Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Represent Patches

Patch representation is crucial in automating various software engineering tasks, like determining patch accuracy or summarizing code changes. While recent research has employed deep learning for patch representation, focusing on token sequences or Abstract Syntax Trees (ASTs), they often miss the change's semantic intent and the context of modified lines. To bridge this gap, we introduce a novel method, Patcherizer. It delves into the intentions of context and structure, merging the surrounding code context with two innovative representations. These capture the intention in code changes and the intention in AST structural modifications pre and post-patch. This holistic representation aptly captures a patch's underlying intentions. Patcherizer employs graph convolutional neural networks for structural intention graph representation and transformers for intention sequence representation. We evaluated Patcherizer's embeddings' versatility in three areas: (1) Patch description generation, (2) Patch accuracy prediction, and (3) Patch intention identification. Our experiments demonstrate the representation's efficacy across all tasks, outperforming state-of-the-art methods. For example, in patch description generation, Patcherizer excels, showing an average boost of 19.39% in BLEU, 8.71% in ROUGE-L, and 34.03% in METEOR scores.
Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance

Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recently, deep learning based terrain generation has emerged, notably the ones based on generative adversarial networks (GAN). However, these methods often struggle to fulfill the requirements of flexible user control and maintain generative diversity for realistic terrain. Therefore, we propose a novel diffusion-based method, namely terrain diffusion network (TDN), which actively incorporates user guidance for enhanced controllability, taking into account terrain features like rivers, ridges, basins, and peaks. Instead of adhering to a conventional monolithic denoising process, which often compromises the fidelity of terrain details or the alignment with user control, a multi-level denoising scheme is proposed to generate more realistic terrains by taking into account fine-grained details, particularly those related to climatic patterns influenced by erosion and tectonic activities. Specifically, three terrain synthesisers are designed for structural, intermediate, and fine-grained level denoising purposes, which allow each synthesiser concentrate on a distinct terrain aspect. Moreover, to maximise the efficiency of our TDN, we further introduce terrain and sketch latent spaces for the synthesizers with pre-trained terrain autoencoders. Comprehensive experiments on a new dataset constructed from NASA Topology Images clearly demonstrate the effectiveness of our proposed method, achieving the state-of-the-art performance. Our code is available at https://github.com/TDNResearch/TDN.
Steerable isobutyric and butyric acid production from CO2 and H2 by Clostridium luticellarii

Abstract Clostridium luticellarii is a recently discovered acetogen that is uniquely capable of producing butyric and isobutyric acid from various substrates (e.g. methanol), but it is unclear which factors influence its (iso)butyric acid production from H2 and CO2. We aimed to investigate the autotrophic metabolism of C. luticellarii by identifying the necessary growth conditions and examining the effects of pH and metabolite levels on product titers and selectivity. Results show that autotrophic growth of C. luticellarii requires the addition of complex nutrient sources and the absence of shaking conditions. Further experiments combined with thermodynamic calculations identified pH as a key parameter governing the direction of metabolic fluxes. At circumneutral pH (~6.5), acetic acid is the sole metabolic end product but C. luticellarii possesses the unique ability to co‐oxidize organic acids such as valeric acid under high H2 partial pressures (>1 bar). Conversely, mildly acidic pH (≤5.5) stimulates the production of butyric and isobutyric acid while partly halting the oxidation of organic acids. Additionally, elevated acetic acid concentrations stimulated butyric and isobutyric acid production up to a combined selectivity of 53 ± 3%. Finally, our results suggest that isobutyric acid is produced by a reversible isomerization of butyric acid, but valeric and caproic acid are not isomerized. These combined insights can inform future efforts to optimize and scale‐up the production of valuable chemicals from CO2 using C. luticellarii.
Conditioning Score-Based Generative Models by Neuro-Symbolic Constraints

Score-based and diffusion models have emerged as effective approaches for both conditional and unconditional generation. Still conditional generation is based on either a specific training of a conditional model or classifier guidance, which requires training a noise-dependent classifier, even when the classifier for uncorrupted data is given. We propose an approach to sample from unconditional score-based generative models enforcing arbitrary logical constraints, without any additional training. Firstly, we show how to manipulate the learned score in order to sample from an un-normalized distribution conditional on a user-defined constraint. Then, we define a flexible and numerically stable neuro-symbolic framework for encoding soft logical constraints. Combining these two ingredients we obtain a general, but approximate, conditional sampling algorithm. We further developed effective heuristics aimed at improving the approximation. Finally, we show the effectiveness of our approach for various types of constraints and data: tabular data, images and time series.
Towards Pervasive Sensing: A multimodal approach via CSI and RGB image modalities fusion

