Miracle-dz/UniFAR-FTUs huggingface dataset:
Sketch for traffic measurement: design, optimization, application and implementation

Network measurement probes the underlying network to support upper-level decisions such as network management, network update, network maintenance, network defense and beyond. Due to the massive, speedy, unpredictable features of network flows, sketches are widely implemented in measurement nodes to record the frequency or estimate the cardinality of flows approximately. At their cores, sketches usually maintain one or multiple counter array(s), and relies on hash functions to select the counter(s) for each flow. Then the space-efficient sketches from the distributed measurement nodes are aggregated to provide statistics of the undergoing flows. Currently, tremendous redesigns and optimizations have been proposed to further improve the sketches for better network measurement performance. However, the existing reviews or surveys mainly focus on one particular aspect of measurement tasks. Researchers and engineers in the network measurement community desire an all-in-one survey which covers the whole processing pipeline of sketch-based network measurement. To this end, we present the first comprehensive survey in this area. We first introduce the preparation of flows for measurement, then detail the most recent investigations of design, aggregation, decoding, application and implementation of sketches for network measurement. To summary the existing efforts, we conduct an in-depth study of the existing literature, covering more than 80 sketch designs and optimization strategies. Furthermore, we conduct a comprehensive analysis and qualitative/quantitative comparison of the sketch designs. Finally, we highlight the open issues for future sketch-based network measurement research.
Using Restricted Boltzmann Machines to Model Molecular Geometries

Precise physical descriptions of molecules can be obtained by solving the Schrodinger equation; however, these calculations are intractable and even approximations can be cumbersome. Force fields, which estimate interatomic potentials based on empirical data, are also time-consuming. This paper proposes a new methodology for modeling a set of physical parameters by taking advantage of the restricted Boltzmann machine's fast learning capacity and representational power. By training the machine on ab initio data, we can predict new data in the distribution of molecular configurations matching the ab initio distribution. In this paper we introduce a new RBM based on the Tanh activation function, and conduct a comparison of RBMs with different activation functions, including sigmoid, Gaussian, and (Leaky) ReLU. Finally we demonstrate the ability of Gaussian RBMs to model small molecules such as water and ethane.
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks

Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices to find solutions to complex problems, such as the ground energy and ground state of strongly-correlated quantum many-body systems. In many applications, it is the optimization of both continuous and discrete parameters that poses a formidable challenge. Using reinforcement learning (RL), we present a hybrid policy gradient algorithm capable of simultaneously optimizing continuous and discrete degrees of freedom in an uncertainty-resilient way. The hybrid policy is modeled by a deep autoregressive neural network to capture causality. We employ the algorithm to prepare the ground state of the nonintegrable quantum Ising model in a unitary process, parametrized by a generalized quantum approximate optimization ansatz: the RL agent solves the discrete combinatorial problem of constructing the optimal sequences of unitaries out of a predefined set and, at the same time, it optimizes the continuous durations for which these unitaries are applied. We demonstrate the noise-robust features of the agent by considering three sources of uncertainty: classical and quantum measurement noise, and errors in the control unitary durations. Our work exhibits the beneficial synergy between reinforcement learning and quantum control.
Transmit Power Pool Design for Grant-Free NOMA-IoT Networks via Deep Reinforcement Learning

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for short-packet internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging due to the absence of closed-loop power control. We design a prototype of transmit power pool (PP) to provide open-loop power control. IoT users acquire their transmit power in advance from this prototype PP solely according to their communication distances. Firstly, a multi-agent deep Q-network (DQN) aided GF-NOMA algorithm is proposed to determine the optimal transmit power levels for the prototype PP. More specifically, each IoT user acts as an agent and learns a policy by interacting with the wireless environment that guides them to select optimal actions. Secondly, to prevent the Q-learning model overestimation problem, double DQN (DDQN) based GF-NOMA algorithm is proposed. Numerical results confirm that the DDQN based algorithm finds out the optimal transmit power levels that form the PP. Comparing with the conventional online learning approach, the proposed algorithm with the prototype PP converges faster under changing environments due to limiting the action space based on previous learning. The considered GF-NOMA system outperforms the networks with fixed transmission power, namely all the users have the same transmit power and the traditional GF with orthogonal multiple access techniques, in terms of throughput.
Learning to Schedule Halide Pipelines for the GPU

