Miracle-dz/UniFAR-FTUs huggingface dataset:
Elevation beamforming performance of a 1.75 D array

1.75 D arrays allow limited beam steering in elevation, which is valuable for three-dimensional (3-D) imaging within a certain field of view. 3-D imaging has important advantage in detecting and visualizing small lesions from multiple viewing angles. Recently, Tetrad Inc has manufactured 1.75 D arrays using the state-of-the-art technologies. The array we have consists of 10 rows in elevation and 128 elements in azimuth, 1280 elements in total. The central frequency of the array is 5 MHz and the bandwidth is 60%. We have constructed 1280 parallel transmission circuitry, 1280 to 40 multiplexing and 40 parallel receiving circuitry. Upon each transmission, 1280-element RF signals were multiplexed to 40 parallel electronic channels, amplified, sampled and stored in the PC. The elevation performance of the array was evaluated through simulations and experiments using a point target. The parameters evaluated were -6 dB, -20 dB beamwidths and grating lobe strength.
A Hierarchical Reinforcement Learning Framework for UAV Path Planning in Tactical Environments

Tackling tactical UAV path planning under radar threat using reinforcement learning involves particular challenges ranging from modeling related difficulties to sparse feedback problem. Learning goal-directed behavior with sparse feedback from complex environments is a fundamental challenge for reinforcement learning algorithms. In this paper we extend our previous work in this area to provide a solution to the problem setting stated above, using Hierarchical Reinforcement Learning (HRL) in a novel way that involves a meta controller for higher level goal assignment and a controller that determines the lower-level actions of the agent. Our meta controller is based on a regression model trained using a state transition scheme that defines the evolution of goal designation, whereas our lower-level controller is based on a Deep Q Network (DQN) and is trained via reinforcement learning iterations. This two-layer framework ensures that an optimal plan for a complex path, organized as multiple goals, is achieved gradually, through piecewise assignment of sub-goals, and thus as a result of a staged, efficient and rigorous procedure.
Clinical Evaluation of Post-Surgical Scar Hyperesthesia; an Exploratory Longitudinal Study

Introduction: Evidence for the objective clinical evaluation of scar hyperesthesia is lacking. This exploratory study investigated the clinical relevance and responsiveness of objective scar evaluation measures in adults following hand surgery. Methods: With ethical approval and consent, participants were enrolled from one NHS hospital. Patient reported and investigator completed scar morphology, cosmesis, pain and function were evaluated at 1- and 4-months post-surgery. Statistical analysis investigated the responsiveness of outcome measures and association of physical measures with the Palmar Pain Severity Scale (PPS). Results: 21 participants enrolled prior to premature study closure due to the COVID-19 pandemic; 13 completed follow up. Scar pain (p=.002); scar interference (PPI [p=.009]) and Brief Pain Inventory (BPI) scores (p=.03) improved. Neuropathic Pain Symptom Inventory (NPSI) scores demonstrated heterogeneity in scar pain; evoked pain predominated. Patient Scar Assessment Questionnaire (PSAQ) indicated improvement in cosmetic dissatisfaction and consciousness (p=.03; p=.003), respectively. Baseline psychological screening scores correlated with scar pain (p=.04), and interference (p< .001). Scar morphology, pliability and inflammation were not associated with scar pain. Significant differences in scar mechanical pain sensitivity (p=.04) and cold pain threshold (p=.05) were identified. Discussion: PPS and PPI scores were responsive in a heterogeneous hand surgery sample. BPI worst pain identified severe pain, suggesting composite scar pain scores are required. The PSAQ robustly measured scar appearance and consciousness. Psychophysical tests of mechanical and thermal sensitivity are potential candidate objective measures of scar hyperesthesia. The NPSI demonstrates clinical utility for exploring scar pain symptoms and may support the elucidation of the drivers of persistent scar pain.
Low dark current small molecule organic photodetectors with selective response to green light