Channel state information (CSI) and RGB (Red, Green, Blue) image are two fundamental modalities for accurate human activity recognition (HAR). CSI data is privacy preserving, independent of viewing angle and lighting, while RGB image data can provide useful information after processed by deep learning (DL) methods. However, both modalities have inherent disadvantages. CSI modality is with high environmental dependence, causing unstable fluctuations in wireless signals. RGB image modality is heavily restricted by camera facing angle and lighting condition. Hence, we propose a general multimodal fusion framework for HAR tasks based on both CSI and RGB image modalities. Distinct from other literatures on multimodal fusion mainly based on vision modalities, we utilized CSI modality from wireless domain along with RGB images to realize more comprehensive human activity sensing. Both early fusion and late fusion strategies are formulated and developed, with multiple models constructed from two modern unimodal frameworks to constitute corresponding fusion models. Experiments are conducted on a synchronous CSI and RGB image human activity dataset and validate the advantage of the proposed multimodal fusion design comparing with unimodal approaches.
A stochastic block model for community detection in attributed networks

Community detection is an important content in complex network analysis. The existing community detection methods in attributed networks mostly focus on only using network structure, while the methods of integrating node attributes is mainly for the traditional community structures, and cannot detect multipartite structures and mixture structures in network. In addition, the model-based community detection methods currently proposed for attributed networks do not fully consider unique topology information of nodes, such as betweenness centrality and clustering coefficient. Therefore, a stochastic block model that integrates betweenness centrality and clustering coefficient of nodes for community detection in attributed networks, named BCSBM, is proposed in this paper. Different from other generative models for attributed networks, the generation process of links and attributes in BCSBM model follows the Poisson distribution, and the probability between community is considered based on the stochastic block model. Moreover, the betweenness centrality and clustering coefficient of nodes are introduced into the process of links and attributes generation. Finally, the expectation maximization algorithm is employed to estimate the parameters of the BCSBM model, and the node-community memberships is obtained through the hard division process, so the community detection is completed. By experimenting on six real-work networks containing different network structures, and comparing with the community detection results of five algorithms, the experimental results show that the BCSBM model not only inherits the advantages of the stochastic block model and can detect various network structures, but also has good data fitting ability due to introducing the betweenness centrality and clustering coefficient of nodes. Overall, the performance of this model is superior to other five compared algorithms.
Inhibition of NPR1 Leads to Shoot Growth Improvement under Low-Calcium Conditions in Arabidopsis

Abstract Under low-Ca conditions, plants accumulate salicylic acid (SA) and induce SA-responsive genes. However, the relationship between SA and low-Ca tolerance remains unclear. Here, we demonstrated that the inhibition or suppression of nonexpressor of pathogenesis-related 1 (NPR1) activity, a major regulator of the SA signaling pathway in the defense response, improves shoot growth under low-Ca conditions. Furthermore, mutations in phytoalexin-deficient 4 (PAD4) or enhanced disease susceptibility 1 (EDS1), which are upstream regulators of NPR1, improved shoot growth under low-Ca conditions, suggesting that NPR1 suppressed growth under low-Ca conditions. In contrast, growth of SA induction–deficient 2-2 (sid2-2), which is an SA-deficient mutant, was sensitive to low Ca levels, suggesting that SA accumulation by SID2 was not related to growth inhibition under low-Ca conditions. Additionally, npr1-1 showed low-Ca tolerance, and the application of tenoxicam—an inhibitor of the NPR1-mediated activation of gene expression—also improved shoot growth under low Ca conditions. The low-Ca tolerance of double mutants pad4-1, npr1-1 and eds1-22 npr1-1 was similar to that of the single mutants, suggesting that PAD4 and EDS1 are involved in the same genetic pathway in suppressing growth under low-Ca conditions as NPR1. Cell death and low-Ca tolerance did not correlate among the mutants, suggesting that growth improvement in the mutants was not due to cell death inhibition. In conclusion, we revealed that NPR1 suppresses plant growth under low-Ca conditions and that the other SA-related genes influence plant growth and cell death.
Demo: A Functional EDSL for Mathematics Visualization That Compiles to JavaScript