We present a new algorithm to automatically generate high-performance GPU implementations of complex imaging and machine learning pipelines, directly from high-level Halide algorithm code. It is fully automatic, requiring no schedule templates or hand-optimized kernels, and it targets a diverse range of computations which is significantly broader than existing autoschedulers. We address the scalability challenge of extending previous approaches to schedule large real world programs, while enabling a broad set of program rewrites that take into account the nested parallelism and memory hierarchy introduced by GPU architectures. We achieve this using a hierarchical sampling strategy that groups programs into buckets based on their structural similarity, then samples representatives to be evaluated, allowing us to explore a large space by only considering a subset of the space, and a pre-pass that 'freezes' decisions for the lowest cost sections of a program, allowing more time to be spent on the important stages. We then apply an efficient cost model combining machine learning, program analysis, and GPU architecture knowledge. Our method scales combinatorially better with respect to the deeper nested parallelism required by GPUs compared to previous work. We evaluate its performance on a diverse suite of real-world imaging and machine learning pipelines. We demonstrate results that are on average 1.66X faster than existing automatic solutions (up to 5X), and competitive with what the best human experts were able to achieve in an active effort to beat our automatic results.
Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs

We present a visually grounded hierarchical planning algorithm for long-horizon manipulation tasks. Our algorithm offers a joint framework of neuro-symbolic task planning and low-level motion generation conditioned on the specified goal. At the core of our approach is a two-level scene graph representation, namely geometric scene graph and symbolic scene graph. This hierarchical representation serves as a structured, object-centric abstraction of manipulation scenes. Our model uses graph neural networks to process these scene graphs for predicting high-level task plans and low-level motions. We demonstrate that our method scales to long-horizon tasks and generalizes well to novel task goals. We validate our method in a kitchen storage task in both physical simulation and the real world. Experiments show that our method achieves over 70% success rate and nearly 90% of subgoal completion rate on the real robot while being four orders of magnitude faster in computation time compared to standard search-based task-and-motion planner. 1
Phase Transition in Modified Newtonian Dynamics (MONDian) Self-Gravitating Systems

We study the statistical mechanics of binary systems under the gravitational interaction of the Modified Newtonian Dynamics (MOND) in three-dimensional space. Considering the binary systems in the microcanonical and canonical ensembles, we show that in the microcanonical systems, unlike the Newtonian gravity, there is a sharp phase transition, with a high-temperature homogeneous phase and a low-temperature clumped binary one. Defining an order parameter in the canonical systems, we find a smoother phase transition and identify the corresponding critical temperature in terms of the physical parameters of the binary system.
Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization

We demonstrate the ability of hybrid regularization methods to automatically avoid the double descent phenomenon arising in the training of random feature models (RFM). The hallmark feature of the double descent phenomenon is a spike in the regularization gap at the interpolation threshold, i.e. when the number of features in the RFM equals the number of training samples. To close this gap, the hybrid method considered in our paper combines the respective strengths of the two most common forms of regularization: early stopping and weight decay. The scheme does not require hyperparameter tuning as it automatically selects the stopping iteration and weight decay hyperparameter by using generalized cross-validation (GCV). This also avoids the necessity of a dedicated validation set. While the benefits of hybrid methods have been well-documented for ill-posed inverse problems, our work presents the first use case in machine learning. To expose the need for regularization and motivate hybrid methods, we perform detailed numerical experiments inspired by image classification. In those examples, the hybrid scheme successfully avoids the double descent phenomenon and yields RFMs whose generalization is comparable with classical regularization approaches whose hyperparameters are tuned optimally using the test data. We provide our MATLAB codes for implementing the numerical experiments in this paper at https://github.com/EmoryMLIP/HybridRFM.
Offline Policy Selection under Uncertainty

The presence of uncertainty in policy evaluation significantly complicates the process of policy ranking and selection in real-world settings. We formally consider offline policy selection as learning preferences over a set of policy prospects given a fixed experience dataset. While one can select or rank policies based on point estimates of their policy values or high-confidence intervals, access to the full distribution over one's belief of the policy value enables more flexible selection algorithms under a wider range of downstream evaluation metrics. We propose BayesDICE for estimating this belief distribution in terms of posteriors of distribution correction ratios derived from stochastic constraints (as opposed to explicit likelihood, which is not available). Empirically, BayesDICE is highly competitive to existing state-of-the-art approaches in confidence interval estimation. More importantly, we show how the belief distribution estimated by BayesDICE may be used to rank policies with respect to any arbitrary downstream policy selection metric, and we empirically demonstrate that this selection procedure significantly outperforms existing approaches, such as ranking policies according to mean or high-confidence lower bound value estimates.
On Approximate Envy-Freeness for Indivisible Chores and Mixed Resources

