Miracle-dz/UniFAR-FTUs huggingface dataset:
Entropy stable discontinuous Galerkin methods for the shallow water equations with subcell positivity preservation

High order schemes are known to be unstable in the presence of shock discontinuities or under-resolved solution features, and have traditionally required additional filtering, limiting, or artificial viscosity to avoid solution blow up. Entropy stable schemes address this instability by ensuring that physically relevant solutions satisfy a semi-discrete entropy inequality independently of discretization parameters. However, additional measures must be taken to ensure that solutions satisfy physical constraints such as positivity. In this work, we present a high order entropy stable discontinuous Galerkin (ESDG) method for the nonlinear shallow water equations (SWE) on two-dimensional (2D) triangular meshes which preserves the positivity of the water heights. The scheme combines a low order positivity preserving method with a high order entropy stable method using convex limiting. This method is entropy stable and well-balanced for fitted meshes with continuous bathymetry profiles.
N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing Cores

Accelerating the neural network inference by FPGA has emerged as a popular option, since the reconfigurability and high performance computing capability of FPGA intrinsically satisfies the computation demand of the fast-evolving neural algorithms. However, the popular neural accelerators on FPGA (e.g., Xilinx DPU) mainly utilize the DSP resources for constructing their processing units, while the rich LUT resources are not well exploited. Via the software-hardware co-design approach, in this work, we develop an FPGA-based heterogeneous computing system for neural network acceleration. From the hardware perspective, the proposed accelerator consists of DSP- and LUT-based GEneral Matrix-Multiplication (GEMM) computing cores, which forms the entire computing system in a heterogeneous fashion. The DSP- and LUT-based GEMM cores are computed w.r. t a unified Instruction Set Architecture (ISA) and unified buffers. Along the data flow of the neural network inference path, the computation of the convolution/fully-connected layer is split into two portions, handled by the DSP- and LUT-based GEMM cores asynchronously. From the software perspective, we mathematically and systematically model the latency and resource utilization of the proposed heterogeneous accelerator, regarding varying system design configurations. Through leveraging the reinforcement learning technique, we construct a framework to achieve end-to-end selection and optimization of the design specification of target heterogeneous accelerator, including workload split strategy, mixed-precision quantization scheme, and resource allocation of DSP- and LUT-core. In virtue of the proposed design framework and heterogeneous computing system, our design outperforms the state-of-the-art Mix&Match design with latency reduced by 1.12-1.32x with higher inference accuracy. The N3H-core is open-sourced at: https://github.com/elliothe/N3H_Core.
Finding Structure in Silence: The Role of Pauses in Aligning Speaker Expectations

The intelligibility of speech relies on the ability of interlocutors to dynamically align their expectations about the rates at which informative changes in signals occur. Exactly how this is achieved remains an open question. We propose that speaker alignment is supported by the statistical structure of spoken signals and show how pauses offer a time-invariant template for structuring speech sequences. Consistent with this, we show that pause distributions in conversational English and Korean provide a memoryless information source. We describe how this can facilitate both the initial structuring and maintenance of predictability in spoken signals over time, and show how the properties of this signal change predictably with speaker experience. These results indicate that pauses provide a structuring signal that interacts with the morphological and rhythmical structure of languages, allowing speakers at all stages of lifespan development to distinguish signal from noise and maintain mutual predictability in time.
Observations with the Southern Connecticut Stellar Interferometer. I. Instrument Description and First Results

We discuss the design, construction, and operation of a new intensity interferometer, based on the campus of Southern Connecticut State University in New Haven, Connecticut. While this paper will focus on observations taken with an original two-telescope configuration, the current instrumentation consists of three portable 0.6 m Dobsonian telescopes with single-photon avalanche diode detectors located at the Newtonian focus of each telescope. Photons detected at each station are time stamped and read out with timing correlators that can give cross-correlations in timing to a precision of 48 ps. We detail our observations to date with the system, which has now been successfully used at our university in 16 nights of observing. Components of the instrument were also deployed on one occasion at Lowell Observatory, where the Perkins and Hall telescopes were made to function as an intensity interferometer. We characterize the performance of the instrument in detail. In total, the observations indicate the detection of a correlation peak at the level of 6. 76σ when observing unresolved stars, and consistency with partial or no detection when observing at a baseline sufficient to resolve the star. Using these measurements, we conclude that the angular diameter of Arcturus is larger than 15 mas and that of Vega is between 0.8 and 17 mas. While the uncertainties are large at this point, both results are consistent with measures from amplitude-based long baseline optical interferometers.
MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection

In this paper we propose a novel socio-inspired convolutional neural network (CNN) deep learning model for image splicing detection. Based on the premise that learning from the detection of coarsely spliced image regions can improve the detection of visually imperceptible finely spliced image forgeries, the proposed model referred to as, MissMarple, is a twin CNN network involving feature-transfer learning. Results obtained from training and testing the proposed model using the benchmark datasets like Columbia splicing, WildWeb, DSO1 and a proposed dataset titled AbhAS consisting of realistic splicing forgeries revealed improvement in detection accuracy over the existing deep learning models.
Development of a three-dimensional road texture measurement method as a first step towards tyre road contact simulation

When analysing tyre vibration excitation and the energy transmission of tyre vibration into vehicle passenger compartments, a three-dimensional road texture profile of the surface course is of special interest. Tyre analyses are performed inside an inner drum test-bench at the Institute of Vehicle System Technology. This paper describes the development of a three-dimensional road texture measurement method in the test-bench. Measured data was converted to the distance domain and interpolated to fit a regular grid, resulting in a resolution of lower than 0.1 mm in the driving direction. Power spectra, Abbott–Firestone curve and the shape factor were determined for the measured road tracks. Analysis of the measured road texture profiles of two concrete road tracks showed that the spectral density of the almost unused drum tracks without potholes has to be approximated by two linear functions, depending on the wavelength ranges. Both textures turned out to have a convex shape but differing maximum texture depth. Convex-shaped textures are usually expected to result in stronger vibration excitations. In order to demonstrate the surface courses’ capacity for tyre vibration excitation, a passenger car tyre was driven on each track. It could be shown that both textures are appropriate for the analysis of tyre vibration characteristics.
RA V-Net: deep learning network for automated liver segmentation

Objective. Segmenting liver from CT images is the first step for doctors to diagnose a patient’s disease. Processing medical images with deep learning models has become a current research trend. Although it can automate segmenting region of interest of medical images, the inability to achieve the required segmentation accuracy is an urgent problem to be solved. Approach. Residual Attention V-Net (RA V-Net) based on U-Net is proposed to improve the performance of medical image segmentation. Composite Original Feature Residual Module is proposed to achieve a higher level of image feature extraction capability and prevent gradient disappearance or explosion. Attention Recovery Module is proposed to add spatial attention to the model. Channel Attention Module is introduced to extract relevant channels with dependencies and strengthen them by matrix dot product. Main results. Through test, evaluation index has improved significantly. Lits2017 and 3Dircadb are chosen as our experimental datasets. On the Dice Similarity Coefficient, RA V-Net exceeds U-Net 0.1107 in Lits2017, and 0.0754 in 3Dircadb. On the Jaccard Similarity Coefficient, RA V-Net exceeds U-Net 0.1214 in Lits2017, and 0.13 in 3Dircadb. Significance. Combined with all the innovations, the model performs brightly in liver segmentation without clear over-segmentation and under-segmentation. The edges of organs are sharpened considerably with high precision. The model we proposed provides a reliable basis for the surgeon to design the surgical plans.
Learning Submodular Objectives for Team Environmental Monitoring

In this paper, we study the well-known team orienteering problem where a fleet of robots collects rewards by visiting locations. Usually, the rewards are assumed to be known to the robots; however, in applications such as environmental monitoring or scene reconstruction, the rewards are often subjective and specifying them is challenging. We propose a framework to learn the unknown preferences of the user by presenting alternative solutions to them, and the user provides a ranking on the proposed alternative solutions. We consider the two cases for the user: 1) a deterministic user which provides the optimal ranking for the alternative solutions, and 2) a noisy user which provides the optimal ranking according to an unknown probability distribution. For the deterministic user we propose a framework to minimize a bound on the maximum deviation from the optimal solution, namely regret. We adapt the approach to capture the noisy user and minimize the expected regret. Finally, we demonstrate the importance of learning user preferences and the performance of the proposed methods in an extensive set of experimental results using real world datasets for environmental monitoring problems.
Aymptotics of eigenvalues of the Neumann-Poincar'e operator in 3D elasticity

For the Neumann-Poincar´e (double layer potential) operator in the three-dimensional elasticity we establish asymptotic formulas for eigenvalues converging to the points of the essential spectrum and discuss geometric and mechanical meaning of coeﬃcients in these formulas. In particular, we establish that for any body, there are inﬁnitely many eigenvalues converging from above to each point of the essential spectrum. On the other hand, if there is a point where the boundary is concave (in particular, if the body contains cavities) then for each point of the essential spectrum there exists a sequence of eigenvalues converging to this point from below. The reasoning is based upon the representation of the Neumann-Poincare operator as a zero order pseudodiﬀerential operator on the boundary and the earlier results by the author on the eigenvalue asymptotics for polynomially compact pseudodiﬀerential operators.
Metallicity in Quasar Broad-line Regions at Redshift ∼ 6

