Miracle-dz/UniFAR-FTUs huggingface dataset:
NeuroMAX: A High Throughput, Multi-Threaded, Log-Based Accelerator for Convolutional Neural Networks

Convolutional neural networks (CNNs) require high throughput hardware accelerators for real time applications owing to their huge computational cost. Most traditional CNN accelerators rely on single core, linear processing elements (PEs) in conjunction with 1D dataflows for accelerating convolution operations. This limits the maximum achievable ratio of peak throughput per PE count to unity. Most of the past works optimize their dataflows to attain close to a 100% hardware utilization to reach this ratio. In this paper, we introduce a high throughput, multi-threaded, log-based PE core. The designed core provides a 200% increase in peak throughput per PE count while only incurring a 6% increase in area overhead compared to a single, linear multiplier PE core with same output bit precision. We also present a 2D weight broadcast dataflow which exploits the multi-threaded nature of the PE cores to achieve a high hardware utilization per layer for various CNNs. The entire architecture, which we refer to as NeuroMAX, is implemented on Xilinx Zynq 7020 SoC at 200 MHz processing clock. Detailed analysis is performed on throughput, hardware utilization, area and power breakdown, and latency to show performance improvement compared to previous FPGA and ASIC designs.
Permutation‐based tests for discontinuities in event studies

We propose using a permutation test to detect discontinuities in an underlying economic model at a known cutoff point. Relative to the existing literature, we show that this test is well suited for event studies based on time‐series data. The test statistic measures the distance between the empirical distribution functions of observed data in two local subsamples on the two sides of the cutoff. Critical values are computed via a standard permutation algorithm. Under a high‐level condition that the observed data can be coupled by a collection of conditionally independent variables, we establish the asymptotic validity of the permutation test, allowing the sizes of the local subsamples to be either be fixed or grow to infinity. In the latter case, we also establish that the permutation test is consistent. We demonstrate that our high‐level condition can be verified in a broad range of problems in the infill asymptotic time‐series setting, which justifies using the permutation test to detect jumps in economic variables such as volatility, trading activity, and liquidity. These potential applications are illustrated in an empirical case study for selected FOMC announcements during the ongoing COVID‐19 pandemic.
Sample, Quantize, and Encode: Timely Estimation Over Noisy Channels

The effects of quantization and coding on the estimation quality of Gauss-Markov processes are considered, with a special attention to the Ornstein-Uhlenbeck process. Samples are acquired from the process, quantized, and then encoded for transmission using either infinite incremental redundancy (IIR) or fixed redundancy (FR) coding schemes. A fixed processing time is consumed at the receiver for decoding and sending feedback to the transmitter. Decoded messages are used to construct a minimum mean square error (MMSE) estimate of the process as a function of time. This is shown to be an increasing functional of the age-of-information (AoI), defined as the time elapsed since the sampling time pertaining to the latest successfully decoded message. Such functional depends on the quantization bits, codewords lengths and receiver processing time. The goal, for each coding scheme, is to optimize sampling times such that the long-term average MMSE is minimized. This is then characterized in the setting of general increasing functionals of AoI, not necessarily corresponding to MMSE, which may be of independent interest in other contexts. We first show that the optimal sampling policy for IIR is such that a new sample is generated only if the AoI exceeds a certain threshold, while for FR it is such that a new sample is delivered just-in-time as the receiver finishes processing the previous one. Enhanced transmissions schemes are then developed in order to exploit the processing times to make new data available at the receiver sooner. For both IIR and FR, it is shown that there exists an optimal number of quantization bits that balances AoI and quantization errors, and hence minimizes the MMSE. It is also shown that for longer receiver processing times, the relatively simpler FR scheme outperforms IIR.
On the validity of complex Langevin method for path integral computations

The complex Langevin (CL) method is a classical numerical strategy to alleviate the numerical sign problem in the computation of lattice field theories. Mathematically, it is a simple numerical tool to compute a wide class of high-dimensional and oscillatory integrals. However, it is often observed that the CL method converges but the limiting result is incorrect. The literature has several unclear or even conflicting statements, making the method look mysterious. By an in-depth analysis of a model problem, we reveal the mechanism of how the CL result turns biased as the parameter changes, and it is demonstrated that such a transition is difficult to capture. Our analysis also shows that the method works for any observables only if the probability density function generated by the CL process is localized. To generalize such observations to lattice field theories, we formulate the CL method on general groups using rigorous mathematical languages for the first time, and we demonstrate that such localized probability density function does not exist in the simulation of lattice field theories for general compact groups, which explains the unstable behavior of the CL method. Fortunately, we also find that the gauge cooling technique creates additional velocity that helps confine the samples, so that we can still see localized probability density functions in certain cases, as significantly broadens the application of the CL method. The limitations of gauge cooling are also discussed. In particular, we prove that gauge cooling has no effect for Abelian groups, and we provide an example showing that biased results still exist when gauge cooling is insufficient to confine the probability density function.
Local Geometric Distortions Resilient Watermarking Scheme Based on Symmetry

