Miracle-dz/UniFAR-FTUs huggingface dataset:
Shadow Extra Dimensions & Fuzzy Branes with an Exorcized Spectrum

Particle physics models with extra dimensions of space (EDS’s) and branes allow to shed new light on naturalness of the electroweak and flavor sectors, with a rich TeV scale phenomenology. Usually, such models are formulated in the framework of local effective field theories. The scope of this article is to propose new model building issues with EDS and branes, arising in the framework of string-inspired infinite derivative quantum field theories (QFT’s), which are perturbatively exorcized (ghost-free) and intrinsically nonlocal above some ultraviolet (UV) scale. First, a toy model with bulk and brane scalar fields is presented. It is shown that a 4D field localized on a δ-like 3-brane is still delocalized in the bulk on a small distance from the brane position, with a penetration length given by the nonlocal length scale. Fields localized on such distant fuzzy branes are thus allowed to interact directly with suppressed couplings, which is impossible in a local QFT. It is also argued that above the nonlocal scale, it should be much more difficult to probe the Kaluza-Klein (KK) tower in an experiment, because of the UV opaqueness of such infinite derivative QFT’s. In this way, a shadow EDS is obtained if the nonlocal and KK scales are close to each other, such that the effects of the KK-excitations are screened compared to a local QFT. After that, directions for model building are given. By using this new possibility of fuzzy branes, a new realization of split fermions in an EDS is presented, allowing to naturally generate flavor hierarchies. Moreover, with a warped EDS, the usual warp transmutation of a brane mass term is revisited, where it is shown that the nonlocal scale is also redshifted. This may provide a new way to solve or reduce the little hierarchy problem related to the Higgs boson mass.
Spin-symmetry broken ground-state of UO2 in DFT+U approach: the SMC method

It turns out that the ground states of some systems are symmetry-broken states in which some property is not symmetrically distributed. In the case of strongly correlated electron systems, that were studied by the DFT+U method, researchers had shown that the total energy of the system is a multi-minima function of input parameters and one has to single out the ground state out of the couples of minimum-energy states. However, the methods already introduced to determine these local minimum states were not able to predict all such states which may include the"true"ground state. In this work, we introduce a new simple and straight-forward method of SMC to find the GS as well as the meta-stable states of 1k-order anti-ferromagnetic configuration for UO$_2$. Using this method, it is shown that the ground state of UO$_2$ system is a spin-symmetry broken state of the electron spin magnetizations of oxygen atoms. Depending on the way we apply the SMC method, we obtain different numbers of meta-stable states, but the same ground states. The energetic properties, geometric properties, the electronic density distributions, and the electronic polarization density distributions of the ground state and the meta-stable states are shown to be different from each other. These properties also are shown to be sensitive to the magnitude of the initial opposite magnetizations of U1 and U2 atoms in the 1k-order anti-ferromagnetic configuration, but the number of meta-stable states as well as the ground-state properties are insensitive to this magnitude. Using the PBEsol-GGA approximation for the exchange-correlation we obtain the ground-state properties in excellent agreement with experiments.
Uniform bounds for $\rm GL(3) \times GL(2)$ $L$-functions

Abstract. In this paper, we prove uniform bounds for GL(3) × GL(2) L-functions in the GL(2) spectral aspect and the t aspect by a delta method. More precisely, let φ be a Hecke–Maass cusp form for SL(3,Z) and f a Hecke–Maass cusp form for SL(2, Z) with the spectral parameter tf . Then for t ∈ R and any ε > 0, we have L(1/2 + it, φ× f) ≪φ,ε (tf + |t|) . Moreover, we get subconvexity bounds for L(1/2 + it, φ× f) whenever |t| − tf ≫ (|t|+ tf ) 3/5+ε.
State Selection Algorithms and Their Impact on The Performance of Stateful Network Protocol Fuzzing

The statefulness property of network protocol implementations poses a unique challenge for testing and verification techniques, including Fuzzing. Stateful fuzzers tackle this challenge by leveraging state models to partition the state space and assist the test generation process. Since not all states are equally important and fuzzing campaigns have time limits, fuzzers need effective state selection algorithms to prioritize progressive states over others. Several state selection algorithms have been proposed but they were implemented and evaluated separately on different platforms, making it hard to achieve conclusive findings. In this work, we evaluate an extensive set of state selection algorithms on the same fuzzing platform that is AFLNet, a state-of-the-art fuzzer for network servers. The algorithm set includes existing ones supported by AFLNet and our novel and principled algorithm called AFLNetLegion. The experimental results on the ProFuzzBench benchmark show that (i) the existing state selection algorithms of AFLNet achieve very similar code coverage, (ii) AFLNetLegion clearly outperforms these algorithms in selected case studies, but (iii) the overall improvement appears insignificant. These are unexpected yet interesting findings. We identify problems and share insights that could open opportunities for future research on this topic.
Learning Inception Attention for Image Synthesis and Image Recognition