We study fair allocation of undesirable indivisible items (or chores) and make three contributions: First, we show that determining the existence of an envy-free allocation is NP-complete even when agents have binary additive valuations. Second, we provide a polynomial-time algorithm for computing an allocation that satisfies envy-freeness up to one chore (EF1) under monotone valuations, correcting a existing proof of the same claim in the literature. A straightforward modification of our algorithm can be used to compute an EF1 allocation for doubly monotone instances (wherein each agent can partition the set of items into objective goods and objective chores). Our third and most important result applies to a mixed resources model consisting of indivisible chores and a divisible, desirable heterogenous resource (metaphorically, a cake). We show that there always exists an allocation that satisfies envy-freeness for mixed resources (EFM) in this setting, complementing an analogous recent result of Bei et al. (AAAI 2020). We also show a similar result in the flipped setting consisting of indivisible goods and a divisible "bad" cake.
Spectral Unmixing With Multinomial Mixture Kernel and Wasserstein Generative Adversarial Loss

This study proposes a novel framework for spectral unmixing by using 1D convolution kernels and spectral uncertainty. High-level representations are computed from data, and they are further modeled with the Multinomial Mixture Model to estimate fractions under severe spectral uncertainty. Furthermore, a new trainable uncertainty term based on a nonlinear neural network model is introduced in the reconstruction step. All uncertainty models are optimized by Wasserstein Generative Adversarial Network (WGAN) to improve stability and capture uncertainty. Experiments are performed on both real and synthetic datasets. The results validate that the proposed method obtains state-of-the-art performance, especially for the real datasets compared to the baselines. Project page at: https://github.com/savasozkan/dscn.
MVFNet: Multi-View Fusion Network for Efficient Video Recognition

Conventionally, spatiotemporal modeling network and its complexity are the two most concentrated research topics in video action recognition. Existing state-of-the-art methods have achieved excellent accuracy regardless of the complexity meanwhile efficient spatiotemporal modeling solutions are slightly inferior in performance. In this paper, we attempt to acquire both efficiency and effectiveness simultaneously. First of all, besides traditionally treating H x W x T video frames as space-time signal (viewing from the Height-Width spatial plane), we propose to also model video from the other two Height-Time and Width-Time planes, to capture the dynamics of video thoroughly. Secondly, our model is designed based on 2D CNN backbones and model complexity is well kept in mind by design. Specifically, we introduce a novel multi-view fusion (MVF) module to exploit video dynamics using separable convolution for efficiency. It is a plug-and-play module and can be inserted into off-the-shelf 2D CNNs to form a simple yet effective model called MVFNet. Moreover, MVFNet can be thought of as a generalized video modeling framework and it can specialize to be existing methods such as C2D, SlowOnly, and TSM under different settings. Extensive experiments are conducted on popular benchmarks (i.e., Something-Something V1 & V2, Kinetics, UCF-101, and HMDB-51) to show its superiority. The proposed MVFNet can achieve state-of-the-art performance with 2D CNN's complexity.
Inverse Problem for Ising Connection Matrix with Long-Range Interaction

In the present paper, we examine Ising systems on d-dimensional hypercube lattices and solve an inverse problem where we have to determine interaction constants of an Ising connection matrix when we know a spectrum of its eigenvalues. In addition, we define restrictions allowing a random number sequence to be a connection matrix spectrum. We use the previously obtained analytical expressions for the eigenvalues of Ising connection matrices accounting for an arbitrary long-range interaction and supposing periodic boundary conditions.
DETERMINATION OF THE LIME REQUIREMENT FOR ACID SOILS IN ONTARIO USING THE SMP BUFFER METHODS

The accuracy of the Shoemaker-McLean-Pratt single buffer method and its double buffer variation was compared for determining the lime requirement of 41 acid mineral soils in Ontario. The single buffer method gave good prediction of the lime requirement when calibrated against Ca(OH)2-titrated soil acidity using a quadratic equation. Key words: Lime requirement, acid soils, SMP buffer
A signal deconvolution method to discriminate smaller nanoparticles in single particle ICP-MS

Single particle ICP-MS (spICP-MS) analysis of inorganic nanoparticles (NPs) cannot accurately distinguish dissolved ion signals and signals from relatively small NPs, although these particles are often more reactive than their larger counterparts. A signal deconvolution method was developed for spICP-MS analysis using gold (Au) NPs of nominally 10, 15 or 30 nm diameter. The signal distributions of dissolved Au standards were parameterised as a function of concentration using a mixed Polyagaussian probability mass function. Dissolved curves were fitted using this parameterisation to the low-intensity signals of samples containing NPs to subtract and deconvolute the dissolved signals from the particle signals. The dissolved signals were quantified in this process. The accuracy of the deconvolution method was confirmed for all NP suspensions studied when comparing the size and number concentration obtained with the deconvolution method with values based on transmission electron microscopy. This method thus allows analysis of NP suspensions with spICP-MS where it was hitherto not possible. The applicability domain lies predominantly with relatively small NPs and/or when a relatively high concentration of dissolved ions of the element of interest is present, where overlapping between dissolved and particulate signals occurs.
A practical route towards fabricating GaN nanowire arrays