We report green-sensitive organic photodetectors consisting of a bulk heterojunction blend of N,N-dimethylquinacridone and dicyanovinyl-terthiophene. Devices incorporating a triphenylamine derivative-based electron blocking layer and a molybdenum oxide hole extracting layer lead to significantly low dark currents (Jd) ∼ 6.41 nA/cm2 at −3 V and high external quantum efficiency of 55.2% at 540 nm wavelength with a narrow full width at half maximum of 146 nm, which is likely to be applicable for full colour organic image sensors. Based on the interfacial energy barrier and temperature dependent current-voltage characteristics, possible origins of the reverse Jd of devices are further described.
Stochastic filtering with moment representation

Stochastic filtering refers to estimating the probability distribution of the latent stochastic process conditioned on the observed measurements in time. In this paper, we introduce a new class of convergent filters that represent the filtering distributions by their moments. The key enablement is a quadrature method that uses orthonormal polynomials spanned by the moments. We prove that this moment-based filter is asymptotically exact in the order of moments, and show that the filter is also computationally efficient and is in line with the state of the art.
Demystifying estimands in cluster-randomised trials

Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned to the study's objectives. Cluster randomised trials require additional attributes to be defined within the estimand compared to individually randomised trials, including whether treatment effects are marginal or cluster specific, and whether they are participant or cluster average. In this paper, we provide formal definitions of estimands encompassing both these attributes using potential outcomes notation and describe differences between them. We then provide an overview of estimators for each estimand, describe their assumptions, and show consistency (i.e. asymptotically unbiased estimation) for a series of analyses based on cluster level summaries. Then, through a reanalysis of a published cluster randomised trial, we demonstrate that the choice of both estimand and estimator can affect interpretation. For instance, the estimated odds ratio ranged from 1.38 (p=0.17) to 1.83 (p=0.03) depending on the target estimand, and for some estimands, the choice of estimator affected the conclusions by leading to smaller treatment effect estimates. We conclude that careful specification of the estimand, along with an appropriate choice of estimator, are essential to ensuring that cluster randomised trials address the right question.
Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View Settings

Reconstructing 3D human heads in low-view settings presents technical challenges, mainly due to the pronounced risk of overfitting with limited views and high-frequency signals. To address this, we propose geometry decomposition and adopt a two-stage, coarse-to-fine training strategy, allowing for progressively capturing high-frequency geometric details. We represent 3D human heads using the zero level-set of a combined signed distance field, comprising a smooth template, a non-rigid deformation, and a high-frequency displacement field. The template captures features that are independent of both identity and expression and is co-trained with the deformation network across multiple individuals with sparse and randomly selected views. The displacement field, capturing individual-specific details, undergoes separate training for each person. Our network training does not require 3D supervision or object masks. Experimental results demonstrate the effectiveness and robustness of our geometry decomposition and two-stage training strategy. Our method outperforms existing neural rendering approaches in terms of reconstruction accuracy and novel view synthesis under low-view settings. Moreover, the pre-trained template serves a good initialization for our model when encountering unseen individuals.
Noncommutative Residues, Equivariant Traces, and Trace Expansions for an Operator Algebra on $\mathbb R^n$

We consider an algebra $\mathscr A$ of Fourier integral operators on $\mathbb R^n$. It consists of all operators $D: \mathscr S(\mathbb R^n)\to \mathscr S(\mathbb R^n)$ on the Schwartz space $\mathscr S(\mathbb R^n)$ that can be written as finite sums $$ D= \sum R_gT_w A, $$ with Shubin type pseudodifferential operators $A$, Heisenberg-Weyl operators $T_w$, $w\in \mathbb C^n$, and lifts $R_g$, $g\in \mathrm U(n)$, of unitary matrices $g$ on $\mathbb C^n$ to operators $R_g$ in the complex metaplectic group. For $D \in \mathscr A$ and a suitable auxiliary Shubin pseudodifferential operator $H$ we establish expansions for $\mathop{\mathrm {Tr}}(D(H-\lambda)^{-K})$ as $|\lambda| \to \infty$ in a sector of $\mathbb C$ for sufficiently large $K$ and of $\mathop{\mathrm {Tr}}(De^{-tH})$ as $t\to 0^+$. We also obtain the singularity structure of the meromorphic extension of $z\mapsto \mathop{\mathrm{Tr}}(DH^{-z})$ to $\mathbb C$. Moreover, we find a noncommutative residue as a suitable coefficient in these expansions and construct from it a family of localized equivariant traces on the algebra.
Irreducible Graded Bimodules over Algebras and a Pierce Decomposition of the Jacobson Radical

