Miracle-dz/UniFAR-FTUs huggingface dataset:
Code Search based on Context-aware Code Translation

Code search is a widely used technique by developers during software development. It provides semantically similar implementations from a large code corpus to developers based on their queries. Existing techniques leverage deep learning models to construct embedding representations for code snippets and queries, respectively. Features such as abstract syntactic trees, control flow graphs, etc., are commonly employed for representing the semantics of code snippets. However, the same structure of these features does not necessarily denote the same semantics of code snippets, and vice versa. In addition, these techniques utilize multiple different word mapping functions that map query words/code tokens to embedding representations. This causes diverged embeddings of the same word/token in queries and code snippets. We propose a novel context-aware code translation technique that translates code snippets into natural language descriptions (called translations). The code translation is conducted on machine instructions, where the context information is collected by simulating the execution of instructions. We further design a shared word mapping function using one single vocabulary for generating embeddings for both translations and queries. We evaluate the effectiveness of our technique, called TranCS, on the CodeSearchNet corpus with 1,000 queries. Experimental results show that TranCS significantly outperforms state-of-the-art techniques by 49.31% to 66.50% in terms of MRR (mean reciprocal rank).
SRP-DNN: Learning Direct-Path Phase Difference for Multiple Moving Sound Source Localization

Multiple moving sound source localization in real-world scenarios remains a challenging issue due to interaction between sources, time-varying trajectories, distorted spatial cues, etc. In this work, we propose to use deep learning techniques to learn competing and time-varying direct-path phase differences for localizing multiple moving sound sources. A causal convolutional recurrent neural network is designed to extract the direct-path phase difference sequence from signals of each microphone pair. To avoid the assignment ambiguity and the problem of uncertain output-dimension encountered when simultaneously predicting multiple targets, the learning target is designed in a weighted sum format, which encodes source activity in the weight and direct-path phase differences in the summed value. The learned direct-path phase differences for all microphone pairs can be directly used to construct the spatial spectrum according to the formulation of steered response power (SRP). This deep neural network (DNN) based SRP method is referred to as SRP-DNN. The locations of sources are estimated by iteratively detecting and removing the dominant source from the spatial spectrum, in which way the interaction between sources is reduced. Experimental results on both simulated and real-world data show the superiority of the proposed method in the presence of noise and reverberation.
Broken symmetry of recruitment fluctuations in marine fishes: L\'evy-stable laws and beyond

Recruitment is calculated by summing random offspring-numbers entering the population, where the number of summands (i.e. spawning population size) is also a random process. A priori, it is not clear that individual reproductive variability would have a significant impact on aggregate measures for monitoring populations. Usually these variations are averaged out in a large population, and the aggregate output is merely influenced by population-wide environmental disturbances such as climate and fisheries. However, such arguments break down if the distribution of the individual offspring numbers is heavy-tailed. In a world with power-law offspring-number distribution with exponent 1 < α < 2, the recruitment distribution has a putative power-law regime in the tail with the same α. The question is to what extent individual reproductive variability can have a noticeable impact on the recruitment under environmentally driven population fluctuations. This question is answered by considering the Lévy-stable fluctuations as embedded in a randomly varying environment. I report fluctuation scaling and asymmetric fluctuations in recruitment of commercially exploited fish stocks throughout the North Atlantic. The linear scaling of recruitment standard deviation with recruitment level implies that the individual reproductive variability is dominated by population fluctuations. The totally asymmetric (skewed to the right) character is a sign of idiosyncratic variation in reproductive success.
Do Language Models Learn Commonsense Knowledge?

Language models (LMs) trained on large amounts of data ( e.g. , Brown et al., 2020; Patwary et al., 2021) have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. Here we aim to better understand the extent to which such models learn commonsense knowledge — a critical component of many NLP applications. To that end, we conduct a systematic and rigorous zero-shot and few-shot commonsense evaluation of pre-trained LMs, where we: (i) carefully control for the LM’s ability to exploit potential surface cues and annotation artefacts, and (ii) account for variations in model performance that arise from non-commonsense related factors. Our ﬁndings highlight the limitations of pre-trained LMs in acquiring commonsense knowledge without task-speciﬁc supervision; furthermore, using larger models — or augmenting the LMs with commonsense knowledge bases at test-time — did not substantially improve their performance. More broadly, our ﬁndings offer valuable lessons and best practices for conducting more rigorous multiple-choice evaluations of pre-trained LMs.
Performance of QUIC Implementations Over Geostationary Satellite Links

