Miracle-dz/UniFAR-FTUs huggingface dataset:
2-descent for Bloch--Kato Selmer groups and rational points on hyperelliptic curves I

This paper introduces explicit Galois cohomological methods for determining the ranks of Bloch--Kato Selmer groups associated to the Tate twists of the 2-adic second \'etale cohomology of the Jacobian of a hyperelliptic curve with a rational Weierstrass point. In particular, this can give a method to determine the rational points on such curves via the Chabauty--Coleman--Kim method. This is applied to answer a question of Bugeaud, Mignotte, Siksek, Stoll and Tengely.
Benchmarking and Analysis of Unsupervised Object Segmentation from Real-World Single Images

In this paper, we study the problem of unsupervised object segmentation from single images. We do not introduce a new algorithm, but systematically investigate the effectiveness of existing unsupervised models on challenging real-world images. We first introduce seven complexity factors to quantitatively measure the distributions of background and foreground object biases in appearance and geometry for datasets with human annotations. With the aid of these factors, we empirically find that, not surprisingly, existing unsupervised models fail to segment generic objects in real-world images, although they can easily achieve excellent performance on numerous simple synthetic datasets, due to the vast gap in objectness biases between synthetic and real images. By conducting extensive experiments on multiple groups of ablated real-world datasets, we ultimately find that the key factors underlying the failure of existing unsupervised models on real-world images are the challenging distributions of background and foreground object biases in appearance and geometry. Because of this, the inductive biases introduced in existing unsupervised models can hardly capture the diverse object distributions. Our research results suggest that future work should exploit more explicit objectness biases in the network design.
CAR: Consolidation, Augmentation and Regulation for Recipe Retrieval

Learning recipe and food image representation in common embedding space is non-trivial but crucial for cross-modal recipe retrieval. In this paper, we propose CAR framework with three novel techniques, i.e., Consolidation, Augmentation and Regulation, for cross-modal recipe retrieval. We introduce adapter layers to consolidate pre-trained CLIP model with much less computation cost than fully cumbersome fine-tuning all the parameters. Furthermore, leveraging on the strong capability of foundation models (i.e., SAM and LLM), we propose to augment recipe and food image by extracting information related to the counterpart. SAM generates image segments corresponding to ingredients in the recipe, while LLM produces a visual imagination description from the recipe, aiming to capture the visual cues of a food image. In addition, we introduce circle loss to regulate cross-modal embedding space, which assigns different penalties for positive and negative pairs. With the extra augmented data from recipe and image, multi-level circle loss is proposed, which applies circle loss not only to original image-recipe pairs, but also to image segments and recipe, visual imagination description and food image as well as any two sections within a recipe. On Recipe1M dataset, our proposed CAR outperforms all the existing methods by a large margin. Extensive ablation studies are conducted to validate the effectiveness of each component of CAR. We will make our code and models publicly available.
Herwig 7.3 Release Note

A new release of the Monte Carlo event generator Herwig (version 7.3) has been launched. This iteration encompasses several enhancements over its predecessor, version 7.2. Noteworthy upgrades include: the implementation of a process-independent electroweak angular-ordered parton shower integrated with QCD and QED radiation; a new recoil scheme for initial-state radiation improving the behaviour of the angular-ordered parton shower; the incorporation of the heavy quark effective theory to refine the hadronization and decay of excited heavy mesons and heavy baryons; a dynamic strategy to regulate the kinematic threshold of cluster splittings within the cluster hadronization model; several improvements to the structure of the cluster hadronization model allowing for refined models; the possibility to extract event-by-event hadronization corrections in a well-defined way; the possibility of using the string model, with a dedicated tune. Additionally, a new tuning of the parton shower and hadronization parameters has been executed. This article discusses the novel features introduced in version 7.3. 0.
Sound Source Localization for a Source inside a Structure using Ac-CycleGAN

We propose a method for sound source localization (SSL) for a source inside a structure using Ac-CycleGAN under unpaired data conditions. The proposed method utilizes a large amount of simulated data and a small amount of actual experimental data to locate a sound source inside a structure in a real environment. An Ac-CycleGAN generator contributes to the transformation of simulated data into real data, or vice versa, using unpaired data from both domains. The discriminator of an Ac-CycleGAN model is designed to differentiate between the transformed data generated by the generator and real data, while also predicting the location of the sound source. Vectors representing the frequency spectrum of the accelerometers (FSAs) measured at three points outside the structure are used as input data and the source areas inside the structure are used as labels. The input data vectors are concatenated vertically to form an image. Labels are defined by dividing the interior of the structure into eight areas with one-hot encoding for each area. Thus, the SSL problem is redefined as an image-classification problem to stochastically estimate the location of the sound source. We show that it is possible to estimate the sound source location using the Ac-CycleGAN discriminator for unpaired data across domains. Furthermore, we analyze the discriminative factors for distinguishing the data. The proposed model exhibited an accuracy exceeding 90\% when trained on 80\% of actual data (12.5\% of simulated data). Despite potential imperfections in the domain transformation process carried out by the Ac-CycleGAN generator, the discriminator can effectively distinguish between transferred and real data by selectively utilizing only those features that generate a relatively small transformation error.
A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting

Despite the progress made in domain adaptation, solving Unsupervised Domain Adaptation (UDA) problems with a general method under complex conditions caused by label shifts between domains remains a formidable task. In this work, we comprehensively investigate four distinct UDA settings including closed set domain adaptation, partial domain adaptation, open set domain adaptation, and universal domain adaptation, where shared common classes between source and target domains coexist alongside domain-specific private classes. The prominent challenges inherent in diverse UDA settings center around the discrimination of common/private classes and the precise measurement of domain discrepancy. To surmount these challenges effectively, we propose a novel yet effective method called Learning Instance Weighting for Unsupervised Domain Adaptation (LIWUDA), which caters to various UDA settings. Specifically, the proposed LIWUDA method constructs a weight network to assign weights to each instance based on its probability of belonging to common classes, and designs Weighted Optimal Transport (WOT) for domain alignment by leveraging instance weights. Additionally, the proposed LIWUDA method devises a Separate and Align (SA) loss to separate instances with low similarities and align instances with high similarities. To guide the learning of the weight network, Intra-domain Optimal Transport (IOT) is proposed to enforce the weights of instances in common classes to follow a uniform distribution. Through the integration of those three components, the proposed LIWUDA method demonstrates its capability to address all four UDA settings in a unified manner. Experimental evaluations conducted on three benchmark datasets substantiate the effectiveness of the proposed LIWUDA method.
Cluster images with AntClust: a clustering algorithm based on the chemical recognition system of ants

We implement AntClust, a clustering algorithm based on the chemical recognition system of ants and use it to cluster images of cars. We will give a short recap summary of the main working principles of the algorithm as devised by the original paper [1]. Further, we will describe how to define a similarity function for images and how the implementation is used to cluster images of cars from the vehicle re-identification data set. We then test the clustering performance of AntClust against DBSCAN, HDBSCAN and OPTICS. Finally one of the core parts in AntClust, the rule set can be easily redefined with our implementation, enabling a way for other bio-inspired algorithms to find rules in an automated process. The implementation can be found on GitLab [9].
Taming Subnet-Drift in D2D-Enabled Fog Learning: A Hierarchical Gradient Tracking Approach

Federated learning (FL) encounters scalability challenges when implemented over fog networks. Semi-decentralized FL (SD-FL) proposes a solution that divides model cooperation into two stages: at the lower stage, device-to-device (D2D) communications is employed for local model aggregations within subnetworks (subnets), while the upper stage handles device-server (DS) communications for global model aggregations. However, existing SD-FL schemes are based on gradient diversity assumptions that become performance bottlenecks as data distributions become more heterogeneous. In this work, we develop semi-decentralized gradient tracking (SD-GT), the first SD-FL methodology that removes the need for such assumptions by incorporating tracking terms into device updates for each communication layer. Analytical characterization of SD-GT reveals convergence upper bounds for both non-convex and strongly-convex problems, for a suitable choice of step size. We employ the resulting bounds in the development of a co-optimization algorithm for optimizing subnet sampling rates and D2D rounds according to a performance-efficiency trade-off. Our subsequent numerical evaluations demonstrate that SD-GT obtains substantial improvements in trained model quality and communication cost relative to baselines in SD-FL and gradient tracking on several datasets.
Twist-Driven Deep-Ultraviolet-Wavelength Exciton Funnel Effect in Bilayer Boron Nitride

Realizing direct-bandgap quantum dots working within the deep-ultraviolet frequency is highly desired for electro-optical and biomedical applications while remaining challenging. In this work, we combine the first-principles many-body perturbation theory and effective Hamiltonian approximation to propose the realization of arrays of deep-ultraviolet excitonic quantum dots in twisted bilayer hexagonal boron nitride. The effective quantum confinement of excitons can reach ~400 meV within small twisting angles, which is about four times larger than those observed in twisted semiconducting transitional metal dichalcogenides. Especially because of enhanced electron-hole attraction, those excitons will accumulate via the so-call exciton funnel effect to the direct-bandgap regime, giving the possibility to better luminescence performance and manipulating coherent arrays of deep-ultraviolet quantum dots.
Detecting DBMS Bugs with Context-Sensitive Instantiation and Multi-Plan Execution