GaN nanowire (NW) arrays have been fabricated by the electrodeless photoelectrochemical (PEC) etching method for the first time. Under appropriate conditions, the etching process is just a dislocation-hunted process, in which the etching solution “digs down” along the threading dislocations, resulting in the formation of GaN NWs by preferentially etching away the defective parts of GaN with dislocations and retaining the flawless parts. The NWs have a density of 1∼2 × 107 cm−2, diameters ranging from 150 nm to 500 nm, and corresponding lengths ranging from 10 μm to 20 μm. Transmission electron microscopy (TEM) indicates that these GaN NWs possess few dislocations. High resolution X-ray diffraction (HRXRD) and micro-Raman measurements show that these GaN NWs are stress-free. Room temperature cathodoluminescence (CL) measurements show a single near-band-edge emission at 367 nm with a full width at half maximum (FWHM) of 8 nm from the NWs, indicating a high optical quality. Additionally, negative piezoelectric current pluses are generated from the GaN NWs when the conductive atomic force microscope is scanned cross the arrays in contact mode. Such GaN NW arrays are promising building blocks for exploring nanodevices with excellent performance.
Intrinsic Image Captioning Evaluation

The image captioning task is about to generate suitable descriptions from images. For this task there can be several challenges such as accuracy, fluency and diversity. However there are few metrics that can cover all these properties while evaluating results of captioning this http URL this paper we first conduct a comprehensive investigation on contemporary metrics. Motivated by the auto-encoder mechanism and the research advances of word embeddings we propose a learning based metrics for image captioning, which we call Intrinsic Image Captioning Evaluation(I2CE). We select several state-of-the-art image captioning models and test their performances on MS COCO dataset with respects to both contemporary metrics and the proposed I2CE. Experiment results show that our proposed method can keep robust performance and give more flexible scores to candidate captions when encountered with semantic similar expression or less aligned semantics. On this concern the proposed metric could serve as a novel indicator on the intrinsic information between captions, which may be complementary to the existing ones.
Source Data-Absent Unsupervised Domain Adaptation Through Hypothesis Transfer and Labeling Transfer

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain. Most existing UDA methods require access to the source data, and thus are not applicable when the data are confidential and not shareable due to privacy concerns. This paper aims to tackle a realistic setting with only a classification model available trained over, instead of accessing to, the source data. To effectively utilize the source model for adaptation, we propose a novel approach called Source HypOthesis Transfer (SHOT), which learns the feature extraction module for the target domain by fitting the target data features to the frozen source classification module (representing classification hypothesis). Specifically, SHOT exploits both information maximization and self-supervised learning for the feature extraction module learning to ensure the target features are implicitly aligned with the features of unseen source data via the same hypothesis. Furthermore, we propose a new labeling transfer strategy, which separates the target data into two splits based on the confidence of predictions (labeling information), and then employ semi-supervised learning to improve the accuracy of less-confident predictions in the target domain. We denote labeling transfer as SHOT++ if the predictions are obtained by SHOT. Extensive experiments on both digit classification and object recognition tasks show that SHOT and SHOT++ achieve results surpassing or comparable to the state-of-the-arts, demonstrating the effectiveness of our approaches for various visual domain adaptation problems. Code will be available at https://github.com/tim-learn/SHOT-plus.
Quantum phase transition in a non-Hermitian XY spin chain with global complex transverse field

In this work, we investigate the quantum phase transition in a non-Hermitian XY spin chain. The phase diagram shows that the critical points of Ising phase transition expand into a critical transition zone after introducing a non-Hermitian effect. By analyzing the non-Hermitian gap and long-range correlation function, one can distinguish different phases by means of different gap features and decay properties of correlation function, a tricky problem in traditional XY model. Furthermore, the results reveal the relationship among different regions of the phase diagram, non-Hermitian energy gap and long-range correlation function.
Demystifying Deep Neural Networks Through Interpretation: A Survey

Modern deep learning algorithms tend to optimize an objective metric, such as minimize a cross entropy loss on a training dataset, to be able to learn. The problem is that the single metric is an incomplete description of the real world tasks. The single metric cannot explain why the algorithm learn. When an erroneous happens, the lack of interpretability causes a hardness of understanding and fixing the error. Recently, there are works done to tackle the problem of interpretability to provide insights into neural networks behavior and thought process. The works are important to identify potential bias and to ensure algorithm fairness as well as expected performance.