Miracle-dz/UniFAR-FTUs huggingface dataset:
Observation of the thermal acoustic breathing modes of a bubble

We report experimental observations of the volume acoustic modes of air bubbles in water, including both the fundamental Minnaert breathing mode and a family of higher-order modes extending into the megahertz frequency range. Bubbles were placed on or near optomechanical sensors having a noise floor substantially determined by ambient medium fluctuations, and which are thus able to detect thermal motions of proximate objects. Bubble motions could be coupled to the sensor through both air (i.e., with the sensor inside the bubble) and water, verifying that sound is radiated by the high-order modes. More speculatively, we also present evidence for Purcell-effect modification of the sensor vibrational spectrum when encapsulated by a bubble.
Paradoxes and Priors in Bayesian Regression

The linear model has been by far the most popular and most attractive choice of a statistical model over the past century, ubiquitous in both frequentist and Bayesian literature. The basic model has been gradually improved over the years to deal with stronger features in the data like multicollinearity, non-linear or functional data patterns, violation of underlying model assumptions etc. One valuable direction pursued in the enrichment of the linear model is the use of Bayesian methods, which blend information from the data likelihood and suitable prior distributions placed on the unknown model parameters to carry out inference. This dissertation studies the modeling implications of many common prior distributions in linear regression, including the popular g prior and its recent ameliorations. Formalization of desirable characteristics for model comparison and parameter estimation has led to the growth of appropriate mixtures of g priors that conform to the seven standard model selection criteria laid out by Bayarri et al. (2012). The existence of some of these properties (or lack thereof) is demonstrated by examining the behavior of the prior under suitable limits on the likelihood or on the prior itself. The first part of the dissertation introduces a new form of an asymptotic limit, the conditional information asymptotic, driven by a situation arising in many practical problems when one or more groups of regression coefficients are much larger than the rest. Under this asymptotic, many prominent “g-type” priors are shown to suffer from two new unsatisfactory behaviors, the Conditional Lindley’s Paradox and Essentially
Physically Accurate Soil Freeze‐Thaw Processes in a Global Land Surface Scheme

The model Soil‐Litter‐Iso (SLI) calculates coupled heat and water transport in soil. It was recently implemented into the Australian land surface model CABLE, which is the land component of the Australian Community Climate and Earth System Simulator (ACCESS). Here we extended SLI to include accurate freeze‐thaw processes in the soil and snow. SLI provides thence an implicit solution of the energy and water balances of soil and snow as a standalone model and within CABLE. The enhanced SLI was tested extensively against theoretical formulations, laboratory experiments, field data, and satellite retrievals. The model performed well for all experiments at wide‐ranging temporal and spatial scales. SLI melts snow faster at the end of the cold season compared to observations though because there is no subgrid variability within SLI given by the implicit, coupled solution of energy and water. Combined CABLE‐SLI shows very realistic dynamics and extent of permafrost on the Northern hemisphere. It illustrated, however, also the limits of possible comparisons between large‐scale land surface models and local permafrost observations. CABLE‐SLI exhibits the same patterns of snow depth and snow water equivalent on the Northern hemisphere compared to satellite‐derived observations but quantitative comparisons depend largely on the given meteorological input fields. Further extension of CABLE‐SLI with depth‐dependence of soil carbon will allow realistic projections of the development of permafrost and frozen carbon stocks in a changing climate.
Should we trust web-scraped data?

The increasing adoption of econometric and machine-learning approaches by empirical researchers has led to a widespread use of one data collection method: web scraping. Web scraping refers to the use of automated computer programs to access websites and download their content. The key argument of this paper is that na\"ive web scraping procedures can lead to sampling bias in the collected data. This article describes three sources of sampling bias in web-scraped data. More specifically, sampling bias emerges from web content being volatile (i.e., being subject to change), personalized (i.e., presented in response to request characteristics), and unindexed (i.e., abundance of a population register). In a series of examples, I illustrate the prevalence and magnitude of sampling bias. To support researchers and reviewers, this paper provides recommendations on anticipating, detecting, and overcoming sampling bias in web-scraped data.
Universal Defensive Underpainting Patch: Making Your Text Invisible to Optical Character Recognition

