Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-scale Feature Aggregation for Crowd Counting

Convolutional Neural Network (CNN) based crowd counting methods have achieved promising results in the past few years. However, the scale variation problem is still a huge challenge for accurate count estimation. In this paper, we propose a multi-scale feature aggregation network (MSFANet) that can alleviate this problem to some extent. Specifically, our approach consists of two feature aggregation modules: the short aggregation (ShortAgg) and the skip aggregation (SkipAgg). The ShortAgg module aggregates the features of the adjacent convolution blocks. Its purpose is to make features with different receptive fields fused gradually from the bottom to the top of the network. The SkipAgg module directly propagates features with small receptive fields to features with much larger receptive fields. Its purpose is to promote the fusion of features with small and large receptive fields. Especially, the SkipAgg module introduces the local self-attention features from the Swin Transformer blocks to incorporate rich spatial information. Furthermore, we present a local-and-global based counting loss by considering the non-uniform crowd distribution. Extensive experiments on four challenging datasets (ShanghaiTech dataset, UCF_CC_50 dataset, UCF-QNRF Dataset, WorldExpo'10 dataset) demonstrate the proposed easy-to-implement MSFANet can achieve promising results when compared with the previous state-of-the-art approaches.
On a question of Davenport and diagonal cubic forms over $\mathbb{F}_q(t)$

Given a non-singular diagonal cubic hypersurface $X\subset\mathbb{P}^{n-1}$ over $\mathbb{F}_q(t)$ with $\mathrm{char} (\mathbb{F}_q)\neq 3$, we show that the number of rational points of height at most $|P|$ is $O(|P|^{3+\varepsilon})$ for $n=6$ and $O(\lvert P \rvert^{2+\varepsilon})$ for $n=4$. In fact, if $n=4$ and $\mathrm{char}(\mathbb{F}_q)>3$ we prove that the number of rational points away from any rational line contained in $X$ is bounded by $O(|P|^{3/2+\varepsilon})$. From the result in $6$ variables we deduce weak approximation for diagonal cubic hypersurfaces for $n\geq 7$ over $\mathbb{F}_q(t)$ when $\mathrm{char}(\mathbb{F}_q)>3$ and handle Waring's problem for cubes in $7$ variables over $\mathbb{F}_q(t)$ when $\mathrm{char}(\mathbb{F}_q)\neq 3$. Our results answer a question of Davenport regarding the number of solutions of bounded height to $x_1^3+x_2^3+x_3^3 = x_4^3+x_5^3+x_6^3$ with $x_i \in \mathbb{F}_q[t]$.
TagRec++: Hierarchical Label Aware Attention Network for Question Categorization

Online learning systems have multiple data repositories in the form of transcripts, books and questions. To enable ease of access, such systems organize the content according to a well defined taxonomy of hierarchical nature (subject-chapter-topic). The task of categorizing inputs to the hierarchical labels is usually cast as a flat multi-class classification problem. Such approaches ignore the semantic relatedness between the terms in the input and the tokens in the hierarchical labels. Alternate approaches also suffer from class imbalance when they only consider leaf level nodes as labels. To tackle the issues, we formulate the task as a dense retrieval problem to retrieve the appropriate hierarchical labels for each content. In this paper, we deal with categorizing questions. We model the hierarchical labels as a composition of their tokens and use an efficient cross-attention mechanism to fuse the information with the term representations of the content. We also propose an adaptive in-batch hard negative sampling approach which samples better negatives as the training progresses. We demonstrate that the proposed approach \textit{TagRec++} outperforms existing state-of-the-art approaches on question datasets as measured by Recall@k. In addition, we demonstrate zero-shot capabilities of \textit{TagRec++} and ability to adapt to label changes.
Automatic Ultrasound Image Segmentation of Supraclavicular Nerve Using Dilated U-Net Deep Learning Architecture

