Miracle-dz/UniFAR-FTUs huggingface dataset:
Cellular Memristive-Output Reservoir (CMOR)

Reservoir computing is a subfield of machine learning in which a complex system, or 'reservoir,' uses complex internal dynamics to non-linearly project an input into a higher-dimensional space. A single trainable output layer then inspects this high-dimensional space for features relevant to perform the given task, such as a classification. Initially, reservoirs were often constructed from recurrent neural networks, but reservoirs constructed from many different elements have been demonstrated. Elementary cellular automata (CA) are one such system which have recently been demonstrated as a powerful and efficient basis which can be used to construct a reservoir. To investigate the feasibility and performance of a monolithic reservoir computing circuit with a fully integrated, programmable read-out layer, we designed, fabricated, and tested a full-custom reservoir computing circuit. This design, the cellular memristive-output reservoir (CMOR), is implemented in 65-nm CMOS technology with integrated front-end-of-the-line (FEOL) resistive random-access memory (ReRAM) used to construct a trainable output layer. We detail the design of this system and present electrical test results verifying its operation and capability to carry out non-linear classifications.
Beam Entropy of 5G Cellular Millimetre-Wave Channels

In this paper, we obtain and study typical beam entropy values for millimetre-wave (mm-wave) channel models using the NYUSIM simulator for frequencies up to 100 GHz for the fifth generation (5G) and beyond 5G cellular communication systems. The beam entropy is used to quantify sparse MIMO channel randomness in beamspace. Lower relative beam entropy channels are suitable for memory- assisted statistically-ranked (MarS) and hybrid radio frequency (RF) beam training algorithms. High beam entropies can potentially be advantageous for low overhead secured radio communications by generating cryptographic keys based on the channel randomness in beamspace, especially for sparse multipleinput multiple- output (MIMO) channels. Urban microcell (UMi) and urban macrocell (UMa) cellular scenarios have been investigated in this work for 28, 60, 73, and 100 GHz carrier frequencies and the rural macrocell (RMa) scenario for 3.5 GHz.
Multi-Scale Convolutions for Learning Context Aware Feature Representations

Finding semantic correspondences is a challenging problem. With the breakthrough of CNNs stronger features are available for tasks like classification but not specifically for the requirements of semantic matching. In the following we present a weakly supervised metric learning approach which generates stronger features by encoding far more context than previous methods. First, we generate more suitable training data using a geometrically informed correspondence mining method which is less prone to spurious matches and requires only image category labels as supervision. Second, we introduce a new convolutional layer which is a learned mixture of differently strided convolutions and allows the network to encode implicitly more context while preserving matching accuracy at the same time. The strong geometric encoding on the feature side enables us to learn a semantic flow network, which generates more natural deformations than parametric transformation based models and is able to jointly predict foreground regions at the same time. Our semantic flow network outperforms current state-of-the-art on several semantic matching benchmarks and the learned features show astonishing performance regarding simple nearest neighbor matching.
STAR: A Structure and Texture Aware Retinex Model

Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives are attributed to the changes in reflectance, while smaller derivatives are emerged in the smooth illumination. In this paper, we utilize exponentiated local derivatives (with an exponent <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>) of an observed image to generate its structure map and texture map. The structure map is produced by been amplified with <inline-formula> <tex-math notation="LaTeX">$\gamma >1$ </tex-math></inline-formula>, while the texture map is generated by been shrank with <inline-formula> <tex-math notation="LaTeX">$\gamma < 1$ </tex-math></inline-formula>. To this end, we design exponential filters for the local derivatives, and present their capability on extracting accurate structure and texture maps, influenced by the choices of exponents <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>. The extracted structure and texture maps are employed to regularize the illumination and reflectance components in Retinex decomposition. A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image. We solve the STAR model by an alternating optimization algorithm. Each sub-problem is transformed into a vectorized least squares regression, with closed-form solutions. Comprehensive experiments on commonly tested datasets demonstrate that, the proposed STAR model produce better quantitative and qualitative performance than previous competing methods, on illumination and reflectance decomposition, low-light image enhancement, and color correction. The code is publicly available at <uri>https://github.com/csjunxu/STAR</uri>.
Probably Approximately Correct Nash Equilibrium Learning

