Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying the Quantum Properties of Hadronic Resonances using Machine Learning

With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also be used to identify its quantum numbers. Convolutional neural networks (CNNs) using jet-images can significantly improve upon existing techniques to identify the quantum chromodynamic (QCD) (`color') as well as the spin of a two-prong resonance using its substructure. Additionally, jet-images are useful in determining what information in the jet radiation pattern is useful for classification, which could inspire future taggers. These techniques improve the categorization of new particles and are an important addition to the growing jet substructure toolkit, for searches and measurements at the LHC now and in the future.
CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution

Modern deep-learning-based lane detection methods are successful in most scenarios but struggling for lane lines with complex topologies. In this work, we propose CondLaneNet, a novel top-to-down lane detection framework that detects the lane instances first and then dynamically predicts the line shape for each instance. Aiming to resolve lane instance-level discrimination problem, we introduce a conditional lane detection strategy based on conditional convolution and row-wise formulation. Further, we design the Recurrent Instance Module(RIM) to overcome the problem of detecting lane lines with complex topologies such as dense lines and fork lines. Benefit from the end-to-end pipeline which requires little post-process, our method has real-time efficiency. We extensively evaluate our method on three benchmarks of lane detection. Results show that our method achieves state-of-the-art performance on all three benchmark datasets. Moreover, our method has the coexistence of accuracy and efficiency, e.g. a 78.14 F1 score and 220 FPS on CULane. Our code is available at https://github.com/aliyun/conditional-lane-detection.
Spectral risk-based learning using unbounded losses

In this work, we consider the setting of learning problems under a wide class of spectral risk (or"L-risk") functions, where a Lipschitz-continuous spectral density is used to flexibly assign weight to extreme loss values. We obtain excess risk guarantees for a derivative-free learning procedure under unbounded heavy-tailed loss distributions, and propose a computationally efficient implementation which empirically outperforms traditional risk minimizers in terms of balancing spectral risk and misclassification error.
Separate But Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data

We propose FedEnhance, an unsupervised federated learning (FL) approach for speech enhancement and separation with non-IID distributed data across multiple clients. We simulate a realworld scenario where each client only has access to a few noisy recordings from a limited and disjoint number of speakers (hence non-IID). Each client trains their model in isolation using mixture invariant training while periodically providing updates to a central server. Our experiments show that our approach achieves competitive enhancement performance compared to IID training on a single device and that we can further facilitate the convergence speed and the overall performance using transfer learning on the server-side. Moreover, we show that we can effectively combine updates from clients trained locally with supervised and unsupervised losses. We also release a new dataset LibriFSD50K and its creation recipe in order to facilitate FL research for source separation problems.
Beachgoers' ability to identify rip currents at a beach in situ

Abstract. Rip currents (“rips”) are the leading cause of drowning on surf beaches worldwide. A major contributing factor is that many beachgoers are unable to identify rip currents. Previous research has attempted to quantify beachgoers' rip identification ability using photographs of rip currents without identifying whether this usefully translates into an ability to identify a rip current in situ at the beach. This study is the first to compare beachgoers ability to identify rip currents in photographs and in situ at a beach in New Zealand (Muriwai Beach) where a channel rip current was present. Only 22 % of respondents were able to identify the in situ rip current. The highest rates of success were for males (33 % ), New Zealand residents (25 % ), and local beach users (29 % ). Of all respondents who were successful at identifying the rip current in situ, 62 % were active surfers/bodyboarders, and 28 % were active beach swimmers. Of the respondents who were able to identify a rip current in two photographs, only 34 % were able to translate this into a successful in situ rip identification, which suggests that the ability to identify rip currents by beachgoers is worse than reported by previous studies involving photographs. This study highlights the difficulty of successfully identifying a rip current in reality and that photographs are not necessarily a useful means of teaching individuals to identify rip currents. It advocates for the use of more immersive and realistic education strategies, such as the use of virtual reality headsets showing moving imagery (videos) of rip currents in order to improve rip identification ability.
Accelerating the SpMV kernel on standard CPUs by exploiting the partially diagonal structures