— Automated object recognition in medical images can facilitate medical diagnosis and treatment. In this paper, we automatically segmented supraclavicular nerves in ultrasound images to assist in injecting peripheral nerve blocks. Nerve blocks are generally used for pain treatment after surgery, where ultrasound guidance is used to inject local anesthetics next to target nerves. This treatment blocks the transmission of pain signals to the brain, which can help improve the rate of recovery from surgery and significantly decrease the requirement for postoperative opioids. However, Ultrasound Guided Regional Anesthesia (UGRA) requires anesthesiologists to visually recognize the actual nerve position in the ultrasound images. This is a complex task given the myriad visual presentations of nerves in ultrasound images, and their visual similarity to many neighboring tissues. In this study, we used an automated nerve detection system for the UGRA Nerve Block treatment. The system can recognize the position of the nerve in ultrasound images using Deep Learning techniques. We developed a model to capture features of nerves by training two deep neural networks with skip connections: two extended U-Net architectures with and without dilated convolutions. This solution could potentially lead to an improved blockade of targeted nerves in regional anesthesia.
Adaptive Learning Rates for Faster Stochastic Gradient Methods

In this work, we propose new adaptive step size strategies that improve several stochastic gradient methods. Our first method (StoPS) is based on the classical Polyak step size (Polyak, 1987) and is an extension of the recent development of this method for the stochastic optimization-SPS (Loizou et al., 2021), and our second method, denoted GraDS, rescales step size by"diversity of stochastic gradients". We provide a theoretical analysis of these methods for strongly convex smooth functions and show they enjoy deterministic-like rates despite stochastic gradients. Furthermore, we demonstrate the theoretical superiority of our adaptive methods on quadratic objectives. Unfortunately, both StoPS and GraDS depend on unknown quantities, which are only practical for the overparametrized models. To remedy this, we drop this undesired dependence and redefine StoPS and GraDS to StoP and GraD, respectively. We show that these new methods converge linearly to the neighbourhood of the optimal solution under the same assumptions. Finally, we corroborate our theoretical claims by experimental validation, which reveals that GraD is particularly useful for deep learning optimization.
Decomposition of galactic X-ray emission with Phox. Contributions from hot gas and X-ray binaries

We provide a numerical framework with which spatially and spectrally accurate representations of X-ray binary populations can be studied from hydrodynamical cosmological simulations. We construct average spectra accounting for a hot gas component and verify the emergence of observed scaling relations between galaxy wide X-ray luminosity ($L_{X}$) and stellar mass ($M_{\star}$) as well as star-formation rate (SFR). Using simulated galaxy halos extracted from the $(48\,h^{-1} \mathrm{cMpc})^3$ volume of the Magneticum Pathfinder cosmological simulations at $z = 0.07$ we generate mock spectra with the X-ray photon-simulator Phox. We extend the Phox code to account for the stellar component in the simulation and study the resulting contribution in composite galactic spectra. Average X-ray luminosity functions are perfectly reproduced up to the one-photon luminosity limit. Comparing our resulting $L_{X}-\mathrm{SFR}-M_{\star}$ relation for X-ray binaries with recent observations of field galaxies in the Virgo galaxy cluster we find significant overlap. Invoking a metallicity dependent model for high-mass X-ray binaries yields an anti-correlation between mass-weighted stellar metallicity and SFR normalized luminosity. The spatial distribution of high-mass X-ray binaries coincides with star-formation regions of simulated galaxies while low-mass X-ray binaries follow the stellar mass surface density. X-ray binary emission is the dominant contribution in the 2-10 keV band in the absence of an actively accreting central super-massive black hole with 50% contribution in the 0.5-2 keV band rivaling the hot gas component. Our modelling remains consistent with observations despite uncertainties connected to our approach. The predictive power and easily extendable framework hold great value for future investigations of galactic X-ray spectra.
Ultrabright and narrowband intra-fiber biphoton source at ultralow pump power