Broad-line regions (BLRs) in high-redshift quasars provide crucial information on chemical enrichment in the early universe. Here we present a study of BLR metallicities in 33 quasars at redshift 5.7 < z < 6.4. Using the near-IR spectra of the quasars obtained from the Gemini telescope, we measure their rest-frame UV emission-line flux and calculate flux ratios. We then estimate BLR metallicities with empirical calibrations based on photoionization models. The inferred median metallicity of our sample is a few times the solar value, indicating that the BLR gas had been highly metal enriched at z ∼ 6. We compare our sample with a low-redshift quasar sample with similar luminosities and find no evidence of redshift evolution in quasar BLR metallicities. This is consistent with previous studies. The Fe ii/Mg ii flux ratio, a proxy for the Fe/α element abundance ratio, shows no redshift evolution as well, further supporting rapid nuclear star formation at z ∼ 6. We also find that the black hole mass–BLR metallicity relation at z ∼ 6 is consistent with the relation measured at 2 < z < 5, suggesting that our results are not biased by a selection effect due to this relation.
The risk in early design method

Risk assessments are necessary to anticipate and prevent accidents from occurring or repeating. Current probabilistic risk assessment methods require mature design proposals to analyse. Since product safety and reliability are affected the most by decisions made during the early design phases, a risk assessment that can be performed with less mature data during these design phases is needed. This study focuses specifically on the relationship between function and risk in early design by presenting a mathematical mapping from product function to risk assessments that can be used in the conceptual design phase. An investigation of a spacecraft orientation subsystem is used to demonstrate the mappings. The results from the study and its spacecraft application yield a preliminary risk assessment method that can be used to identify and assess risks as early as the conceptual phase of design. The preliminary risk assessment presented in this paper is a tool that will aid designers by identifying risks as well as reducing the subjectivity of the likelihood and consequence value from a risk element, will provide four key risk element properties (design parameter, failure mode, likelihood, and consequence) for numerous risk elements with simple calculations, and will provide a means for inexperienced designers to effectively address risk in the conceptual design phase.
Quantum noise of gravitons and stochastic force on geodesic separation

In this work we consider the eﬀects of gravitons and their ﬂuctuations on the dynamics of two masses using the Feynman-Vernon inﬂuence functional formalism, applied to nonequilibrium quantum ﬁeld theory and semiclassical stochastic gravity earlier by Calzetta, Hu and Verdaguer [1–3], and most recently, to this problem by Parikh, Wilczek and Zahariade [4–6]. The Hadamard function of the gravitons yields the noise kernel acting as a stochastic tensorial force in a Langevin equation governing the motion of the separation of the two masses. The ﬂuctuations of the separation due to the graviton noise are then solved for various quantum states including the Minkowski vacuum, thermal, coherent and squeezed states. The previous considerations of Parikh et al. are only for some selected modes of the graviton, while in this work we have included all graviton modes and polarizations. We comment on the possibility of detecting these ﬂuctuations in primordial gravitons using interferometors with long baselines in deep space experiments.
Network Graph Based Neural Architecture Search

Neural architecture search enables automation of architecture design. Despite its success, it is computationally costly and does not provide an insight on how to design a desirable architecture. Here we propose a new way of searching neural network where we search neural architecture by rewiring the corresponding graph and predict the architecture performance by graph properties. Because we do not perform machine learning over the entire graph space and use predicted architecture performance to search architecture, the searching process is remarkably efficient. We find graph based search can give a reasonably good prediction of desirable architecture. In addition, we find graph properties that are effective to predict architecture performance. Our work proposes a new way of searching neural architecture and provides insights on neural architecture design.
Floquet-engineered pair and single-particle filters in the Fermi-Hubbard model

We investigate the Fermi-Hubbard model with a Floquet-driven impurity in the form of a local time-oscillating potential. For strong attractive interactions a stable formation of pairs is observed. These pairs show a completely different transmission behavior than the transmission that is observed for the single unpaired particles. Whereas in the high frequency limit the single particles show a maximum of the transition at low driving amplitudes, the pairs display a pronounced maximum transmission when the amplitude of the driving lies close to the ratio of the interaction U and the driving frequency {\omega}. We use the distinct transmission behaviour to design filters for pairs or single particles, respectively. For example one can totally block the transmission of single particles through the driven impurity and allow only for the transmission of pairs. We quantify the quality of the designed filters.
Segmentation-Reconstruction-Guided Facial Image De-occlusion

Occlusions are very common in face images in the wild, leading to the degraded performance of face-related tasks. Although much effort has been devoted to removing occlusions from face images, the varying shapes and textures of occlusions still challenge the robustness of current methods. As a result, current methods either rely on manual occlusion masks or only apply to specific occlusions. This paper proposes a novel face de-occlusion model based on face segmentation and 3D face reconstruction, which is robust to arbitrary kinds of face occlusions. The proposed model consists of a 3D face reconstruction module, a face segmentation module, and an image generation module. With the face prior and the occlusion mask predicted by the first two, respectively, the image generation module can faithfully recover the missing facial textures. To supervise the training, we further build a large occlusion dataset, with both manually labeled and synthetic occlusions. Qualitative and quantitative results demonstrate the effectiveness and robustness of the proposed method.
An efficient biomimetic swimming robot capable of multiple gaits of locomotion : design, modelling and fabrication.

