Miracle-dz/UniFAR-FTUs huggingface dataset:
Out-Of-Distribution Generalization on Graphs: A Survey

—Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis, i.e., testing and training graph data are identically distributed. However, this in-distribution hypothesis can hardly be satisﬁed in many real-world graph scenarios where the model performance substantially degrades when there exist distribution shifts between testing and training graph data. To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the in-distribution hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area. First, we provide a formal problem deﬁnition of OOD generalization on graphs. Second, we categorize existing methods into three classes from conceptually different perspectives, i.e., data, model, and learning strategy, based on their positions in the graph machine learning pipeline, followed by detailed discussions for each category. We also review the theories related to OOD generalization on graphs and introduce the commonly used graph datasets for thorough evaluations. Finally, we share our insights on future research directions. This paper is the ﬁrst systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.
Entanglement-assisted concatenated quantum codes

Significance Code concatenation that uses two or more short component codes is a significant method for designing powerful codes. Concatenated classical codes are not only asymptotically good in theory, but also have been widely used in practice. However, construction of concatenated quantum codes (CQCs) suffers severe orthogonality constraints. We introduce entanglement to concatenated codes and propose entanglement-assisted CQCs (EACQCs). The enhanced performance of EACQCs under noisy ebit conditions demonstrates considerable advantages in both quantum communication and quantum computation. Among them, we show that EACQCs can beat the best-known quantum codes, either standard or entanglement-assisted. Moreover, we derive a high threshold of 47% when the error probability of ebits is 1% to that of qubits.
Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy

Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions representing transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.
Modular Multi–Source Prediction of Drug Side–Effects With DruGNN

Drug Side–Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side–effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time, money, and health of the participants. Drug side–effects are triggered by complex biological processes involving many different entities, from drug structures to protein–protein interactions. To predict their occurrence, it is necessary to integrate data from heterogeneous sources. In this work, such heterogeneous data is integrated into a graph dataset, expressively representing the relational information between different entities, such as drug molecules and genes. The relational nature of the dataset represents an important novelty for drug side–effect predictors. Graph Neural Networks (GNNs) are exploited to predict DSEs on our dataset with very promising results. GNNs are deep learning models that can process graph–structured data, with minimal information loss, and have been applied on a wide variety of biological tasks. Our experimental results confirm the advantage of using relationships between data entities, suggesting interesting future developments in this scope. The experimentation also shows the importance of specific subsets of data in determining associations between drugs and side–effects.
XFBoost: Improving Text Generation with Controllable Decoders

Multimodal conditionality in transformer-based natural language models has demonstrated state-of-the-art performance in the task of product description generation. Recent approaches condition a language model on one or more images and other textual metadata to achieve near-human performance for describing products from e-commerce stores. However, generated descriptions may exhibit degrees of inaccuracy or even contradictory claims relative to the inputs of a given product. In this paper, we propose a controllable language generation framework called Extract-Finetune-Boost (XFBoost), which addresses the problem of inaccurate low-quality inference. By using visual semantic attributes as constraints at the decoding stage of the generation process and finetuning the language model with policy gradient techniques, the XFBoost framework is found to produce significantly more descriptive text with higher image relevancy, outperforming baselines and lowering the frequency of factually inaccurate descriptions. We further demonstrate the application of XFBoost to online learning wherein human-in-the-loop critics improve language models with active feedback.
Absence and recovery of cost-precision tradeoff relations in quantum transport

The operation of many classical and quantum systems in nonequilibrium steady state is constrained by cost-precision (dissipation-fluctuation) tradeoff relations, delineated by the thermodynamic uncertainty relation (TUR). However, coherent quantum electronic nanojunctions can escape such a constraint, showing finite charge current and nonzero entropic cost with vanishing current fluctuations. Here, we analyze the absence, and restoration, of cost-precision tradeoff relations in fermionic nanojunctions under different affinities: voltage and temperature biases. With analytic work and simulations, we show that both charge and energy currents can display the absence of cost-precision tradeoff if we engineer the transmission probability as a boxcar function—with a perfect transmission and hard energy cutoffs. Specifically for charge current under voltage bias, the standard TUR may be immediately violated as we depart from equilibrium, and it is exponentially suppressed with increased voltage. However, beyond idealized, hard-cutoff energy-filtered transmission functions, we show that realistic models with soft cutoffs or imperfect transmission functions follow cost-precision tradeoffs, and eventually recover the standard TUR sufficiently far from equilibrium. The existence of cost-precision tradeoff relations is thus suggested as a generic feature of realistic nonequilibrium quantum transport junctions.
Flexible-Modal Face Anti-Spoofing: A Benchmark

