Miracle-dz/UniFAR-FTUs huggingface dataset:
Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders

Self-supervised representation learning techniques have been developing rapidly to make full use of unlabeled images. They encode images into rich features that are oblivious to downstream tasks. Behind their revolutionary representation power, the requirements for dedicated model designs and a massive amount of computation resources expose image encoders to the risks of potential model stealing attacks - a cheap way to mimic the well-trained encoder performance while circumventing the demanding requirements. Yet conventional attacks only target supervised classifiers given their predicted labels and/or posteriors, which leaves the vulnerability of unsupervised encoders unexplored. In this paper, we first instantiate the conventional stealing attacks against encoders and demonstrate their severer vulnerability compared with downstream classifiers. To better leverage the rich representation of encoders, we further propose Cont-Steal, a contrastive-learning-based attack, and validate its improved stealing effectiveness in various experiment settings. As a takeaway, we appeal to our community's attention to the intellectual property protection of representation learning techniques, especially to the defenses against encoder stealing attacks like ours.11See our code in https://github.com/zeyangsha/Cont-Steal.
Observation of the p-wave Shape Resonance

We observe a p -wave resonance in collisions between metastable neon and rovibrationally ground state HD molecules at a collision energy of k B × 22 mK. This is the ﬁrst observation of the lowest quasibound angular momentum state in molecular collisions. This measurement was enabled by the reduction of the lowest collision energy achieved without laser cooling, using the phase-space correlation in cold molecular beams. We demonstrate that contrary to higher l -state resonances, the p -wave resonance allows sensitive probing of the leading term of the van der Waals intermolecular interaction. Furthermore, the same sensitivity to the long range part of the interaction potential allows an accurate estimation of p -wave resonance lifetime using only fundamental constants and the dispersion coeﬃcient.
Effects of surface tension reduction on wind-wave growth and air–water scalar transfer

Abstract Effects of surface tension reduction on wind-wave growth are investigated using direct numerical simulations of air–water two-phase turbulent flows. The incompressible Navier–Stokes equations for air and water sides are solved using an arbitrary Lagrangian–Eulerian method with boundary-fitted moving grids. The wave growth of finite-amplitude and non-breaking gravity–capillary waves, whose wavelength is less than 0.07 m, is simulated for two cases of different surface tensions under a low-wind-speed condition of several metres per second. The results show that significant wave height for the smaller surface tension case increases faster than that for the larger surface tension case. Energy fluxes for gravity and capillary wave scales reveal that when the surface tension is reduced, the energy transfer from the significant gravity waves to capillary waves decreases, and the significant waves accumulate more energy supplied by wind. This results in faster wave growth for the smaller surface tension case. The effect on the scalar transfer across the air–water interface is also investigated. The results show that the scalar transfer coefficient on the water side decreases due to the surface tension reduction. The decrease is caused by suppression of turbulence in the water side. In order to support the conjecture, the surface tension effect is compared with laboratory experiments in a small wind-wave tank.
Coded Compressed Sensing With List Recoverable Codes for the Unsourced Random Access

We consider a coded compressed sensing approach for the unsourced random access and replace the outer tree code proposed by Amalladinne et al. (2020) with the list recoverable code capable of correcting <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> errors. A finite-length random coding bound for such codes is derived. The numerical experiments in the single-antenna quasi-static Rayleigh fading channel show that transition to list recoverable codes correcting <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> errors improves the performance of the coded compressed sensing scheme by 7–10 dB compared to the tree code-based scheme. We propose two practical constructions of outer codes. The first is a modification of the tree code called <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-tree code. It utilizes the same code structure, and a key difference is a decoder capable of correcting up to <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> errors. The second is based on the Reed–Solomon codes and Guruswami–Sudan list decoding algorithm. The first scheme provides energy efficiency very close to the random coding bound when the decoding complexity (number of decoding paths) is unbounded. But when we restrict the number of decoding paths with a practical value, the second scheme outperforms the first one. Both schemes improve the performance of a tree code-based scheme for a small and moderate number of active users.
Sparsification of Decomposable Submodular Functions

Submodular functions are at the core of many machine learning and data mining tasks. The underlying submodular functions for many of these tasks are decomposable, i.e., they are sum of several simple submodular functions. In many data intensive applications, however, the number of underlying submodular functions in the original function is so large that we need prohibitively large amount of time to process it and/or it does not even fit in the main memory. To overcome this issue, we introduce the notion of sparsification for decomposable submodular functions whose objective is to obtain an accurate approximation of the original function that is a (weighted) sum of only a few submodular functions. Our main result is a polynomial-time randomized sparsification algorithm such that the expected number of functions used in the output is independent of the number of underlying submodular functions in the original function. We also study the effectiveness of our algorithm under various constraints such as matroid and cardinality constraints. We complement our theoretical analysis with an empirical study of the performance of our algorithm.
Gaussian fluctuations of replica overlap in directed polymers