As an efficient watermark attack method, geometric distortions destroy the synchronization between the watermark encoder and decoder. Local geometric distortion is a considerable challenge in the watermarking field. Although many geometric distortion resilient watermarking schemes have been proposed, few perform well against local geometric distortions, such as random bending attacks (RBAs). To address this problem, this paper proposes a novel watermark synchronization process and a corresponding watermarking scheme. In our scheme, the watermark bits are represented by random patterns. The message is encoded to obtain a watermark unit, and the watermark unit is flipped to generate a symmetrical watermark. Then, the symmetrical watermark is additively embedded into the spatial domain of the host image. In watermark extraction, we first obtain the theoretical mean-square error minimized estimation of the watermark. Then, an autoconvolution function is applied to this estimation to detect the symmetry and obtain a watermark unit map. According to this map, the watermark can be accurately synchronized, and then extraction can be performed. Experimental results demonstrate the excellent robustness of the proposed watermarking scheme to local geometric distortions, global geometric distortions, common image processing operations, and some kinds of combined attacks.
Speed of Social Learning from Reviews in Non-Stationary Environments

Potential buyers of a product or service tend to first browse feedback from previous consumers through review platforms. This behavior is modeled by a market of Bayesian consumers with heterogeneous preferences, who sequentially decide whether to buy an item based on reviews of previous buyers. While the belief of the item quality in simple settings is known to converge to its true value, this paper extends this result to more general cases, besides providing convergence rates. In practice, the quality of an item may change over time as new competitors can appear in the market or the product/service can undergo modifications. This paper studies such dynamics with changing points model and shows that the cost of learning remains low, when expressed in total utility earned by consumers.
Internet Traffic Volumes are Not Gaussian—They are Log-Normal: An 18-Year Longitudinal Study With Implications for Modelling and Prediction

Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that traffic obeys the log-normal distribution which is a better fit than the Gaussian distribution commonly claimed in the literature. We also investigate an alternative heavy-tailed distribution (the Weibull) and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which exhibit a poor fit for all distributions tried and show that this is often due to traffic outages or links that hit maximum capacity. We demonstrate that the data we look at is stationary if we consider samples of 15-minute long or even 1-hour long. This gives confidence that we can use the distributions for estimation and modelling purposes. We demonstrate the utility of our findings in two contexts: predicting that the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) and predicting 95th percentile pricing. We also show that the log-normal distribution is a better predictor than Gaussian or Weibull distributions in both contexts.
Outer space for RAAGs

For any right-angled Artin group $A_{\Gamma}$ we construct a finite-dimensional space $\mathcal{O}_{\Gamma}$ on which the group $\text{Out}(A_{\Gamma})$ of outer automorphisms of $A_{\Gamma}$ acts properly. We prove that $\mathcal{O}_{\Gamma}$ is contractible, so that the quotient is a rational classifying space for $\text{Out}(A_{\Gamma})$. The space $\mathcal{O}_{\Gamma}$ blends features of the symmetric space of lattices in $\mathbb{R}^n$ with those of Outer space for the free group $F_n$. Points in $\mathcal{O}_{\Gamma}$ are locally CAT(0) metric spaces that are homeomorphic (but not isometric) to certain locally CAT(0) cube complexes, marked by an isomorphism of their fundamental group with $A_{\Gamma}$.
Ultrastructural studies on the Myelospermaceae fam. nov., with a new species of Myelosperma

Ultrastructural and morphological studies have been carried out on M. tumidum , the type species of Myelosperma. Asci are provided with a distinctive refractive subapical ring, which differs from that found in other unitunicate families. The ascospores are reniform in all species and surrounded by mucilaginous sheaths of various shapes. Numerous ascomata also develop in a circle around a common central pore. At the Ultrastructural level the ascus apex comprises an electron-transparent thickening incorporating a subapical electron-dense hemispherical ring-like structure and the ascus wall is continuous over the apex. This type of ring is compared with those found in representative species in other related families. Based on these observations, a new family, the Myelospermaceae, is introduced, to accommodate Myelosperma. The rationale behind this is discussed, and M. alata is described to accommodate a remarkable new species from the Seychelles. Arguments for the possible inclusion of Manokwaria, Palmicola and Pemphidium in the Myelospermaceae are given.
Estimation for High-Dimensional Multi-Layer Generalized Linear Model - Part I: The Exact MMSE Estimator