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from presentation attacks. Benefitted from the maturing camera sensors, single-modal (RGB) and multi-modal (e.g., RGB+Depth) FAS has been applied in various scenarios with different configurations of sensors/modalities. Existing single- and multi-modal FAS methods usually separately train and deploy models for each possible modality scenario, which might be redundant and inefficient. Can we train a unified model, and flexibly deploy it under various modality scenarios? In this paper, we establish the first flexible-modal FAS benchmark with the principle ‘train one for all’. To be specific, with trained multi-modal (RGB+Depth+IR) FAS models, both intra- and cross-dataset testings are conducted on four flexible-modal sub-protocols (RGB, RGB+Depth, RGB+IR, and RGB+Depth+IR). We also investigate prevalent deep models and feature fusion strategies for flexible-modal FAS. We hope this new benchmark will facilitate the future research of the multi-modal FAS. The protocols and codes are available at https://github.com/ZitongYu/Flex-Modal-FAS.
Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark

The research of open-domain dialog systems has been greatly prospered by neural models trained on large-scale corpora, however, such corpora often introduce various safety problems (e.g., offensive languages, biases, and toxic behaviors) that significantly hinder the deployment of dialog systems in practice. Among all these unsafe issues, addressing social bias is more complex as its negative impact on marginalized populations is usually expressed implicitly, thus requiring normative reasoning and rigorous analysis. In this paper, we focus our investigation on social bias detection of dialog safety problems. We first propose a novel Dial-Bias Frame for analyzing the social bias in conversations pragmatically, which considers more comprehensive bias-related analyses rather than simple dichotomy annotations. Based on the proposed framework, we further introduce CDail-Bias Dataset that, to our knowledge, is the first well-annotated Chinese social bias dialog dataset. In addition, we establish several dialog bias detection benchmarks at different label granularities and input types (utterance-level and context-level). We show that the proposed in-depth analyses together with these benchmarks in our Dial-Bias Frame are necessary and essential to bias detection tasks and can benefit building safe dialog systems in practice.
Disentangling Successor Features for Coordination in Multi-agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) is a promising framework for solving complex tasks with many agents. However, a key challenge in MARL is defining private utility functions that ensure coordination when training decentralized agents. This challenge is especially prevalent in unstructured tasks with sparse rewards and many agents. We show that successor features can help address this challenge by disentangling an individual agent's impact on the global value function from that of all other agents. We use this disentanglement to compactly represent private utilities that support stable training of decentralized agents in unstructured tasks. We implement our approach using a centralized training, decentralized execution architecture and test it in a variety of multi-agent environments. Our results show improved performance and training time relative to existing methods and suggest that disentanglement of successor features offers a promising approach to coordination in MARL.
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability

Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of modifying existing nodes or edges, i.e., Graph Modification Attack (GMA). Although GIA has achieved promising results, little is known about why it is successful and whether there is any pitfall behind the success. To understand the power of GIA, we compare it with GMA and find that GIA can be provably more harmful than GMA due to its relatively high flexibility. However, the high flexibility will also lead to great damage to the homophily distribution of the original graph, i.e., similarity among neighbors. Consequently, the threats of GIA can be easily alleviated or even prevented by homophily-based defenses designed to recover the original homophily. To mitigate the issue, we introduce a novel constraint -- homophily unnoticeability that enforces GIA to preserve the homophily, and propose Harmonious Adversarial Objective (HAO) to instantiate it. Extensive experiments verify that GIA with HAO can break homophily-based defenses and outperform previous GIA attacks by a significant margin. We believe our methods can serve for a more reliable evaluation of the robustness of GNNs.
Supersymmetric Lattice Theories: Contribution to Snowmass 2022

In this white paper we summarise the construction and applications of lattice theories possessing exact supersymmetry focusing, in particular, on N=4 Yang-Mills theory. Lattice formulations of this theory allow for numerical simulation of the theory at strong coupling and hence give a window on non-perturbative physics away from the planar limit. This has important applications to our understanding of holographic approaches to quantum gravity and conformal field theories.
NORM: An FPGA-based Non-volatile Memory Emulation Framework for Intermittent Computing

