Miracle-dz/UniFAR-FTUs huggingface dataset:
Antibiotic Resistance Influences Growth Rates and Cross-Tolerance to Lactic Acid in Escherichia coli O157:H7 H1730.

Escherichia coli O157:H7-contaminated beef has been implicated in numerous foodborne outbreaks. Contamination occurs despite the use of antimicrobial interventions such as lactic acid (LA). In addition, resistance to antibiotics such as ampicillin and streptomycin among isolates has been frequently reported. The influence of antibiotic resistance (ABR) on growth rates and cross-tolerance of lettuce isolate E. coli O157:H7 H1730 to LA was evaluated. Antibiotic-resistant strain variants were generated by conferring resistance to either ampicillin (ampC) or streptomycin (strepC) or both ampicillin and streptomycin (ampC strepC) through incremental exposure to the antibiotics. Ampicillin resistance was also conferred by plasmid transformation to generate the ampP and ampP strepC strains. The minimum inhibitory concentration of LA on all the strains evaluated was 0.375% v/v. The lag phase duration of all strains except E. coli O157:H7 ampP strepC increased with increasing concentration of LA. The ampP strepC and ampC strains were most tolerant to 5% LA with declines in the cell population of 2.86 and 2.56 log CFU/mL, respectively (p < 0.05). The ampP strepC strain was the most tolerant when evaluated by the live/dead viability assay. The addition of the efflux pump inhibitor, carbonyl cyanide m-chlorophenylhydrazone, with 2.5% LA resulted in a significant increase in sensitivity in the no resistance (NR) wild-type and ampC strains, resulting in 6.62 and 6.65 log CFU/mL reduction, respectively, while the highly tolerant ampP strepC strain had a 2.90 log CFU/mL decrease. Tolerance to LA was significantly influenced by both the ABR profile of the strain and LA concentration. The results from this study indicate that E. coli O157:H7 strains with certain ABR profiles might be more tolerant to LA.
Design of Linear Magnetic Position Sensor Used in Permanent Magnet Linear Machine With Consideration of Manufacturing Tolerances

This paper proposes the design of a linear magnetic position sensor (LMPS) to detect the mover position of the permanent magnet linear synchronous machines (PMLSMs). The working principle of the LMPS is based on the well-known Hall-Effect. The Hall-Effect-based design of LMPS is considered because it provides a low cost and relatively harsh environment susceptible alternative for the position detection for long track PMLSM system. The initial design was performed using 3D-finite element analysis and genetic algorithm-based deterministic optimization. Based on the initial model, four LMPS were manufactured and tested under the output constraint of peak flux density > 0.1T and total harmonic distortion < 3%. From the test results, it is concluded that the manufactured LMPS suffered significant deviation in output compared to the simulation model. These anomalies arise because of the manufacturing tolerances. To further improve the design and manufacturing process of the LMPS considering the effect of tolerance, a reliability-based robust design optimization (RBRDO) of the LMPS is proposed. The results of RBRDO confirms its usefulness for the design process of the LMPS in terms of undesired design non-compliance cost and great robustness without sacrificing the accuracy of the output signal.
Test-Time Adaptation via Conjugate Pseudo-labels

Test-time adaptation (TTA) refers to adapting neural networks to distribution shifts, with access to only the unlabeled test samples from the new domain at test-time. Prior TTA methods optimize over unsupervised objectives such as the entropy of model predictions in TENT [Wang et al., 2021], but it is unclear what exactly makes a good TTA loss. In this paper, we start by presenting a surprising phenomenon: if we attempt to meta-learn the best possible TTA loss over a wide class of functions, then we recover a function that is remarkably similar to (a temperature-scaled version of) the softmax-entropy employed by TENT. This only holds, however, if the classifier we are adapting is trained via cross-entropy; if trained via squared loss, a different best TTA loss emerges. To explain this phenomenon, we analyze TTA through the lens of the training losses's convex conjugate. We show that under natural conditions, this (unsupervised) conjugate function can be viewed as a good local approximation to the original supervised loss and indeed, it recovers the best losses found by meta-learning. This leads to a generic recipe that can be used to find a good TTA loss for any given supervised training loss function of a general class. Empirically, our approach consistently dominates other baselines over a wide range of benchmarks. Our approach is particularly of interest when applied to classifiers trained with novel loss functions, e.g., the recently-proposed PolyLoss, where it differs substantially from (and outperforms) an entropy-based loss. Further, we show that our approach can also be interpreted as a kind of self-training using a very specific soft label, which we refer to as the conjugate pseudolabel. Overall, our method provides a broad framework for better understanding and improving test-time adaptation. Code is available at https://github.com/locuslab/tta_conjugate.
Transient superconductivity in three-dimensional Hubbard systems by combining matrix-product states and self-consistent mean-field theory

