Miracle-dz/UniFAR-FTUs huggingface dataset:
Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks

Defect detection is one of the most important yet challenging tasks in the quality control stage in the manufacturing sector. In this work, we introduce a Tensor Convolutional Neural Network (T-CNN) and examine its performance on a real defect detection application in one of the components of the ultrasonic sensors produced at Robert Bosch's manufacturing plants. Our quantum-inspired T-CNN operates on a reduced model parameter space to substantially improve the training speed and performance of an equivalent CNN model without sacrificing accuracy. More specifically, we demonstrate how T-CNNs are able to reach the same performance as classical CNNs as measured by quality metrics, with up to fifteen times fewer parameters and 4% to 19% faster training times. Our results demonstrate that the T-CNN greatly outperforms the results of traditional human visual inspection, providing value in a current real application in manufacturing.
Approximation in H\"older Spaces

For a modulus of continuity $\omega$ and normed spaces $X,Y$ we introduce and study the subspaces $\dot{\operatorname{VC}}^{0,\omega}_{\Gamma}(X,Y)$ of vanishing scales $\Gamma\in \{\operatorname{small},\operatorname{large},\operatorname{far}\}$ of the homogeneous H\"{o}lder space $\dot{C}^{0,\omega}(X,Y). $ For many couples $X$ and $Y$, we characterize the subspaces that admit approximations by smooth, Lipschitz and boundedly supported functions in terms of these three vanishing scales. In the particular case $X=\mathbb{R}^n,$ we identify $\dot{\operatorname{VC}}^{0,\omega}_{\Gamma}(\mathbb{R}^n,Y) = \operatorname{VMO}^{\omega}_{\Gamma}(\mathbb{R}^n,Y)$ with the corresponding vanishing mean oscillation spaces, thus transferring these approximations also to these spaces.
A Generative AI Assistant to Accelerate Cloud Migration

We present a tool that leverages generative AI to accelerate the migration of on-premises applications to the cloud. The Cloud Migration LLM accepts input from the user specifying the parameters of their migration, and outputs a migration strategy with an architecture diagram. A user study suggests that the migration LLM can assist inexperienced users in finding the right cloud migration profile, while avoiding complexities of a manual approach.
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection

Previous research on behaviour-based attack detection on networks of IoT devices has resulted in machine learning models whose ability to adapt to unseen data is limited, and often not demonstrated. In this paper we present an approach for modelling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance. First, we present an improved rolling window approach for feature extraction, and introduce a multi-step feature selection process that reduces overfitting. Second, we build and test models using isolated train and test datasets, thereby avoiding common data leaks that have limited the generalizability of previous models. Third, we rigorously evaluate our methodology using a diverse portfolio of machine learning models, evaluation metrics and datasets. Finally, we build confidence in the models by using explainable AI techniques, allowing us to identify the features that underlie accurate detection of attacks.
The Boomerang protocol: A Decentralised Privacy-Preserving Verifiable Incentive Protocol

In the era of data-driven economies, incentive systems and loyalty programs, have become ubiquitous in various sectors, including advertising, retail, travel, and financial services. While these systems offer advantages for both users and companies, they necessitate the transfer and analysis of substantial amounts of sensitive data. Privacy concerns have become increasingly pertinent, necessitating the development of privacy-preserving incentive protocols. Despite the rising demand for secure and decentralized systems, the existing landscape lacks a comprehensive solution. We propose the Boomerang protocol, a novel decentralized privacy-preserving incentive protocol that leverages cryptographic black box accumulators to securely store user interactions within the incentive system. Moreover, the protocol employs zero-knowledge proofs based on BulletProofs to transparently compute rewards for users, ensuring verifiability while preserving their privacy. To further enhance public verifiability and transparency, we utilize a smart contract on a Layer 1 blockchain to verify these zero-knowledge proofs. The careful combination of black box accumulators with selected elliptic curves in the zero-knowledge proofs makes the Boomerang protocol highly efficient. Our proof of concept implementation shows that we can handle up to 23.6 million users per day, on a single-threaded backend server with financial costs of approximately 2 USD. Using the Solana blockchain we can handle 15.5 million users per day with approximate costs of 0.00011 USD per user. The Boomerang protocol represents a significant advancement in privacy-preserving incentive protocols, laying the groundwork for a more secure and privacy-centric future.
Clonorchiasis and opisthorchiasis: epidemiology, transmission, clinical features, morbidity, diagnosis, treatment, and control