Optical Character Recognition (OCR) enables automatic text extraction from scanned or digitized text images, but it also makes it easy to pirate valuable or sensitive text from these images. Previous methods to prevent OCR piracy by distorting characters in text images are impractical in real-world scenarios, as pirates can capture arbitrary portions of the text images, rendering the defenses ineffective. In this work, we propose a novel and effective defense mechanism termed the Universal Defensive Underpainting Patch (UDUP) that modifies the underpainting of text images instead of the characters. UDUP is created through an iterative optimization process to craft a small, fixed-size defensive patch that can generate non-overlapping underpainting for text images of any size. Experimental results show that UDUP effectively defends against unauthorized OCR under the setting of any screenshot range or complex image background. It is agnostic to the content, size, colors, and languages of characters, and is robust to typical image operations such as scaling and compressing. In addition, the transferability of UDUP is demonstrated by evading several off-the-shelf OCRs. The code is available at https://github.com/QRICKDD/UDUP.
On the characteristics of optimal transfers.

In the past 50 years the scientists have been developing and analysing methods and new algorithms that optimise an interplanetary trajectory according to one or more objectives. Within this field, in 1963 Lawden derived, from Pontryagin's minimum principle, the so-called `primer vector theory'. The main goal of this thesis is to develop a theoretical understanding of Lawden's theory, getting an insight into the optimality of a trajectory when mid-course corrections need to be applied. The novelty of the research is represented by a different approach to the primer vector theory, which simplifies the structure of the problem.
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP

Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing objects from an open set of categories. One way to address this challenge is to leverage multi-modal models, such as CLIP, to provide image and text features in a shared embedding space, which bridges the gap between closed-vocabulary and open-vocabulary recognition. Hence, existing methods often adopt a two-stage framework to tackle the problem, where the inputs first go through a mask generator and then through the CLIP model along with the predicted masks. This process involves extracting features from images multiple times, which can be ineffective and inefficient. By contrast, we propose to build everything into a single-stage framework using a shared Frozen Convolutional CLIP backbone, which not only significantly simplifies the current two-stage pipeline, but also remarkably yields a better accuracy-cost trade-off. The proposed FC-CLIP, benefits from the following observations: the frozen CLIP backbone maintains the ability of open-vocabulary classification and can also serve as a strong mask generator, and the convolutional CLIP generalizes well to a larger input resolution than the one used during contrastive image-text pretraining. When training on COCO panoptic data only and testing in a zero-shot manner, FC-CLIP achieve 26.8 PQ, 16.8 AP, and 34.1 mIoU on ADE20K, 18.2 PQ, 27.9 mIoU on Mapillary Vistas, 44.0 PQ, 26.8 AP, 56.2 mIoU on Cityscapes, outperforming the prior art by +4.2 PQ, +2.4 AP, +4.2 mIoU on ADE20K, +4.0 PQ on Mapillary Vistas and +20.1 PQ on Cityscapes, respectively. Additionally, the training and testing time of FC-CLIP is 7.5x and 6.6x significantly faster than the same prior art, while using 5.9x fewer parameters. FC-CLIP also sets a new state-of-the-art performance across various open-vocabulary semantic segmentation datasets. Code at https://github.com/bytedance/fc-clip
Topological Mott insulator in the odd-integer filled Anderson lattice model with Hatsugai-Kohmoto interactions