Today’s intermittent computing systems operate by relying only on harvested energy accumulated in their tiny energy reservoirs, typically capacitors. An intermittent device dies due to a power failure when there is no energy in its capacitor and boots again when the harvested energy is sufficient to power its hardware components. Power failures prevent the forward progress of computation due to the frequent loss of computational state. To remedy this problem, intermittent computing systems comprise built-in fast non-volatile memories with high write endurance to store information that persists despite frequent power failures. However, the lack of design tools makes fast-prototyping these systems difficult. Even though FPGAs are common platforms for fast prototyping and behavioral verification of continuously powered architectures, they do not target prototyping intermittent computing systems. This article introduces a new FPGA-based framework, named NORM (Non-volatile memORy eMulator), to emulate and verify the behavior of any intermittent computing system that exploits fast non-volatile memories. Our evaluation showed that NORM can be used to emulate and validate FeRAM-based transiently powered hardware architectures successfully.
SemiRetro: Semi-template framework boosts deep retrosynthesis prediction

Recently, template-based (TB) and template-free (TF) molecule graph learning methods have shown promising results to retrosynthesis. TB methods are more accurate using pre-encoded reaction templates, and TF methods are more scalable by decomposing retrosynthesis into subproblems, i.e., center identification and synthon completion. To combine both advantages of TB and TF, we suggest breaking a full-template into several semi-templates and embedding them into the two-step TF framework. Since many semi-templates are reduplicative, the template redundancy can be reduced while the essential chemical knowledge is still preserved to facilitate synthon completion. We call our method SemiRetro, introduce a new GNN layer (DRGAT) to enhance center identification, and propose a novel self-correcting module to improve semi-template classification. Experimental results show that SemiRetro significantly outperforms both existing TB and TF methods. In scalability, SemiRetro covers 98.9\% data using 150 semi-templates, while previous template-based GLN requires 11,647 templates to cover 93.3\% data. In top-1 accuracy, SemiRetro exceeds template-free G2G 4.8\% (class known) and 6.0\% (class unknown). Besides, SemiRetro has better training efficiency than existing methods.
IoT Smart Plant Monitoring, Watering and Security System

Interest in home gardening has burgeoned since governments around the world-imposed lockdowns to suppress the spread of COVID-19. Nowadays, most families start to do gardening during this lockdown season because they can grow vegetables and fruits or any other plants that they want in their day-to-day life. So, they can survive without spending money on online grocery shopping for fruits and vegetables during this lockdown season. In Sri Lanka, home gardening was a trend during the past couple of months due to this pandemic. Most of the families were trying to do gardening for their needs. But the problem is, nowadays the government is trying to release those restrictions to start day-to-day work in Sri Lanka. With this situation, people are starting to do their jobs and they do not have time to spend in their gardens continuing their gardening. We thought about this problem and tried to find a solution to continue the gardening work while doing their jobs. The major concern is people cannot monitor their plants every time and protect their garden. So, we decided to automate the garden work. With our new solution, gardeners can monitor some important factors like the plant's healthiness, soil moisture level, air humidity level, and the surrounding temperature and water their garden from anywhere in the world at any time by using our app. Plant health has a significant impact on plant development, production, and quality of agricultural goods. The goal of this study is to create an automated system that can identify the presence of illness in plants based on variations in plant leaf health state is created utilizing sensors such as temperature, humidity, and color.
Optimizing Nanopore Sequencing for Rapid Detection of Microbial Species and Antimicrobial Resistance in Patients at Risk of Surgical Site Infections

