Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-equilibrium structural dynamics of supercoiled DNA plasmids exhibits asymmetrical relaxation

Abstract Many cellular processes occur out of equilibrium. This includes site-specific unwinding in supercoiled DNA, which may play an important role in gene regulation. Here, we use the Convex Lens-induced Confinement (CLiC) single-molecule microscopy platform to study these processes with high-throughput and without artificial constraints on molecular structures or interactions. We use two model DNA plasmid systems, pFLIP-FUSE and pUC19, to study the dynamics of supercoiling-induced secondary structural transitions after perturbations away from equilibrium. We find that structural transitions can be slow, leading to long-lived structural states whose kinetics depend on the duration and direction of perturbation. Our findings highlight the importance of out-of-equilibrium studies when characterizing the complex structural dynamics of DNA and understanding the mechanisms of gene regulation.
Impact of ceftazidime/avibactam versus best available therapy on mortality from infections caused by carbapenemase-producing Enterobacterales (CAVICOR study).

BACKGROUND Infections caused by carbapenemase-producing Enterobacterales (CPE) are not well represented in pivotal trials with ceftazidime/avibactam. The best strategy for the treatment of these infections is unknown. METHODS We conducted a multicentre retrospective observational study of patients who received ≥48 h of ceftazidime/avibactam or best available therapy (BAT) for documented CPE infections. The primary outcome was 30 day crude mortality. Secondary outcomes were 21 day clinical response and microbiological response. A multivariate logistic regression model was used to identify factors predictive of 30 day crude mortality. A propensity score to receive treatment with ceftazidime/avibactam was used as a covariate in the analysis. RESULTS The cohort included 339 patients with CPE infections. Ceftazidime/avibactam treatment was used in 189 (55.8%) patients and 150 (44.2%) received BAT at a median of 2 days after diagnosis of infection. In multivariate analysis, ceftazidime/avibactam treatment was associated with survival (OR 0.41, 95% CI 0.20-0.80; P = 0.01), whereas INCREMENT-CPE scores of >7 points (OR 2.57, 95% CI 1.18-1.5.58; P = 0.01) and SOFA score (OR 1.20, 95% CI 1.08-1.34; P = 0.001) were associated with higher mortality. In patients with INCREMENT-CPE scores of >7 points, ceftazidime/avibactam treatment was associated with lower mortality compared with BAT (16/73, 21.9% versus 23/49, 46.9%; P = 0.004). Ceftazidime/avibactam was also an independent factor of 21 day clinical response (OR 2.43, 95% CI 1.16-5.12; P = 0.02) and microbiological eradication (OR 0.40, 95% CI 0.18-0.85; P = 0.02). CONCLUSIONS Ceftazidime/avibactam is an effective alternative for the treatment of CPE infections, especially in patients with INCREMENT-CPE scores of >7 points. A randomized controlled trial should confirm these findings.
Incidence of Contrast-Induced Nephropathy and Post-Operative Outcomes in Patients Undergoing Chimney Endovascular Aortic Aneurysm Repair

Chimney endovascular aortic aneurysm repair (ch-EVAR) has become a valid alternative to treat complex aneurysms but the occurrence of contrast-induced kidney injury (CI-AKI) is poorly known. This study investigated the incidence and the impact of CI-AKI on post-operative outcomes after ch-EVAR. Consecutive patients who underwent ch-EVAR between July 2010 and 2021 were retrospectively included. CI-AKI was defined based on plasma creatinine levels within 7 days after the intervention according to the “Kidney Disease Improving Global Outcomes” (KDIGO) classification. Among 102 patients included, CI-AKI occurred in 14 cases (13.7%). The 30-day post-operative mortality and complications were significantly higher in patients who developed CI-AKI compared with those who did not (50 vs 9.1%, P = .001 and 57.1 vs 20.5%, P = .007). Over a median follow-up of 24 months (3-39), overall mortality was also significantly higher (78.6 vs 33.0%, P = .002). The pre-operative platelet-to-lymphocyte ratio (PLR) was significantly higher in patients who developed CI-AKI (224.5 vs 147.6, P = .008). CI-AKI is frequent after ch-EVAR and is associated with worse post-operative outcomes. This should increase awareness of clinicians to optimize preventive and therapeutic strategies.
Efficiently Sampling and Estimating from Substructures using Linear Algebraic Queries