We combine matrix-product-state (MPS) and mean-field (MF) methods to model the real-time evolution of a three-dimensional (3D) extended Hubbard system formed from one-dimensional (1D) chains arrayed in parallel with weak coupling in-between them. This approach allows us to treat much larger 3D systems of correlated fermions out-of-equilibrium over a much more extended real-time domain than previous numerical approaches. We deploy this technique to study the evolution of the system as its parameters are tuned from a charge-density wave phase into the superconducting regime, which allows us to investigate the formation of transient non-equilibrium superconductivity. In our ansatz, we use MPS solutions for chains as input for a self-consistent time-dependent MF scheme. In this way, the 3D problem is mapped onto an effective 1D Hamiltonian that allows us to use the MPS efficiently to perform the time evolution, and to measure the BCS order parameter as a function of time. Our results confirm previous findings for purely 1D systems that for such a scenario a transient superconducting state can occur.
Orbital and Spin Components of the Angular Momentum of a General Electromagnetic Field

A decomposition of the angular momentum of the classical electromagnetic field into orbital and spin components that is manifestly gauge invariant and general has been obtained. This is done by decomposing the electric field into its longitudinal and transverse parts by means of the Helmholtz theorem. The orbital and spin components of the angular momentum of any specified electromagnetic field can be found from this prescription.
Can Causal (and Counterfactual) Reasoning improve Privacy Threat Modelling?

Causal questions often permeate in our day-to-day activities. With causal reasoning and counterfactual intuition, privacy threats can not only be alleviated but also prevented. In this paper, we discuss what is causal and counterfactual reasoning and how this can be applied in the field of privacy threat modelling (PTM). We believe that the future of PTM relies on how we can causally and counterfactually imagine cybersecurity threats and incidents.
DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations. It leverages a labeled source domain dataset as well as unlabeled target domain images to learn a segmentation network. In this paper, we observe two main issues of the existing domain-invariant learning framework. (1) Being distracted by the feature distribution alignment, the network cannot focus on the segmentation task. (2) Fitting source domain data well would compromise the target domain performance. To address these issues, we propose DecoupleNet that alleviates source domain overfitting and enables the final model to focus more on the segmentation task. Furthermore, we put forward Self-Discrimination (SD) and introduce an auxiliary classifier to learn more discriminative target domain features with pseudo labels. Finally, we propose Online Enhanced Self-Training (OEST) to contextually enhance the quality of pseudo labels in an online manner. Experiments show our method outperforms existing state-of-the-art methods, and extensive ablation studies verify the effectiveness of each component. Code is available at https://github.com/dvlab-research/DecoupleNet.
Learning Topological Interactions for Multi-Class Medical Image Segmentation

Deep learning methods have achieved impressive performance for multi-class medical image segmentation. However, they are limited in their ability to encode topological interactions among different classes (e.g., containment and exclusion). These constraints naturally arise in biomedical images and can be crucial in improving segmentation quality. In this paper, we introduce a novel topological interaction module to encode the topological interactions into a deep neural network. The implementation is completely convolution-based and thus can be very efficient. This empowers us to incorporate the constraints into end-to-end training and enrich the feature representation of neural networks. The efficacy of the proposed method is validated on different types of interactions. We also demonstrate the generalizability of the method on both proprietary and public challenge datasets, in both 2D and 3D settings, as well as across different modalities such as CT and Ultrasound. Code is available at: https://github.com/TopoXLab/TopoInteraction
Design of a Compact V-Band Transceiver and Antenna for Millimeter-Wave Imaging Systems

In this paper, we introduce a synthetic aperture radar (SAR)-based millimeter-wave imaging system for high-resolution imaging of human skin, in addition to traditional nondestructive testing applications. The imaging system, which operates in the 58–64-GHz frequency range, forms the basic building block of a future millimeter-wave camera suitable for imaging burned skin and skin cancers. The system is composed of a coherent transceiver and two antennas, principally enabling a quasi-monostatic measurement configuration. The designed antenna is an antipodal Vivaldi antenna with 5.0–8.0-dB gain and half-power beamwidths of ~79.0<sup>°</sup> and ~76.0<sup>°</sup>in its <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>– and <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-planes, respectively. The transceiver is designed using a complete system-on-chip transmitter and receiver capable of <inline-formula> <tex-math notation="LaTeX">$I/Q$ </tex-math></inline-formula> detection. The fully designed imaging system has 47 dB of dynamic range and is phase-calibrated using a robust wideband calibration procedure based on single-frequency SAR imaging approach. Examples of images produced with this system include burned pigskin skin and a small balsa wood panel with four small embedded rubber pieces.
VisQuiz: Exploring Feedback Mechanisms to Improve Graphical Perception