QUIC was recently standardized as RFC 9000, but the performance of QUIC over geostationary satellite links is problematic due to the non-applicability of Performance Enhancing Proxies. As of today, there are more than a dozen of different QUIC implementations. So far performance evaluations of QUIC over satellite links were limited to specific QUIC implementations. By deploying a modified version of the IETF QUIC-Interop-Runner, this paper evaluates the performance of multiple QUIC implementations over multiple geostationary satellite links. This includes two emulated ones (with and without packet loss) and two real ones. The results show that the goodput achieved with QUIC over geostationary satellite links is very poor in general, and especially poor when there is packet loss. Some implementations fail completely and the performance of the other implementations varies greatly. The performance depends on both client and server implementation.
Robust Nonparametric Distribution Forecast with Backtest-Based Bootstrap and Adaptive Residual Selection

Distribution forecast can quantify forecast uncertainty and provide various forecast scenarios with their corresponding estimated probabilities. Accurate distribution forecast is crucial for planning – for example when making production capacity or inventory allocation decisions. We propose a practical and robust distribution forecast framework that relies on backtest-based bootstrap and adaptive residual selection. The proposed approach is robust to the choice of the underlying forecasting model, accounts for uncertainty around the input covariates, and relaxes the independence between residuals and covariates assumption. It reduces the Absolute Coverage Error by more than 63% compared to the classic bootstrap approaches and by 2%−32% compared to a variety of State-of-the-Art deep learning approaches on in-house product sales data and M4-hourly competition data.
Generalizing continuous flexible Kokotsakis belts of the isogonal type

Kokotsakis studied the following problem in 1932: Given is a rigid closed polygonal line (planar or non-planar), which is surrounded by a polyhedral strip, where at each polygon vertex three faces meet. Determine the geometries of these closed strips with a continuous mobility. On the one side, we generalize this problem by allowing the faces, which are adjacent to polygon line-segments, to be skew; i.e to be non-planar. But on the other side, we restrict to the case where the four angles associated with each polygon vertex fulfill the so-called isogonality condition that both pairs of opposite angles are equal or supplementary. In more detail, we study the case where the polygonal line is a skew quad, as this corresponds to a (3x3) building block of a so-called V-hedra composed of skew quads. The latter also gives a positive answer to a question posed by Robert Sauer in his book of 1970 whether continuous flexible skew quad surfaces exist.
Deep optical study of the mixed-morphology supernova remnant G 132.7+1.3 (HB3)

We present optical ccd images of the large supernova remnant (SNR) G132.7+1.3 (HB3) covering its full extent for the first time, in the emission lines of Hα +[N ii], [S ii] and [O iii], where new and known filamentary and diffuse structures are detected. These observations are supplemented by new low-resolution long-slit spectra and higher-resolution images in the same emission lines. Both the flux-calibrated images and spectra confirm that the optical emission originates from shock-heated gas since the [S ii]/Hα > 0.4. Our findings are also consistent with the recently developed emission line ratio diagnostics for distinguishing SNRs from H ii regions. A multi-wavelength comparison among our optical data and relevant observations in radio, X-rays, γ-rays and CO bands, provided additional evidence on the interaction of HB3 with the surrounding clouds and clarified the borders of the SNR and the adjacent cloud. We discuss the supernova (SN) properties and evolution that led to the current observables of HB3 and we show that the remnant has most likely passed at the pressure driven snowplow phase. The estimated SN energy was found to be (3.7 ± 1.5) × 1051 erg and the current SNR age (5.1 ± 2.1) × 104 yrs. We present an alternative scenario according to which the SNR evolved in the wind bubble cavity excavated by the progenitor star and currently is interacting with its density walls. We show that the overall mixed morphology properties of HB3 can be explained if the SN resulted by a Wolf-Rayet progenitor star with mass $\sim 34 \rm ~M_{\rm \odot }$.
A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

The Computer Assisted Sperm Analysis (CASA) plays a crucial role in male reproductive health diagnosis and Infertility treatment. With the development of the computer industry in recent years, a great of accurate algorithms are proposed. With the assistance of those novel algorithms, it is possible for CASA to achieve a faster and higher quality result. Since image processing is the technical basis of CASA, including pre-processing,feature extraction, target detection and tracking, these methods are important technical steps in dealing with CASA. The various works related to Computer Assisted Sperm Analysis methods in the last 30 years (since 1988) are comprehensively introduced and analysed in this survey. To facilitate understanding, the methods involved are analysed in the sequence of general steps in sperm analysis. In other words, the methods related to sperm detection (localization) are first analysed, and then the methods of sperm tracking are analysed. Beside this, we analyse and prospect the present situation and future of CASA. According to our work, the feasible for applying in sperm microscopic video of methods mentioned in this review is explained. Moreover, existing challenges of object detection and tracking in microscope video are potential to be solved inspired by this survey.
Physicochemical, Biochemical and organoleptic analysis of pineapple cultivars MD2 and MORRIS