Recently, a quantum anomalous Hall state at odd integer filling in moir\'e stacked MoTe$_2$/WSe$_2$ was convincingly interpreted as a topological Mott insulator state appearing due to strong interactions in {\it band} basis [P. Mai, J. Zhao, B. E. Feldman, and P. W. Phillips, Nat. Commun. {\bf 14}, 5999 (2023)]. In this work, we aim to analyze the formation of a topological Mott insulator due to interactions in {\it orbital} basis instead, being more natural for systems where interactions originate from the character of $f$ or $d$ orbitals rather than band flatness. For that reason, we study an odd-integer filled Anderson lattice model incorporating odd-parity hybridization between orbitals with different degrees of correlations introduced in the Hatsugai-Kohmoto spirit. We demonstrate that a topological Mott insulating state can be realized in a considered model only when weak intra- and inter-orbital correlations involving dispersive states are taken into account. Interestingly, we find that all topological transitions between trivial and topological Mott insulating phases are not accompanied by a spectral gap closing, consistent with a phenomenon called {\it first-order topological transition}. Instead, they are signaled by a kink developed in spectral function at one of the time reversal invariant momenta. We believe that our approach can provide insightful phenomenology of topological Mott insulators in spin-orbit coupled $f$ or $d$ electron systems.
Compact Bloch mappings on the complex unit disc

The known duality of the space of Bloch complex-valued functions on the open complex unit disc $\mathbb{D}$ is addressed under a new approach with the introduction of the concepts of Bloch molecules and Bloch-free Banach space of $\mathbb{D}$. We introduce the notion of compact Bloch mapping from $\mathbb{D}$ to a complex Banach space and establish its main properties: invariance by M\"obius transformations, linearization from the Bloch-free Banach space of $\mathbb{D}$, factorization of their derivatives, inclusion properties, Banach ideal property and transposition on the Bloch function space. We state Bloch versions of the classical theorems of Schauder, Gantmacher and Davis-Figiel-Johnson-Pelczy\'nski.
EFFECT OF GROWTH REGULATORS ON YIELD AND QUALITY OF WINTER GUAVA CV. ALLAHABAD SAFEDA.

Sandeep Kaur 1 and Amarjeet Kaur 2 . 1. MSc research student Department of Horticulture, Khalsa College, (GNDU)Amritsar-143001. 2. Assistant Professor Department of Horticulture, Khalsa College, (GNDU)Amritsar-143001. Manuscript Info Abstract . Manuscript History
An equilibrated estimator for mixed finite element discretizations of the curl-curl problem

We propose a new a posteriori error estimator for mixed finite element discretizations of the curl-curl problem. This estimator relies on a Prager--Synge inequality, and therefore leads to fully guaranteed constant-free upper bounds on the error. The estimator is also locally efficient and polynomial-degree-robust. The construction is based on patch-wise divergence-constrained minimization problems, leading to a cheap embarrassingly parallel algorithm. Crucially, the estimator operates without any assumption on the topology of the domain, and unconventional arguments are required to establish the reliability estimate. Numerical examples illustrate the key theoretical results, and suggest that the estimator is suited for mesh adaptivity purposes.
UGainS: Uncertainty Guided Anomaly Instance Segmentation

A single unexpected object on the road can cause an accident or may lead to injuries. To prevent this, we need a reliable mechanism for finding anomalous objects on the road. This task, called anomaly segmentation, can be a stepping stone to safe and reliable autonomous driving. Current approaches tackle anomaly segmentation by assigning an anomaly score to each pixel and by grouping anomalous regions using simple heuristics. However, pixel grouping is a limiting factor when it comes to evaluating the segmentation performance of individual anomalous objects. To address the issue of grouping multiple anomaly instances into one, we propose an approach that produces accurate anomaly instance masks. Our approach centers on an out-of-distribution segmentation model for identifying uncertain regions and a strong generalist segmentation model for anomaly instances segmentation. We investigate ways to use uncertain regions to guide such a segmentation model to perform segmentation of anomalous instances. By incorporating strong object priors from a generalist model we additionally improve the per-pixel anomaly segmentation performance. Our approach outperforms current pixel-level anomaly segmentation methods, achieving an AP of 80.08% and 88.98% on the Fishyscapes Lost and Found and the RoadAnomaly validation sets respectively. Project page: https://vision.rwth-aachen.de/ugains
BlindSage: Label Inference Attacks against Node-level Vertical Federated Graph Neural Networks

