Miracle-dz/UniFAR-FTUs huggingface dataset:
An experimental study on Synthetic Tabular Data Evaluation

(Abstract) In this paper, we present the findings of our investigation into various methodologies for measuring the similarity of synthetic data generated from real data samples. Our focus is on cases where the synthetic data has a significantly larger number of samples than the real data, which presents a unique challenge in terms of evaluating the reliability of the generated data. We evaluate commonly used global metrics from the literature and introduce a novel approach based on topological data analysis. This approach has several advantages for addressing the complexity of high-dimensional synthetic data with a greatly increased sample size. It allows for the study of geometric properties without considering quantitative distance function values, and focuses on the concept of "closeness" between points. We also propose an approach using Eigen vectors to evaluate the level of noise in synthetic data and assess the similarity to the original data.
Implicit Training of Energy Model for Structure Prediction

Most deep learning research has focused on developing new model and training procedures. On the other hand the training objective has usually been restricted to combinations of standard losses. When the objective aligns well with the evaluation metric, this is not a major issue. However when dealing with complex structured outputs, the ideal objective can be hard to optimize and the efficacy of usual objectives as a proxy for the true objective can be questionable. In this work, we argue that the existing inference network based structure prediction methods ( Tu and Gimpel 2018; Tu, Pang, and Gimpel 2020) are indirectly learning to optimize a dynamic loss objective parameterized by the energy model. We then explore using implicit-gradient based technique to learn the corresponding dynamic objectives. Our experiments show that implicitly learning a dynamic loss landscape is an effective method for improving model performance in structure prediction.
Conjugate Product Graphs for Globally Optimal 2D-3D Shape Matching

We consider the problem of finding a continuous and non-rigid matching between a 2D contour and a 3D mesh. While such problems can be solved to global optimality by finding a shortest path in the product graph between both shapes, existing solutions heavily rely on unrealistic prior assumptions to avoid degenerate solutions (e.g. knowledge to which region of the 3D shape each point of the 2D contour is matched). To address this, we propose a novel 2D-3D shape matching formalism based on the conjugate prod-uct graph between the 2D contour and the 3D shape. Doing so allows us for the first time to consider higher-order costs, i.e. defined for edge chains, as opposed to costs de-fined for single edges. This offers substantially more flexi-bility, which we utilise to incorporate a local rigidity prior. By doing so, we effectively circumvent degenerate solutions and thereby obtain smoother and more realistic matchings, even when using only a one-dimensional feature descrip-tor. Overall, our method finds globally optimal and contin-uous 2D-3D matchings, has the same asymptotic complex-ity as previous solutions, produces state-of-the-art results for shape matching and is even capable of matching partial shapes. Our code is publicly available.11https://github.com/paulOnoah/sm-2D3D
Explicit/implicit fluid/structure staggered procedures with a structural predictor and fluid subcycling for 2D inviscid aeroelastic simulations

Field time integrators with second-order-accurate numerical schemes for both the fluid and the structure are considered for unsteady Euler aeroelastic computations. We show that if these schemes are simply coupled and used straightforwardly with subcycling, then accuracy and stability properties may be lost. We present new coupling staggered procedures where momentum conservation is enforced at the interface. This is done by using a structural predictor. Continuity of structural and fluid grid displacements is not satisfied at the fluid/structure interface. However, we show on a two-degree-of-freedom aerofoil that this new type of method has many advantages, e.g. accuracy of conservation at the interface and extended stability. The supersonic flutter of a flat panel is simulated in order to numerically prove that the algorithm gives accurate results with arbitrary subcycling for the fluid in the satisfying limit of 30 time steps per period of coupled oscillation. © 1997 John Wiley & Sons, Ltd.
Collection, measurement, and analysis of airborne magnetic particulates from pollution in the environment (invited)