In this short note, we prove a central limit theorem for a type of replica overlap of the Brownian directed polymer in a Gaussian random environment, in the low temperature regime and in all dimensions. The proof relies on a superconcentration result for the KPZ equation driven by a spatially molliﬁed noise, which is inspired by the recent work of Chatterjee [14].
Toward a More Complete Optical Census of Active Galactic Nuclei via Spatially Resolved Spectroscopy

While emission line flux ratio diagnostics are the most common technique for identifying active galactic nuclei (AGNs) in optical spectra, applying this approach to single-fiber spectra of galaxies can omit entire subpopulations of AGNs. Here, we use spatially resolved spectroscopy from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey to construct a sample of 10 galaxies where Baldwin–Philips–Terlevich line flux ratio diagnostics classify each galaxy’s central 3″ spectrum as LINER or star-forming, while >10% of the spaxels in the galaxy’s MaNGA footprint are classified as Seyfert. We obtain Chandra observations of these 10 galaxies with off-nuclear Seyfert regions to determine whether AGNs are actually present in them. Our main result is that 7–10 (depending on strictness of criteria) of the galaxies host one or more X-ray AGNs, even though none of them were classified as AGNs based on their single-fiber optical spectra. We find that these AGNs were not identified in the single-fiber spectra because they are AGNs in the nuclei of companion galaxies, low-luminosity AGNs, dust-obscured AGNs, and/or flickering AGNs. In summary, we find that off-nuclear AGN signatures may increase the number of known AGNs by a factor of two over what conventional single nuclear fiber spectra identify. Our results show that spatially resolved spectroscopy can be leveraged to reveal a more complete census of AGNs that are traditionally missed by single-fiber spectra.
TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning

Image fusion is a technique to integrate information from multiple source images with complementary information to improve the richness of a single image. Due to insufficient task-specific training data and corresponding ground truth, most existing end-to-end image fusion methods easily fall into overfitting or tedious parameter optimization processes. Two-stage methods avoid the need of large amount of task-specific training data by training encoder-decoder network on large natural image datasets and utilizing the extracted features for fusion, but the domain gap between natural images and different fusion tasks results in limited performance. In this study, we design a novel encoder-decoder based image fusion framework and propose a destruction-reconstruction based self-supervised training scheme to encourage the network to learn task-specific features. Specifically, we propose three destruction-reconstruction self-supervised auxiliary tasks for multi-modal image fusion, multi-exposure image fusion and multi-focus image fusion based on pixel intensity non-linear transformation, brightness transformation and noise transformation, respectively. In order to encourage different fusion tasks to promote each other and increase the generalizability of the trained network, we integrate the three self-supervised auxiliary tasks by randomly choosing one of them to destroy a natural image in model training. In addition, we design a new encoder that combines CNN and Transformer for feature extraction, so that the trained model can exploit both local and global information. Extensive experiments on multi-modal image fusion, multi-exposure image fusion and multi-focus image fusion tasks demonstrate that our proposed method achieves the state-of-the-art performance in both subjective and objective evaluations. The code will be publicly available soon.
Surrogate-assisted distributed swarm optimisation for computationally expensive models

Advances in parallel and distributed computing have enabled e ﬃ cient implementation of distributed swarm and evolutionary algorithms for complex and computationally expensive models. Evolutionary algorithms provide gradient-free optimisation which is beneﬁcial for models that do not have such information available, for instance geoscientiﬁc landscape evolution models. However, such models are so computationally expensive that even distributed swarm and evolutionary algorithms with the power of parallel computing struggle. We need to incorporate e ﬃ cient strategies such as surrogate assisted optimisation that further improves their performance; however this becomes a challenge given parallel processing and inter-process communication for implementing surrogate training and prediction. In this paper, we implement surrogate-based estimation of ﬁtness evaluation in distributed swarm optimisation over a parallel computing architecture. Our results demonstrate very promising results for benchmark functions and geoscientiﬁc landscape evolution models. We obtain reduction in computationally time while retaining optimisation solution accuracy though the use of surrogates in a parallel computing environment.
DMF-Net: Dual-Branch Multi-Scale Feature Fusion Network for copy forgery identification of anti-counterfeiting QR code