Image synthesis and image recognition have witnessed remarkable progress, but often at the expense of computationally expensive training and inference. Learning light-weight yet expressive deep model has emerged as an important and interesting direction. Inspired by the well-known split-transform-aggregate design heuristic in the Inception building block, this paper proposes a Skip-Layer Inception Module (SLIM) that facilitates efﬁcient learning of image synthesis models, and a same-layer variant (dubbed as SLIM too) as a stronger alternative to the well-known ResNeXts for image recognition. The proposed method is built on the idea of developing spatially-adaptive feature modulation/attention in a network. Speciﬁcally, SLIM is done from an input source feature map to a target one, either skip-layer or same-layer. In SLIM, the input feature map is ﬁrst split into a number of groups (e.g., 4). Each group is then transformed to a latent style vector (via channel-wise attention) and a latent spatial mask (via spatial attention). The learned latent masks and latent style vectors are aggregated to modulate the target feature map. For generative learning, SLIM is built on a recently proposed lightweight Generative Adversarial Networks (i.e., FastGANs) which present a skip-layer excitation (SLE) module. For few-shot image synthesis tasks, the proposed SLIM achieves better performance than the SLE work and other related methods. For one-shot image synthesis tasks, it shows stronger capability of preserving image structures than prior arts such as the SinGANs. For image classiﬁcation tasks, the proposed SLIM is used as a drop-in replacement for convolution layers in ResNets (resulting the ResNeXt-like models) and achieves better accuracy in the ImageNet-1000 dataset, with signiﬁcantly smaller model
An Efficient and Robust Committee Structure for Sharding Blockchain

Nowadays, sharding is deemed a promising way to save traditional blockchain protocols from their low scalability. However, such a technique also brings several potential risks and a huge communication burden. An improper design may give rise to an inconsistent state among different committees. Further, the communication burden arising from cross-shard transactions, unfortunately, reduces the system's performance. In this article, we first summarize five essential issues that all sharding blockchain designers face. For each issue, we discuss its key challenge and propose our suggested solutions. In order to break the performance bottlenecks, we design a committee structure and propose a reputation mechanism for selecting leaders. The term reputation in our design reflects each node's honest computation resources. In addition, we present a recovery procedure in case the leader is malicious. Theoretically, we prove that the system is robust under our design. Further simulation results also support this. In addition, the results show that selecting leaders by reputation can dramatically improve the system's performance.
Infinite wide (finite depth) Neural Networks benefit from multi-task learning unlike shallow Gaussian Processes - an exact quantitative macroscopic characterization

We prove in this paper that wide ReLU neural networks (NNs) with at least one hidden layer optimized with (cid:96) 2 -regularization on the parameters enforces multi-task learning due to representation-learning – also in the limit width to inﬁnity. This is in contrast to multiple other idealized settings discussed in the literature where wide (ReLU)-NNs loose their ability to beneﬁt from multi-task learning in the limit width to inﬁnity. We deduce the multi-task learning ability from proving an exact quantitative macroscopic characterization of the learned NN in function space.
XLPN: Efficient and Scalable Cross-Ledger Protocols for the Topological Consortium of Permissioned Blockchains

While increasingly more application-specific blockchains, or ledgers, are being implemented and deployed, exchanging information between these ledgers remains an open problem. Existing cross-ledger protocols (XLPs) exhibit a variety of limitations such as scalability, liveness, efficiency, among others. This paper proposes a new XLP, namely XLPN-22, which introduces a global topology for the consortium of ledgers to achieve better efficiency and scalability of cross-ledger data exchanges. In this work, we prove the safety and liveness of XLPN-22 and analyze its theoretical complexity. We also implement XLPN-22 on SciChain ledgers and evaluate it on up to 128 nodes, 8 ledgers, and 16,000 transactions. Experimental results show that XLPN-22 outperforms two baseline protocols, namely VLDB-20 and PODC-18, by 18--50\% and 64--84\%, respectively.
Deep Facial Synthesis: A New Challenge

—The goal of this paper is to conduct a comprehensive study on the facial sketch synthesis (FSS) problem. However, due to the high costs in obtaining hand-drawn sketch datasets, there lacks a complete benchmark for assessing the development of FSS algorithms over the last decade. As such, we ﬁrst introduce a high-quality dataset for FSS, named FS2K , which consists of 2,104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difﬁculty, diversity, and scalability, and should thus facilitate the progress of FSS research. Second, we present the largest-scale FSS study by investigating 139 classical methods, including 24 handcrafted feature based facial sketch synthesis approaches, 37 general neural-style transfer methods, 43 deep image-to-image translation methods, and 35 image-to-sketch approaches. Besides, we elaborate comprehensive experiments for existing 19 cutting-edge models. Third, we present a simple baseline for FSS, named FSGAN . With only two straightforward components, i.e. , facial-aware masking and style-vector expansion, FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset, by a large margin. Finally, we conclude with lessons learned over the past years, and point out several unsolved challenges. Our open-source code is available at https://github.com/DengPingFan/FSGAN.
Total reflection of a flare-driven quasi-periodic extreme ultraviolet wave train at a coronal hole boundary