Magnetic particulates in the environment, collected from surfaces and directly from the atmosphere, are reported. A significant percentage of magnetic material in many air‐borne samples consists of spherical magnetite from coal‐burning utilities and from iron/steel manufacturing with particle diameters 2–10 μm which correspond to average coercive fields of ∼100 Oe and remanence to saturation ratios of ∼0.1. The amount of air‐borne magnetic material that settles to the ground varies inversely with distance from its source.
Data Leakage and Evaluation Issues in Micro-Expression Analysis

Micro-expressions have drawn increasing interest lately due to various potential applications. The task is, however, difficult as it incorporates many challenges from the fields of computer vision, machine learning and emotional sciences. Due to the spontaneous and subtle characteristics of micro-expressions, the available training and testing data are limited, which make evaluation complex. We show that data leakage and fragmented evaluation protocols are issues among the micro-expression literature. We find that fixing data leaks can drastically reduce model performance, in some cases even making the models perform similarly to a random classifier. To this end, we go through common pitfalls, propose a new standardized evaluation protocol using facial action units with over 2000 micro-expression samples, and provide an open source library that implements the evaluation protocols in a standardized manner. Code is publicly available in https://github.com/tvaranka/meb.
Attention-based Class Activation Diffusion for Weakly-Supervised Semantic Segmentation

Extracting class activation maps (CAM) is a key step for weakly-supervised semantic segmentation (WSSS). The CAM of convolution neural networks fails to capture long-range feature dependency on the image and result in the coverage on only foreground object parts, i.e., a lot of false negatives. An intuitive solution is ``coupling'' the CAM with the long-range attention matrix of visual transformers (ViT) We find that the direct ``coupling'', e.g., pixel-wise multiplication of attention and activation, achieves a more global coverage (on the foreground), but unfortunately goes with a great increase of false positives, i.e., background pixels are mistakenly included. This paper aims to tackle this issue. It proposes a new method to couple CAM and Attention matrix in a probabilistic Diffusion way, and dub it AD-CAM. Intuitively, it integrates ViT attention and CAM activation in a conservative and convincing way. Conservative is achieved by refining the attention between a pair of pixels based on their respective attentions to common neighbors, where the intuition is two pixels having very different neighborhoods are rarely dependent, i.e., their attention should be reduced. Convincing is achieved by diffusing a pixel's activation to its neighbors (on the CAM) in proportion to the corresponding attentions (on the AM). In experiments, our results on two challenging WSSS benchmarks PASCAL VOC and MS~COCO show that AD-CAM as pseudo labels can yield stronger WSSS models than the state-of-the-art variants of CAM.
Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design

A major research plan entitled “Integrated research on the ecohydrological process of the Heihe River Basin” was launched by the National Natural Science Foundation of China in 2010. One of the key aims of this research plan is to establish a research platform that integrates observation, data management, and model simulation to foster twenty-first-century watershed science in China. Based on the diverse needs of interdisciplinary studies within this research plan, a program called the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was implemented. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class watershed observing system, and to enhance the applicability of remote sensing in integrated ecohydrological studies and water resource management at the basin scale. This paper introduces the background, scientific objectives, and experimental design of HiWATER. The instrumental setting and airborne mission plans a.
Classical-quantum correspondence of special and extraordinary-log criticality: Villain's bridge