Nonclassical photon sources of high brightness are key components of quantum communication technologies. We here demonstrate the generation of narrowband, nonclassical photon pairs by employing spontaneous four-wave mixing in an optically-dense ensemble of cold atoms within a hollow-core fiber. The brightness of our source approaches the limit of achievable generated spectral brightness at which successive photon pairs start to overlap in time. For a generated spectral brightness per pump power of up to 2 × 109 pairs/(s MHz mW) we observe nonclassical correlations at pump powers below 100 nW and a narrow bandwidth of 2π × 6.5 MHz. In this regime we demonstrate that our source can be used as a heralded single-photon source. By further increasing the brightness we enter the regime where successive photon pairs start to overlap in time and the cross-correlation approaches a limit corresponding to thermal statistics. Our approach of combining the advantages of atomic ensembles and waveguide environments is an important step toward photonic quantum networks of ensemble-based elements.
Selecting Valid Instrumental Variables in Linear Models with Multiple Exposure Variables: Adaptive Lasso and the Median-of-Medians Estimator

In a linear instrumental variables (IV) setting for estimating the causal eﬀects of multiple confounded exposure/treatment variables on an outcome, we investigate the adaptive Lasso method for selecting valid instrumental variables from a set of available instruments that may contain invalid ones. An instrument is invalid if it fails the exclusion conditions and enters the model as an explanatory variable. We extend the results as developed in Windmeijer et al. (2019) for the single exposure model to the multiple exposures case. In particular we propose a median-of-medians estimator and show that the conditions on the minimum number of valid instruments under which this estimator is consistent for the causal eﬀects are only moderately stronger than the simple majority rule that applies to the median estimator for the single exposure case. The adaptive Lasso method which uses the initial median-of-medians estimator for the penalty weights achieves consistent selection with oracle properties of the resulting IV estimator. This is conﬁrmed by some Monte Carlo simulation results. We apply the method to estimate the causal eﬀects of educational attainment and cognitive ability on body mass index (BMI) in a Mendelian Randomization setting.
Locally Adaptive Bayesian Isotonic Regression using Half Shrinkage Priors

Isotonic regression or monotone function estimation is a problem of estimating function values under monotonicity constraints, which appears naturally in many scientific fields. This paper proposes a new Bayesian method with global‐local shrinkage priors for estimating monotone function values. Specifically, we introduce half shrinkage priors for positive valued random variables and assign them for the first‐order differences of function values. We also develop fast and simple Gibbs sampling algorithms for full posterior analysis. By incorporating advanced shrinkage priors, the proposed method is adaptive to local abrupt changes or jumps in target functions. We show this adaptive property theoretically by proving that the posterior mean estimators are robust to large differences and that asymptotic risk for unchanged points can be improved. Finally, we demonstrate the proposed methods through simulations and applications to a real data set.This article is protected by copyright. All rights reserved.
The Relationship Between Surface Pressure Spectra and Vorticity in a Turbulent Boundary Layer

The modeling of surface pressure wave number spectra beneath a turbulent boundary layer is reviewed and reconsidered in terms of the vorticity in the flow. Using a solution based on the vorticity equation and Squires theorem, which was originally given by Chase(1991), it is shown that the complete solution for surface pressure spectrum can be specified using the non-linear turbulence-turbulence interaction terms as sources. It is then shown that the surface pressure can be directly related to the vorticity in the flow. The results are checked against a Direct Numerical Simulation of a channel flow configuration. It is shown that the vorticity associated with the wall shear stress decays rapidly over a distance of about seven wall units and so the outer vorticity dominates in the majority of the flow. The vorticity correlation functions are evaluated and it is shown that the length scale of the vorticity normal the wall is only about ten wall units so the flow can be modeled as the superposition of uncorrelated vortex sheets. Finally a model for the surface pressure wave number spectrum is developed by modeling the vorticity spectrum based on the distribution of root mean square (rms) vorticity in the flow, and an integral length scale that is about ten wall units. Using inputs from the numerical simulation, a good fit to existing empirical models for surface pressure spectra is obtained.
Defect-controlled Fermi-level tuning in half-Heusler topological semimetals

