Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer Vision-Based Guidance Assistance Concept for Plowing Using RGB-D Camera

This paper proposes a concept of computer vision-based guidance assistance for agricultural vehicles to increase the accuracy in plowing and reduce driver’s cognitive burden in long-lasting tillage operations. Plowing is a common agricultural practice to prepare the soil for planting in many countries and it can take place both in the spring and the fall. Since plowing operation requires high traction forces, it causes increased energy consumption. Moreover, longer operation time due to unnecessary maneuvers leads to higher fuel consumption. To provide necessary information for the driver and the control unit of the tractor, a first concept of furrow detection system based on an RGB-D camera was developed.
Quantum control of nuclear-spin qubits in a rapidly rotating diamond

Nuclear spins in certain solids couple weakly to their environment, making them attractive candidates for quantum information processing and inertial sensing. When coupled to the spin of an optically-active electron, nuclear spins can be rapidly polarized, controlled and read via lasers and radiofrequency fields. Possessing coherence times of several milliseconds at room temperature, nuclear spins hosted by a nitrogen-vacancy center in diamond are thus intriguing systems to observe how classical physical rotation at quantum timescales affects a quantum system. Unlocking this potential is hampered by precise and inflexible constraints on magnetic field strength and alignment in order to optically induce nuclear polarization, which restricts the scope for further study and applications. In this work, we demonstrate optical nuclear spin polarization and rapid quantum control of nuclear spins in a diamond physically rotating at $1\,$kHz, faster than the nuclear spin coherence time. Free from the need to maintain strict field alignment, we are able to measure and control nuclear spins in hitherto inaccessible regimes, such as in the presence of a large, time-varying magnetic field that makes an angle of more than $100^\circ$ to the nitrogen-lattice vacancy axis. The field induces spin mixing between the electron and nuclear states of the qubits, decoupling them from oscillating rf fields. We are able to demonstrate that coherent spin state control is possible at any point of the rotation, and even for up to six rotation periods. We combine continuous dynamical decoupling with quantum feedforward control to eliminate decoherence induced by imperfect mechanical rotation. Our work liberates a previously inaccessible degree of freedom of the NV nuclear spin, unlocking new approaches to quantum control and rotation sensing.
A Hybrid Reduced Order Model for nonlinear LES filtering

We develop a Reduced Order Model (ROM) for a Large Eddy Simulation (LES) approach that combines a three-step algorithm called Evolve-Filter-Relax (EFR) with a computationally efficient finite volume method. The main novelty of our ROM lies in the use within the EFR algorithm of a nonlinear, deconvolution-based indicator function that identifies the regions of the domain where the flow needs regularization. The ROM we propose is a hybrid projection/data-driven strategy: a classical Proper Orthogonal Decomposition Galerkin projection approach for the reconstruction of the velocity and the pressure fields and a data-driven reduction method to approximate the indicator function used by the nonlinear differential filter. This data-driven technique is based on interpolation with Radial Basis Functions. We test the performance of our ROM approach on two benchmark problems: 2D and 3D unsteady flow past a cylinder at Reynolds number 0<= Re<= 100. The accuracy of the ROM is assessed against results obtained with the full order model for velocity, pressure, indicator function and time evolution of the aerodynamics coefficients.
Rigidity for circle diffeomorphisms with breaks satisfying a Zygmund smoothness condition

Let f and f̃ be two circle diffeomorphisms with a break point, with the same irrational rotation number of bounded type, the same size of the break c and satisfying a certain Zygmund type smoothness condition depending on a parameter γ > 2. We prove that under a certain condition imposed on the break size c, the diffeomorphisms f and f̃ are Cγ -smoothly conjugate to each other, where ωγ(δ) = | log δ| .
Free–free absorption in hot relativistic flows: application to fast radio bursts

Magnetic flares create hot relativistic shocks outside the light cylinder radius of a magnetized star. Radio emission produced in such a shock or at a radius smaller than the shock undergoes free–free absorption while passing through the shocked medium. In this work, we demonstrate that this free–free absorption can lead to a negative drift in the frequency-time spectra. Whether it is related to the downward drift pattern observed in fast radio bursts (FRBs) is unclear. However, if the FRB down-drifting is due to this mechanism then it will be pronounced in those shocks that have isotropic kinetic energies $ \gtrsim 10^{44}$ erg. In this model, for an internal shock with a Lorentz factor ∼100, the normalized drift rate $|{\rm DR_{\rm obs}}|/\nu _{\rm mean}$ is ∼10−2 per ms, where νmean is the central frequency of the radio pulses. The corresponding radius of the shocked shell is, therefore, in the range of 1010 cm and 1011 cm. This implies that, for an outflow consisting of hydrogen ion, the upper limit on the mass of the relativistic shocks is a few × 10−10 M⊙, which is considerably low compared to that ejected from SGR 1806-20 during the 2004 outburst.
Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey

This is a tutorial and survey paper on Boltzmann Machine (BM), Restricted Boltzmann Machine (RBM), and Deep Belief Network (DBN). We start with the required background on probabilistic graphical models, Markov random field, Gibbs sampling, statistical physics, Ising model, and the Hopfield network. Then, we introduce the structures of BM and RBM. The conditional distributions of visible and hidden variables, Gibbs sampling in RBM for generating variables, training BM and RBM by maximum likelihood estimation, and contrastive divergence are explained. Then, we discuss different possible discrete and continuous distributions for the variables. We introduce conditional RBM and how it is trained. Finally, we explain deep belief network as a stack of RBM models. This paper on Boltzmann machines can be useful in various fields including data science, statistics, neural computation, and statistical physics.
Template-based Chatbot for Agriculture Related FAQs

Agriculture is the fundamental industry of the society, which is the basis of food supply and an important source of employment and GDP increase. However, the insufficient expert can not fulfill the demand of farmers. To address this problem, we design a chatbot to answer frequently asked questions in the Agriculture field. Template-based questions will be answered by AIML while LSA is used for other service-based questions. This chatbot will assist farmers by dealing with industry problems conveniently and efficiently.
Extrapolation Estimation for Nonparametric Regression with Measurement Error

For the nonparametric regression models with covariates contaminated with normal measurement errors, this paper proposes an extrapolation algorithm to estimate the nonparametric regression functions. By applying the conditional expectation directly to the kernel-weighted least squares of the deviations between the local linear approximation and the observed responses, the proposed algorithm successfully bypasses the simulation step needed in the classical simulation extrapolation method, thus significantly reducing the computational time. It is noted that the proposed method also provides an exact form of the extrapolation function, but the extrapolation estimate generally cannot be obtained by simply setting the extrapolation variable to negative one in the fitted extrapolation function if the bandwidth is less than the standard deviation of the measurement error. Large sample properties of the proposed estimation procedure are discussed, as well as simulation studies and a real data example being conducted to illustrate its applications.
Double Deep Q-learning Based Real-Time Optimization Strategy for Microgrids

The uncertainties from distributed energy resources (DERs) bring significant challenges to the real-time operation of microgrids. In addition, due to the nonlinear constraints in the AC power flow equation and the nonlinearity of the battery storage model, etc., the optimization of the microgrid is a mixed-integer nonlinear programming (MINLP) problem. It is challenging to solve this kind of stochastic nonlinear optimization problem. To address the challenge, this paper proposes a deep reinforcement learning (DRL) based optimization strategy for the real-time operation of the microgrid. Specifically, we construct the detailed operation model for the microgrid and formulate the real-time optimization problem as a Markov Decision Process (MDP). Then, a double deep Q network (DDQN) based architecture is designed to solve the MINLP problem. The proposed approach can learn a near-optimal strategy only from the historical data. The effectiveness of the proposed algorithm is validated by the simulations on a 10-bus microgrid system and a modified IEEE 69-bus microgrid system. The numerical simulation results demonstrate that the proposed approach outperforms several existing methods.
On the data persistency of replicated erasure codes in distributed storage systems

This paper studies the fundamental problem of data persistency for a general family of redundancy schemes in distributed storage systems, called replicated erasure codes. Namely, we analyze two strategies of replicated erasure codes distribution: random and symmetric. For both strategies we derive closed analytical and asymptotic formulas for expected data persistency despite nodes failure.
Remember What You have drawn: Semantic Image Manipulation with Memory

Image manipulation with natural language, which aims to manipulate images with the guidance of language descriptions, has been a challenging problem in the fields of computer vision and natural language processing (NLP). Currently, a number of efforts have been made for this task, but their performances are still distant away from generating realistic and text-conformed manipulated images. Therefore, in this paper, we propose a memory-based Image Manipulation Network (MIM-Net), where a set of memories learned from images is introduced to synthesize the texture information with the guidance of the textual description. We propose a two-stage network with an additional reconstruction stage to learn the latent memories efficiently. To avoid the unnecessary background changes, we propose a Target Localization Unit (TLU) to focus on the manipulation of the region mentioned by the text. Moreover, to learn a robust memory, we further propose a novel randomized memory training loss. Experiments on the four popular datasets show the better performance of our method compared to the existing ones.
End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection

Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are known to reside in specific subbands and temporal segments. Various approaches can be used to capture and model such artefacts, however, none works well across a spectrum of diverse spoofing attacks. Reliable detection then often depends upon the fusion of multiple detection systems, each tuned to detect different forms of attack. In this paper we show that better performance can be achieved when the fusion is performed within the model itself and when the representation is learned automatically from raw waveform inputs. The principal contribution is a spectro-temporal graph attention network (GAT) which learns the relationship between cues spanning different sub-bands and temporal intervals. Using a model-level graph fusion of spectral (S) and temporal (T) sub-graphs and a graph pooling strategy to improve discrimination, the proposed RawGAT-ST model achieves an equal error rate of 1.06 % for the ASVspoof 2019 logical access database. This is one of the best results reported to date and is reproducible using an open source implementation.
High-Payload Online Identification and Adaptive Control for an Electrically-actuated Quadruped Robot

Quadruped robots manifest great potential to traverse rough terrains with payload. Numerous traditional control methods for legged dynamic locomotion are modelbased and exhibit high sensitivity to model uncertainties and payload variations. Therefore, high-performance model parameter estimation becomes indispensable. However, the inertia parameters of payload are usually unknown and dynamically changing when the quadruped robot is deployed in versatile tasks. To address this problem, online identification of the inertia parameters and the Center of Mass (CoM) position of the payload for the quadruped robots draw an increasing interest. This study presents an adaptive controller based on the online payload identification for the high payload capacity (the ratio between payload and robot’s self-weight) quadruped locomotion. We name it as Adaptive Controller for Quadruped Locomotion (ACQL), which consists of a recursive update law and a control law. ACQL estimates the external forces and torques induced by the payload online. The estimation is incorporated in inverse-dynamics-based Quadratic Programming (QP) to realize a trotting gait. As such, the tracking accuracy of the robot’s CoM and orientation trajectories are improved. The proposed method, ACQL, is verified in a real quadruped robot platform. Experiments prove the estimation efficacy for the payload weighing from 20 kg to 75 kg and loaded at different locations of the robot’s torso.
Design Space Exploration of Sparse Accelerators for Deep Neural Networks

—Novel architectures for deep learning exploit both activation and weight sparsity to improve the performance of DNN inference. However, this speedup usually brings non-negligible overheads which diminish the efﬁciency of such designs when running dense models. These overheads speciﬁcally are exacerbated for low precision accelerators with optimized SRAM size per core. This paper examines the design space trade-offs of such accelerators aiming to achieve competitive performance and efﬁciency metrics for all four combinations of dense or sparse activation/weight tensors. To do so, we systematically examine overheads of supporting sparsity on top of an optimized dense core. These overheads are modeled based on parameters that indicate how a multiplier can borrow a nonzero operation from the neighboring multipliers or future cycles. As a result of this exploration, we identify a few promising designs that perform better than prior work. Our ﬁndings suggest that even a best design targeting dual sparsity yields 20%-30% drop in power efﬁciency when performing on single sparse models, i.e., those with only sparse weight or sparse activation tensors. We introduce novel techniques to reuse resources of the same core to maintain high performance and efﬁciency when running single sparsity or dense models. We call this hybrid design Grifﬁn . Grifﬁn is 1.2, 3.0, 3.1, and 1.4 × more power efﬁcient than state-of-the-art sparse architectures, for dense, weight-only sparse, activation-only sparse, and dual sparse models, respectively.
Gravitational radiation from accelerating jets

Non-spherical rapid acceleration of mass (or energy) to a relativistic velocity is a natural source of gravitational radiation. Such conditions arise in both long and short gamma-ray bursts whose central engine ejects relativistic jets. The resulting gravitational wave signal is of a memory type, rising to a finite level (of order 4GE/r) over a duration that corresponds to the longer of either the injection time and the acceleration time of the jet. We explore the properties of such signals and their potential detectability. Unfortunately, the expected signals are below the frequency band of Advanced LIGO-Virgo-Kagra, and above LISA. However, they fall within the range of the planned BBO and DECIGO. While current sensitivity is marginal for the detection of jet gravitational wave signals from GRBs, hidden relativistic jets that exist within some core collapse SNe could be detected. Such a detection would reveal the acceleration mechanism and the activity of the central engine, which cannot be explored directly in any other way.
Selective MPC: Distributed Computation of Differentially Private Key-Value Statistics

Key-value data is a naturally occurring data type that has not been thoroughly investigated in the local trust model. Existing local differentially private (LDP) solutions for computing statistics over key-value data suffer from the inherent accuracy limitations of each user adding their own noise. Multi-party computation (MPC) maintains better accuracy than LDP and similarly does not require a trusted central party. However, naively applying MPC to key-value data results in prohibitively expensive computation costs. In this work, we present selective multi-party computation, a novel approach to distributed computation that leverages DP leakage to efficiently and accurately compute statistics over key-value data. By providing each party with a view of a random subset of the data, we can capture subtractive noise. We prove that our protocol satisfies pure DP and is provably secure in the combined DP/MPC model. Our empirical evaluation demonstrates that we can compute statistics over 10,000 keys in 20 seconds and can scale up to 30 servers while obtaining results for a single key in under a second.
Poisoning Online Learning Filters: DDoS Attacks and Countermeasures