There has been much recent progress on exotic surface critical behavior, yet the classical-quantum correspondence of special and extraordinary-log criticality remains largely unclear. Employing worm Monte Carlo simulations, we explore the surface criticality at an emergent superfluid-Mott insulator critical point in the Villain representation, which is believed to connect classical and quantum O(2) critical systems. We observe a special transition with the thermal and magnetic renormalization exponents $y_t=0.58(1)$ and $y_h=1.690(1)$ respectively, which are close to recent estimates from models with discrete spin variables. The existence of extraordinary-log universality is evidenced by the critical exponent $\hat{q}=0.58(2)$ from two-point correlation and the renormalization-group parameter $\alpha=0.28(1)$ from superfluid stiffness, which obey the scaling relation of extraordinary-log critical theory and recover the logarithmic finite-size scaling of critical superfluid stiffness in open-edge quantum Bose-Hubbard model. Our results bridge recent observations of surface critical behavior in the classical statistical mechanical models [Parisen Toldin, Phys. Rev. Lett. 126, 135701 (2021); Hu $et$ $al.$, $ibid.$ 127, 120603 (2021); Parisen Toldin $et$ $al.$, $ibid.$ 128, 215701 (2022)] and the open-edge quantum Bose-Hubbard model [Sun $et$ $al.$, Phys. Rev. B 106, 224502 (2022)].
ISAC Meets SWIPT: Multi-functional Wireless Systems Integrating Sensing, Communication, and Powering

This paper unifies integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT), by investigating a new multi-functional multiple-input multiple-output (MIMO) system integrating wireless sensing, communication, and powering. In this system, one multi-antenna hybrid access point (H-AP) transmits wireless signals to communicate with one multi-antenna information decoding (ID) receiver, wirelessly charge one multi-antenna energy harvesting (EH) receiver, and perform radar target sensing based on the echo signal at the same time. Under this setup, we aim to reveal the fundamental performance tradeoff limits among sensing, communication, and powering, in terms of the estimation Cramer-Rao bound (CRB), achievable communication rate, and harvested energy level, respectively. In particular, we consider two different target models for radar sensing, namely the point and extended targets, for which we are interested in estimating the target angle and the complete target response matrix, respectively. For both models, we define the achievable CRB-rate-energy (C-R-E) region and characterize its Pareto boundary by maximizing the achievable rate at the ID receiver, subject to the estimation CRB requirement for target sensing, the harvested energy requirement at the EH receiver, and the maximum transmit power constraint at the H-AP. We obtain the well-structured optimal transmit covariance solutions to the two formulated problems by applying advanced convex optimization techniques. Numerical results show the optimal C-R-E region boundary achieved by our proposed design, as compared to the benchmark schemes based on time switching and eigenmode transmission (EMT).
TuRaN: True Random Number Generation Using Supply Voltage Underscaling in SRAMs

Prior works propose SRAM-based TRNGs that extract entropy from SRAM arrays. SRAM arrays are widely used in a majority of specialized or general-purpose chips that perform the computation to store data inside the chip. Thus, SRAM-based TRNGs present a low-cost alternative to dedicated hardware TRNGs. However, existing SRAM-based TRNGs suffer from 1) low TRNG throughput, 2) high energy consumption, 3) high TRNG latency, and 4) the inability to generate true random numbers continuously, which limits the application space of SRAM-based TRNGs. Our goal in this paper is to design an SRAM-based TRNG that overcomes these four key limitations and thus, extends the application space of SRAM-based TRNGs. To this end, we propose TuRaN, a new high-throughput, energy-efficient, and low-latency SRAM-based TRNG that can sustain continuous operation. TuRaN leverages the key observation that accessing SRAM cells results in random access failures when the supply voltage is reduced below the manufacturer-recommended supply voltage. TuRaN generates random numbers at high throughput by repeatedly accessing SRAM cells with reduced supply voltage and post-processing the resulting random faults using the SHA-256 hash function. To demonstrate the feasibility of TuRaN, we conduct SPICE simulations on different process nodes and analyze the potential of access failure for use as an entropy source. We verify and support our simulation results by conducting real-world experiments on two commercial off-the-shelf FPGA boards. We evaluate the quality of the random numbers generated by TuRaN using the widely-adopted NIST standard randomness tests and observe that TuRaN passes all tests. TuRaN generates true random numbers with (i) an average (maximum) throughput of 1.6Gbps (1.812Gbps), (ii) 0.11nJ/bit energy consumption, and (iii) 278.46us latency.
Cavity Optomechanical Bistability with an Ultrahigh Reflectivity Photonic Crystal Membrane