Three-dimensional topological semimetals host a range of interesting quantum phenomena related to band crossing and band touching that give rise to Dirac or Weyl fermions, and could be engineered into novel technological applications. Harvesting the full potential of these materials in applications will depend on our ability to position the Fermi level near the symmetry-protected band crossings or touchings so that exotic spin and charge transport properties are manifest. Using ﬁrst-principles calculations based on density functional theory, we investigate how point defects impact the Fermi level position in two representative half-Heusler topological semimetals, PtLuSb and PtLuBi; we explore how intrinsic defects can be used to tune the Fermi level, and explain recent observations based on Hall measurements in bulk and thin ﬁlms. Under typical growth conditions we show that Pt vacancies are the most abundant intrinsic defects, leading to excess hole densities that place the Fermi level signiﬁcantly below the expected position in the pristine material. Suggestions for tuning the Fermi level by tuning chemical potentials are discussed.
Cross-chain between a Parent Chain and Multiple Side Chains

In certain Blockchain systems, multiple Blockchains are required to operate cooperatively for security, performance, and capacity considerations. This invention defines a cross-chain mechanism where a main Blockchain issues the tokens, which can then be transferred and used in multiple side Blockchains to drive their operations. A set of witnesses are created to securely manage the token exchange across the main chain and multiple side chains. The system decouples the consensus algorithms between the main chain and side chains. We also discuss the coexistence of the main tokens and the native tokens in the side chains.
Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network

Automatic segmentation of left atrial (LA) scars from late gadolinium enhanced CMR images is a crucial step for atrial fibrillation (AF) recurrence analysis. However, delineating LA scars is tedious and error-prone due to the variation of scar shapes. In this work, we propose a boundary-aware LA scar segmentation network, which is composed of two branches to segment LA and LA scars, respectively. We explore the inherent spatial relationship between LA and LA scars. By introducing a Sobel fusion module between the two segmentation branches, the spatial information of LA boundaries can be propagated from the LA branch to the scar branch. Thus, LA scar segmentation can be performed condition on the LA boundaries regions. In our experiments, 40 labeled images were used to train the proposed network, and the remaining 20 labeled images were used for evaluation. The network achieved an average Dice score of 0.608 for LA scar segmentation.
PEPPER

Recommender systems are proving to be an invaluable tool for extracting user-relevant content helping users in their daily activities (e.g., finding relevant places to visit, content to consume, items to purchase). However, to be effective, these systems need to collect and analyze large volumes of personal data (e.g., location check-ins, movie ratings, click rates . etc.), which exposes users to numerous privacy threats. In this context, recommender systems based on Federated Learning (FL) appear to be a promising solution for enforcing privacy as they compute accurate recommendations while keeping personal data on the users' devices. However, FL, and therefore FL-based recommender systems, rely on a central server that can experience scalability issues besides being vulnerable to attacks. To remedy this, we propose PEPPER, a decentralized recommender system based on gossip learning principles. In PEPPER, users gossip model updates and aggregate them asynchronously. At the heart of PEPPER reside two key components: a personalized peer-sampling protocol that keeps in the neighborhood of each node, a proportion of nodes that have similar interests to the former and a simple yet effective model aggregation function that builds a model that is better suited to each user. Through experiments on three real datasets implementing two use cases: a location check-in recommendation and a movie recommendation, we demonstrate that our solution converges up to 42% faster than with other decentralized solutions providing up to 9% improvement on average performance metric such as hit ratio and up to 21% improvement on long tail performance compared to decentralized competitors.
Pikachu: Securing PoS Blockchains from Long-Range Attacks by Checkpointing into Bitcoin PoW using Taproot