Anti-counterfeiting QR codes are widely used in people's work and life, especially in product packaging. However, the anti-counterfeiting QR code has the risk of being copied and forged in the circulation process. In reality, copying is usually based on genuine anti-counterfeiting QR codes, but the brands and models of copiers are diverse, and it is extremely difficult to determine which individual copier the forged anti-counterfeiting code come from. In response to the above problems, this paper proposes a method for copy forgery identification of anti-counterfeiting QR code based on deep learning. We first analyze the production principle of anti-counterfeiting QR code, and convert the identification of copy forgery to device category forensics, and then a Dual-Branch Multi-Scale Feature Fusion network is proposed. During the design of the network, we conducted a detailed analysis of the data preprocessing layer, single-branch design, etc., combined with experiments, the specific structure of the dual-branch multi-scale feature fusion network is determined. The experimental results show that the proposed method has achieved a high accuracy of copy forgery identification, which exceeds the current series of methods in the field of image forensics.
Variational inference for quantifying inter-observer variability in segmentation of anatomical structures

Lesions or organ boundaries visible through medical imaging data are often ambiguous, thus resulting in significant variations in multi-reader delineations, i.e., the source of aleatoric uncertainty. In particular, quantifying the inter-observer variability of manual annotations with Magnetic Resonance (MR) Imaging data plays a crucial role in establishing a reference standard for various diagnosis and treatment tasks. Most segmentation methods, however, simply model a mapping from an image to its single segmentation map and do not take the disagreement of annotators into consideration. In order to account for inter-observer variability, without sacrificing accuracy, we propose a novel variational inference framework to model the distribution of plausible segmentation maps, given a specific MR image, which explicitly represents the multi-reader variability. Specifically, we resort to a latent vector to encode the multi-reader variability and counteract the inherent information loss in the imaging data. Then, we apply a variational autoencoder network and optimize its evidence lower bound (ELBO) to efficiently approximate the distribution of the segmentation map, given an MR image. Experimental results, carried out with the QUBIQ brain growth MRI segmentation datasets with seven annotators, demonstrate the effectiveness of our approach.
Performance Improvement of Pi-gate SOI MOSFET Transistor using High-k Dielectric with Metal Gate

ABSTRACT Pi-gate silicon-on insulator (SOI) MOSFET transistors have emerged as novel devices due to its simple architecture and better performance, better control over short-channel effects, and reduced power dissipation due to reduced gate leakage currents. As the oxide thickness scales below 2 nm, leakage currents due to tunnelling increase drastically, leading to high power consumption and reduced device reliability. Replacing the SiO2 gate oxide with a high-k material allows increased gate capacitance without the associated leakage effects. In this paper, we present the results of a 3D numerical simulation of the Pi-gate SOI MOSFET transistor. 3D device structure, based on the technology SOI is described and simulated by using SILVACO TCAD tools, and we compare the electrical characteristics results for titanium nitride (TiN) fabricated on Al2O3 (k ∼ 9), HfO2 (k ∼ 20), and La2O3 (k ∼ 30) gate dielectric. Excellent dielectric properties such as high-k constant, low leakage current, threshold voltage, and electrical characteristics were demonstrated. From the simulation result, it was shown that HfO2 is the best dielectric material with metal gate TiN.
Fine-grained network traffic prediction from coarse data

ICT systems provide detailed information on computer network traffic. However, due to storage limitations, some of the information on past traffic is often only retained in an aggregated form. In this paper we show that Linear Gaussian State Space Models yield simple yet effective methods to make predictions based on time series at different aggregation levels. The models link coarse-grained and fine-grained time series to a single model that is able to provide fine-grained predictions. Our numerical experiments show up to 3.7 times improvement in expected mean absolute forecast error when forecasts are made using, instead of ignoring, additional coarse-grained observations. The forecasts are obtained in a Bayesian formulation of the model, which allows for provisioning of a traffic prediction service with highly informative priors obtained from coarse-grained historical data.
XAI Model for Accurate and Interpretable Landslide Susceptibility

Landslides are notoriously difficult to predict. Deep neural networks (DNNs) models are more accurate than statistical models. However, they are uninterpretable, making it difficult to extract mechanistic information about landslide controls in the modeled region. We developed an explainable AI (XAI) model to assess landslide susceptibility that is computationally simple and features high accuracy. We validated it on three different regions of eastern Himalaya that are highly susceptible to landslides. SNNs are computationally much simpler than DNNs, yet achieve similar performance while offering insights regarding the relative importance of landslide control factors in each region. Our analysis highlighted the importance of: 1) the product of slope and precipitation rate and 2) topographic aspects that contribute to high susceptibility in landslide areas. These identified controls suggest that strong slope-climate couplings, along with microclimates, play more dominant roles in eastern Himalayan landslides. The model outperforms physically-based stability and statistical models.
UxNB-Enabled Cell-Free Massive MIMO with HAPS-Assisted Sub-THz Backhauling

