Miracle-dz/UniFAR-FTUs huggingface dataset:
Antenna Array Enabled Space/Air/Ground Communications and Networking for 6G

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/air/ground communications and networking.
Adaptive Control of Underactuated Planar Pronking Hexapod

Underactuated legged robots depict highly nonlinear and complex dynamical behaviors that create significant challenges in accurately modeling system dynamics using both first principles and system identification approaches. Hence, it makes a more substantial challenge to design stabilizing controllers. If physical parameters on mathematical models have miscalibrations due to uncertainty in identifying and modeling processes, designed controllers could perform poorly or even result in unstable responses. Moreover, these parameters can certainly change-over-time due to operation and environmental conditions. In that respect, analogous to a living organism modifying its behavior in response to novel conditions, adapting/updating system parameters, such as spring constant, to compensate for modeling errors could provide the advantage of constructing a stable gait level controller without needing “exact” dynamical parameter values. This paper presents an online, model-based adaptive control approach for an underactuated planar hexapod robot’s pronking behavior adopted from antelope species. We show through systematic simulation studies that the adaptive control policy is robust to high levels of parameter uncertainties compared to a non-adaptive model-based dead-beat controller.
DeepAg: Deep Learning Approach for Measuring the Effects of Outlier Events on Agricultural Production and Policy

Quantitative metrics that measure the global econ-omy's equilibrium have strong and interdependent relationships with the agricultural supply chain and international trade flows. Sudden shocks in these processes caused by outlier events such as trade wars, pandemics, or weather can have complex effects on the global economy. In this paper, we propose a novel framework, namely: DeepAg, that employs econometrics and measures the effects of outlier events detection using Deep Learning (DL) to determine relationships between commonplace financial indices (such as the DowJones), and the production values of agricultural commodities (such as Cheese and Milk). We employed a technique called Long Short-Term Memory (LSTM) networks successfully to predict commodity production with high accuracy and also present five popular models (regression and boosting) as baselines to measure the effects of outlier events. The results indicate that DeepAg with outliers' considerations (using Isolation Forests) outperforms baseline models, as well as the same model without outliers detection. Outlier events make a considerable impact when predicting commodity production with respect to financial indices. Moreover, we present the implications of DeepAg on public policy, provide insights for policymakers and farmers, and for operational decisions in the agricultural ecosystem. Data are collected, models developed, and the results are recorded and presented.
Frame Hydrodynamics of Biaxial Nematics from Molecular-Theory-Based Tensor Models

Starting from a dynamic tensor model about two second-order tensors, we derive the frame hydrodynamics for the biaxial nematic phase using the Hilbert expansion. The coefficients in the frame model are derived from those in the tensor model. The energy dissipation of the tensor model is maintained in the frame model. The model is reduced to the Ericksen--Leslie model if the biaxial bulk energy minimum of the tensor model is reduced to a uniaxial one.
An Efficient Estimation of Time‐Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation

It is crucially important to estimate unknown parameters in process‐based models by integrating observation and numerical simulation. For many applications in earth system sciences, a parameter estimation method which allows parameters to temporally change is required. In the present paper, an efficient and practical method to estimate time‐varying parameters of relatively low dimensional models is presented. In the newly proposed method, called Hybrid Offline Online Parameter Estimation with Particle Filtering (HOOPE‐PF), an inflation method to maintain the spread of ensemble members in a Sampling‐Importance‐Resampling Particle Filter (SIRPF) is improved using a non‐parametric posterior probabilistic distribution of time‐invariant parameters obtained by comparing simulated and observed climatology. HOOPE‐PF outperforms the original SIRPF in synthetic experiments with toy models and a real‐data experiment with a conceptual hydrological model when an ensemble size is small. The advantage of HOOPE‐PF is that its performance is not greatly affected by the size of perturbation to be added to ensemble members to maintain their spread while it is important to get the optimal performance in the original particle filter. Since HOOPE‐PF is the extension of the existing particle filter which has been extensively applied to many models in earth system sciences such as land, ecosystem, hydrology, and paleoclimate reconstruction, HOOPE‐PF can be applied to improve the simulation of these process‐based models by considering time‐varying model parameters.
Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead

Goal oriented dialogue systems have become a prominent customer-care interaction channel for most businesses. However, not all interactions are smooth, and customer intent misunderstanding is a major cause of dialogue failure. We show that intent prediction can be improved by training a deep text-to-text neural model to generate successive user utterances from unlabeled dialogue data. For that, we define a multi-task training regime that utilizes successive user-utterance generation to improve the intent prediction. Our approach achieves the reported improvement due to two complementary factors: First, it uses a large amount of unlabeled dialogue data for an auxiliary generation task. Second, it uses the generated user utterance as an additional signal for the intent prediction model. Lastly, we present a novel look-ahead approach that uses user utterance generation to improve intent prediction in inference time. Specifically, we generate counterfactual successive user utterances for conversations with ambiguous predicted intents, and disambiguate the prediction by reassessing the concatenated sequence of available and generated utterances.
Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Tasks

This paper introduces a discrete-continuous action space to learn insertion primitives for robotic assembly tasks. Primitives are sequences of elementary actions with certain exit conditions, such as “pushing down the peg until contact”. Since the primitive is an abstraction of robot control commands and encodes human prior knowledge, it reduces the exploration difficulty and yields better learning efficiency. In this paper, we learn robot assembly skills via primitives. Specifically, we formulate insertion primitives as parameterized actions: hybrid actions consisting of discrete primitive types and continuous primitive parameters. Compared with the previous work using a set of discretized parameters for each primitive, the agent in our method can freely choose primitive parameters from a continuous space, which is more flexible and efficient. To learn these insertion primitives, we propose Twin-Smoothed Multi-pass Deep Q-Network (TS-MP-DQN), an advanced version of MP-DQN with twin Q-network to reduce the Q-value over-estimation. Extensive experiments are conducted in the simulation and real world for validation. From experiment results, our approach achieves higher success rates than three baselines: MP-DQN with parameterized actions, primitives with discrete parameters, and continuous velocity control. Furthermore, learned primitives are robust to sim-to-real transfer and can generalize to challenging assembly tasks such as tight round peg-hole and complex shaped electric connectors with promising success rates. Experiment videos are available at https://msc.berkeley. edu/research/insertion-primitives.html.
Neural ODE and DAE Modules for Power System Dynamic Modeling

—The time-domain simulation is the fundamental tool for power system transient stability analysis. Accurate and reliable simulations rely on accurate dynamic component modeling. In practical power systems, dynamic component modeling has long faced the challenges of model determination and model calibration, especially with the rapid development of renewable generation and power electronics. In this paper, based on the general framework of neural ordinary differential equations (ODEs), a modified neural ODE module and a neural differential-algebraic equations (DAEs) module for power system dynamic component modeling are proposed. The modules adopt an autoencoder to raise the dimension of state variables, model the components’ dynamics with artificial neural networks (ANNs), and keep the numerical integration structure. In the neural DAE module, an additional ANN is used to calculate injection currents. The neural models can be easily integrated into time-domain simulations. With datasets consisting of sampled curves of input variables and output variables, the proposed modules can be used to fulfill the tasks of parameter inference, physics-data-integrated modeling, black-box modeling, etc., and can be easily integrated into power system dynamic simulations. Some simple numerical tests are carried out in the IEEE-39 system and prove the validity and potentiality of the proposed modules.
Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI - Should Different Clinical Objectives Mandate Different Loss Functions?

We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, require different loss functions for optimal performance due to inherent differences in their clinical objectives. We investigate distribution, region and boundary-based loss functions for both tasks across 200 patient exams from the publicly-available ProstateX dataset. For evaluation, we conduct a thorough comparative analysis of model predictions and calibration, measured with respect to multi-class volume segmentation of the prostate anatomy (whole-gland, transitional zone, peripheral zone), as well as, patient-level diagnosis and lesion-level detection of clinically significant prostate cancer. Notably, we find that distribution-based loss functions (in particular, focal loss) are well-suited for diagnostic or panoptic segmentation tasks such as lesion detection, primarily due to their implicit property of inducing better calibration. Meanwhile, (with the exception of focal loss) both distribution and region/boundary-based loss functions perform equally well for anatomical or semantic segmentation tasks, such as quantification of organ shape, size and boundaries.
Circle Representation for Medical Object Detection

