed in the early universe, suggesting that rapid quenching in the present epoch is driven by a similar mechanism to the one at high redshift.
Base-Rate Fallacy Redux and a Deep Dive Review in Cybersecurity

This paper examines the current state of the science underlying cybersecurity research with an emphasis on the non-signature-based intrusion detection domain. First, the paper re-examines the base-rate fallacy originally published by Axelsson, putting the impact of false positives into context. Given the relative high numbers of false positives, the paper argues for deeper analysis of false positives, akin to the analysis that true positives are treated to. The second section of the paper examines the metrics being used to analyze non-signature intrusion detection techniques, the current status quo of employed metrics, and the impact of the status quo on scientific advancement. Finally, the paper analyzes the use of online attack graphs and their applicability, especially in scenarios of constrained environments, such as Internet of Things devices. The use of offline attack graphs in such constrained environments is also examined. In essence, a deep dive review identified multiple areas throughout the field in which the effectiveness and validity of the scientific method can be greatly improved, e.g., through removal of logical fallacies.