Miracle-dz/UniFAR-FTUs huggingface dataset:
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search

Neural architecture search (NAS) has achieved breakthrough success in a great number of applications in the past few years. It could be time to take a step back and analyze the good and bad aspects in the field of NAS. A variety of algorithms search architectures under different search space. These searched architectures are trained using different setups, e.g., hyper-parameters, data augmentation, regularization. This raises a comparability problem when comparing the performance of various NAS algorithms. NAS-Bench-101 has shown success to alleviate this problem. In this work, we propose an extension to NAS-Bench-101: NAS-Bench-201 with a different search space, results on multiple datasets, and more diagnostic information. NAS-Bench-201 has a fixed search space and provides a unified benchmark for almost any up-to-date NAS algorithms. The design of our search space is inspired by the one used in the most popular cell-based searching algorithms, where a cell is represented as a directed acyclic graph. Each edge here is associated with an operation selected from a predefined operation set. For it to be applicable for all NAS algorithms, the search space defined in NAS-Bench-201 includes all possible architectures generated by 4 nodes and 5 associated operation options, which results in 15,625 neural cell candidates in total. The training log using the same setup and the performance for each architecture candidate are provided for three datasets. This allows researchers to avoid unnecessary repetitive training for selected architecture and focus solely on the search algorithm itself. The training time saved for every architecture also largely improves the efficiency of most NAS algorithms and presents a more computational cost friendly NAS community for a broader range of researchers. We provide additional diagnostic information such as fine-grained loss and accuracy, which can give inspirations to new designs of NAS algorithms. In further support of the proposed NAS-Bench-102, we have analyzed it from many aspects and benchmarked 10 recent NAS algorithms, which verify its applicability.
New Competitive Analysis Results of Online List Scheduling Algorithm

Online algorithm has been an emerging area of interest for researchers in various domains of computer science. The online $m$-machine list scheduling problem introduced by Graham has gained theoretical as well as practical significance in the development of competitive analysis as a performance measure for online algorithms. In this paper, we study and explore the performance of Graham's online \textit{list scheduling algorithm(LSA)} for independent jobs. In the literature, \textit{LSA} has already been proved to be $2-\frac{1}{m}$ competitive, where $m$ is the number of machines. We present two new upper bound results on competitive analysis of \textit{LSA}. We obtain upper bounds on the competitive ratio of $2-\frac{2}{m}$ and $2-\frac{m^2-m+1}{m^2}$ respectively for practically significant two special classes of input job sequences. Our analytical results can motivate the practitioners to design improved competitive online algorithms for the $m$-machine list scheduling problem by characterization of real life input sequences.
Mechanism of resonant enhancement of electron drift in nanometer semiconductor superlattices subjected to electric and inclined magnetic fields

We address the increase of electron drift velocity that arises in semiconductor superlattices (SLs) subjected to constant electric and magnetic fields. It occurs if the magnetic field possesses nonzero components both along and perpendicular to the SL axis and the Bloch oscillations along the SL axis become resonant with cyclotron rotation in the transverse plane. It is a phenomenon of considerable interest, so that it is important to understand the underlying mechanism. In an earlier Letter (Phys. Rev. Lett. 114, 166802 (2015)) we showed that, contrary to a general belief that drift enhancement occurs through chaotic diffusion along a stochastic web (SW) within semiclassical collisionless dynamics, the phenomenon actually arises through a non-chaotic mechanism. In fact, any chaos that occurs tends to reduce the drift. We now provide fuller details, elucidating the mechanism in physical terms, and extending the investigation. In particular, we: (i) demonstrate that pronounced drift enhancement can still occur even in the complete absence of an SW; (ii) show that, where an SW does exist and its characteristic slow dynamics comes into play, it suppresses the drift enhancement even before strong chaos is manifested; (iii) generalize our theory for non-small temperature, showing that heating does not affect the enhancement mechanism and accounting for some earlier numerical observations; (iv) demonstrate that certain analytic results reported previously are incorrect; (v) provide an extended critical review of the subject and closely related issues; and (vi) discuss some challenging problems for the future.
Key-dependent Security of Stream Ciphers