In this paper, we explore the design and evaluation of feedback for graphical perception tasks, called VisQuiz. Using a quiz-like metaphor, we design feedback for a typical visualization comparison experiment, showing participants their answer alongside the correct answer in an animated sequence in each trial, as well as summary feedback at the end of trial sections. To evaluate VisQuiz, we conduct a between-subjects experiment, including three stages of 40 trials each with a control condition that included only summary feedback. Results from $n=80$ participants show that once participants started receiving trial feedback (Stage 2) they performed significantly better with bubble charts than those in the control condition. This effect carried over when feedback was removed (Stage 3). Results also suggest an overall trend of improved performance due to feedback. We discuss these findings in the context of other visualization literacy efforts, and possible future work at the intersection of visualization, feedback, and learning. Experiment data and analysis scripts are available at the following repository https://osf.io/jys5d/
When Is TTS Augmentation Through a Pivot Language Useful?

Developing Automatic Speech Recognition (ASR) for low-resource languages is a challenge due to the small amount of transcribed audio data. For many such languages, audio and text are available separately, but not audio with transcriptions. Using text, speech can be synthetically produced via text-to-speech (TTS) systems. However, many low-resource languages do not have quality TTS systems either. We propose an alter-native: produce synthetic audio by running text from the target language through a trained TTS system for a higher-resource pivot language. We investigate when and how this technique is most effective in low-resource settings. In our experiments, using several thousand synthetic TTS text-speech pairs and duplicating authentic data to balance yields optimal results. Our findings suggest that searching over a set of candidate pivot languages can lead to marginal improvements and that, surpris-ingly, ASR performance can by harmed by increases in measured TTS quality. Application of these findings improves ASR by 64.5% and 45.0% character error reduction rate (CERR) re-spectively for two low-resource languages: Guaran´ı and Suba.
Densely Constrained Depth Estimator for Monocular 3D Object Detection

Estimating accurate 3D locations of objects from monocular images is a challenging problem because of lacking depth. Previous work shows that utilizing the object's keypoint projection constraints to estimate multiple depth candidates boosts the detection performance. However, the existing methods can only utilize vertical edges as projection constraints for depth estimation. So these methods only use a small number of projection constraints and produce insufficient depth candidates, leading to inaccurate depth estimation. In this paper, we propose a method that utilizes dense projection constraints from edges of any direction. In this way, we employ much more projection constraints and produce considerable depth candidates. Besides, we present a graph matching weighting module to merge the depth candidates. The proposed method DCD (Densely Constrained Detector) achieves state-of-the-art performance on the KITTI and WOD benchmarks. Code is released at https://github.com/BraveGroup/DCD.
Estimating bias and mean squared error of kernel density estimator

We propose a generalized smooth bootstrap scheme for estimating the bias By and mean square error My of a kernel density estimator, at y, based on i.i. d data. A number of existing bootstrap schemes are special case of our proposal. For a fairly broad class of kernel and bandwidth hn, we obtain the rates at which E h B � By − 1 i2 and E h M � y My − 1 i2 converge to zero as n (sample size) is increased, where B � y and M � y are the proposed estimators of By and My respectively. We obtain conditions under which the estimators have infinite asymptotic relative accuracy (in L2 sense) in comparison to corresponding plug-in estimators. Simulations reveal that if y is a mode or an anti-mode and log 10 (hn) ≤ −0.5, then performance of both M � y and M A y (plug-in estimator of My) are satisfactory. On the other hand, when y is in the tail region, M � y is the only estimator which successfully imitates the important features (e.g. existence of multiple minima) of My as a function of hn.
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognition

In this work, we consider the problem of cross-domain 3D action recognition in the open-set setting, which has been rarely explored before. Specifically, there is a source domain and a target domain that contain the skeleton sequences with different styles and categories, and our purpose is to cluster the target data by utilizing the labeled source data and unlabeled target data. For such a challenging task, this paper presents a novel approach dubbed CoDT to collaboratively cluster the domain-shared features and target-specific features. CoDT consists of two parallel branches. One branch aims to learn domain-shared features with supervised learning in the source domain, while the other is to learn target-specific features using contrastive learning in the target domain. To cluster the features, we propose an online clustering algorithm that enables simultaneous promotion of robust pseudo label generation and feature clustering. Furthermore, to leverage the complementarity of domain-shared features and target-specific features, we propose a novel collaborative clustering strategy to enforce pair-wise relationship consistency between the two branches. We conduct extensive experiments on multiple cross-domain 3D action recognition datasets, and the results demonstrate the effectiveness of our method.
Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification

Contrastive Vision-Language Pre-training, known as CLIP, has provided a new paradigm for learning visual representations using large-scale image-text pairs. It shows impressive performance on downstream tasks by zero-shot knowledge transfer. To further enhance CLIP's adaption capability, existing methods proposed to fine-tune additional learnable modules, which significantly improves the few-shot performance but introduces extra training time and computational resources. In this paper, we propose a training-free adaption method for CLIP to conduct few-shot classification, termed as Tip-Adapter, which not only inherits the training-free advantage of zero-shot CLIP but also performs comparably to those training-required approaches. Tip-Adapter constructs the adapter via a key-value cache model from the few-shot training set, and updates the prior knowledge encoded in CLIP by feature retrieval. On top of that, the performance of Tip-Adapter can be further boosted to be state-of-the-art on ImageNet by fine-tuning the cache model for 10$\times$ fewer epochs than existing methods, which is both effective and efficient. We conduct extensive experiments of few-shot classification on 11 datasets to demonstrate the superiority of our proposed methods. Code is released at https://github.com/gaopengcuhk/Tip-Adapter.
Inertial focusing of a dilute suspension in pipe flow

The dynamics of rigid particle suspensions in a wall-bounded laminar flow present several nontrivial and intriguing features, including particle ordering, lateral transport, and the appearance of stable, preferential locations like the Segr\'{e}-Silberberg annulus. The formation of more than one annulus is a particularly puzzling phenomenon that is still not fully explained. Here, we present numerical simulation results of a dilute suspension of particles in (periodic) pipe flow based on the lattice Boltzmann and the discrete element methods (DEM). Our simulations provide access to the full radial position history of the particles while traveling downstream. This allows to accurately quantify the transient and steady states. We observe the formation of the secondary, inner annulus and show that its position invariably shifts toward the Segr\'{e}-Silberberg one if the channel is sufficiently long, proving that it is, in fact, a transient feature for Reynolds numbers ($Re$) up to $600$. We quantify the variation of the channel focusing length ($L_s/2R$) with $Re$. Interestingly and unlike the theoretical prediction for a point-like particle, we observe that $L_s/2R$ increases with $Re$ for both the single particle and the suspension.
Thoughts on child safety on commodity platforms

The explosion of global social media and online communication platforms has changed how we interact with each other and as a society, bringing with it new security and privacy challenges. Like all technologies, these platforms can be abused and they are routinely used to attempt to cause harm at scale. One of the most significant offence types that is enabled by these platforms is child sexual abuse both scaling existing abuse and enabling entirely new types of online-only abuse where the impacts on the victim are equally catastrophic. Many platforms invest significantly in combating this heinous crime on their platforms, referring confirmed evidence of illegality to law enforcement. The introduction of end-toend encryption and similar technologies breaks many of the mitigations in place today and this has led to a debate around the apparent dichotomy of good child safety and good general user privacy and security. This debate has concentrated on the problem of detecting offenders sharing known abuse imagery using a technique known as client side scanning. We will show that the real problem of online child sexual abuse is much more complex than offender image sharing, providing a new set of ‘harm archetypes’ to better group harms into categories that have similar technical characteristics and, as far as we are able, bring more clarity to the processes currently used by platforms and law enforcement in relation to child sexual abuse content and the real world impacts. We explore, at a high level, a variety of techniques that could be used as part of any potential solution and examine the benefits and disbenefits that may accrue in various use cases, and use a hypothetical service as an example of how various techniques could be brought together to provide both user privacy and security, while protecting child safety and enabling law enforcement action. We recognise that some of the techniques discussed require further research and work needs to be done to create a framework that can be used for consistent evaluation of various techniques on specific platforms and services. These are highlighted at the end of the paper. This paper is not intended to represent UK Government policy, but we hope it will lead to a balanced and informed debate that will help inform global policy in this area.
Efficient Dependency Analysis for Rule-Based Ontologies

Several types of dependencies have been proposed for the static analysis of existential rule ontologies, promising insights about com-putational properties and possible practical uses of a given set of rules, e.g., in ontology-based query answering. Unfortunately, these dependencies are rarely implemented, so their potential is hardly realised in practice. We focus on two kinds of rule dependencies – positive reliances and restraints – and design and implement optimised algorithms for their eﬃcient computation. Experiments on real-world ontologies of up to more than 100,000 rules show the scalability of our approach, which lets us realise several previously proposed applications as practical case studies. In particular, we can analyse to what extent rule-based bottom-up approaches of reasoning can be guaranteed to yield redundancy-free “lean” knowledge graphs (so-called cores ) on practical ontologies.