Visualizations are a critical part of mathematics practice and education, and computers and open-source web technologies provide accessible ways to create high-quality mathematics visualizations at virtually no cost. However libraries and languages to create visualizations for mathematics are typically fine-grained, low-level, and targeted to vector graphics domain experts or web developers, not mathematics students or teachers or end-users. We present demos of Sylvester: a functional domain-specific language interface to the JSXGraph visualization library embedded in F# that emphasizes readability, composability, and the ability of end-users to easily create and manipulate elements of high-quality interactive mathematics visualizations without needing vector graphics or web development domain knowledge.
Platelet‐derived microvesicles induce intracellular calcium mobilization in human platelets

Platelet‐derived microvesicles (PMVs) represent a significant proportion of microvesicles in circulation and have been linked to various pathophysiological complications. Recent research suggests that PMVs carry significant amounts of cargo that can affect cellular functions by influencing calcium oscillations in target cells. As calcium is involved in multiple cellular processes, including hemostasis and thrombosis, this study aimed to investigate the impact of PMVs on platelet calcium mobilization. The study found that PMVs increase platelet intracellular calcium levels via both intracellular storage and extracellular space in a dose‐dependent manner. The study highlighted the critical role of the dense tubular system, acidic vacuoles, mitochondrial stores, and store‐operated calcium entry (SOCE) in PMV‐mediated calcium release in human platelets. Moreover, the study revealed that PMV‐induced calcium rise in platelets does not occur via sarcoendoplasmic reticulum calcium ATPase, and extracellular calcium addition further increases the calcium level in platelets, demonstrating the involvement of SOCE. These findings provide insights into the platelet stimulation signaling mechanisms and contributes to our understanding of platelet and cell behavior when exposed to PMV‐rich environments.
Modeling the impact of universal TB molecular testing and timing of TB preventive treatment during ART initiation in South Africa

Objectives: Targeted universal tuberculosis (TB) testing can improve TB detection among people with HIV. This approach is being scaled up in South Africa through Xpert MTB/RIF Ultra testing for individuals starting antiretroviral therapy and annually thereafter. Clarity is needed on how Universal Xpert testing may affect TB preventive treatment (TPT) provision, and on whether TPT should be delayed until TB is ruled out. Design: State-transition microsimulation. Methods: We simulated a cohort of South African patients being screened for TB while entering HIV care. We compared clinical and cost outcomes between four TB screening algorithms: symptom-based, C-reactive protein-based, and Universal Xpert testing with either simultaneous or delayed TPT initiation. Results: Prompt TB treatment initiation among simulated patients with TB increased from 26% (24–28%) under symptom screening to 53% (50–56%) with Universal Xpert testing. Universal Xpert testing led to increased TPT uptake when TPT initiation was simultaneous, but to approximately 50% lower TPT uptake if TPT was delayed. Universal Xpert with simultaneous TPT prevented incident TB compared to either symptom screening (median 17 cases averted per 5000 patients) or Universal Xpert with delayed TPT (median 23 averted). Universal Xpert with Simultaneous TPT cost approximately $39 per incremental TPT course compared to Universal Xpert with delayed TPT. Conclusions: Universal Xpert testing can promote timely treatment for newly diagnosed people with HIV who have active TB. Pairing universal testing with immediate TPT will improve the promptness, uptake, and preventive effects of TPT. Simultaneous improvements to TB care cascades are needed to maximize impact.
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction

Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer vision, and molecular modeling. We extend this paradigm by utilizing LLMs for material property prediction by introducing our model Materials Informatics Transformer (MatInFormer). Specifically, we introduce a novel approach that involves learning the grammar of crystallography through the tokenization of pertinent space group information. We further illustrate the adaptability of MatInFormer by incorporating task-specific data pertaining to Metal-Organic Frameworks (MOFs). Through attention visualization, we uncover the key features that the model prioritizes during property prediction. The effectiveness of our proposed model is empirically validated across 14 distinct datasets, hereby underscoring its potential for high throughput screening through accurate material property prediction.
One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions

A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. The downstream effects of ADM systems critically depend on the decisions made during a systems' design, implementation, and evaluation, as biases in data can be mitigated or reinforced along the modeling pipeline. Many of these decisions are made implicitly, without knowing exactly how they will influence the final system. To study this issue, we draw on insights from the field of psychology and introduce the method of multiverse analysis for algorithmic fairness. In our proposed method, we turn implicit decisions during design and evaluation into explicit ones and demonstrate their fairness implications. By combining decisions, we create a grid of all possible"universes"of decision combinations. For each of these universes, we compute metrics of fairness and performance. Using the resulting dataset, one can investigate the variability and robustness of fairness scores and see how and which decisions impact fairness. We demonstrate how multiverse analyses can be used to better understand fairness implications of design and evaluation decisions using an exemplary case study of predicting public health care coverage for vulnerable populations. Our results highlight how decisions regarding the evaluation of a system can lead to vastly different fairness metrics for the same model. This is problematic, as a nefarious actor could optimise or"hack"a fairness metric to portray a discriminating model as fair merely by changing how it is evaluated. We illustrate how a multiverse analysis can help to address this issue.
Myofascial acupuncture versus routine acupuncture for mechanical neck pain: a protocol for a multicentre randomised controlled trial