The control of the cryptography is more than ever a recurrent issue. As the current international regulation does not apply in the signatory countries, the concept of enforcing backdoors in encryption system is reborn with more strength. This paper deals with a particular class of stream cipher backdoors. This class, under a different form, has been widely used by the industry in the 80s and 90s in the context of the export control rules imposed by the US to the Western countries. We propose here a new system -- called BSEA-2, with a 128-bit secret key -- which is a seemingly minor modification of BSEA-1, a system proposed in \cite{filiol_bsea1}. BSEA-2 illustrates, in a simple and didactic~\textemdash~it has been also designed for a MSc cryptanalysis course~\textemdash~but efficient way the concept of key-dependent cryptographic security. The aim is to keep control on encryption means that a country/provider could provide to another country/client for which the secret key are also provided. With such systems, changing the key class results in downgrading the cryptographic security.
Exact Semidefinite Programming Bounds for Packing Problems

In this paper we give an algorithm to round the floating point output of a semidefinite programming solver to a solution over the rationals or a quadratic extension of the rationals. We apply this to get sharp bounds for packing problems, and we use these sharp bounds to prove that certain optimal packing configurations are unique up to rotations. In particular, we show that the configuration coming from the $\mathsf{E}_8$ root lattice is the unique optimal code with minimal angular distance $\pi/3$ on the hemisphere in $\mathbb R^8$, and we prove that the three-point bound for the $(3, 8, \vartheta)$-spherical code, where $\vartheta$ is such that $\cos \vartheta = (2\sqrt{2}-1)/7$, is sharp by rounding to $\mathbb Q[\sqrt{2}]$. We also use our machinery to compute sharp upper bounds on the number of spheres that can be packed into a larger sphere.
CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data

ABSTRACT CircSpaceTime is the only R package, currently available, that implements Bayesian models for spatial and spatio-temporal interpolation of circular data. Such data are often found in applications where, among the many, wind directions, animal movement directions, and wave directions are involved. To analyse such data, we need models for observations at locations and times t, as the so-called geostatistical models, providing structured dependence assumed to decay in distance and time. The approach we take begins with Gaussian processes defined for linear variables over space and time. Then, we use either wrapping or projection to obtain processes for circular data. The models are cast as hierarchical, with fitting and inference within a Bayesian framework. Altogether, this package implements work developed by a series of papers, by Jona Lasinio, Mastrantonio, Wang, and Gelfand. All procedures are written using Rcpp. Estimates are obtained by MCMC, allowing parallelized multiple chains run. The implementation of the proposed models is considerably improved on the simple routines adopted in the research papers. As original running examples, for the spatial and spatio-temporal settings, we use wind directions datasets over central Italy.
On the Improved Rates of Convergence for Matérn-Type Kernel Ridge Regression with Application to Calibration of Computer Models

Kernel ridge regression is an important nonparametric method for estimating smooth functions. We introduce a new set of conditions, under which the actual rates of convergence of the kernel ridge regression estimator under both the L_2 norm and the norm of the reproducing kernel Hilbert space exceed the standard minimax rates. An application of this theory leads to a new understanding of the Kennedy-O'Hagan approach for calibrating model parameters of computer simulation. We prove that, under certain conditions, the Kennedy-O'Hagan calibration estimator with a known covariance function converges to the minimizer of the norm of the residual function in the reproducing kernel Hilbert space.
Multi-lane Detection Using Instance Segmentation and Attentive Voting