Box representation has been extensively used for object detection in computer vision. Such representation is efficacious but not necessarily optimized for biomedical objects (e.g., glomeruli), which play an essential role in renal pathology. In this paper, we propose a simple circle representation for medical object detection and introduce CircleNet, an anchor-free detection framework. Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant. When detecting glomeruli and nuclei on pathological images, the proposed circle representation achieved superior detection performance and be more rotation-invariant, compared with the bounding box. The code has been made publicly available: https://github.com/hrlblab/CircleNet.
Theory of non-equilibrium ‘hot’ carriers in direct band-gap semiconductors under continuous illumination

The interplay between the illuminated excitation of carriers and subsequent thermalization and recombination leads to the formation of non-equilibrium distributions for the ‘hot’ carriers and to heating of both electrons, holes and phonons. In spite of the fundamental and practical importance of these processes, there is no theoretical framework which encompasses all of them and provides a clear prediction for the non-equilibrium carrier distributions. Here, a self-consistent theory accounting for the interplay between excitation, thermalization, and recombination in continuously-illuminated semiconductors is presented, enabling the calculation of non-equilibrium carrier distributions. We show that counter-intuitively, distributions deviate more from equilibrium under weak illumination than at high intensities. We mimic two experimental procedures to extract the carrier temperatures and show that they yield different dependence on illumination. Finally, we provide an accurate way to evaluate photoluminescence efficiency, which, unlike conventional models, predicts correctly the experimental results. These results provide a starting point towards examining how non-equilibrium features will affect properties hot-carrier based application.
Galois cohomology and component group of a real reductive group

Let G be a connected reductive group over the field of real numbers R. Using results of our previous joint paper, we compute combinatorially the first Galois cohomology set H^1(R,G) in terms of reductive Kac labelings. Moreover, we compute the group of connected components \pi_0 G(R) of the real Lie group G(R) and the maps in exact sequences containing \pi_0 G(R) and H^1(R,G).
Face sketch to photo translation using generative adversarial networks

Translating face sketches to photo-realistic faces is an interesting and essential task in many applications like law enforcement and the digital entertainment industry. One of the most important challenges of this task is the inherent differences between the sketch and the real image such as the lack of color and details of the skin tissue in the sketch. With the advent of adversarial generative models, an increasing number of methods have been proposed for sketch-to-image synthesis. However, these models still suffer from limitations such as the large number of paired data required for training, the low resolution of the produced images, or the unrealistic appearance of the generated images. In this paper, we propose a method for converting an input facial sketch to a colorful photo without the need for any paired dataset. To do so, we use a pre-trained face photo generating model to synthesize high-quality natural face photos and employ an optimization procedure to keep high-fidelity to the input sketch. We train a network to map the facial features extracted from the input sketch to a vector in the latent space of the face generating model. Also, we study different optimization criteria and compare the results of the proposed model with those of the state-of-the-art models quantitatively and qualitatively. The proposed model achieved 0.655 in the SSIM index and 97.59% rank-1 face recognition rate with higher quality of the produced images.
New constraints on the minimum mass for thermonuclear lithium burning in brown dwarfs

The theory of substellar evolution predicts that there is a sharp mass boundary between lithium and non-lithium brown dwarfs, not far below the substellar-mass limit. The imprint of thermonuclear burning is carved on the surface lithium abundance of substellar-mass objects during the first few hundred million years of their evolution, leading to a sharp boundary between lithium and non-lithium brown dwarfs, so-called, the lithium test. New optical spectroscopic observations of the binaries DENIS+J063001.4-184014 and DENIS+J225210.7-173013 obtained using the 10.4-m Gran Telescopio de Canarias are reported here. They allow us to re-determine their combined optical spectral types (M9.5 and L6.5, respectively) and to search for the presence of the LiI resonance doublet. The non detection of the LiI feature in the combined spectrum of DENIS\,J063001.4$-$184014AB is converted into estimates for the depletion of lithium in the individual components of this binary system. In DENIS\,J225210.7$-$173013AB we report the detection of a weak LiI feature which we tentatively ascribe as arising from the contribution of the T3.5-type secondary. Combining our results with data for seven other brown dwarf binaries in the literature treated in a self-consistent way, we confirm that there is indeed a sharp transition in mass for lithium depletion in brown dwarfs, as expected from theoretical calculations. We estimate such mass boundary is observationally located at 51.48$^{+0.22}_{-4.00}$ $M_\mathrm{Jup}$, which is lower than the theoretical determinations.
SSMF: Shifting Seasonal Matrix Factorization