Surgical site infections (SSI) are a significant burden to patients and health care systems. They increase mortality rates, length of hospital stays, and associated health care costs. ABSTRACT Surgical site infections (SSI) are a significant burden to patients and health care systems. We evaluated the use of Nanopore sequencing (NS) to rapidly detect microbial species and antimicrobial resistance (AMR) genes present in intraoperative bile aspirates. Bile aspirates from 42 patients undergoing pancreatic head resection were included. Three methods of DNA extraction using mechanical cell lysis or protease cell lysis were compared to determine the optimum method of DNA extraction. The impact of host DNA depletion, sequence run duration, and use of different AMR gene databases was also assessed. To determine clinical value, NS results were compared to standard culture (SC) results. NS identified microbial species in all culture positive samples. Mechanical lysis improved NS detection of cultured species from 60% to 76%, enabled detection of fungal species, and increased AMR predictions. Host DNA depletion improved detection of streptococcal species and AMR correlation with SC. Selection of AMR database influenced the number of AMR hits and resistance profile of 13 antibiotics. AMR prediction using CARD and ResFinder 4.1 correctly predicted 79% and 81% of the bile antibiogram, respectively. Sequence run duration positively correlated with detection of AMR genes. A minimum of 6 h was required to characterize the biliary microbes, resulting in a turnaround time of 14 h. Rapid identification of microbial species and AMR genes can be achieved by NS. NS results correlated with SC, suggesting that NS may be useful in guiding early antimicrobial therapy postsurgery. IMPORTANCE Surgical site infections (SSI) are a significant burden to patients and health care systems. They increase mortality rates, length of hospital stays, and associated health care costs. To reduce the risk of SSI, surgical patients are administered broad-spectrum antibiotics that are later adapted to target microbial species detected at the site of surgical incision. Use of broad-spectrum antibiotics can be harmful to the patient. We wanted to develop a rapid method of detecting microbial species and their antimicrobial resistance phenotypes. We developed a method of detecting microbial species and predicting resistance phenotypes using Nanopore sequencing. Results generated using Nanopore sequencing were similar to current methods of detection but were obtained in a significantly shorter amount of time. This suggests that Nanopore sequencing could be used to tailor antibiotics in surgical patients and reduce use of broad-spectrum antibiotics.
New Diminutive Eocene Lizard Reveals High K-Pg Survivorship and Taxonomic Diversity of Stem Xenosaurs in North America

ABSTRACT We describe a new diminutive early Eocene lizard, Blutwurstia oliviae, gen. et sp. nov., on the basis of associated cranial and postcranial remains from the Clarks Fork Basin of Wyoming. Results from phylogenetic analyses suggest that B. oliviae is on the stem of knob-scaled lizards (Xenosaurus), a relict extant clade of specialized, stenotopic crevice-dwellers from Mexico and Central America. Results further suggest that B. oliviae is basal to all other previously described pan-xenosaurs (members of Pan-Xenosaurus, the total clade of Xenosaurus) except species of Entomophontes, to which it is closely related. Given that B. oliviae and Entomophontes are known from a limited fossil record, with only one recovered element (the maxilla) in common, the level of support for this relationship is surprisingly high. We use a posteriori time-calibrated trees and ghost lineages (maximum parsimony) and divergence time estimates under the fossilized birth-death process (Bayesian inference) to infer patterns of extinction across the Cretaceous-Paleogene (K-Pg) boundary in Pan-Xenosaurus, including those consistent with pseudoextinction. Whereas the fossil record documents a single lineage in the latest Cretaceous, results from analyses using these analytical methods suggest that three or more species existed, with high survivorship across the K-Pg boundary. The surviving lineages were apparently present at proximal to intermediate distance from the Chicxulub impact site, thought to have a causal relationship with extinctions across the K-Pg boundary. The premaxilla and dorsal vertebrae of E. incrustatus and B. oliviae, respectively, independently suggest that each of these taxa had a depressed body form consistent with extant crevice-dwelling squamates, which may have played a role in the high survivorship of pan-xenosaur lineages across the K-Pg boundary.
Supply–Demand Docking Joint Decision of Cross-border E-commerce under the Influence of Internet Celebrity

ABSTRACT While cross-border e-commerce (CBEC) is booming in the world today, there are many obstacles hindering the development of the market, such as fake commodities and inferior goods, unreliable and lengthy transit times, and so on. The emergence of Internet celebrity brings new vitality to CBEC, as they build a bridge of trust between brand merchants and consumers. At the same time, the more complex CBEC supply chain with Internet celebrity involved needs to be integrated. This study propose a business-to-business-to-consumer (B2B2C) supply–demand docking business model and accordingly four incentive mechanisms (namely high-quality production incentive, Internet celebrity marketing incentive, equity sharing incentive and cooperative voting mechanism) to achieve the CBEC supply chain vertical integration. Our analysis shows that a higher commission proportion of CBEC platform results in lower quality improvement level and demand, and higher sale price and consumer experience level. We also find that joint decision-making actually realizes Pareto improvement of decentralized decision-making. Moreover, the introduction of cooperative voting mechanism ensures the fair expression of the interest demands of participants in the joint decision, providing a win-win situation for all parties. This study provides a sufficient basis for the vital role of Internet celebrity in CBEC.