Autonomous driving is becoming one of the leading industrial research areas. Therefore many automobile companies are coming up with semi to fully autonomous driving solutions. Among these solutions, lane detection is one of the vital driver-assist features that play a crucial role in the decision-making process of the autonomous vehicle. A variety of solutions have been proposed to detect lanes on the road, which ranges from using hand-crafted features to the state-of-the-art end-to-end trainable deep learning architectures. Most of these architectures are trained in a traffic constrained environment. In this paper, we propose a novel solution to multi -lane detection, which outperforms state of the art methods in terms of both accuracy and speed. To achieve this, we also offer a dataset with a more intuitive labeling scheme as compared to other benchmark datasets. Using our approach, we are able to obtain a lane segmentation accuracy of 99.87% running at 54.53 fps (average).
On the fundamental limitations of imaging with evanescent waves

There has been significant interest in imaging and focusing schemes that use evanescent waves to beat the diffraction limit, such as those employing negative refractive index materials or hyperbolic metamaterials. The fundamental issue with all such schemes is that the evanescent waves quickly decay between the imaging system and sample, leading to extremely weak field strengths. Using an entropic definition of spot size which remains well defined for arbitrary beam profiles, we derive rigorous bounds on this evanescent decay. In particular, we show that the decay length is only $w / \pi e \approx 0.12 w$, where $w$ is the spot width in the focal plane, or $\sqrt{A} / 2 e \sqrt{\pi} \approx 0.10 \sqrt{A}$, where $A$ is the spot area. Practical evanescent imaging schemes will thus most likely be limited to focal distances less than or equal to the spot width.
Attentive batch normalization for lstm-based acoustic modeling of speech recognition

Batch normalization (BN) is an effective method to accelerate model training and improve the generalization performance of neural networks. In this paper, we propose an improved batch normalization technique called attentive batch normalization (ABN) in Long Short Term Memory (LSTM) based acoustic modeling for automatic speech recognition (ASR). In the proposed method, an auxiliary network is used to dynamically generate the scaling and shifting parameters in batch normalization, and attention mechanisms are introduced to improve their regularized performance. Furthermore, two schemes, frame-level and utterance-level ABN, are investigated. We evaluate our proposed methods on Mandarin and Uyghur ASR tasks, respectively. The experimental results show that the proposed ABN greatly improves the performance of batch normalization in terms of transcription accuracy for both languages.
A Novel Self-Supervised Re-labeling Approach for Training with Noisy Labels

The major driving force behind the immense success of deep learning models is the availability of large datasets along with their clean labels. This is very difficult to obtain and thus has motivated research on training deep neural networks in the presence of label noise. In this work, we build upon the seminal work in this area, Co-teaching and propose a simple, yet efficient approach termed mCT- S2R (modified co-teaching with self-supervision and relabeling) for this task. Firstly, to deal with significant amount of noise in the labels, we propose to use self-supervision to generate robust features without using any labels. Furthermore, using a parallel network architecture, an estimate of the clean labeled portion of the data is obtained. Finally, using this data, a portion of the estimated noisy labeled portion is re-labeled, before resuming the network training with the augmented data. Extensive experiments on three standard datasets show the effectiveness of the proposed framework.
DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices

Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements like step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist where noise remains an unsolved problem. Here, we develop a multi-task deep learning method to assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation (AF). We train our algorithm on over one million simulated unlabeled physiological signals and fine-tune on a curated dataset of over 500K labeled signals from over 100 individuals from 3 different wearable devices. We demonstrate that in comparison with a traditional random forest-based approach (precision:0.24, recall:0.58, f1:0.34, auPRC:0.44) and a single task CNN (precision:0.59, recall:0.69, f1:0.64, auPRC:0.68) our architecture using unsupervised transfer learning through convolutional denoising autoencoders dramatically improves the performance of AF detection in participants at rest (pr:0.94, rc:0.98, f1:0.96, auPRC:0.96). In addition, we validate algorithm performance on a prospectively derived replication cohort of ambulatory subjects using data derived from an independently engineered device. We show that two-stage training can help address the unbalanced data problem common to biomedical applications where large well-annotated datasets are scarce. In conclusion, though a combination of simulation and transfer learning and we develop and apply a multitask architecture to the problem of AF detection from wearable wrist sensors demonstrating high levels of accuracy and a solution for the vexing challenge of mechanical noise.
Bounds on the Distinguishing Number of Orthogonality Graphs