SUMMARY Clonorchis sinensis, Opisthorchis viverrini, and Opisthorchis felineus are important liver flukes that cause a considerable public health burden in eastern Asia, southeastern Asia, and eastern Europe, respectively. The life cycles are complex, involving humans, animal reservoirs, and two kinds of intermediate hosts. An interplay of biological, cultural, ecological, economic, and social factors drives transmission. Chronic infections are associated with liver and biliary complications, most importantly cholangiocarcinoma. With regard to diagnosis, stool microscopy is widely used in epidemiologic surveys and for individual diagnosis. Immunologic techniques are employed for screening purposes, and molecular techniques facilitate species differentiation in reference laboratories. The mainstay of control is preventive chemotherapy with praziquantel, usually combined with behavioral change through information, education and communication, and environmental control. Tribendimidine, a drug registered in the People’s Republic of China for soil-transmitted helminth infections, shows potential against both C. sinensis and O. viverrini and, hence, warrants further clinical development. Novel control approaches include fish vaccine and biological control. Considerable advances have been made using multi-omics which may trigger the development of new interventions. Pressing research needs include mapping the current distribution, disentangling the transmission, accurately estimating the disease burden, and developing new diagnostic and treatment tools, which would aid to optimize control and elimination measures.
Prophage enhances the ability of deep-sea bacterium Shewanella psychrophila WP2 to utilize D-amino acid

ABSTRACT Prophages are prevalent in the marine bacterial genomes and reshape the physiology and metabolism of their hosts. However, whether and how prophages influence the microbial degradation of D-amino acids (D-AAs), which is one of the widely distributed recalcitrant dissolved organic matters (RDOMs) in the ocean, remain to be explored. In this study, we addressed this issue in a representative marine bacterium, Shewanella psychrophila WP2 (WP2), and its integrated prophage SP1. Notably, compared to the WP2 wild-type strain, the SP1 deletion mutant of WP2 (WP2ΔSP1) exhibited a significantly lower D-glutamate (D-Glu) consumption rate and longer lag phase when D-Glu was used as the sole nitrogen source. The subsequent transcriptome analysis identified 1,523 differentially expressed genes involved in diverse cellular processes, especially that multiple genes related to inorganic nitrogen metabolism were highly upregulated. In addition, the dynamic profiles of ammonium, nitrate, and nitrite were distinct between the culture media of WP2 and WP2ΔSP1. Finally, we provide evidence that SP1 conferred a competitive advantage to WP2 when D-Glu was used as the sole nitrogen source and SP1-like phages may be widely distributed in the global ocean. Taken together, these findings offer novel insight into the influences of prophages on host metabolism and RDOM cycling in marine environments. IMPORTANCE This work represents the first exploration of the impact of prophages on the D-amino acid (D-AA) metabolism of deep-sea bacteria. By using S. psychrophila WP2 and its integrated prophage SP1 as a representative system, we found that SP1 can significantly increase the catabolism rate of WP2 to D-glutamate and produce higher concentrations of ammonium, resulting in faster growth and competitive advantages. Our findings not only deepen our understanding of the interaction between deep-sea prophages and hosts but also provide new insights into the ecological role of prophages in refractory dissolved organic matter and the nitrogen cycle in deep oceans. This work represents the first exploration of the impact of prophages on the D-amino acid (D-AA) metabolism of deep-sea bacteria. By using S. psychrophila WP2 and its integrated prophage SP1 as a representative system, we found that SP1 can significantly increase the catabolism rate of WP2 to D-glutamate and produce higher concentrations of ammonium, resulting in faster growth and competitive advantages. Our findings not only deepen our understanding of the interaction between deep-sea prophages and hosts but also provide new insights into the ecological role of prophages in refractory dissolved organic matter and the nitrogen cycle in deep oceans.
Crystal structure of (E)-1-(4-(benzyloxy)-2-hydroxyphenyl)-3-(dimethylamino)prop-2-en-1-one, C18H19NO3