Given taxi-ride counts information between departure and destination locations, how can we forecast their future demands? In general, given a data stream of events with seasonal patterns that innovate over time, how can we effectively and efficiently forecast future events? In this paper, we propose Shifting Seasonal Matrix Factorization approach, namely SSMF, that can adaptively learn multiple seasonal patterns (called regimes), as well as switching between them. Our proposed method has the following properties: (a) it accurately forecasts future events by detecting regime shifts in seasonal patterns as the data stream evolves; (b) it works in an online setting, i.e., processes each observation in constant time and memory; (c) it effectively realizes regime shifts without human intervention by using a lossless data compression scheme. We demonstrate that our algorithm outperforms state-of-the-art baseline methods by accurately forecasting upcoming events on three real-world data streams.
The Impact of Dust on the Sizes of Galaxies in the Epoch of Reionization

We study the sizes of galaxies in the Epoch of Reionization using a sample of ∼100, 000 galaxies from the BlueTides cosmological hydrodynamical simulation from z = 7 to 11. We measure the galaxy sizes from stellar mass and luminosity maps, defining the effective radius as the minimum radius which could enclose the pixels containing 50% of the total mass/light in the image. We find an inverse relationship between stellar mass and effective half-mass radius, suggesting that the most massive galaxies are more compact and dense than lower mass galaxies, which have flatter mass distributions. We find a mildly negative relation between intrinsic far-ultraviolet luminosity and size, while we find a positive size–luminosity relation when measured from dust-attenuated images. This suggests that dust is the predominant cause of the observed positive size–luminosity relation, with dust preferentially attenuating bright sight lines resulting in a flatter emission profile and thus larger measured effective radii. We study the size–luminosity relation across the rest-frame ultraviolet and optical, and find that the slope decreases at longer wavelengths; this is a consequence of the relation being caused by dust, which produces less attenuation at longer wavelengths. We find that the far-ultraviolet size–luminosity relation shows mild evolution from z = 7 to 11, and galaxy size evolves with redshift as R∝(1 + z)−m, where m = 0.662 ± 0.009. Finally, we investigate the sizes of z = 7 quasar host galaxies, and find that while the intrinsic sizes of quasar hosts are small relative to the overall galaxy sample, they have comparable sizes when measured from dust-attenuated images.
Parametric Variational Linear Units (PVLUs) in Deep Convolutional Networks

The Rectified Linear Unit is currently a state-of-the-art activation function in deep convolutional neural networks. To combat ReLU's dying neuron problem, we propose the Parametric Variational Linear Unit (PVLU), which adds a sinusoidal function with trainable coefficients to ReLU. Along with introducing nonlinearity and non-zero gradients across the entire real domain, PVLU acts as a mechanism of fine-tuning when implemented in the context of transfer learning. On a simple, non-transfer sequential CNN, PVLU substitution allowed for relative error decreases of 16.3% and 11.3% (without and with data augmentation) on CIFAR-100. PVLU is also tested on transfer learning models. The VGG-16 and VGG-19 models experience relative error reductions of 9.5% and 10.7% on CIFAR-10, respectively, after the substitution of ReLU with PVLU. When training on Gaussian-filtered CIFAR-10 images, similar improvements are noted for the VGG models. Most notably, fine-tuning using PVLU allows for relative error reductions up to and exceeding 10% for near state-of-the-art residual neural network architectures on the CIFAR datasets.
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations

In malware behavioral analysis, the list of accessed and created files very often indicates whether the examined file is malicious or benign. However, malware authors are trying to avoid detection by generating random filenames and/or modifying used filenames with new versions of the malware. These changes represent real-world adversarial examples. The goal of this work is to generate realistic adversarial examples and improve the classifier's robustness against these attacks. Our approach learns latent representations of input strings in an unsupervised fashion and uses gradient-based adversarial attack methods in the latent domain to generate adversarial examples in the input domain. We use these examples to improve the classifier's robustness by training on the generated adversarial set of strings. Compared to classifiers trained only on perturbed latent vectors, our approach produces classifiers that are significantly more robust without a large trade-off in standard accuracy.