Sparse Matrix Vector multiplication (SpMV) is one of basic building blocks in scientific computing, and acceleration of SpMV has been continuously required. In this research, we aim for accelerating SpMV on recent CPUs for sparse matrices that have a specific sparsity structure, namely a diagonally structured sparsity pattern. We focus a hybrid storage format that combines the DIA and CSR formats, so-called the HDC format. First, we recall the importance of introducing cache blocking techniques into HDC-based SpMV kernels. Next, based on the observation of the cache blocked kernel, we present a modified version of the HDC formats, which we call the M-HDC format, in which partial diagonal structures are expected to be more efficiently picked up. For these SpMV kernels, we theoretically analyze the expected performance improvement based on performance models. Then, we conduct comprehensive experiments on state-of-the-art multi-core CPUs. By the experiments using typical matrices, we clarify the detailed performance characteristics of each SpMV kernel. We also evaluate the performance for matrices appearing in practical applications and demonstrate that our approach can accelerate SpMV for some of them. Through the present paper, we demonstrate the effectiveness of exploiting partial diagonal structures by the M-HDC format as a promising approach to accelerating SpMV on CPUs for a certain kind of practical sparse matrices.
Uncover Common Facial Expressions in Terracotta Warriors: A Deep Learning Approach

Can advanced deep learning technologies be applied to analyze some ancient humanistic arts? Can deep learning technologies be directly applied to special scenes such as facial expression analysis of Terracotta Warriors? The big challenging is that the facial features of the Terracotta Warriors are very different from today's people. We found that it is very poor to directly use the models that have been trained on other classic facial expression datasets to analyze the facial expressions of the Terracotta Warriors. At the same time, the lack of public high-quality facial expression data of the Terracotta Warriors also limits the use of deep learning technologies. Therefore, we firstly use Generative Adversarial Networks (GANs) to generate enough high-quality facial expression data for subsequent training and recognition. We also verify the effectiveness of this approach. For the first time, this paper uses deep learning technologies to find common facial expressions of general and postured Terracotta Warriors. These results will provide an updated technical means for the research of art of the Terracotta Warriors and shine lights on the research of other ancient arts.
The explicit formula of the distributions of the nonoverlapping words and its applications to statistical tests for random numbers

Bassino et al. 2010 and Regnier et al. 1998 showed the generating functions of the distributions of the number of the occurrences of words (distributions of words for short) in finite string in the form of rational functions. However the coefficients of the expansion of the rational functions are complicated and we do not have a simple formula of the exact distributions of words from rational functions. In this paper we study the finite dimensional generating functions of the distribution of nonoverlapping words for each fixed sample size and show the explicit formula of the distributions of words for Bernoulli model. We demonstrate that 1) the tests based on the distributions of words reject the random number generator in BSD Library with p-value almost zero and 2) computation of the distributions of words in the human DNA size strings.
Hypergeometric Functions at Unit Argument: Simple Derivation of Old and New Identities

The main goal of this paper is to derive a number of identities for the generalized hypergeometric function evaluated at unity and for certain terminating multivariate hypergeometric functions from the symmetries and other properties of Meijer's $G$ function. For instance, we recover two- and three-term Thomae relations for ${}_3F_2$, give two- and three-term transformations for ${}_4F_3$ with one unit shift and ${}_5F_4$ with two unit shifts in the parameters, establish multi-term identities for general ${}_{p}F_{p-1}$ and several transformations for terminating Kamp\'e de F\'eriet and Srivastava $F^{(3)}$ functions. We further present a presumably new formula for analytic continuation of ${}_pF_{p-1}(1)$ in parameters and reveal somewhat unexpected connections between the generalized hypergeometric functions and the generalized and ordinary Bernoulli polynomials. Finally, we exploit some recent duality relations for the generalized hypergeometric and $q$-hypergeometric functions to derive multi-term relations for terminating series.
ALMA and NOEMA constraints on synchrotron nebular emission from embryonic superluminous supernova remnants and radio–gamma-ray connection