Abstract C18H19NO3, monoclinic, P21/c (no. 14), a = 8.9529(3) Å, b = 13.5984(4) Å, c = 12.8403(5) Å, β = 101.572(4)°, R gt (F) = 0.0458, wR ref (F2) = 0.1395, V = 1532.54(9) Å3, Z = 4, T = 150 K.
Robust Training of Temporal GNNs using Nearest Neighbour based Hard Negatives

Temporal graph neural networks Tgnn have exhibited state-of-art performance in future-link prediction tasks. Training of these TGNNs is enumerated by uniform random sampling based unsupervised loss. During training, in the context of a positive example, the loss is computed over uninformative negatives, which introduces redundancy and sub-optimal performance. In this paper, we propose modified unsupervised learning of Tgnn, by replacing the uniform negative sampling with importance-based negative sampling. We theoretically motivate and define the dynamically computed distribution for a sampling of negative examples. Finally, using empirical evaluations over three real-world datasets, we show that Tgnn trained using loss based on proposed negative sampling provides consistent superior performance.
Text Representation Models based on the Spatial Distributional Properties of Word Embeddings

In the current digital era, about 80% of the digital data which is being generated is unstructured and unlabeled natural language text. In the development cycle of information retrieval and text mining applications, text representation is the most fundamental and critical step, as its effectiveness directly impacts the application’s performance. The existing traditional text representation frameworks are mostly frequency distribution-based. In this work, we explored the spatial distribution of word embeddings and proposed two text representational models. The experimental demonstrated that proposed models perform consistently better at text mining tasks compared to baseline methods.
Knowledge Management Framework Over Low Resource Indian Colloquial Language Audio Contents

We introduce Graama-Kannada Audio Search, a knowledge management framework for low-resource Indian colloquial language audio content. Rural social communities often contain rich knowledge, but it can often be inaccessible on the internet as it is primarily oral. Organizations such as Namma Halli Radio1 have collected an audio corpus of a few hours containing community interactions spoken in colloquial language. A simple and efficient platform to store, organize, and query knowledge in such audio can greatly help even for people with low literacy levels. In this work, we fine-tune state-of-the-art Automatic Speech Recognition (ASR) models using limited audio data to reduce the Word Error Rate (WER) for colloquial audio data to acquire transcripts for the audio, followed by creating an interface to search for keywords using simple fuzzy matching technique for n-gram inputs.
Better Graph Embeddings for Enterprise Graphs

Graph embeddings are scalable and performant node representations in a graph. Fast Random Projections (FastRP) is claimed to be thousands of times faster to generate embeddings compared to random walk-based algorithms like DeepWalk and Node2Vec, while achieving comparable performance on various downstream tasks. In this paper, we consider graph embeddings for enterprise graphs—the communication graphs which are constructed using emails, meetings, documents, etc., as graph nodes which are connected if they share a common contact (person) or topic (keyword). We consider meeting-to-email, meeting-to-document, and email-to-document recommendations as the downstream tasks. If we use the FastRP algorithm to get relevant entities (e.g., relevant emails for a given meeting), we get a large number of false positives, i.e., emails with high embedding similarity with the meetings but not actually relevant as per the ground-truth. We present a modified FastRP algorithm where we delay the random projections to improve the relevance of the FastRP algorithm with slight additional cost. Specifically, using the real enterprise data, we show that embedding performance can be improved by more than 10% (Recall@N) with slight increase in the embedding generation cost. Currently, we are using this algorithm to generate embeddings of billions of entities for millions of Microsoft customers and powering a number of recommendation applications.
New User Product Recommendation for Q-commerce via Hierarchical Cross-Domain Learning