Given an unknown $n \times n$ matrix $A$ having non-negative entries, the \emph{inner product} (IP) oracle takes as inputs a specified row (or a column) of $A$ and a vector $v \in \mathbb{R}^{n}$, and returns their inner product. A derivative of IP is the induced degree query in an unknown graph $G=(V(G), E(G))$ that takes a vertex $u \in V(G)$ and a subset $S \subseteq V(G)$ as input and reports the number of neighbors of $u$ that are present in $S$. The goal of this paper is to understand the strength of the inner product oracle. Our results in that direction are as follows: (I) IP oracle can solve bilinear form estimation, i.e., estimate the value of ${\bf x}^{T}A\bf{y}$ given two vectors ${\bf x},\, {\bf y} \in \mathbb{R}^{n}$ with non-negative entries and can sample almost uniformly entries of a matrix with non-negative entries; (ii) We tackle for the first time weighted edge estimation and weighted sampling of edges that follow as an application to the bilinear form estimation and almost uniform sampling problems, respectively; (iii) induced degree query, a derivative of IP can solve edge estimation and an almost uniform edge sampling in induced subgraphs. To the best of our knowledge, these are the first set of Oracle-based query complexity results for induced subgraphs. We show that IP/induced degree queries over the whole graph can simulate local queries in any induced subgraph; (iv) Apart from the above, we also show that IP can solve several problems related to matrix, like testing if the matrix is diagonal, symmetric, doubly stochastic, etc.
Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet

In this paper we present Scylla, a methodology for domain adaptation of Neural Machine Translation (NMT) systems that make use of a multilingual FrameNet enriched with qualia relations as an external knowledge base. Domain adaptation techniques used in NMT usually require fine-tuning and in-domain training data, which may pose difficulties for those working with lesser-resourced languages and may also lead to performance decay of the NMT system for out-of-domain sentences. Scylla does not require fine-tuning of the NMT model, avoiding the risk of model over-fitting and consequent decrease in performance for out-of-domain translations. Two versions of Scylla are presented: one using the source sentence as input, and another one using the target sentence. We evaluate Scylla in comparison to a state-of-the-art commercial NMT system in an experiment in which 50 sentences from the Sports domain are translated from Brazilian Portuguese to English. The two versions of Scylla significantly outperform the baseline commercial system in HTER.
A Rule-based Operational Semantics of Graph Query Languages

We consider a core language of graph queries. These queries are seen as formulas to be solved with respect to graph-oriented databases. For this purpose, we first define a graph query algebra where some operations over graphs and sets of graph homomorphisms are specified. Then, the notion of pattern is introduced to represent a kind of recursively defined formula over graphs. The syntax and formal semantics of patterns are provided. Afterwards, we propose a new sound and complete calculus to solve patterns. This calculus, which is based on a rewriting system, develops only one derivation per pattern to be solved. Our calculus is generic in the sense that it can be adapted to different kinds of graph databases provided that the notions of graph and graph homomorphism (match) are well defined.
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization

Human intervention is an effective way to inject human knowledge into the training loop of reinforcement learning, which can bring fast learning and ensured training safety. Given the very limited budget of human intervention, it remains challenging to design when and how human expert interacts with the learning agent in the training. In this work, we develop a novel human-in-the-loop learning method called Human-AI Copilot Optimization (HACO). To allow the agent's sufficient exploration in the risky environments while ensuring the training safety, the human expert can take over the control and demonstrate how to avoid probably dangerous situations or trivial behaviors. The proposed HACO then effectively utilizes the data both from the trial-and-error exploration and human's partial demonstration to train a high-performing agent. HACO extracts proxy state-action values from partial human demonstration and optimizes the agent to improve the proxy values meanwhile reduce the human interventions. The experiments show that HACO achieves a substantially high sample efficiency in the safe driving benchmark. HACO can train agents to drive in unseen traffic scenarios with a handful of human intervention budget and achieve high safety and generalizability, outperforming both reinforcement learning and imitation learning baselines with a large margin. Code and demo videos are available at: https://decisionforce.github. io/HACO/.
The Adaptive Spectral Koopman Method for Dynamical Systems

Dynamical systems have a wide range of applications in mechanics, electrical engineering, chemistry, and so on. In this work, we propose the adaptive spectral Koopman (ASK) method to solve nonlinear autonomous dynamical systems. This novel numerical method leverages the spectral-collocation (i.e., pseudo-spectral) method and properties of the Koopman operator to obtain the solution of a dynamical system. Specifically, this solution is represented as a linear combination of the multiplication of Koopman operator's eigenfunctions and eigenvalues, and these eigenpairs are approximated by the spectral method. Unlike conventional time evolution algorithms such as Euler's scheme and the Runge-Kutta scheme, ASK is mesh-free, and hence is more flexible when evaluating the solution. Numerical experiments demonstrate high accuracy of ASK for solving one-, two- and three-dimensional dynamical systems.
PGE: Robust Product Graph Embedding Learning for Error Detection