Blockchain systems based on a reusable resource, such as proof-of-stake (PoS), provide weaker security guarantees than those based on proof-of-work. Specifically, they are vulnerable to long-range attacks, where an adversary can corrupt prior participants in order to rewrite the full history of the chain. To prevent this attack on a PoS chain, we propose a protocol that checkpoints the state of the PoS chain to a proof-of-work blockchain such as Bitcoin. Our checkpointing protocol hence does not rely on any central authority. Our work uses Schnorr signatures and leverages Bitcoin recent Taproot upgrade, allowing us to create a checkpointing transaction of constant size. We argue for the security of our protocol and present an open-source implementation that was tested on the Bitcoin testnet.
On m-bi-interior ideals of an ordered semigroup

In this article, we concurrently explore the class of m -bi-ideals as well as the class of m interior ideals in an ordered semigroup. We call these class of ideals as m -bi-interior ideals. Here, m -simple, m -regular ordered semigroups and their various subclasses get characterized in diverse manners as per their m -bi-interior ideals.
Reconstruction of Low Dimensional Electronic States by Altering the Chemical Arrangement at the SrTiO3 Surface

Developing reliable methods for modulating the electronic structure of the 2D electron gas (2DEG) in SrTiO3 is crucial for utilizing its full potential and inducing novel properties. Herein, it is shown that relatively simple surface preparation reconstructs the 2DEG at the SrTiO3 (STO) surface, leading to a Lifshitz‐like transition. Combining experimental methods, such as angle‐resolved photoemission spectroscopy (ARPES) and X‐ray photoemission spectroscopy with ab initio calculations, that the modulation of the surface band structures can be effectively achieved via transforming the chemical composition at the atomic scale is found. In addition, ARPES experiments demonstrate that vacuum ultraviolet light can be efficiently employed to alter the band renormalization of the 2DEG system and control the electron‐phonon interaction . This study provides a robust and straightforward route to stabilize and tune the low‐dimensional electronic structure via the chemical degeneracy of the STO surface.
Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs

Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this paper, a novel vRAN reconfiguration problem is formulated to jointly reconfigure the functional splits of the base stations (BSs), locations of the virtualized central units (vCUs) and distributed units (vDUs), their resources, and the routing for each BS data flow. The objective is to minimize the long-term total network operation cost while adapting to the varying traffic demands and resource availability. In the first step, testbed measurements are performed to study the relationship between the traffic demands and computing resources, which reveals high variance and depends on the platform and its load. Consequently, finding the perfect model of the underlying system is non-trivial. Therefore, to solve the proposed problem, a deep reinforcement learning (RL)-based framework is proposed and developed using model-free RL approaches. Moreover, the problem consists of multiple BSs sharing the same resources, which results in a multi-dimensional discrete action space and leads to a combinatorial number of possible actions. To overcome this curse of dimensionality, action branching architecture, which is an action decomposition method with a shared decision module followed by neural network is combined with Dueling Double Deep Q-network (D3QN) algorithm. Simulations are carried out using an O-RAN compliant model and real traces of the testbed. Our numerical results show that the proposed framework successfully learns the optimal policy that adaptively selects the vRAN configurations, where its learning convergence can be further expedited through transfer learning even in different vRAN systems. It also offers significant cost savings by up to 59% of a static benchmark, 35% of Deep Deterministic Policy Gradient with discretization, and 76% of non-branching D3QN.
Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property Prediction

Recent advances in machine learning have enabled accurate prediction of chemical properties. However, supervised machine learning methods in this domain often suffer from the label scarcity problem, due to the expensive nature of labelling chemical property experimentally. This research modifies state-of-the-art molecule generation method - Junction Tree Variational Autoencoder (JT-VAE) to facilitate semi-supervised learning on chemical property prediction. Furthermore, we force some latent variables to take on consistent and interpretable purposes such as representing toxicity via this partial supervision. We leverage JT-VAE architecture to learn an interpretable representation optimal for tasks ranging from molecule property prediction to conditional molecule generation, using a partially labelled dataset.