The recent advancements in machine learning have led to a wave of interest in adopting online learning-based approaches for long-standing attack mitigation issues. In particular, DDoS attacks remain a significant threat to network service availability even after more than two decades. These attacks have been well studied under the assumption that malicious traffic originates from a single attack profile. Based on this premise, malicious traffic characteristics are assumed to be considerably different from legitimate traffic. Consequently, online filtering methods are designed to learn network traffic distributions adaptively and rank requests according to their attack likelihood. During an attack, requests rated as malicious are precipitously dropped by the filters. In this paper, we conduct the first systematic study on the effects of data poisoning attacks on online DDoS filtering; introduce one such attack method, and propose practical protective countermeasures for these attacks. We investigate an adverse scenario where the attacker is"crafty", switching profiles during attacks and generating erratic attack traffic that is ever-shifting. This elusive attacker generates malicious requests by manipulating and shifting traffic distribution to poison the training data and corrupt the filters. To this end, we present a generative model MimicShift, capable of controlling traffic generation while retaining the originating traffic's intrinsic properties. Comprehensive experiments show that online learning filters are highly susceptible to poisoning attacks, sometimes performing much worse than a random filtering strategy in this attack scenario. At the same time, our proposed protective countermeasure diminishes the attack impact.
Individual Survival Curves with Conditional Normalizing Flows

Survival analysis, or time-to-event modelling, is a classical statistical problem that has garnered a lot of interest for its practical use in epidemiology, demographics or actuarial sciences. Recent advances on the subject from the point of view of machine learning have been concerned with precise per-individual predictions instead of population studies, driven by the rise of individualized medicine. We introduce here a conditional normalizing flow based estimate of the time-to-event density as a way to model highly flexible and individualized conditional survival distributions. We use a novel hierarchical formulation of normalizing flows to enable efficient fitting of flexible conditional distributions without overfitting and show how the normalizing flow formulation can be efficiently adapted to the censored setting. We experimentally validate the proposed approach on a synthetic dataset as well as four open medical datasets and an example of a common financial problem.
CKConv: Learning Feature Voxelization for Point Cloud Analysis

Despite the remarkable success of deep learning, optimal convolution operation on point cloud remains indeﬁ-nite due to its irregular data structure. In this paper, we present Cubic Kernel Convolution (CKConv) that learns to voxelize the features of local points by exploiting both continuous and discrete convolutions. Our continuous convolution uniquely employs a 3D cubic form of kernel weight representation that splits a feature into voxels in embedding space. By consecutively applying discrete 3D convolu-tions on the voxelized features in a spatial manner, preceding continuous convolution is forced to learn spatial feature mapping, i.e., feature voxelization. In this way, geometric information can be detailed by encoding with sub-divided features, and our 3D convolutions on these ﬁxed structured data do not suffer from discretization artifacts thanks to voxelization in embedding space. Furthermore, we propose a spatial attention module, Local Set Attention (LSA), to provide comprehensive structure awareness within the local point set and hence produce representative features. By learning feature voxelization with LSA, CK-Conv can extract enriched features for effective point cloud analysis. We show that CKConv has great applicability to point cloud processing tasks including object classiﬁcation, object part segmentation, and scene semantic segmentation with state-of-the-art results.
Adaptive Denoising via GainTuning

Deep convolutional neural networks (CNNs) for image denoising are usually trained on large datasets. These models achieve the current state of the art, but they have difficulties generalizing when applied to data that deviate from the training distribution. Recent work has shown that it is possible to train denoisers on a single noisy image. These models adapt to the features of the test image, but their performance is limited by the small amount of information used to train them. Here we propose"GainTuning", in which CNN models pre-trained on large datasets are adaptively and selectively adjusted for individual test images. To avoid overfitting, GainTuning optimizes a single multiplicative scaling parameter (the"Gain") of each channel in the convolutional layers of the CNN. We show that GainTuning improves state-of-the-art CNNs on standard image-denoising benchmarks, boosting their denoising performance on nearly every image in a held-out test set. These adaptive improvements are even more substantial for test images differing systematically from the training data, either in noise level or image type. We illustrate the potential of adaptive denoising in a scientific application, in which a CNN is trained on synthetic data, and tested on real transmission-electron-microscope images. In contrast to the existing methodology, GainTuning is able to faithfully reconstruct the structure of catalytic nanoparticles from these data at extremely low signal-to-noise ratios.