Context. A ﬂare-driven quasi-periodic extreme ultraviolet wave train totally reﬂected at a coronal hole boundary was well imaged on both temporal and spatial scales by AIA / SDO. Aims. We aim to investigate the driving mechanisms of the quasi-periodic wave train and demonstrate the total reﬂection e ﬀ ect at the coronal hole boundary. Methods. The speeds of the incident and reﬂected wave trains are studied. The periodic correlation of the wave trains with the related ﬂare is probed. We compare the measured incidence angle and the estimated critical angle. Results. We ﬁnd that the periods of the incident and reﬂected wave trains are both about 100 seconds. The excitation of the quasi- periodic wave train was possibly due to the intermittent energy release in the associated ﬂare since its period is similar to that of the quasi-periodic pulsations in the associated ﬂare. Our observational results show that the reﬂection of the wave train at the boundary of the coronal hole was a total reﬂection because the measured incidence and critical angles satisfy the theory of total reﬂection: the incidence angle is smaller than the critical angle.
An Analysis of an Alternative Pythagorean Expected Win Percentage Model: Applications Using Major League Baseball Team Quality Simulations

Background. Contests are games in which the players compete for a prize and exert effort to increase their probability of winning. For sport contests, analysts often use the Pythagorean expected win percentage model to estimate teams’ expected wins (quality). We ask if there are alternative contest models that minimize error or information loss from misspecification and outperform the Pythagorean model. Aim. This article aims to use simulated data to select the optimal expected win percentage model among the choice of relevant alternatives. The choices include the traditional Pythagorean model and the difference-form contest success function (CSF). Method. We simulate 1,000 iterations of the 2014 MLB season for the purpose of estimating and analyzing alternative models of expected win percentage (team quality). We use the open-source, Strategic Baseball Simulator and develop an AutoHotKey script that programmatically executes the SBS application, chooses the correct settings for the 2014 season, enters a unique ID for the simulation data file, and iterates these steps 1,000 times. We estimate expected win percentage using the traditional Pythagorean model, as well as the difference-form CSF model that is used in game theory and public choice economics. Each model is estimated while accounting for fixed (team) effects. Result. We find that the difference-form CSF model outperforms the traditional Pythagorean model in terms of explanatory power and in terms of misspecification-based information loss as estimated by the Akaike Information Criterion. Through parametric estimation, we further confirm that the simulator yields realistic statistical outcomes. Conclusion. The simulation methodology offers the advantage of greatly improved sample size. As the season is held constant, our simulation-based statistical inference also allows for estimation and model comparison without the (time series) issue of nonstationarity. The results suggest that improved win (productivity) estimation can be achieved through alternative CSF specifications.
Integrating crystalline-silicon photovoltaic cells into decorative glazing

Photovoltaic cells are often perceived as an ugly addition to glazing. Improvements could make photovoltaics more attractive for use in architecture, increasing the opportunities to generate electricity on the surfaces of buildings. This research demonstrates methods of integrating crystalline-silicon photovoltaic cells into decorative glazing. The aim was to explore the use of a combination of opaque, reflective and coloured materials with crystalline-silicon photovoltaic cells in glazing designs, to enhance appearance whilst maintaining good photovoltaic generation. Colour was incorporated through the use of Lumogen F dyes (BASF) added to the photovoltaic encapsulants Sylgard 184 (Dow Corning) and EVA (ethylene vinyl acetate: Solutia Vistasolar Fastcure 486.00). The absorption and emission properties of these new material combinations were measured. The dyes degraded quickly under both accelerated and outdoor testing. Glazing designs were created that incorporated Lumogen dyes, plus opaque and reflective materials. The opaque and reflective materials were used to disguise the square photovoltaic cells. This demonstrated that crystalline-silicon photovoltaic cells could be assimilated into a wide variety of architectural glazing. Reflective materials and fluorescent dyes were shown to improve maximum electrical current from widely-spaced crystalline-silicon photovoltaic cells. Further work is recommended to establish durable material combinations for use with photovoltaics in decorative glazing.
Deep Learning Models for Knowledge Tracing: Review and Empirical Evaluation