We consider a multiagent noncooperative game with agents’ objective functions being affected by uncertainty. Following a data driven paradigm, we represent uncertainty by means of scenarios and seek a robust Nash equilibrium solution. We treat the Nash equilibrium computation problem within the realm of probably approximately correct learning. Building upon recent developments in scenario-based optimization, we accompany the computed Nash equilibrium with a priori and a posteriori probabilistic robustness certificates, providing confidence that the computed equilibrium remains unaffected (in probabilistic terms) when a new uncertainty realization is encountered. For a wide class of games, we also show that the computation of the so called compression set—which is at the core of scenario-based optimization—can be directly obtained as a byproduct of the proposed methodology. We demonstrate the efficacy of our approach on an electric vehicle charging control problem.
A Global Solution Method for Decentralized Multi-Area SCUC and Savings Allocation Based on MILP Value Functions

To address the issue that Lagrangian dual function based algorithms cannot guarantee convergence and global optimality for decentralized multi-area security constrained unit commitment (M-SCUC) problems, a novel decomposition and coordination method using MILP (mixed integer linear programming) value functions is proposed in this paper. Each regional system operator sets the tie-line power injections as variational parameters in its regional SCUC model, and utilizes a finite algorithm to generate a MILP value function, which returns the optimal generation cost for any given interchange scheduling. With the value functions available from all system operators, theoretically, a coordinator is able to derive a globally optimal interchange scheduling. Since power exchanges may alter the financial position of each area considerably from what it would have been via scheduling independently, we then propose a fair savings allocation method using the values functions derived above and the Shapley value in cooperative game theory. Numerical experiments on a two-area 12-bus system and a three-area 457-bus system are carried out. The validness of the value functions based method is verified for the decentralized M-SCUC problems. The outcome of savings allocation is compared with that of the locational marginal cost based method.
Danger of using fully homomorphic encryption: A look at Microsoft SEAL

Fully homomorphic encryption is a promising crypto primitive to encrypt your data while allowing others to compute on the encrypted data. But there are many well-known problems with fully homomorphic encryption such as CCA security and circuit privacy problem. Despite these problems, there are still many companies are currently using or preparing to use fully homomorphic encryption to build data security applications. It seems that the full homomorphic encryption is very close to practicality and these problems can be easily mitigated in implementation. Although the those problems are well known in theory, there is no public discussion of their actual impact on real application. Our research shows that there are many security pitfalls in fully homomorphic encryption from the perspective of practical application. The security problems of a fully homomorphic encryption in a real application is more severe than imagined. In this paper, we will take Microsoft SEAL as an examples to introduce the security pitfalls of fully homomorphic encryption from the perspective of implementation and practical application
The Sub-Eddington Boundary for the Quasar Mass–Luminosity Plane: A Theoretical Perspective

By exploring more than sixty thousand quasars from the Sloan Digital Sky Survey Data Release 5, Steinhardt & Elvis discovered a sub-Eddington boundary and a redshift-dependent drop-off at higher black hole mass, possible clues to the growth history of massive black holes. Our contribution to this special issue of Universe amounts to an application of a model for black hole accretion and jet formation to these observations. For illustrative purposes, we include ~100,000 data points from the Sloan Digital Sky Survey Data Release 7 where the sub-Eddington boundary is also visible and propose a theoretical picture that explains these features. By appealing to thin disk theory and both the lower accretion efficiency and the time evolution of jetted quasars compared to non-jetted quasars in our “gap paradigm”, we explain two features of the sub-Eddington boundary. First, we show that a drop-off on the quasar mass-luminosity plane for larger black hole mass occurs at all redshifts. But the fraction of jetted quasars is directly related to the merger function in this paradigm, which means the jetted quasar fraction drops with decrease in redshift, which allows us to explain a second feature of the sub-Eddington boundary, namely a redshift dependence of the slope of the quasar mass–luminosity boundary at high black hole mass stemming from a change in radiative efficiency with time. We are able to reproduce the mass dependence of, as well as the oscillating behavior in, the slope of the sub-Eddington boundary as a function of time. The basic physical idea involves retrograde accretion occurring only for a subset of the more massive black holes, which implies that most spinning black holes in our model are prograde accretors. In short, this paper amounts to a qualitative overview of how a sub-Eddington boundary naturally emerges in the gap paradigm.
Not at issue any more