Federated learning enables collaborative training of machine learning models by keeping the raw data of the involved workers private. One of its main objectives is to improve the models' privacy, security, and scalability. Vertical Federated Learning (VFL) offers an efficient cross-silo setting where a few parties collaboratively train a model without sharing the same features. In such a scenario, classification labels are commonly considered sensitive information held exclusively by one (active) party, while other (passive) parties use only their local information. Recent works have uncovered important flaws of VFL, leading to possible label inference attacks under the assumption that the attacker has some, even limited, background knowledge on the relation between labels and data. In this work, we are the first (to the best of our knowledge) to investigate label inference attacks on VFL using a zero-background knowledge strategy. To concretely formulate our proposal, we focus on Graph Neural Networks (GNNs) as a target model for the underlying VFL. In particular, we refer to node classification tasks, which are widely studied, and GNNs have shown promising results. Our proposed attack, BlindSage, provides impressive results in the experiments, achieving nearly 100% accuracy in most cases. Even when the attacker has no information about the used architecture or the number of classes, the accuracy remained above 85% in most instances. Finally, we observe that well-known defenses cannot mitigate our attack without affecting the model's performance on the main classification task.
Performance analysis of multichannel surface plasmon resonance sensor with dual coating of conducting metal oxide

Abstract. We proposed a six-channel photonic plasmonic biosensor with bimetallic coating of metal oxide, which operates in the infrared region of the spectrum. The outer surface of the channel has been coated with an indium tin oxide (ITO) material layer followed by another coat of Ta2O5 and TiO2 plasmonic material. The study of light-matter interaction in compact and wider region is carried out, and the sensor performance using bimetallic coating of conducting metal oxides is numerically analyzed. Results show that the compact sensor has high sensitivity and easy liquid infiltration governed by pressure and capillary action. Moreover, wavelength sensitivity is numerically investigated for various structural parameters. The proposed sensor design has maximum wavelength sensitivity of 5000 nm/RIU for ITO/TiO2 plasmonic material.
Experience with combination of nimotuzumab and intensity-modulated radiotherapy in patients with locoregionally advanced nasopharyngeal carcinoma

Aim To evaluate the efficacy and safety of using nimotuzumab in combination with intensity-modulated radiotherapy (IMRT) in the primary treatment of locoregionally advanced nasopharyngeal carcinoma. Methods Between December 2009 and December 2013, 38 newly diagnosed patients with stage III–IV nasopharyngeal carcinoma were treated with IMRT and nimotuzumab concomitantly. The distribution of disease was stage III in 20 (52.6%), stage IV A in 9 (23.7%), and stage IV B in 9 (23.7%). All the patients received at least two cycles of cisplatin-based neoadjuvant chemotherapy followed by nimotuzumab 200 mg/week concurrently with IMRT. Acute and late radiation-related toxicities were graded according to the Acute and Late Radiation Morbidity Scoring Criteria of Radiation Therapy Oncology Group. Results With a median follow-up of 39.7 months (range, 13.3–66.5 months), the estimated 3-year local recurrence-free survival, regional recurrence-free survival, distant metastasis-free survival, progression failure-free survival, and overall survival rates were 92.8%, 92.9%, 89.5%, 78.7%, and 87.5%, respectively. The median cycle for nimotuzumab addition was 6 weeks. Grade 3 radiation-induced mucositis accounted for 36.8% of treated people. No skin rash and infusion reaction were observed, distinctly from what is reported in cetuximab-treated patients. Conclusion Nimotuzumab plus IMRT showed promising outcomes in terms of locoregional control and survival, without increasing the incidence of radiation-related toxicities for patients.
HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar

Detecting human presence indoors with millimeter-wave frequency-modulated continuous-wave (FMCW) radar faces challenges from both moving and stationary clutter. This work proposes a robust and real-time capable human presence and out-of-distribution (OOD) detection method using 60 GHz short-range FMCW radar. HOOD solves the human presence and OOD detection problems simultaneously in a single pipeline. Our solution relies on a reconstruction-based architecture and works with radar macro and micro range-Doppler images (RDIs). HOOD aims to accurately detect the presence of humans in the presence or absence of moving and stationary disturbers. Since HOOD is also an OOD detector, it aims to detect moving or stationary clutters as OOD in humans' absence and predicts the current scene's output as"no presence. "HOOD performs well in diverse scenarios, demonstrating its effectiveness across different human activities and situations. On our dataset collected with a 60 GHz short-range FMCW radar, we achieve an average AUROC of 94.36%. Additionally, our extensive evaluations and experiments demonstrate that HOOD outperforms state-of-the-art (SOTA) OOD detection methods in terms of common OOD detection metrics. Importantly, HOOD also perfectly fits on Raspberry Pi 3B+ with an ARM Cortex-A53 CPU, which showcases its versatility across different hardware environments. Videos of our human presence detection experiments are available at: https://muskahya.github. io/HOOD
Global dynamics of two models for Quintom Friedman-Lema\^itre-Robertson-Walker Universes

We comprehensively analyze the dynamics for the gravitational field equations for the Chiral-Quintom theory in a Friedman-Lema\^itre-Robertson-Walker cosmology with an additional matter source. We consider a new set of dimensionless variables and write the field equations in the equivalent form of an algebraic-differential system. Specifically, we consider two families of quintom models where the two scalar fields interact in the kinetic sector. We mathematically focus on the dynamical effect of spatial curvature. Physically, we find two periods of inflation related to the Universe's early and late-time acceleration phases.
AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification

Deep learning (DL) has been successfully applied to encrypted network traffic classification in experimental settings. However, in production use, it has been shown that a DL classifier's performance inevitably decays over time. Re-training the model on newer datasets has been shown to only partially improve its performance. Manually re-tuning the model architecture to meet the performance expectations on newer datasets is time-consuming and requires domain expertise. We propose AutoML4ETC, a novel tool to automatically design efficient and high-performing neural architectures for encrypted traffic classification. We define a novel, powerful search space tailored specifically for the early classification of encrypted traffic using packet header bytes. We show that with different search strategies over our search space, AutoML4ETC generates neural architectures that outperform the state-of-the-art encrypted traffic classifiers on several datasets, including public benchmark datasets and real-world TLS and QUIC traffic collected from the Orange mobile network. In addition to being more accurate, AutoML4ETC's architectures are significantly more efficient and lighter in terms of the number of parameters. Finally, we make AutoML4ETC publicly available for future research.
Non-formally integrable centers admitting an algebraic inverse integrating factor

We study the existence of a class of inverse integrating factor for a family of non-formally integrable systems whose lowest-degree quasi-homogeneous term is a Hamiltonian vector field. Once the existence of an inverse integrating factor is established, we study the systems having a center. Among others, we characterize the centers of the perturbations of the system \begin{document}$ -y^3\partial_x+x^3\partial_y$\end{document} having an algebraic inverse integrating factor.
Intraseasonal variation of the East Asian summer monsoon associated with the Madden–Julian Oscillation

We investigate the daily variability of the East Asian summer monsoon (EASM) by projecting daily wind anomaly data onto the two major modes of an interannual multivariate Empirical Orthogonal Functions analysis. Mode 1, closely resembling the Pacific–Japan (PJ) pattern and referred to as PJ‐mode, transits from positive to negative phase around mid‐summer consistent with the Meiyu rains predominantly being an early summer phenomenon. Mode 2, which is influenced by the Indian summer monsoon (ISM) and referred to as ISM‐mode, peaks in late July and early August and is associated with rainfall farther north over China. We then analyze the relation between the intraseasonal variation of the EASM and the Madden–Julian Oscillation (MJO) by analyzing circulation anomalies following MJO events. In the lower troposphere, the circulation anomalies associated with the MJO most strongly project on the PJ‐mode. MJO phases 1–4 (5–8) favor the positive (negative) phase of the PJ‐mode by favoring the anticyclonic (cyclonic) anomalies over the subtropical western North Pacific. In the upper troposphere, the circulation anomalies associated with the MJO project mainly on the ISM‐mode.