In this paper, we propose a cell-free scheme for unmanned aerial vehicle (UAV) base stations (BSs) to manage the severe intercell interference between terrestrial users and UAV-BSs of neighboring cells. Since the cell-free scheme requires enormous bandwidth for backhauling, we propose to use the sub-terahertz (sub-THz) band for the backhaul links between UAV-BSs and central processing unit (CPU). Also, because the sub-THz band requires a reliable line-of-sight link, we propose to use a high altitude platform station (HAPS) as a CPU. At the first time-slot of the proposed scheme, users send their messages to UAVs at the sub-6 GHz band. The UAVs then apply match-filtering and power allocation. At the second time-slot, at each UAV, orthogonal resource blocks are allocated for each user at the sub-THz band, and the signals are sent to the HAPS after analog beamforming. In the HAPS receiver, after analog beamforming, the message of each user is decoded. We formulate an optimization problem that maximizes the minimum signal-to-interference-plus-noise ratio of users by finding the optimum allocated power as well as the optimum locations of UAVs. Simulation results demonstrate the superiority of the proposed scheme compared with aerial cellular and terrestrial cell-free baseline schemes.
Dynamic Bandwidth Allocation for PON Slicing with Performance-Guaranteed Online Convex Optimization

The emergence of diverse network applications demands more flexible and responsive resource allocation for networks. Network slicing is a key enabling technology that provides each network service with a tailored set of network resources to satisfy specific service requirements. The focus of this paper is the network slicing of access networks realized by Passive Optical Networks (PONs). This paper proposes a learning-based Dynamic Bandwidth Allocation (DBA) algorithm for PON access networks, considering slice-awareness, demand-responsiveness, and allocation fairness. Our online convex optimization-based algorithm learns the implicit traffic trend over time and determines the most robust window allocation that reduces the average latency. Our simulation results indicate that the proposed algorithm reduces the average latency by prioritizing delay-sensitive and heavily-loaded ONUs while guaranteeing a minimal window allocation to all ONUs.
Wannier Functions Dually Localized in Space and Energy

Wannier functions are real-space representations of Bloch orbitals that provide a useful picture for chemical bonding and offer a localized description of single-particle wave functions. There is a unitary freedom in the construction of Wannier functions from Bloch orbitals, which can be chosen to produce Wannier functions that have advantageous properties. A popular choice for this freedom is the minimization of spatial variance, which leads to maximally localized Wannier functions. We minimize a weighted sum of the spatial and energy variances, yielding what we call dually localized Wannier functions. Localization in energy results in Wannier functions that are associated with a particular energy and allows for dually localized Wannier functions to be comprised of occupied and unoccupied spaces simulatneously. This can lead to Wannier functions that are fractionally occupied, which is a key feature that is used in correcting the delocalization error in density functional approximations. We show how this type of localization results in bonding (antibonding) functions for the occupied (unoccupied) spaces around the frontier energy of silicon in the diamond lattice and of molecular ethylene. Dually localized Wannier functions therefore offer a relevant description for chemical bonding and are well suited to orbital-dependent methods that associate Wannier functions with specific energy ranges without the need to consider the occupied and unoccupied spaces separately.
The Companion of Enrico’s Chart for Phase Noise and Two-Sample Variances

Phase noise and frequency (in)stability both describe the fluctuation of stable periodic signals from somewhat different standpoints. Frequency is unique compared to other domains of metrology, in which its fluctuations of interest span at least 14 orders of magnitude, from <inline-formula> <tex-math notation="LaTeX">$10^{-4}$ </tex-math></inline-formula> in a mechanical watch to <inline-formula> <tex-math notation="LaTeX">$10^{-18}$ </tex-math></inline-formula> in atomic clocks. The frequency span of interest is some 12–15 orders of magnitude from <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Hz to GHz Fourier frequency for phase noise, while the time span over which the fluctuations occur ranges from sub- <inline-formula> <tex-math notation="LaTeX">$\mathrm {\mu s}$ </tex-math></inline-formula> to years integration time for variances. Because this domain is ubiquitous in science and technology, a common language and tools suitable to the variety mentioned are a challenge. This article is at once: 1) a tutorial; 2) a review covering the most important facts about phase noise, frequency noise, and two-sample (Allan and Allan-like) variances; and 3) a user guide to “Enrico’s Chart of Phase Noise and Two-Sample Variances. ” In turn, the Chart is a reference card collecting the most useful concepts, formulas, and plots in a single A4/A-size sheet, intended to be a staple on the desk of whoever works with these topics. The Chart is available under Creative Commons 4.0 CC-BY-NC-ND license from Zenodo, DOI: 10.5281/zenodo.4399218. A wealth of auxiliary material is available for free on Enrico’s home page: <uri>https://rubiola.org</uri>.