In this work, we address the new user cold start problem in the product recommendation system for a quick-commerce (q-commerce) grocery delivery service. Traditional recommendation systems built on user-product interaction data tend to perform poorly for new users owing to data sparsity. This has led to the emergence of cross-domain recommendation systems, which leverage the data from a relatively richer ‘source’ domain to improve recommendations in the target domain. In our case, online food delivery, being one of the more evolved services of our platform, is the source domain to the online grocery delivery referenced above. This enables us to leverage the data from the food domain. There is a large body of literature on cross-domain recommendation systems which typically involves learning a cross-domain mapping function between the customer or the item embeddings in the two domains and leveraging that mapping function to derive the embeddings for new users in the target domain. We show that such approaches are sub-optimal where sales distribution is long-tailed, making the embeddings noisy. This is further aggravated when applied to q-commerce settings where location-specific geographical and cultural diversities have to be considered. Given these nuances, we propose a neural network-based hierarchical cross-domain mapper, which leverages the inherent hierarchy in the item taxonomy and learns multiple mapping functions between customers’ food category preferences and product category preferences in the grocery domain. These category-level preferences are then used to personalize product recommendations in the grocery domain through a learning-to-rank model. We show that the proposed algorithm outperforms the embedding mapping-based approach and the popularity-based baseline by 30% and 8% respectively, in terms of NDCG in an offline experiment and 4% improvement in conversion in an online A/B experiment which is significant at our scale.
WRKY gene family of Licorice (Glycyrrhiza uralensis): identification and expression analysis in response to abiotic stress

Abstract WRKY transcription factors (TF) control a variety of biological processes in higher plants, including abiotic and biotic stress responses. WRKY genes have been identified and functionally characterized in many plant species during the past ten years, but despite this progress, few investigations have focused on this TF family in licorice (Glycyrrhiza uralensis Fisch.). This study identified 72 WRKY genes from the licorice genome and classified them into three groups. The gene structure analysis indicated that the GuWRKY genes contain from one to eight introns, with the majority having two introns and three exons. Conserved motif analysis identified the conserved WRKY domain in most GuWRKYs. Further analysis of the promoter region revealed that almost all GuWRKYs had plant hormone- or stress-responsive cis-regulatory elements, with differences in the number of elements among the groups. The role of 10 selected GuWRKYs in regulating abiotic stress responses was further explored via quantitative real-time PCR (qRT-PCR) after low-temperature (4 °C), salt (150 mmol·L−1 NaCl), drought (15% polyethylene glycol 6000) treatments at 3, 6, 12, and 24 h. Thus, our study of the WRKY gene family in licorice provides a basis for further research on the structure and function of WRKY genes.
Spatially Resolved Transcriptomic Signatures of Hippocampal Subregions and Arc-Expressing Ensembles in Active Place Avoidance Memory

The rodent hippocampus is a spatially organized neuronal network that supports the formation of spatial and episodic memories. We conducted bulk RNA sequencing and spatial transcriptomics experiments to measure gene expression changes in the dorsal hippocampus following the recall of active place avoidance (APA) memory. Through bulk RNA sequencing, we examined the gene expression changes following memory recall across the functionally distinct subregions of the dorsal hippocampus. We found that recall induced differentially expressed genes (DEGs) in the CA1 and CA3 hippocampal subregions were enriched with genes involved in synaptic transmission and synaptic plasticity, while DEGs in the dentate gyrus (DG) were enriched with genes involved in energy balance and ribosomal function. Through spatial transcriptomics, we examined gene expression changes following memory recall across an array of spots encompassing putative memory-associated neuronal ensembles marked by the expression of the IEGs Arc, Egr1, and c-Jun. Within samples from both trained and untrained mice, the subpopulations of spatial transcriptomic spots marked by these IEGs were transcriptomically and spatially distinct from one another. DEGs detected between Arc+ and Arc-spots exclusively in the trained mouse were enriched in several memory-related gene ontology terms, including “regulation of synaptic plasticity” and “memory. ” Our results suggest that APA memory recall is supported by regionalized transcriptomic profiles separating the CA1 and CA3 from the DG, transcriptionally and spatially distinct IEG expressing spatial transcriptomic spots, and biological processes related to synaptic plasticity as a defining the difference between Arc+ and Arc-spatial transcriptomic spots.
Downstream-of-gene (DoG) transcripts contribute to an imbalance in the cancer cell transcriptome