This paper reconsiders the theoretical interpretation of an observation made by AnderBois et al. (2011) that the (not)-at-issue status of appositive relative clauses (ARCs) depends on their position in the sentence: Sentence-final ARCs are “more at-issue” and more able to be targeted by, for instance, direct denial than sentencemedial ARCs. It is shown that this prediction can be derived from a general theory of discourse interpretation. In the proposed view, (not)-at-issue status of ARCs boils down to their salience in discourse and accessibility for attachment of new discourse material as regulated by standard discourse mechanisms such as the Right Frontier Constraint (Polanyi, 1988). The present paper explores the theoretical implications of this approach. One of them is that the (not)-at-issue status of one and the same piece of content can change in time. What is at issue at one point in discourse may not be at issue any more at another point. This and other consequences of the proposed approach call into question some widely held assumptions about not-at-issue content and open up a new perspective on the relationship between attachment, at-issueness, and projection.
Simple Swarm Foraging Algorithm Based on Gradient Computation

Swarm foraging is a common test case application for multi-robot systems. In this paper we present a novel algorithm for controlling swarm robots with limited communication range and storage capacity to efficiently search for and retrieve targets within an unknown environment. In our approach, robots search using random walk and adjust their turn probability based on attraction and repulsion signals they sense from other robots. We compared our algorithm with five different variations reflecting absence or presence of attractive and/or repulsive communication signals. Our results show that best performance is achieved when both signals are used by robots for communication. Furthermore, we show through hardware experiments how the communication model we used in the simulation could be realized on real robots.
Cosmology with stacked cluster weak lensing and cluster–galaxy cross-correlations

Cluster weak lensing is a sensitive probe of cosmology, particularly the amplitude of matter clustering σ8 and matter density parameter Ωm. The main nuisance parameter in a cluster weak lensing cosmological analysis is the scatter between the true halo mass and the relevant cluster observable, denoted $\sigma _{\ln M_\mathrm{ c}}$. We show that combining the cluster weak lensing observable ΔΣ with the projected cluster–galaxy cross-correlation function wp,cg and galaxy autocorrelation function wp,gg can break the degeneracy between σ8 and $\sigma _{\ln M_\mathrm{ c}}$ to achieve tight, per cent-level constraints on σ8. Using a grid of cosmological N-body simulations, we compute derivatives of ΔΣ, wp,cg, and wp,gg with respect to σ8, Ωm, $\sigma _{\ln M_\mathrm{ c}}$, and halo occupation distribution (HOD) parameters describing the galaxy population. We also compute covariance matrices motivated by the properties of the Dark Energy Survey cluster and weak lensing survey and the BOSS CMASS galaxy redshift survey. For our fiducial scenario combining ΔΣ, wp,cg, and wp,gg measured over 0.3−30.0 h−1 Mpc, for clusters at z = 0.35−0.55 above a mass threshold Mc ≈ 2 × 1014 h−1 M⊙, we forecast a $1.4{{\ \rm per\ cent}}$ constraint on σ8 while marginalizing over $\sigma _{\ln M_\mathrm{ c}}$ and all HOD parameters. Reducing the mass threshold to 1 × 1014 h−1 M⊙ and adding a z = 0.15−0.35 redshift bin sharpens this constraint to $0.8{{\ \rm per\ cent}}$. The small-scale (rp < 3.0 h−1 Mpc) ‘mass function’ and large-scale (rp > 3.0 h−1 Mpc) ‘halo-mass cross-correlation’ regimes of ΔΣ have comparable constraining power, allowing internal consistency tests from such an analysis.
Protecting Elections by Recounting Ballots

Complexity of voting manipulation is a prominent topic in computational social choice. In this work, we consider a two-stage voting manipulation scenario. First, a malicious party (an attacker) attempts to manipulate the election outcome in favor of a preferred candidate by changing the vote counts in some of the voting districts. Afterwards, another party (a defender), which cares about the voters' wishes, demands a recount in a subset of the manipulated districts, restoring their vote counts to their original values. We investigate the resulting Stackelberg game for the case where votes are aggregated using two variants of the Plurality rule, and obtain an almost complete picture of the complexity landscape, both from the attacker's and from the defender's perspective.
Measurement-Based Online Available Bandwidth Estimation Employing Reinforcement Learning