DBMS bugs can cause serious consequences, posing severe security and privacy concerns. This paper works towards the detection of memory bugs and logic bugs in DBMSs, and aims to solve the two innate challenges, including how to generate semantically correct SQL queries in a test case, and how to propose effective oracles to capture logic bugs. To this end, our system proposes two key techniques. The first key technique is called context-sensitive instantiation, which considers all static semantic requirements (including but not limited to the identifier type used by existing systems) to generate semantically valid SQL queries. The second key technique is called multi-plan execution, which can effectively capture logic bugs. Given a test case, multi-plan execution makes the DBMS execute all query plans instead of the default optimal one, and compares the results. A logic bug is detected if a difference is found among the execution results of the executed query plans. We have implemented a prototype system called Kangaroo and applied it to three widely used and well-tested DBMSs, including SQLite, PostgreSQL, and MySQL. Our system successfully detected 50 new bugs. The comparison between our system with the state-of-the-art systems shows that our system outperforms them in terms of the number of generated semantically valid SQL queries, the explored code paths during testing, and the detected bugs.
Pulsed voltage cold atmospheric plasma jet and goldnanoparticles enhance cytotoxic anticancer effect

Efficient and biologically safe mode of cold atmospheric plasma jet (CAPJ) is crucial for the development of CAPJ-based anticancer therapy. In the experiment and numerical simulations, by changing the pulse duration of a positive-pulsed voltage, we found the optimal CAPJ modes with a regular streamer propagation and a maximum discharge current at a temperature T<42 ◦C. These CAPJs substantially suppressed the viability of cancer cells. To enhance a cytotoxic effect of CAPJ treatment, gold nanoparticles (NPs) were added to the cells before and after the CAPJ exposure. Combination of CAPJ, generated with the positive-pulsed voltage, and gold nanoparticles decreased the viability of NCI-H23 epithelial-like lung adenocarcinoma, A549 lung adenocarcinoma, BrCCh4e-134 breast adenocarcinoma and uMel1 uveal melanoma cells. Polyethylene glycol-modified nanoparticles with attached fluorescent labels were used to visualize the uptake of NPs. We demonstrated that NPs efficiently entered the cells when were added to the cells just before CAPJ exposure or up to two hours afterwards. The efficiency of NPs penetration into cells positively correlated with the induced cytotoxic effect: it was maximal when NPs was added to cells right before or immediately after CAPJ exposure. Summarizing, the treatment with optimal CAPJ modes in combination with modified NPs, bearing the cancer-addressed molecules and therapeutics may be the next strategy of strengthening the CAPJ-based antitumor approaches.
BELT: Old-School Backdoor Attacks can Evade the State-of-the-Art Defense with Backdoor Exclusivity Lifting

Deep neural networks (DNNs) are susceptible to backdoor attacks, where malicious functionality is embedded to allow attackers to trigger incorrect classifications. Old-school backdoor attacks use strong trigger features that can easily be learned by victim models. Despite robustness against input variation, the robustness however increases the likelihood of unintentional trigger activations. This leaves traces to existing defenses, which find approximate replacements for the original triggers that can activate the backdoor without being identical to the original trigger via, e.g., reverse engineering and sample overlay. In this paper, we propose and investigate a new characteristic of backdoor attacks, namely, backdoor exclusivity, which measures the ability of backdoor triggers to remain effective in the presence of input variation. Building upon the concept of backdoor exclusivity, we propose Backdoor Exclusivity LifTing (BELT), a novel technique which suppresses the association between the backdoor and fuzzy triggers to enhance backdoor exclusivity for defense evasion. Extensive evaluation on three popular backdoor benchmarks validate, our approach substantially enhances the stealthiness of four old-school backdoor attacks, which, after backdoor exclusivity lifting, is able to evade six state-of-the-art backdoor countermeasures, at almost no cost of the attack success rate and normal utility. For example, one of the earliest backdoor attacks BadNet, enhanced by BELT, evades most of the state-of-the-art defenses including ABS and MOTH which would otherwise recognize the backdoored model.
Autoencoding Labeled Interpolator, Inferring Parameters From Image, And Image From Parameters

The Event Horizon Telescope (EHT) provides an avenue to study black hole accretion flows on event-horizon scales. Fitting a semi-analytical model to EHT observations requires the construction of synthetic images, which is computationally expensive. This study presents an image generation tool in the form of a generative machine learning model, which extends the capabilities of a variational autoencoder. This tool can rapidly and continuously interpolate between a training set of images and can retrieve the defining parameters of those images. Trained on a set of synthetic black hole images, our tool showcases success in both interpolating black hole images and their associated physical parameters. By reducing the computational cost of generating an image, this tool facilitates parameter estimation and model validation for observations of black hole system.
Electron Magneto-Hydrodynamics in Graphene