Downstream-of-gene (DoG) transcripts are an emerging class of noncoding RNAs. However, it remains largely unknown how DoG RNA production is regulated and whether alterations in DoG RNA signatures exist in major cancers. Here, through transcriptomic analyses of matched tumors and non-neoplastic tissues and cancer cell lines, we reveal a comprehensive catalogue of DoG RNA signatures. Through separate lines of evidence, we support the biological importance of DoG RNAs in carcinogenesis. First, we reveal DoG RNAs are tissue-specific and differentially expressed in tumors versus paired normal tissues with their respective host genes involved in tumor promoting versus tumor suppressor pathways. Second, increased DoG RNA number and length is associated with poor patient prognosis. Third, depletion of essential enzyme Topoisomerase I in colon cancer alters RNA polymerase II chromatin engagement leading to termination defects and induction of DoG RNAs. Our results underlie the significance of DoG RNAs in diversifying the cancer transcriptome.
Imaging spectroscopy reveals topographic variability effects on grassland functional traits and drought responses

Functional traits and their variations are essential indicators of plant metabolism, growth, distribution, and survival and determine how a plant and an ecosystem function. Under the same climatic condition, traits can vary largely between species and within the same species growing in different topographic conditions. When drought stress occurs, plants that grow in these conditions may respond differently as their topography-driven tolerance and adaptability differ. Insights into topographic variability-driven trait variation and drought response can improve our prediction of ecosystem functioning and ecological impacts. Imaging spectroscopy allows accurate detection of plant species, retrieval of functional traits, and characterization of topography-driven and drought impacts on trait variation across space. However, the use of this data in a heterogeneous grassland ecosystem is challenging as species are small, high mixed, spectrally and texturally similar, and highly varied with small-scale variation in topography. In this paper, we introduce the first study that explores the use of high-resolution airborne imaging spectroscopy to characterize the variation of common traits, including chlorophylls (Chl), carotenoids (Car), Chl/Car ratio, water content (WC), and leaf area index (LAI), across topographic gradients and under drought stress at the species level in a heterogeneous grassland. The results reveal that there were significant relationships between functional traits and topographic variability, and the degree of the relationships deferred among species and under different environmental conditions. The results also show that drought-induced trait responses varied significantly within and between species, especially between drought-tolerant invasive species and native species, between lower and upper slope positions. The study contributes greatly to the advancement in understanding biological and ecological processes for a better prediction of ecosystem functioning under stressed environments.
SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN

Spatially resolved transcriptomics or proteomics data have the potential to contribute fundamental insights into the mechanisms underlying physiologic and pathological processes. However, analysis of these data capable of relating spatial information, multiplexed markers, and their observed phenotypes remains technically challenging. To analyze these relationships, we developed SORBET, a deep learning framework that leverages recent advances in graph neural networks (GNN). We apply SORBET to predict tissue phenotypes, such as response to immunotherapy, across different disease processes and different technologies including both spatial proteomics and transcriptomics methods. Our results show that SORBET accurately learns biologically meaningful relationships across distinct tissue structures and data acquisition methods. Furthermore, we demonstrate that SORBET facilitates understanding of the spatially-resolved biological mechanisms underlying the inferred phenotypes. In sum, our method facilitates mapping between the rich spatial and marker information acquired from spatial ‘omics technologies to emergent biological phenotypes. Moreover, we provide novel techniques for identifying the biological processes that comprise the predicted phenotypes.