An accurate and fast estimation of the available bandwidth in a network with varying cross-traffic is a challenging task. The accepted probing tools, based on the fluid-flow model of a bottleneck link with first-in, first-out multiplexing, estimate the available bandwidth by measuring packet dispersions. The estimation becomes more difficult if packet dispersions deviate from the assumptions of the fluid-flow model in the presence of non-fluid bursty cross-traffic, multiple bottleneck links, and inaccurate time-stamping. This motivates us to explore the use of machine learning tools for available bandwidth estimation. Hence, we consider reinforcement learning and implement the single-state multi-armed bandit technique, which follows the ε-greedy algorithm to find the available bandwidth. Our measurements and tests reveal that our proposed method identifies the available bandwidth with high precision. Furthermore, our method converges to the available bandwidth under a variety of notoriously difficult conditions, such as heavy traffic burstiness, different cross-traffic intensities, multiple bottleneck links, and in networks where the tight link and the bottleneck link are not same. Compared to the piece-wise linear network a model based direct probing technique that employs a Kalman filter, our method shows more accurate estimates and faster convergence in certain network scenarios and does not require measurement noise statistics.
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence. This task is usually done in a pipeline manner, with aspect term extraction performed first, followed by sentiment predictions toward the extracted aspect terms. While easier to develop, such an approach does not fully exploit joint information from the two subtasks and does not use all available sources of training information that might be helpful, such as document-level labeled sentiment corpus. In this paper, we propose an interactive multi-task learning network (IMN) which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level. Unlike conventional multi-task learning methods that rely on learning common features for the different tasks, IMN introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables. Experimental results demonstrate superior performance of the proposed method against multiple baselines on three benchmark datasets.
Task Switching between Face Categorizations: An Advance Preparation Effect

This study examined how advance preparation modulates our ability to switch between face categorizations. The study included three switching experiments with different pairs of facial categorization tasks. In experiment 1, subjects switched between gender and occupation categorizations. Results showed a larger switch cost for the occupation task. In experiment 2, participants categorized emotion and gender categorizations. Results yielded a larger switch cost for the gender task. In experiment 3, subjects performed emotion and occupation categorization task. There was a larger switch cost for the occupation task. The overall results of experiments indicated that harder task yielded a larger switch cost than the easier task. Moreover, these switch costs can be reduced with sufficient preparation time. This study is the first investigation into advance preparation effect during switching between tasks of social significance. We discuss why asymmetries reduce with an advance preparation during face categorization tasks.
Linear Aggregation in Tree-Based Estimators

Abstract Regression trees and their ensemble methods are popular methods for nonparametric regression: they combine strong predictive performance with interpretable estimators. To improve their utility for locally smooth response surfaces, we study regression trees and random forests with linear aggregation functions. We introduce a new algorithm that finds the best axis-aligned split to fit linear aggregation functions on the corresponding nodes, and we offer a quasilinear time implementation. We demonstrate the algorithm’s favorable performance on real-world benchmarks and in an extensive simulation study, and we demonstrate its improved interpretability using a large get-out-the-vote experiment. We provide an open-source software package that implements several tree-based estimators with linear aggregation functions. Supplementary materials for this article are available online.
Noisy-As-Clean: Learning Unsupervised Denoising from the Corrupted Image

In the past few years, supervised networks have achieved promising performance on image denoising. These methods learn image priors and synthetic noise statistics from plenty pairs of noisy and clean images. Recently, several unsupervised denoising networks are proposed only using external noisy images for training. However, the networks learned from external data inherently suffer from the domain gap dilemma, i.e., the image priors and noise statistics are very different between the training data and the corrupted test images. This dilemma becomes more clear when dealing with the signal dependent realistic noise in real photographs. In this work, we provide a statistically useful conclusion: it is possible to learn an unsupervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images. This is achieved by proposing a"Noisy-As-Clean"strategy: taking the corrupted image as"clean"target and the simulated noisy images (based on the corrupted image) as inputs. Extensive experiments show that the unsupervised denoising networks learned with our"Noisy-As-Clean"strategy surprisingly outperforms previous supervised networks on removing several typical synthetic noise and realistic noise. The code will be publicly released.
Improving temporal action proposal generation by using high performance computing

—Temporal action proposal generation is an important and challenging problem in computer vision. The biggest challenge for the task is generating proposals with precise temporal boundaries. To address these difﬁculties, we improved the algo-rithm based on boundary sensitive network. The popular temporal convolution network today overlooked the original meaning of the single video feature vector. We proposed a new temporal convolution network called Multipath Temporal ConvNet (MTN), which consists of two parts i.e. Multipath DenseNet and SE-ConvNet, can extract more useful information from the video database. Besides, to respond to the large memory occupation and a large number of videos, we abandon traditional parameter server parallel architecture and introduce high performance computing into temporal action proposal generation. To achieve this, we implement ring parallel architecture by Massage Passing Interface (MPI) acting on our method. Compared to parameter server architecture, our parallel architecture has higher efﬁciency on temporal action detection task with multiple GPUs, which is signiﬁcant to dealing with large-scale video database. We conduct experiments on ActivityNet-1.3 and THUMOS14, where our method outperforms other state-of-art temporal action detection methods with high recall and high temporal precision.