We consider the hydrodynamic flow of an electron fluid in a channel formed in a two-dimensional electron gas (2DEG) with no-slip boundary conditions. To generate vorticity in the fluid the flow is influenced by an array of micromagnets on the top of 2DEG. We analyse the viscous boundary layer and demonstrate anti-Poiseuille behaviour in this region. Furthermore we predict a longitudinal Hall effect, where a periodic magnetic field generates a voltage modulation in the direction of transport. From the experimental point of view we propose a method for a precise measurements of the properties of different electron fluids. The results are applicable to graphene away from the charge neutrality point and to semiconductors.
Equivariant algebraic $K$-theory of symmetric monoidal Mackey functors

We provide a unifying approach to different constructions of the algebraic $K$-theory of equivariant symmetric monoidal categories. A consequence of our work is that every connective genuine $G$-spectrum is equivalent to the equivariant algebraic $K$-theory of categorical Mackey functors of Bohmann-Osorno.
Turbulent flow of Newtonian and non-Newtonian fluids in rough channels: Part I: Roughness effect on Newtonian fluids

Direct Numerical Simulations (DNS) of turbulent channel flow at a shear Reynolds number of $Re_{*}=360$ for Newtonian and Herschel-Bulkley fluids in smooth and rough channels has been performed. The rough surface was made of irregular undulations modeled with the immersed surface method. The rough surface was such that the ratio of the channel half-height to the RMS roughness height is 48, and the RMS and the maximum crest and trough heights are 7.5 and 23 wall units, respectively. This part, limited to Newtonian turbulent flows, has demonstrated the generic nature of the rough surface that is ideal for studying non-Newtonian flows in practical applications. The results confirm that turbulence in the outer layer is not directly affected by the rough surface. The roughness effects on the turbulence quantities are confined to the layer between 0 and 25 wall units; beyond which, the profiles collapse with those for smooth pipes. In the roughness sublayer, the streamwise normal Reynolds stress is reduced while the spanwise and wall-normal components are increased. The Reynolds shear stress increases significantly, causing a proportional increase in turbulence production near the wall affecting the dominant terms in the turbulent kinetic energy budget. The friction factor using the Colebrook-White correlation calculated by specifying the sand-grain roughness as 7.5 wall units predicts the friction velocity and the bulk velocity accurately. The streaky structures that exist near smooth walls were observed to be broken by the roughness elements, leading to a denser population of coherent structures near the wall, which increase the velocity fluctuations. The coherent structures developed in the roughness layer do not seem to penetrate in to the outer layer, and no evidence could be found as to the direct impact of the roughness layer on the outer one.
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging

Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example in medical imaging. Privacy Enhancing Technologies (PETs), such as Differential Privacy (DP), aim to circumvent these susceptibilities. DP is the strongest possible protection for training models while bounding the risks of inferring the inclusion of training samples or reconstructing the original data. DP achieves this by setting a quantifiable privacy budget. Although a lower budget decreases the risk of information leakage, it typically also reduces the performance of such models. This imposes a trade-off between robust performance and stringent privacy. Additionally, the interpretation of a privacy budget remains abstract and challenging to contextualize. In this study, we contrast the performance of AI models at various privacy budgets against both, theoretical risk bounds and empirical success of reconstruction attacks. We show that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible. We thus conclude that not using DP -- at all -- is negligent when applying AI models to sensitive data. We deem those results to lie a foundation for further debates on striking a balance between privacy risks and model performance.
Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration

Real-world scenarios are usually accompanied by continuously appearing classes with scare labeled samples, which require the machine learning model to incrementally learn new classes and maintain the knowledge of base classes. In this Few-Shot Class-Incremental Learning (FSCIL) scenario, existing methods either introduce extra learnable components or rely on a frozen feature extractor to mitigate catastrophic forgetting and overfitting problems. However, we find a tendency for existing methods to misclassify the samples of new classes into base classes, which leads to the poor performance of new classes. In other words, the strong discriminability of base classes distracts the classification of new classes. To figure out this intriguing phenomenon, we observe that although the feature extractor is only trained on base classes, it can surprisingly represent the semantic similarity between the base and unseen new classes. Building upon these analyses, we propose a simple yet effective Training-frEE calibratioN (TEEN) strategy to enhance the discriminability of new classes by fusing the new prototypes (i.e., mean features of a class) with weighted base prototypes. In addition to standard benchmarks in FSCIL, TEEN demonstrates remarkable performance and consistent improvements over baseline methods in the few-shot learning scenario. Code is available at: https://github.com/wangkiw/TEEN