It is well known that the ring radical theory can be approached via language of modules. In this work, we present some generalizations of classical results from module theory, in the two-sided and graded sense. Let $\mathsf{G}$ be a group, $\mathbb{F}$ an algebraically closed field with $\mathsf{char}(\mathbb{F})=0$, $\mathfrak{A}$ a finite dimensional $\mathsf{G}$-graded associative $\mathbb{F}$-algebra and $\mathsf{M}$ a $\mathsf{G}$-graded unitary $\mathfrak{A}$-bimodule. We proved that if $\mathfrak{A}=M_n(\mathbb{F}^\sigma[\mathsf{H}])$ with a canonical elementary $\mathsf{G}$-grading, where $\mathsf{H}$ is a finite abelian subgroup of $\mathsf{G}$ and $\sigma\in\mathsf{Z}^2(\mathsf{H},\mathbb{F}^*)$, then $\mathsf{M}$ being irreducible graded implies that there exists a nonzero homogeneous element $w\in\mathsf{M}$ satisfying $\mathsf{M}=\mathfrak{B}w$ and $\mathfrak{B} w= w\mathfrak{B}$. Another result we proved generalizes the last one: if $\mathsf{G}$ is abelian, $\mathfrak{A}$ is simple graded and $\mathsf{M}$ is finitely generated, then there exist nonzero homogeneous elements $w_1, w_2,\dots,w_n\in\mathsf{M}$ such that \begin{equation}\nonumber \mathsf{M}=\mathfrak{A} w_1\oplus\mathfrak{A} w_2\oplus \cdots \oplus \mathfrak{A} w_n \ , \end{equation} where $w_i \mathfrak{A}=\mathfrak{A} w_i\neq0$ for all $i=1, 2,\dots,n$, and each $\mathfrak{A} w_i$ is irreducible. The elements $w_i$'s are associated with the irreducible characters of $\mathsf{G}$. We also describe graded bimodules over graded semisimple algebras. And we finish by presenting a Pierce decomposition of the graded Jacobson radical of any finite dimensional $\mathbb{F}$-algebra with a $\mathsf{G}$-grading.
GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism

Large-scale graphs with billions of edges are ubiquitous in many industries, science, and engineering fields such as recommendation systems, social graph analysis, knowledge base, material science, and biology. Graph neural networks (GNN), an emerging class of machine learning models, are increasingly adopted to learn on these graphs due to their superior performance in various graph analytics tasks. Mini-batch training is commonly adopted to train on large graphs, and data parallelism is the standard approach to scale mini-batch training to multiple GPUs. In this paper, we argue that several fundamental performance bottlenecks of GNN training systems have to do with inherent limitations of the data parallel approach. We then propose split parallelism, a novel parallel mini-batch training paradigm. We implement split parallelism in a novel system called gsplit and show that it outperforms state-of-the-art systems such as DGL, Quiver, and PaGraph.
Reduced Absorption Due to Defect-Localized Interlayer Excitons in Transition-Metal Dichalcogenide–Graphene Heterostructures