This two-part work considers the minimum means square error (MMSE) estimation problem for a high dimensional multi-layer generalized linear model (ML-GLM), which resembles a feed-forward fully connected deep learning network in that each of its layer mixes up the random input with a known weighting matrix and activates the results via non-linear functions, except that the activation here is stochastic and following some random distribution. Part I of the work focuses on the exact MMSE estimator, whose implementation is long known infeasible. For this exact estimator, an asymptotic analysis on the performance is carried out using a new replica method that is refined from certain aspects. A decoupling principle is then established, suggesting that, in terms of joint input-and-estimate distribution, the original estimation problem of multiple-input multiple-output is indeed identical to a simple single-input single-output one subjected to additive white Gaussian noise (AWGN) only. The variance of the AWGN is further shown to be determined by some coupled equations, whose dependency on the weighting and activation is given explicitly and analytically. Comparing to existing results, this paper is the first to offer a decoupling principle for the ML-GLM estimation problem. To further address the implementation issue of an exact solution, Part II proposes an approximate estimator, ML-GAMP, whose per-iteration complexity is as low as GAMP, while its asymptotic MSE (if converged) is as optimal as the exact MMSE estimator.
Localising change points in piecewise polynomials of general degrees

In this paper we are concerned with a sequence of univariate random variables with piecewise polynomial means and independent sub-Gaussian noise. The underlying polynomials are allowed to be of arbitrary but fixed degrees. We propose a two-step estimation procedure based on the $\ell_0$-penalisation and provide upper bounds on the localisation error. We complement these results by deriving information-theoretic lower bounds, which show that our two-step estimators are nearly minimax rate-optimal. We also show that our estimator enjoys near optimally adaptive performance by attaining individual localisation errors depending on the level of smoothness at individual change points of the underlying signal.
Studies on the Extracellular Polysaccharides (EPS) Produced in vitro by Pseudomonas phaseolicola I. Indications for a Polysaccharide Resembling Alginic Acid in Seven P. syringae Pathovars

Extracellular polysaccharides (EPS) produced by Pseudomonas syringae pv. phaseolicola are obviously composed of two main components: the long known levan consisting of fructose, and a mannuronan consisting mainly of mannuronic acid (manA), thus resembling alginic acid (alginate). The identification of manA was established by TLC utilizing different developing systems, and by cellulose acetate electrophoresis in different buffers. References were authentic uronic acids and hydrolyzed authentic alginate. A rough quantification of the “alginate” present in crude EPS was achieved with a selective colour reaction which largely excluded compounds other than uronic acids. Levan was only synthesized with sucrose as primary carbon source. When grown on several other sugars and related compounds “alginate” was the predominant component of the EPS. Additionally, rhamnose, fucose, glucose and amino sugars were found in some instances in hydrolysates of crude EPS, suggesting the release of lipopolysaccharides (LPS) from the bacterial cell walls during culture. Growth on carbon sources not related to sugars resulted in these “LPS” as the main constituent of EPS. After cultivation with sucrose, the “alginate” was restricted to the “slime” fraction of the EPS. In the “capsular” fraction, levan was predominating. A screening program revealed the capacity to synthesize the “alginate” in six additional P. syringae pathovars: pisi, lachrymans, aptata, tomato, syringae, and glycinea. All of the strains tested so far produced levan from sucrose, however, the “alginate” was formed not by all of them. There was a tendency that fresh isolates produced more “alginate” than strains subcultured for an extended time in vitro. This was also true for the total amount of EPS.
HELPING BEHAVIOUR IN COOPERATIVELY-BREEDING GREEN WOODHOOPOES: SELECTED OR UNSELECTED TRAIT?

1. I experimentally examined components of the stimulus-response mechanism and their effects on feeding rates to nestlings in cooperative singular-breeding green woodhoopoes Phoeniculus purpureus, and showed that both auditory and visual stimuli played important roles. 2. Breeding status did not influence the way in which adults responded to the various stimuli. 3. Adult woodhoopoes fed foreign nestlings which had been transferred as frequently and with as much food as their own nestlings. 4. Observational evidence from two different studies showed that recent-immigrant nonbreeders provided young with as much food as other established nonbreeders. Thus, there is no evidence that nonparental feeding behaviour is related to nest-site specificity, kin discrimination, or prior association with breeders in this species. 5. Demographic data gathered over a period of eight years (1981-1989) for a total of 258 "group years" suggested that helpers did not gain any direct or indirect fitness benefits as a result of their nonparental feeding contributions to young per se. 6. I conclude that nonparental feeding behaviour has persisted as unmodified parental feeding behaviour in this species and suggest that this may be the case for most other singular cooperative-breeding species.
Graph Neural Network for Video-Query based Video Moment Retrieval