Photonic crystal (PhC) membranes patterned with sub‐wavelength periods offer a unique combination of high reflectivity, low mass, and high mechanical quality factor. Using a PhC membrane as one mirror of a Fabry–Perot cavity, a finesse as high as F=35000(500)$F=35\,000(500)$ is demonstrated, corresponding to a record high PhC reflectivity of R=0.999835(6)$R=0.999835(6)$ and an optical quality factor of Qopt≈109$Q_{\text{opt}}\approx 10^9$ . The fundamental mechanical frequency is 426 kHz, more than twice the optical linewidth, placing it firmly in the resolved‐sideband regime required for ground‐state optical cooling. The mechanical quality factor in vacuum is Q=1.1(1)×106$Q=1.1(1)\times 10^6$ , allowing values of the single‐photon cooperativity as high as C0=6.6×10−3${\cal C}_0=6.6\times 10^{-3}$ . Optomechanical bistability is easily observed as hysteresis in the cavity transmission. As the input power is raised well beyond the bistability threshold, dynamical backaction induces strong mechanical oscillation above 1 MHz, even in the presence of air damping. This platform will facilitate advances in optomechanics, precision sensing, and applications of optomechanically‐induced bistability.
Algorithm of Earth-centered Earth-fixed coordinates to geodetic coordinates

The transformation between Earth-centered Earth-fixed (ECEF) coordinates and geodetic coordinates is required in many applications. This correspondence presents an iterative approach using the Newton-Raphson (NR) method that has good efficiency and accuracy and is free from singularity and nonconvergence except in a small region near the center of the Earth. Comparisons with other well-known approximate methods are made in complexity and performance.
Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic

Streamflow observation data is vital for flood monitoring, agricultural, and settlement planning. However, such streamflow data are commonly plagued with missing observations due to various causes such as harsh environmental conditions and constrained operational resources. This problem is often more pervasive in under-resourced areas such as Sub-Saharan Africa. In this work, we reconstruct streamflow time series data through bias correction of the GEOGloWS ECMWF streamflow service (GESS) forecasts at ten river gauging stations in Benin Republic. We perform bias correction by fitting Quantile Mapping, Gaussian Process, and Elastic Net regression in a constrained training period. We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in low predictive skill over the ten Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior skill relative to traditional imputation by Random Forest, k-Nearest Neighbour, and GESS lookup. The findings of this work provide a basis for integrating global GESS streamflow data into operational early-warning decision-making systems (e.g., flood alert) in countries vulnerable to drought and flooding due to extreme weather events.
Terahertz Channel Measurement and Analysis on a University Campus Street

Owning abundant bandwidth resource, the Tera-hertz (0.1-10 THz) band is a promising spectrum to support sixth-generation (6G) and beyond communications. As the foundation of channel study in the spectrum, channel measurement is ongoing in covering representative 6G communication scenarios and promising THz frequency bands. In this paper, a wideband channel measurement in an L-shaped university campus street is conducted at 306–321 GHz and 356–371 GHz. In particular, ten line-of-sight (LoS) and eight non-line-of-sight (NLoS) points are measured at the two frequency bands, respectively. In total, 6480 channel impulse responses (CIRs) are obtained from the measurement, based on which multi-path propagation in the L-shaped roadway in the THz band is elaborated to identify major scatterers of walls, vehicles, etc. in the environment and their impact on multi-path components (MPCs). Furthermore, outdoor THz channel characteristics in the two frequency bands are analyzed, including path losses, shadow fading, cluster pa-rameters, delay spread and angular spread. In contrast with the counterparts in the similar outdoor scenario at lower frequencies, the results verify the sparsity of MPCs at THz frequencies and indicate smaller power spreads in both temporal and spatial domains in the THz band.
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors. From a psychological perspective, emotions are the expression of affect or feelings during a short period, while sentiments are formed and held for a longer period. However, most existing works study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. In this paper, we propose a multimodal sentiment knowledge-sharing framework (UniMSE) that unifies MSA and ERC tasks from features, labels, and models. We perform modality fusion at the syntactic and semantic levels and introduce contrastive learning between modalities and samples to better capture the difference and consistency between sentiments and emotions. Experiments on four public benchmark datasets, MOSI, MOSEI, MELD, and IEMOCAP, demonstrate the effectiveness of the proposed method and achieve consistent improvements compared with state-of-the-art methods.
Contact and Rubbing of Flat Surfaces