Associating atomic vacancies to excited-state transport phenomena in two-dimensional semiconductors demands a detailed understanding of the exciton transitions involved. We study the effect of such defects on the electronic and optical properties of WS2–graphene and MoS2–graphene van der Waals heterobilayers, employing many-body perturbation theory. We find that chalcogen defects and the graphene interface radically alter the optical properties of the transition-metal dichalcogenide in the heterobilayer, due to a combination of dielectric screening and the many-body nature of defect-induced intralayer and interlayer optical transitions. By analyzing the intrinsic radiative rates of the subgap excitonic features, we show that while defects introduce low-lying optical transitions, resulting in excitons with non-negligible oscillator strength, they decrease the optical response of the pristine-like transition-metal dichalcogenide intralayer excitons. Our findings relate excitonic features with interface design for defect engineering in photovoltaic and transport applications.
Finding a good tree to burn

The burning number of a graph $G$ is the smallest positive integer $k$ such that the vertex set of $G$ can be covered with balls of radii $0, 1, \dots, k-1$. A well-known conjecture by Bonato, Janssen and Roshabin states that any connected graph on $n$ vertices has burning number at most $\lceil \sqrt{n} \rceil$. It was recently shown by Norin and Turcotte that the conjecture holds up to a factor of $1+o(1)$. In this note, we demonstrate how this result can be applied to determine the asymptotic value of the burning number for graph classes with given minimum degree. This is based on an observation about connected $2$-hop dominating sets, which may be of independent interest.
Effective black box adversarial attack with handcrafted kernels

We propose a new, simple framework for crafting adversarial examples for black box attacks. The idea is to simulate the substitution model with a non-trainable model compounded of just one layer of handcrafted convolutional kernels and then train the generator neural network to maximize the distance of the outputs for the original and generated adversarial image. We show that fooling the prediction of the first layer causes the whole network to be fooled and decreases its accuracy on adversarial inputs. Moreover, we do not train the neural network to obtain the first convolutional layer kernels, but we create them using the technique of F-transform. Therefore, our method is very time and resource effective.
Enhancing Peak Network Traffic Prediction via Time-Series Decomposition

For network administration and maintenance, it is critical to anticipate when networks will receive peak volumes of traffic so that adequate resources can be allocated to service requests made to servers. In the event that sufficient resources are not allocated to servers, they can become prone to failure and security breaches. On the contrary, we would waste a lot of resources if we always allocate the maximum amount of resources. Therefore, anticipating peak volumes in network traffic becomes an important problem. However, popular forecasting models such as Autoregressive Integrated Moving Average (ARIMA) forecast time-series data generally, thus lack in predicting peak volumes in these time-series. More than often, a time-series is a combination of different features, which may include but are not limited to 1) Trend, the general movement of the traffic volume, 2) Seasonality, the patterns repeated over some time periods (e.g. daily and monthly), and 3) Noise, the random changes in the data. Considering that the fluctuation of seasonality can be harmful for trend and peak prediction, we propose to extract seasonalities to facilitate the peak volume predictions in the time domain. The experiments on both synthetic and real network traffic data demonstrate the effectiveness of the proposed method.
4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping

Millimeter wave radar can measure distances, directions, and Doppler velocity for objects in harsh conditions such as fog. The 4D imaging radar with both vertical and horizontal data resembling an image can also measure objects' height. Previous studies have used 3D radars for ego-motion estimation. But few methods leveraged the rich data of imaging radars, and they usually omitted the mapping aspect, thus leading to inferior odometry accuracy. This letter presents a real-time imaging radar inertial odometry and mapping method, iRIOM, based on the submap concept. To deal with moving objects and multipath reflections, we use the graduated non-convexity method to robustly and efficiently estimate ego-velocity from a single scan. To measure the agreement between sparse non-repetitive radar scan points and submap points, the distribution-to-multi-distribution distance for matches is adopted. The ego-velocity, scan-to-submap matches are fused with the 6D inertial data by an iterative extended Kalman filter to get the platform's 3D position and orientation. A loop closure module is also developed to curb the odometry module's drift. To our knowledge, iRIOM based on the two modules is the first 4D radar inertial SLAM system. On our and third-party data, we show iRIOM's favorable odometry accuracy and mapping consistency against the FastLIO-SLAM and the EKFRIO. Also, the ablation study reveal the benefit of inertial data versus the constant velocity model, and scan-to-submap matching versus scan-to-scan matching.
CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout

Recent advances have shown promise in merging neural radiance fields (NeRFs) with pre-trained diffusion models for text-to-3D object generation. However, one enduring challenge is their inadequate capability to accurately parse and regenerate consistent multi-object environments. Specifically, these models encounter difficulties in accurately representing quantity and style prompted by multi-object texts, often resulting in a collapse of the rendering fidelity that fails to match the semantic intricacies. Moreover, amalgamating these elements into a coherent 3D scene is a substantial challenge, stemming from generic distribution inherent in diffusion models. To tackle the issue of 'guidance collapse' and enhance consistency, we propose a novel framework, dubbed CompoNeRF, by integrating an editable 3D scene layout with object specific and scene-wide guidance mechanisms. It initiates by interpreting a complex text into an editable 3D layout populated with multiple NeRFs, each paired with a corresponding subtext prompt for precise object depiction. Next, a tailored composition module seamlessly blends these NeRFs, promoting consistency, while the dual-level text guidance reduces ambiguity and boosts accuracy. Noticeably, the unique modularity of CompoNeRF permits NeRF decomposition. This enables flexible scene editing and recomposition into new scenes based on the edited layout or text prompts. Utilizing the open source Stable Diffusion model, CompoNeRF not only generates scenes with high fidelity but also paves the way for innovative multi-object composition using editable 3D layouts. Remarkably, our framework achieves up to a 54\% improvement in performance, as measured by the multi-view CLIP score metric. Code is available at https://github.com/hbai98/Componerf.
2D Floor Plan Segmentation Based on Down-sampling

In recent years, floor plan segmentation has gained significant attention due to its wide range of applications in floor plan reconstruction and robotics. In this paper, we propose a novel 2D floor plan segmentation technique based on a down-sampling approach. Our method employs continuous down-sampling on a floor plan to maintain its structural information while reducing its complexity. We demonstrate the effectiveness of our approach by presenting results obtained from both cluttered floor plans generated by a vacuum cleaning robot in unknown environments and a benchmark of floor plans. Our technique considerably reduces the computational and implementation complexity of floor plan segmentation, making it more suitable for real-world applications. Additionally, we discuss the appropriate metric for evaluating segmentation results. Overall, our approach yields promising results for 2D floor plan segmentation in cluttered environments.
Non-Gaussianity in rapid-turn multi-field inflation

We show that theories of inflation with multiple, rapidly turning fields can generate large amounts of non-Gaussianity. We consider a general theory with two fields, an arbitrary field-space metric, and a potential that supports sustained, rapidly turning field trajectories. Our analysis accounts for non-zero field cross-correlation and does not fix the power spectra of curvature and isocurvature perturbations to be equal at horizon crossing. Using the δN formalism, we derive a novel, analytical formula for bispectrum generated from multi-field mixing on super-horizon scales. Rapid-turn inflation can produce a bispectrum with several potentially large contributions that are not necessarily of the local shape. We exemplify the applicability of our formula with a fully explicit model and show that the new contributions indeed can generate a large amplitude of local non-Gaussianity, f NL loc ∼ 𝒪(1). These results will be important when interpreting the outcomes of future observations.
Clustering based on Mixtures of Sparse Gaussian Processes

Creating low dimensional representations of a high dimensional data set is an important component in many machine learning applications. How to cluster data using their low dimensional embedded space is still a challenging problem in machine learning. In this article, we focus on proposing a joint formulation for both clustering and dimensionality reduction. When a probabilistic model is desired, one possible solution is to use the mixture models in which both cluster indicator and low dimensional space are learned. Our algorithm is based on a mixture of sparse Gaussian processes, which is called Sparse Gaussian Process Mixture Clustering (SGP-MIC). The main advantages to our approach over existing methods are that the probabilistic nature of this model provides more advantages over existing deterministic methods, it is straightforward to construct non-linear generalizations of the model, and applying a sparse model and an efficient variational EM approximation help to speed up the algorithm.