A graph $G$ is said to be $d$-distinguishable if there is a labeling of the vertices with $d$ labels so that only the trivial automorphism preserves the labels. The smallest such $d$ is the distinguishing number, Dist($G$). A subset of vertices $S$ is a determining set for $G$ if every automorphism of $G$ is uniquely determined by its action on $S$. The size of a smallest determining set for $G$ is called the determining number, Det($G$). The orthogonality graph $\Omega_{2k}$ has vertices which are bitstrings of length $2k$ with an edge between two vertices if they differ in precisely $k$ bits. This paper shows that Det($\Omega_{2k}$) $= 2^{2k-1}$ and that if $\binom{m}{2} \geq 2k$ then $2<$ Dist($\Omega_{2k}$) $\leq m$.
On the modelling, linear stability, and numerical simulation for advection-diffusion-reaction in poroelastic media

We perform the linear growth analysis for a new PDE-based model for poromechanical processes (formulated in mixed form using the solid deformation, fluid pressure, and total pressure) interacting with diffusing and reacting solutes in the medium. We find parameter regions that lead to interesting behaviour of the coupled system. These mutual dependences between deformation and diffusive patterns are of substantial relevance in the study of morphoelastic changes in biomaterials. We provide a set of computational examples in 2D and 3D that can be used to form a better understanding on how, and up to which extent, the deformations of the porous structure dictate the generation and suppression of spatial patterning dynamics, also related to the onset of mechanochemical waves.
AGN feedback in the FR II galaxy 3C 220.1

We present results from a deep (174 ks) Chandra observation of the FR-II radio galaxy 3C 220.1, the central brightest cluster galaxy (BCG) of a kT ∼ 4 keV cluster at z = 0.61. The temperature of the hot cluster medium drops from ∼5.9 to ∼3.9 keV at ∼35 kpc radius, while the temperature at smaller radii may be substantially lower. The central active galactic nucleus (AGN) outshines the whole cluster in X-rays, with a bolometric luminosity of 2.0 × 1046 erg s−1 (∼10 per cent of the Eddington rate). The system shows a pair of potential X-ray cavities ∼35 kpc east and west of the nucleus. The cavity power is estimated within the range of 1.0 × 1044 and 1.7 × 1045 erg s−1, from different methods. The X-ray enhancements in the radio lobes could be due to inverse Compton emission, with a total 2–10 keV luminosity of ∼8.0 × 1042 erg s−1. We compare 3C 220.1 with other cluster BCGs, including Cygnus A, as there are few BCGs in rich clusters hosting an FR-II galaxy. We also summarize the jet power of FR-II galaxies from different methods. The comparison suggests that the cavity power of FR-II galaxies likely underestimates the jet power. The properties of 3C 220.1 suggest that it is at the transition stage from quasar-mode feedback to radio-mode feedback.
Introduction to Nonsmooth Analysis and Optimization

These notes aim to give an introduction to generalized derivative concepts useful in deriving necessary optimality conditions and numerical algorithms for infinite-dimensional nondifferentiable optimization problems that arise in inverse problems, imaging, and PDE-constrained optimization. They cover convex subdifferentials, Fenchel duality, monotone operators and resolvents, Moreau--Yosida regularization as well as Clarke and (briefly) limiting subdifferentials. Both first-order (proximal point and splitting) methods and second-order (semismooth Newton) methods are treated. In addition, differentiation of set-valued mapping is discussed and used for deriving second-order optimality condition. The required background from functional analysis and calculus of variations is also briefly summarized.
Ensemble emotion recognizing with multiple modal physiological signals