Although product graphs (PGs) have gained increasing attentions in recent years for their successful applications in product search and recommendations, the extensive power of PGs can be limited by the inevitable involvement of various kinds of errors. Thus, it is critical to validate the correctness of triples in PGs to improve their reliability. Knowledge graph (KG) embedding methods have strong error detection abilities. Yet, existing KG embedding methods may not be directly applicable to a PG due to its distinct characteristics: (1) PG contains rich textual signals, which necessitates a joint exploration of both text information and graph structure; (2) PG contains a large number of attribute triples, in which attribute values are represented by free texts. Since free texts are too flexible to define entities in KGs, traditional way to map entities to their embeddings using ids is no longer appropriate for attribute value representation; (3) Noisy triples in a PG mislead the embedding learning and significantly hurt the performance of error detection. To address the aforementioned challenges, we propose an end-to-end noise-tolerant embedding learning framework, PGE, to jointly leverage both text information and graph structure in PG to learn embeddings for error detection. Experimental results on real-world product graph demonstrate the effectiveness of the proposed framework comparing with the state-of-the-art approaches.
Enhancing Affective Representations Of Music-Induced Eeg Through Multimodal Supervision And Latent Domain Adaptation

The study of Music Cognition and neural responses to music has been invaluable in understanding human emotions. Brain signals, though, manifest a highly complex structure that makes processing and retrieving meaningful features challenging, particularly of abstract constructs like affect. Moreover, the performance of learning models is undermined by the limited amount of available neuronal data and their severe inter-subject variability. In this paper we extract efficient, personalized affective representations from EEG signals during music listening. To this end, we employ music signals as a supervisory modality to EEG, aiming to project their semantic correspondence onto a common representation space. We utilize a bi-modal framework by combining an LSTM-based attention model to process EEG and a pre-trained model for music tagging, along with a reverse domain discriminator to align the distributions of the two modalities, further constraining the learning process with emotion tags. The resulting framework can be utilized for emotion recognition both directly, by performing supervised predictions from either modality, and indirectly, by providing relevant music samples to EEG input queries. The experimental findings show the potential of enhancing neuronal data through stimulus information for recognition purposes and yield insights into the distribution and temporal variance of music-induced affective features.
A Grounded Theory of Coordination in Remote-First and Hybrid Software Teams

While the long-term effects of the COVID-19 pandemic on software professionals and organizations are difficult to predict, it seems likely that working from home, remote-first teams, distributed teams, and hybrid (part-remote/part-office) teams will be more common. It is therefore important to investigate the challenges that software teams and organizations face with new remote and hybrid work. Consequently, this paper reports a year-long, participant-observation, constructivist grounded theory study investigating the impact of working from home on software development. This study resulted in a theory of software team coordination. Briefly, shifting from in-office to at-home work fundamentally altered coordination within software teams. While group cohesion and more effective communication appear protective, coordination is under-mined by distrust, parenting and communication bricolage. Poor coordination leads to numerous problems including misunderstandings, help requests, lower job satisfaction among team members, and more illdefined tasks. These problems, in turn, reduce overall project success and prompt professionals to alter their software development processes (in this case, from Scrum to Kanban). Our findings suggest that software organizations with many remote employees can improve performance by encouraging greater engagement within teams and supporting employees with family and childcare responsibilities.
Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach

The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is significant to propose an efficient scheduling approach to help EVs arrive faster. Existing vehicle-centric scheduling approaches aim to recommend the optimal paths for EVs based on the current traffic status while the road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. With the intuition that real-time vehicle-road information interaction and strategy coordination can bring more benefits, we propose LEVID, a LEarning-based cooperative VehIcle-roaD scheduling approach including a real-time route planning module and a collaborative traffic signal control module, which interact with each other and make decisions iteratively. The real-time route planning module adapts the artificial potential field method to address the real-time changes of traffic signals and avoid falling into a local optimum. The collaborative traffic signal control module leverages a graph attention reinforcement learning framework to extract the latent features of different intersections and abstract their interplay to learn cooperative policies. Extensive experiments based on multiple real-world datasets show that our approach outperforms the state-of-the-art baselines.
Cooperative Artificial Intelligence

In the future, artificial learning agents are likely to become increasingly widespread in our society. They will interact with both other learning agents and humans in a variety of complex settings including social dilemmas. We argue that there is a need for research on the intersection between game theory and artificial intelligence, with the goal of achieving cooperative artificial intelligence that can navigate social dilemmas well. We consider the problem of how an external agent can promote cooperation between artificial learners by distributing additional rewards and punishments based on observing the learners’ actions. We propose a rule for automatically learning how to create right incentives by considering the players’ anticipated parameter updates. Using this learning rule leads to cooperation with high social welfare in matrix games in which the agents would otherwise learn to defect with high probability. We show that the resulting cooperative outcome is stable in certain games even if the planning agent is turned off after a given number of episodes, while other games require ongoing intervention to maintain mutual cooperation. Finally, we reflect on what the goals of multi-agent reinforcement learning should be in the first place, and discuss the necessary building blocks towards the goal of building cooperative AI.
Direct reconstruction of the band structure of a one-dimensional optical lattice with thermal atoms