Introduction Mechanical neck pain (MNP) is defined as pain in the area of the neck and/or neck-shoulder provoked by body mechanics and which adversely affects physical, psychological and social function. The treatments for MNP are limited. Previous studies and clinical experience have indicated that myofascial acupuncture might be a better treatment option for MNP, but the efficacy is controversial. Therefore, our aim is to compare the efficacy of myofascial acupuncture and routine acupuncture for MNP. Methods and analysis The study is a multicentre, prospective randomised clinical trial. Patients will be recruited from four tertiary hospitals in China. A total of 438 participants with MNP will be randomly assigned into two groups, namely the ‘Sancai-Tianbu’ myofascial acupuncture group and the routine acupuncture group, at a ratio of 1:1. Each group will receive the acupuncture treatment twice a week for 21 days, totalling six sessions. The primary outcome will be the Visual Analogue Scale score. The secondary outcomes will be the Neck Disability Index, the cervical range of motion and the MOS 36-Item Short Form Health Survey. The assessments will be performed at baseline (immediately after allocation), pretreatment (5 min before every treatment), post-treatment (within 10 min after every treatment), postcourse (within 1 day after the course), and at 1, 3 and 6 months after the course. All patients will be included in the intent-to-treat analysis. Repeated-measure analysis of covariance will be used to determine the effects of the intervention on the outcome measures. Ethics and dissemination Ethics approval was obtained from China Aerospace Science & Industry Corporation 731 Hospital, with permission number 2022-0204-01. Written informed consent will be obtained from the enrolled patients. Trial results will be disseminated in peer-reviewed publications. Trial registration number ChiCTR2200061453.
TNF‐like weak inducer of apoptosis inhibition is comparable to IL‐13 blockade in ameliorating atopic dermatitis inflammation

Targeting IL‐13 is highly efficacious in patients with Th2‐biased atopic dermatitis (AD), but inhibition of other inflammatory molecules might also limit disease. We investigated the importance of the TNF family cytokine TNF‐like weak inducer of apoptosis (TWEAK; TNFSF12) to keratinocyte dysregulation and the pathogenesis of AD in mice and also tested if blocking TWEAK has a similar therapeutic effect as targeting IL‐13.
Learning with Multi-modal Gradient Attention for Explainable Composed Image Retrieval

We consider the problem of composed image retrieval that takes an input query consisting of an image and a modification text indicating the desired changes to be made on the image and retrieves images that match these changes. Current state-of-the-art techniques that address this problem use global features for the retrieval, resulting in incorrect localization of the regions of interest to be modified because of the global nature of the features, more so in cases of real-world, in-the-wild images. Since modifier texts usually correspond to specific local changes in an image, it is critical that models learn local features to be able to both localize and retrieve better. To this end, our key novelty is a new gradient-attention-based learning objective that explicitly forces the model to focus on the local regions of interest being modified in each retrieval step. We achieve this by first proposing a new visual image attention computation technique, which we call multi-modal gradient attention (MMGrad) that is explicitly conditioned on the modifier text. We next demonstrate how MMGrad can be incorporated into an end-to-end model training strategy with a new learning objective that explicitly forces these MMGrad attention maps to highlight the correct local regions corresponding to the modifier text. By training retrieval models with this new loss function, we show improved grounding by means of better visual attention maps, leading to better explainability of the models as well as competitive quantitative retrieval performance on standard benchmark datasets.
Data-Aided Channel Estimation Utilizing Gaussian Mixture Models

In this work, we propose two methods that utilize data symbols in addition to pilot symbols for improved channel estimation quality in a multi-user system, so-called semi-blind channel estimation. To this end, a subspace is estimated based on all received symbols and utilized to improve the estimation quality of a Gaussian mixture model-based channel estimator, which solely uses pilot symbols for channel estimation. Both of the proposed approaches allow for parallelization. Even the precomputation of estimation filters, which is beneficial in terms of computational complexity, is enabled by one of the proposed methods. Numerical simulations for real channel measurement data available to us show that the proposed methods outperform the studied state-of-the-art channel estimators.