Physiological signals that provide the objective repression of human affective states are attracted increasing attention in the emotion recognition field. However, the single signal is difficult to obtain completely and accurately description for emotion. Multiple physiological signals fusing models, building the uniform classification model by means of consistent and complementary information from different emotions to improve recognition performance. Original fusing models usually choose the particular classification method to recognition, which is ignoring different distribution of multiple signals. Aiming above problems, in this work, we propose an emotion classification model through multiple modal physiological signals for different emotions. Features are extracted from EEG, EMG, EOG signals for characterizing emotional state on valence and arousal levels. For characterization, four bands filtering theta, beta, alpha, gamma for signal preprocessing are adopted and three Hjorth parameters are computing as features. To improve classification performance, an ensemble classifier is built. Experiments are conducted on the benchmark DEAP datasets. For the two-class task, the best result on arousal is 94.42\%, the best result on valence is 94.02\%, respectively. For the four-class task, the highest average classification accuracy is 90.74, and it shows good stability. The influence of different peripheral physiological signals for results is also analyzed in this paper.
Ripple: A Practical Declarative Programming Framework for Serverless Compute

Serverless computing has emerged as a promising alternative to infrastructure- (IaaS) and platform-as-a-service (PaaS)cloud platforms for applications with ample parallelism and intermittent activity. Serverless promises greater resource elasticity, significant cost savings, and simplified application deployment. All major cloud providers, including Amazon, Google, and Microsoft, have introduced serverless to their public cloud offerings. For serverless to reach its potential, there is a pressing need for programming frameworks that abstract the deployment complexity away from the user. This includes simplifying the process of writing applications for serverless environments, automating task and data partitioning, and handling scheduling and fault tolerance. We present Ripple, a programming framework designed to specifically take applications written for single-machine execution and allow them to take advantage of the task parallelism of serverless. Ripple exposes a simple interface that users can leverage to express the high-level dataflow of a wide spectrum of applications, including machine learning (ML) analytics, genomics, and proteomics. Ripple also automates resource provisioning, meeting user-defined QoS targets, and handles fault tolerance by eagerly detecting straggler tasks. We port Ripple over AWS Lambda and show that, across a set of diverse applications, it provides an expressive and generalizable programming framework that simplifies running data-parallel applications on serverless, and can improve performance by up to 80x compared to IaaS/PaaS clouds for similar costs.
Tensor-Tensor Products for Optimal Representation and Compression

In this era of big data, data analytics and machine learning, it is imperative to find ways to compress large data sets such that intrinsic features necessary for subsequent analysis are not lost. The traditional workhorse for data dimensionality reduction and feature extraction has been the matrix SVD, which presupposes that the data has been arranged in matrix format. Our main goal in this study is to show that high-dimensional data sets are more compressible when treated as tensors (aka multiway arrays) and compressed via tensor-SVDs under the tensor-tensor product structures in (Kilmer and Martin, 2011; Kernfeld et al., 2015). We begin by proving Eckart Young optimality results for families of tensor-SVDs under two different truncation strategies. As such optimality properties can be proven in both matrix and tensor-based algebras, a fundamental question arises: does the tensor construct subsume the matrix construct in terms of representation efficiency? The answer is yes, as shown when we prove that a tensor-tensor representation of an equal dimensional spanning space can be superior to its matrix counterpart. We then investigate how the compressed representation provided by the truncated tensor-SVD is related both theoretically and in compression performance to its closest tensor-based analogue, truncated HOSVD (De Lathauwer et al., 2000; De Lathauwer and Vandewalle, 2004), thereby showing the potential advantages of our tensor-based algorithms. Finally, we propose new tensor truncated SVD variants, namely multi-way tensor SVDs, provide further approximated representation efficiency and discuss under which conditions they are considered optimal. We conclude with a numerical study demonstrating the utility of the theory.