The interpretation of certain phenomena occuring at nominally flat surfaces in stationary or sliding contact is dependent on the assumed distribution of the real area of contact between the surfaces. Since there is little direct evidence on which to base an estimate of this distribution, the approach used is to set up a simple model and compare the deduced theory (e.g., the deduced dependence of the experimental observables on the load) with the experimental evidence. The main conclusions are as follows. (a) The electrical contact resistance depends on the model used to represent the surfaces; the most realistic model is one in which increasing the load increases both the number and size of the contact areas. (b) In general, mechanical wear should also depend on the model. However, in wear experiments showing the simplest behavior, the wear rate is proportional to the load, and these results can be explained by assuming removal of lumps at contact areas formed by plastic deformation; moreover, this particular deduction is independent of the assumed model. This suggests that a basic assumption of previous theories, that increasing the load increases the number of contacts without affecting their average size, is redundant.
A Lightweight Domain Adaptive Absolute Pose Regressor Using Barlow Twins Objective

Identifying the camera pose for a given image is a challenging problem with applications in robotics, autonomous vehicles, and augmented/virtual reality. Lately, learning-based methods have shown to be effective for absolute camera pose estimation. However, these methods are not accurate when generalizing to different domains. In this paper, a domain adaptive training framework for absolute pose regression is introduced. In the proposed framework, the scene image is augmented for different domains by using generative methods to train parallel branches using Barlow Twins objective. The parallel branches leverage a lightweight CNN-based absolute pose regressor architecture. Further, the efficacy of incorporating spatial and channel-wise attention in the regression head for rotation prediction is investigated. Our method is evaluated with two datasets, Cambridge landmarks and 7Scenes. The results demonstrate that, even with using roughly 24 times fewer FLOPs, 12 times fewer activations, and 5 times fewer parameters than MS-Transformer, our approach outperforms all the CNN-based architectures and achieves performance comparable to transformer-based architectures. Our method ranks 2nd and 4th with the Cambridge Landmarks and 7Scenes datasets, respectively. In addition, for augmented domains not encountered during training, our approach significantly outperforms the MS-transformer. Furthermore, it is shown that our domain adaptive framework achieves better performance than the single branch model trained with the identical CNN backbone with all instances of the unseen distribution.
Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers

Large-scale transformer models have become the de-facto architectures for various machine learning applications, e.g., CV and NLP. However, those large models also introduce prohibitive training costs. To mitigate this issue, we propose a novel random and layerwise token dropping method (random-LTD), which skips the computation of a subset of the input tokens at all middle layers. Particularly, random-LTD achieves considerable speedups and comparable accuracy as the standard training baseline. Compared to other token dropping methods, random-LTD does not require (1) any importance score-based metrics, (2) any special token treatment (e.g., [CLS]), and (3) many layers in full sequence length training except the first and the last layers. Besides, a new LayerToken learning rate schedule is proposed for pretraining problems that resolve the heavy tuning requirement for our proposed training mechanism. Finally, we demonstrate that random-LTD can be applied to broader applications, including GPT and BERT pretraining as well as ViT and GPT finetuning tasks. Our results show that random-LTD can save about 33.3% theoretical compute cost and 25.6% wall-clock training time while achieving similar zero-shot evaluations on GPT-31.3B as compared to baseline.