We report on a simple method to reconstruct the band structure of a 1D optical lattice using a thermal cloud with a momentum spread of about two-photon recoils. We image the momentum distribution of a thermal cloud exposed to a standing wave potential using time-of-flight absorption images and observe unique features. With the support of numerical calculations, we explain their appearance and show how they can be used to reconstruct the full band-structure directly. While this can serve as a precise lattice depth calibration tool, we additionally propose a method to estimate the lattice depth in a single-shot manner.
Statistical and topological summaries aid disease detection for segmented retinal vascular images

Disease complications can alter vascular network morphology and disrupt tissue functioning. Microvascular diseases of the retina are assessed by visual inspection of retinal images, but this can be challenging when diseases exhibit silent symptoms or patients cannot attend in‐person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease when trained on statistical and topological summaries of segmented retinal vascular images.
Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections

In recent years, great efforts have been devoted to deep imitation learning for autonomous driving control, where raw sensory inputs are directly mapped to control actions. However, navigating through densely populated intersections remains a challenging task due to uncertainty caused by uncertain traffic participants. We focus on autonomous navigation at crowded intersections that require interaction with pedestrians. A multi-task conditional imitation learning framework is proposed to adapt both lateral and longitudinal control tasks for safe and efficient interaction. A new benchmark called IntersectNav is developed and human demonstrations are provided. Empirical results show that the proposed method can achieve a success rate gain of up to 30% compared to the state-of-the-art.
Data-Driven Mitigation of Adversarial Text Perturbation

Social networks have become an indispensable part of our lives, with billions of people producing ever-increasing amounts of text. At such scales, content policies and their enforcement become paramount. To automate moderation, questionable content is detected by Natural Language Processing (NLP) classifiers. However, high-performance classifiers are hampered by misspellings and adversarial text perturbations. In this paper, we classify intentional and unintentional adversarial text perturbation into ten types and propose a deobfuscation pipeline to make NLP models robust to such perturbations. We propose Continuous Word2Vec (CW2V), our data-driven method to learn word embeddings that ensures that perturbations of words have embeddings similar to those of the original words. We show that CW2V embeddings are generally more robust to text perturbations than embeddings based on character ngrams. Our robust classification pipeline combines deobfuscation and classification, using proposed defense methods and word embeddings to classify whether Facebook posts are requesting engagement such as likes. Our pipeline results in engagement bait classification that goes from 0.70 to 0.67 AUC with adversarial text perturbation, while character ngram-based word embedding methods result in downstream classification that goes from 0.76 to 0.64.
Using Illustrations to Communicate Differential Privacy Trust Models: An Investigation of Users' Comprehension, Perception, and Data Sharing Decision

Proper communication is key to the adoption and implementation of differential privacy (DP). However, a prior study found that laypeople did not understand the data perturbation processes of DP and how DP noise protects their sensitive personal information. Consequently, they distrusted the techniques and chose to opt out of participating. In this project, we designed explanative illustrations of three DP models (Central DP, Local DP, Shuffler DP) to help laypeople conceptualize how random noise is added to protect individuals' privacy and preserve group utility. Following pilot surveys and interview studies, we conducted two online experiments (N = 595) examining participants' comprehension, privacy and utility perception, and data-sharing decisions across the three DP models. Besides the comparisons across the three models, we varied the noise levels of each model. We found that the illustrations can be effective in communicating DP to the participants. Given an adequate comprehension of DP, participants preferred strong privacy protection for a certain type of data usage scenarios (i.e., commercial interests) at both the model level and the noise level. We also obtained empirical evidence showing participants' acceptance of the Shuffler DP model for data privacy protection. Our findings have implications for multiple stakeholders for user-centered deployments of differential privacy, including app developers, DP model developers, data curators, and online users.
PETCI: A Parallel English Translation Dataset of Chinese Idioms

Idioms are an important language phenomenon in Chinese, but idiom translation is notoriously hard. Current machine translation models perform poorly on idiom translation, while idioms are sparse in many translation datasets. We present PETCI, a parallel English translation dataset of Chinese idioms, aiming to improve idiom translation by both human and machine. The dataset is built by leveraging human and machine effort. Baseline generation models show unsatisfactory abilities to improve translation, but structure-aware classification models show good performance on distinguishing good translations. Furthermore, the size of PETCI can be easily increased without expertise. Overall, PETCI can be helpful to language learners and machine translation systems.