Knowledge tracing models, which are used to estimate students’ ability or knowledge based on data collected from students’ work on learning-related tasks, are a widely studied area within the educational data mining domain. In this work, we review and evaluate a body of deep learning knowledge tracing (DLKT) models with openly available and widely-used data sets, and with a novel data set of students learning to program. The evaluated DLKT models have been reimplemented for assessing reproducibility of previously reported results as well as for assessing the level of detail with which the models and their evaluations have been reported in previously published articles. The DLKT models are tested with different input and output layer variations found in the compared models that are independent of the main architectures of the models and also with different maximum attempt count options. Several metrics to compare and contrast the results are used to reflect on the quality and appropriateness of the evaluated knowledge tracing models. The evaluated knowledge tracing models include Vanilla-DKT, two variants of Long Short-Term Memory Deep Knowledge Tracing (LSTM-DKT), two variants of Dynamic Key-Value Memory Network (DKVMN), and Self-Attentive Knowledge Tracing (SAKT). We evaluate logistic regression, Bayesian Knowledge Tracing (BKT) and simple non-learning models as baselines. Our empirical evaluation suggests that while the DLKT models with tuned hyperparameters in general outperform non deep learning based models, the relative differences between the DLKT models are subtle and often vary between datasets. Specifically, we observe that no model consistently outperforms all other models in all datasets. Our results also show that simple non-learning models such as mean prediction can yield better performance than more sophisticated knowledge tracing models, especially in terms of accuracy, in some datasets. Further, our metric and hyperparameter analysis shows that the metric used to select the best model hyperparameters has a noticeable effect on the performance of the models, and also that some metrics appear more favorable than others for certain models. We also study the effect of input and output layer variations on model performance, and analyze the impact of filtering out long attempt sequences that has been implicitly and explicitly used in some studies. We further discuss the effect of non-model properties such as randomness and hardware on model performance, and finally, we discuss model performance replicability and related issues including pitfalls and suggest practices for future work. Our model implementations, evaluation code, and data are published as a part of this work.
Two approaches for the passive charge management of contactless test masses

Free floating test masses (TM) of inertial reference instruments accumulate charge mainly through the triboelectric effect during separation from their housings and, if in the space environment, from cosmic radiation. These charges will degrade the accuracy of high sensitivity accelerometers and drag-free sensors. We demonstrate in ground testing two passive bipolar charge management systems using photoelectrons emitted from gold coated surfaces under illumination by ultraviolet light emitting diodes (UV-LEDs) with 255 nm, 275 nm, and 295 nm central wavelengths. The first method uses fast photoelectrons, generated by two 255 nm UV-LEDs with adjustable-intensity (through fine-tuning of their excitation currents) and illuminating the TM and its housing respectively. A second technique uses slow photoelectrons generated by one UV-LED, of either 275 nm or 295 nm, directed at the TM. Fast and slow electrons are defined as having kinetic energies after photoemission above and below eVTMmax , where VTMmax is the maximum allowable potential required for normal operation of the TM. In its optimized configurations and following an exposure of <30 s to UV, the fast-photoelectron control system converges to zero TM potential from ±1 V with a drift of ≈1.5 mV/day. The slow-photoelectron system requires about 5 min to converge to zero TM potential from 100 mV, with a similar drift of ≈2.0 mV/day. For reference, VTMmax≅80mV for the LISA and LISA pathfinder sensors. Insight into the slow photoelectrons charge control method is provided by our simple Gaussian model of the work function for gold films (that approximates their quasi-triangular measured shape): 4.6 eV center, 0.2 eV standard deviation, and 4.30 eV and 4.90 eV cutoffs. The longest wavelength (lowest energy) photoemission is obtained with the 295 nm LED (11 nm FWHM) at about 285 nm (4.35 eV), where only about 3.5% of the photons have an energy above the photoelectric threshold. While the two-source UV fast photoelectron method is a modification of the LISA Pathfinder approach, the slow photoelectron method is a new passive charge management technique. These two passive techniques were developed for instruments without sensing and activation systems. For instruments with electric fields surrounding the TM and more complex geometries additional adaptations are required.
Stochastic Layers in Vision Transformers

We introduce fully stochastic layers in vision transformers, without causing any severe drop in performance. The additional stochasticity boosts the robustness of visual features and strengthens privacy. In this process, linear layers with fully stochastic parameters are used, both during training and inference, to transform the feature activations of each multilayer perceptron. Such stochastic linear operations preserve the topological structure, formed by the set of tokens passing through the shared multilayer perceptron. This operation encourages the learning of the recognition task to rely on the topological structures of the tokens, instead of their values, which in turn offers the desired robustness and privacy of the visual features. In this paper, we use our features for three different applications, namely, adversarial robustness, network calibration, and feature privacy. Our features offer exciting results on those tasks. Furthermore, we showcase an experimental setup for federated and transfer learning, where the vision transformers with stochastic layers are again shown to be well behaved. Our source code will be made publicly available.