According to Malaysian Pineapple Industry Board (MPIB), pineapple industry is one of the important agricultural sectors in Malaysia thus plays a role in national earnings as one of the world top pineapple suppliers. In Malaysia, pineapple cultivars include Maspine, Morris, Morris Gajah, Gandul, Sarawak, Josapine, N36, Yankee and MD2. Each cultivar has its own physical and phytochemical uniqueness. Before 'MD2' was introduced, 'Morris' was one of the popular cultivars for fresh consumption. This study was geared toward comparing physicochemical, biochemical and organoleptic evaluations of two commercial pineapple cultivars 'MD2' and 'Morris'. Six fruits of each cultivars were purchased at commercial maturity stage which is about 20-40% yellowing of fruit peel and flesh of fruits were used for evaluations. Results of physicochemical analysis showed that 'MD2' is the sweeter and less astringent variety compared to 'Morris' and the same was also observed biochemically where antioxidant capacity, protein content and bromelain enzymatic activity were higher in 'MD2'. For organic acids, 'MD2' had a slightly lower content of oxalic and malic than 'Morris'. Sensory analysis had also supported 'MD2' as the most preferred among the two cultivars where all the attributes such as aroma, flavor, sweetness, off-taste, texture and overall preference scored higher compared to 'Morris'. As a conclusion, 'MD2' compares very well against 'Morris', hence its cultivation in Malaysia for potential local and international market should be encouraged not only due to their higher consumers' preference but nutritional benefits. All the mean differences observed between the cultivars were statistically significant except for protein content and organic acids quantification.
The Intra-Group Baryons in the LEO I Pair From the VST Early-Type GAlaxy Survey

In this paper we present the deep, wide-field and multi-band imaging of the LEO I pair NGC 3379-NGC 3384, from the VST Early-type GAlaxy Survey (VEGAS). The main goal of this study is to map the intra-group baryons in the pair, in the form of diffuse light and globular clusters (GCs). Taking advantage from the large covered area, which extends for ∼ 3.9 square degrees around the pair, and the long integration time, we can map the light distribution out to ∼ 63 kpc and down to ∼ 30 mag/arcsec2 in the g band and ∼ 29 mag/arcsec2 in the r band, deeper than previous data available for this target. The map of the intra-group light (IGL) presents two very faint (μ g ∼ 28-29 mag/arcsec2) streams protruding from the brightest group member NGC 3379 and elongated toward North-West and South. We estimate that the fraction of the stellar halo around NGC 3379 plus the intra-group light is ∼ 17 ± 2 % in both g and r bands, with an average color g-r = 0.75 ± 0.04 mag. The color distribution of the GCs appears multi-modal, with two dominant peaks at (u-r) = 1.8 mag and (u-r) = 2.1 mag, respectively. The GC population stretches from North-East to South-West and from North-West to South of the pair, in the last case overlapping with the streams of IGL, as well as the PNe distribution found by Hartke et al. (Hartke et al., A&A, 2020, 642, A46) and Hartke et al. (Hartke et al., 2022). Since these structures are elongated in the direction of the two nearby galaxies M96 and NGC 3338, they could be the remnant of a past gravitational interactions with the pair.
Multi-Modal Data Fusion in Enhancing Human-Machine Interaction for Robotic Applications: A Survey

Human-machine interaction has been around for several decades now, with new applications emerging every day. One of the major goals that remain to be achieved is designing an interaction similar to how a human interacts with another human. Therefore, there is a need to develop interactive systems that could replicate a more realistic and easier human-machine interaction. On the other hand, developers and researchers need to be aware of state-of-the-art methodologies being used to achieve this goal. We present this survey to provide researchers with state-of-the-art data fusion technologies implemented using multiple inputs to accomplish a task in the robotic application domain. Moreover, the input data modalities are broadly classified into uni-modal and multi-modal systems and their application in myriad industries, including the health care industry, which contributes to the medical industry's future development. It will help the professionals to examine patients using different modalities. The multi-modal systems are differentiated by a combination of inputs used as a single input, e.g., gestures, voice, sensor, and haptic feedback. All these inputs may or may not be fused, which provides another classification of multi-modal systems. The survey concludes with a summary of technologies in use for multi-modal systems.
IPD: An Incremental Prototype based DBSCAN for large-scale data with cluster representatives

DBSCAN is a fundamental density-based clustering technique that identifies any arbitrary shape of the clusters. However, it becomes infeasible while handling big data. On the other hand, centroid-based clustering is important for detecting patterns in a dataset since unprocessed data points can be labeled to their nearest centroid. However, it can not detect non-spherical clusters. For a large data, it is not feasible to store and compute labels of every samples. These can be done as and when the information is required. The purpose can be accomplished when clustering act as a tool to identify cluster representatives and query is served by assigning cluster labels of nearest representative. In this paper, we propose an Incremental Prototype-based DBSCAN (IPD) algorithm which is designed to identify arbitrary-shaped clusters for large-scale data. Additionally, it chooses a set of representatives for each cluster.
Postexplosion Positive Jet-feedback Activity in Inner Ejecta of Core Collapse Supernovae