In this paper, we focus on Video Query based Video Moment Retrieval (VQ-VMR) task, which uses a query video clip as input to retrieve a semantic relative video clip in another untrimmed long video. we find that in VQ-VMR datasets, there exists a phenomenon showing that there does not exist consistent relationship between feature similarity by frame and feature similarity by video, which affects the feature fusion among frames. However, existing VQ-VMR methods do not fully consider it. Taking this phenomenon into account, in this article, we treat video features as a graph by concatenating the query video feature and proposal video feature along time dimension, where each timestep is treated as a node, each row of the feature matrix is treated as feature of each node. Then, with the power of graph neural networks, we propose a Multi-Graph Feature Fusion Module to fuse the relation feature of this graph. After evaluating our method on ActivityNet v1.2 dataset and Thumos14 dataset, we find that our proposed method outperforms the state of art methods.
Results from the Alfvén Eigenmode Active Diagnostic during the 2019-2020 JET deuterium campaign

This paper presents results of extensive analysis of mode excitation observed during the operation of the Alfvén Eigenmode Active Diagnostic (AEAD) in the JET tokamak during the 2019-2020 deuterium campaign. Six of eight toroidally spaced antennas, each with independent power and phasing, were successful in actively exciting stable MHD modes in 479 plasmas. In total, magnetic resonances were detected with up to fourteen fast magnetic probes. In this work, we present the calculations of resonant frequencies f 0, damping rates γ < 0, and toroidal mode numbers n, spanning the parameter ranges f 0≈ 30–250 kHz, −γ ≈ 0–13 kHz, and |n|≤30 . In general, good agreement is seen between the resonant and the calculated toroidal Alfvén Eigenmode frequencies, and between the toroidal mode numbers applied by the AEAD and estimated of the excited resonances. We note several trends in the database: the probability of resonance detection decreases with plasma current and external heating power; the normalized damping rate increases with edge safety factor but decreases with external heating. These results provide key information to prepare future experimental campaigns and to better understand the physics of excitation and damping of Alfvén Eigenmodes in the presence of alpha particles during the upcoming DT campaign, thereby extrapolating with confidence to future tokamaks.
HERBICIDE EFFECTS ON PLANT DISEASE

To be effective, herbicides they must have strong biological activity against plants. Indirectly through their effects on plants, herbicides can influence almost any process or interaction of the plant, including its susceptibility to plant diseases. In some cases, herbicides also have direct effects on plant pathogens. In this short review, we will discuss both types of effect, and, where possible, provide the mechanism for the effect. We provide a separate section for glyphosate because of the relatively large amount of information on the effects of this very important herbicide on plant diseases and the fact that there have been many seemingly conflicting claims of the effect of glyphosate on diseases in glyphosate-resistant crops (Duke & Cerdeira, 2005). The topic of herbicide effects on plant diseases has been reviewed previously, either as a single topic (e.g., Altman, 1991; Altman & Campbell, 1979; Katan & Eshel, 1973; Levesque et al., 1992) or as part of a more extensive review on secondary effects of pesticides (several of these are in the book by Altman, 1993) or chemical effects on microbial weed biocontrol agents (Hoagland, 1996). There have been no recent reviews of this topic, but a large amount of information has not been generated since the last review of this topic, except in the case of glyphosate.
Structure and Properties of Thermoresponsive Diblock Copolymers Embedded with Metal Oxide Nanoparticles

Nanostructured polymer-metal oxide composites are a current research area of great importance due to its highlight applications in sensors, optics, catalysts and drug delivery. Particularly the use of thermoresponsive polymers gives more flexibilities and possibilities in the design and construction of polymer templates. In the present investigation, the structure and magnetic properties of hybrid metal oxide/DBC films composed of two kinds of polystyrene-block-poly (N-isopropylacrylamide)(PS-b-PNIPAM) diblock copolymers (DBCs) with PS and PNIPAM as the major polymer domains respectively, and iron oxide were investigated. The thermoresponsive PNIPAM has a lower critical solution temperature (LCST) in aqueous solution at 32{\deg}C, which enables the controllable volume ratio of PS and PNIPAM in the structure of PS-b-PNIPAM diblock copolymers (DBCs). Thus, a temperature and humidity controlling cell was designed and built for precisely tuning the block structure of PS-b-PNIPAM DBCs, which was investigated by in-situ small-angle X-ray scattering (SAXS) and grazing-incidence small-angle X-ray scattering (GISAXS) measurements. The superparamagnetic behavior of the heat-treated hybrid iron oxide/PS-b-PNIPAM DBC films was investigated using a superconducting quantum interference device (SQUID) magnetometer.