Replacing humans with underwater robots for accomplishing marine tasks such as oceanic supervision and undersea operations have been an endeavour from long time ago. Hence, a number of underwater robots have been developed. Among those underwater robots, developing biomimetic swimming robots has been appealing for many researchers and institutes since these robots have shown superior performance. Biomimetic swimming robots have higher swimming efficiency, manoeuvrability and noiseless performance. However, the existing biomimetic swimming robots are specialised for a single gait of locomotion like cruising, manoeuvrability and accelerating while for efficient accomplishment of marine tasks, an underwater robot needs to have multiple gaits of locomotion. In order to develop multiple-gaited swimming robots, the optimal characteristics of each gait of swimming must be combined together, whereas the combination is not usually possible. The problem needs to be addressed during the design process. Moreover, the optimality of the actuation mechanism of robots that do not utilise any artificial muscle could be assured using the mathematical model employed for simulation of their swimming behaviour. However, the existing models are incomplete and, accordingly, not reliable since their assumptions like the constant speed of flow around the fish robot could be used when the average speed of the flow is determined during experiment while before development of robots, the flow speed is not known. In addition to that, the simulation results must be optimised using the experimental observations in nature and analytical results while the optimisation algorithms are based on one fitness function. The aforementioned problems as well as the fabrication challenges of freeswimming biomimetic robots are addressed in a development process of multiple-gaited fish-mimetic robots introduced by the author in this thesis. This development method engages the improvement of all development steps of fish robots including design, mathematical modelling, optimisation and fabrication steps. In this thesis, the aforementioned steps are discussed and the contributions of the method for each step are introduced. As an outcome of the project, two prototypes of fish robots called UC-Ika 1 & 2 are built.
Oracle Linguistic Graphs Complement a Pretrained Transformer Language Model: A Cross-formalism Comparison

We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. With an ensemble setup consisting of a pretrained Transformer and ground-truth graphs from one of 7 different formalisms, we ﬁnd that, overall, semantic constituency structures are most useful to language modeling performance—outpacing syntactic constituency structures as well as syntactic and semantic dependency structures. Further, effects vary greatly depending on part-of-speech class. In sum, our ﬁndings point to promising tendencies in neuro-symbolic language modeling and invite future research quantifying the design choices made by different formalisms.
Boosted Dense Retriever

We propose DrBoost, a dense retrieval ensemble inspired by boosting. DrBoost is trained in stages: each component model is learned sequentially and specialized by focusing only on retrieval mistakes made by the current ensemble. The final representation is the concatenation of the output vectors of all the component models, making it a drop-in replacement for standard dense retrievers at test time. DrBoost enjoys several advantages compared to standard dense retrieval models. It produces representations which are 4x more compact, while delivering comparable retrieval results. It also performs surprisingly well under approximate search with coarse quantization, reducing latency and bandwidth needs by another 4x. In practice, this can make the difference between serving indices from disk versus from memory, paving the way for much cheaper deployments.
Generative Adversarial Networks for Data Generation in Structural Health Monitoring

Structural Health Monitoring (SHM) has been continuously benefiting from the advancements in the field of data science. Various types of Artificial Intelligence (AI) methods have been utilized to assess and evaluate civil structures. In AI, Machine Learning (ML) and Deep Learning (DL) algorithms require plenty of datasets to train; particularly, the more data DL models are trained with, the better output it yields. Yet, in SHM applications, collecting data from civil structures through sensors is expensive and obtaining useful data (damage associated data) is challenging. In this paper, one-dimensional (1-D) Wasserstein loss Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) is utilized to generate damage-associated vibration datasets that are similar to the input. For the purpose of vibration-based damage diagnostics, a 1-D Deep Convolutional Neural Network (1-D DCNN) is built, trained, and tested on both real and generated datasets. The classification results from the 1-D DCNN on both datasets resulted in being very similar to each other. The presented work in this paper shows that, for the cases of insufficient data in DL or ML-based damage diagnostics, 1-D WDCGAN-GP can successfully generate data for the model to be trained on.