We conduct three-dimensional hydrodynamical simulations of weak jets that we launch into a core collapse supernova (CCSN) ejecta half an hour after the explosion and find that the interaction of the fast jets with the CCSN ejecta creates high-pressure zones that induce a backflow that results in mass accretion onto the newly born neutron star. In cases of weak jets, with a total power of ≈1045–1046 erg, the backflow mass accretion might power them up to more energetic jets by an order of magnitude. In total, the jets of the two postexplosion jet-launching episodes have enough energy to influence the morphology of the very inner ejecta, with a mass of ≈0.1 M ⊙. Our results imply that in some, probably a minority of, CCSN remnants the very inner regions might display a bipolar structure that results from postexplosion weak jets. The regions outside this part might display the morphology of jittering jets.
Misinformation Detection in Social Media Video Posts

With the growing adoption of short-form video by social media platforms, reducing the spread of misinformation through video posts has become a critical challenge for social media providers. In this paper, we develop methods to detect misinformation in social media posts, exploiting modalities such as video and text. Due to the lack of large-scale public data for misinformation detection in multi-modal datasets, we collect 160,000 video posts from Twitter, and leverage self-supervised learning to learn expressive representations of joint visual and textual data. In this work, we propose two new methods for detecting semantic inconsistencies within short-form social media video posts, based on contrastive learning and masked language modeling. We demonstrate that our new approaches outperform current state-of-the-art methods on both artificial data generated by random-swapping of positive samples and in the wild on a new manually-labeled test set for semantic misinformation.
When Does A Spectral Graph Neural Network Fail in Node Classification?

Spectral Graph Neural Networks (GNNs) with various graph filters have received extensive affirmation due to their promising performance in graph learning problems. However, it is known that GNNs do not always perform well. Although graph filters provide theoretical foundations for model explanations, it is unclear when a spectral GNN will fail. In this paper, focusing on node classification problems, we conduct a theoretical analysis of spectral GNNs performance by investigating their prediction error. With the aid of graph indicators including homophily degree and response efficiency we proposed, we establish a comprehensive understanding of complex relationships between graph structure, node labels, and graph filters. We indicate that graph filters with low response efficiency on label difference are prone to fail. To enhance GNNs performance, we provide a provably better strategy for filter design from our theoretical analysis - using data-driven filter banks, and propose simple models for empirical validation. Experimental results show consistency with our theoretical results and support our strategy.
Code Generation for Unknown Libraries via Reading API Documentations

Open-domain code generation is a challenging problem because the set of functions and classes that we use are frequently changed and extended in programming communities. We consider the challenge of code generation for unknown libraries without additional training. In this paper, we explore a framework of code generation that can refer to relevant API documentations like human programmers to handle unknown libraries. As a first step of this direction, we implement a model that can extract relevant code signatures from API documentations based on a natural language intent and copy primitives from the extracted signatures. Moreover, to evaluate code generation for unknown libraries and our framework, we extend an existing dataset of open-domain code generation and resplit it so that the evaluation data consist of only examples using the libraries that do not appear in the training data. Experiments on our new split show that baseline encoder-decoder models cannot generate code using primitives of unknown libraries as expected. In contrast, our model outperforms the baseline on the new split and can properly generate unknown primitives when extracted code signatures are noiseless.
Formulating Beurling LASSO for Source Separation via Proximal Gradient Iteration

Beurling LASSO generalizes the LASSO problem to finite Radon measures regularized via their total variation. Despite its theoretical appeal, this space is hard to parametrize, which poses an algorithmic challenge. We propose a formulation of continuous convolutional source separation with Beurling LASSO that avoids the explicit computation of the measures and instead employs the duality transform of the proximal mapping.
Increasing Loudness in Audio Signals: A Perceptually Motivated Approach to Preserve Audio Quality

We present a method to maintain the subjective perception of volume of audio signals and, at the same time, reduce their absolute peak value. We focus on achieving this without compromising the perceived audio quality. This is specially useful, for example, to maximize the perceived reproduction level of loudspeakers where simply amplifying the signal amplitude, and hence their peak value, is limited due to already constrained physical designs. In particular, we minimize the absolute peak value subject to a constraint based on auditory masking. This limits the perceptual difference between the original and the modified signals. Moreover, this constraint can be tuned and allows to control the resulting audio quality. We show results comparing loudness and audio quality as a function of peak reduction. These results suggest that our method presents the best trade-off between loudness and audio quality when compared against classical methods based on compression and clipping.