Fast-rotating pulsars and magnetars have been suggested as the central engines of superluminous supernovae (SLSNe) and fast radio bursts, and this scenario naturally predicts non-thermal synchrotron emission from their nascent pulsar wind nebulae (PWNe). We report results of high-frequency radio observations with ALMA and NOEMA for three SLSNe (SN 2015bn, SN 2016ard, and SN 2017egm), and present a detailed theoretical model to calculate non-thermal emission from PWNe with an age of ∼1−3 yr. We find that the ALMA data disfavours a PWN model motivated by the Crab nebula for SN 2015bn and SN 2017egm, and argue that this tension can be resolved if the nebular magnetization is very high or very low. Such models can be tested by future MeV–GeV gamma-ray telescopes such as AMEGO.
Effect of Some Phosphorus Fertilizer Packages on Growth and Yield ofSome Egyptian Wheat Varieties

Two pot experiments and field experiment were carried out at Agric. Res. Station, Fac. Agric. , Al-Azhar Univ. Nasr City and El-Aleg region, Kaliobia governorate, Egypt, during 2011/2012, 2012/2013 and 2013/2014 to investigate the influence of phosphorus fertilizer packages as bio fertilizer on the productivity of wheat varieties. The three studied phosphorus fertilizer packages treatments were phosphorene, humic acid 6%, phosphorene+ humic 6% acid and control (without P) as well as three wheat varieties (Sakha-93, Masry-1 and Banisweif). Complete randomized design was applied for the pot experiments, whereas, split plot design was conducted for the experimental field. Results showed significant differences between the three tested wheat varieties Masr-1 variety exceeded sakha-93 and Baniswif- 6 in grain yield characters. Moreover, Baniswif-6 gave grains quality characters higher than Sakha-93 and Masr-1 varieties in cache season under pot and field experiments, as for phosphorus treatments, results indicated that, there were significant differences between the various phosphorus fertilizer packages treatments in both seasons under pot and field experiments. P4 and P3 treatments recorded the highest values of growth, yield, and yield components as compared with P1 treatment in the three seasons in pot or field experiments. The interaction effect between (var. X P. packages) showed that must of the studied characters were significant by the interaction between the two studied factors.
Effects of Reduced Nitrogen Fertilization and Irrigation on Structure and Physicochemical Properties of Starch in Two Bread Wheat Cultivars

Nitrogen (N) fertilization and irrigation are significant agronomic factors affecting wheat production, but little information is available on the effects of reduced N fertilization and irrigation on internal starch structure and physicochemical properties associated with the quality of wheat-based foods. In this study, reduced N fertilization and irrigation were separately applied to investigate their effects on composition and morphological changes, crystalline and external region structure features, swelling power, and gelatinization characteristics of starch granules in bread wheat, with a high N-use-efficiency and water-saving wheat cultivar Zhongmai 175 and a widely grown cultivar Jingdong 17. Compared with a non-N control, reduced N fertilization did not change the crystallinity type and short-range ordered degree of starch; however, it significantly increased relative crystallinity, swelling power and gelatinization enthalpy, whereas amylose content and transition temperatures were decreased. Under reduced irrigation, more small starch granules with compact arrangements appeared in comparison with non-water control. Relative crystallinity, swelling power and gelatinization enthalpy of starch were increased, whereas short-range ordered degree and transition temperatures were decreased. Moreover, the starch of the two cultivars appeared to differ in response to both the N and water treatments. The findings indicated that reduced N fertilization or irrigation markedly influenced the structure and physicochemical characteristics of wheat starch, providing important information for developing elite cultivars with high N and water use efficiency and outstanding starch quality.
Sufficient conditions for compactness of the
∂-Neumann operator on high level forms

By establishing a unified estimate of the twisted KohnMorrey-Hörmander estimate and the q-pseudoconvex Ahn-Zampieri estimate, we discuss variants of Property (Pq) of Catlin and Property (P̃q) of McNeal on the boundary of a smooth pseudoconvex domain in C for certain high level forms. These variant conditions on the one side, imply L-compactness of the ∂-Neumann operator on the associated domain, on the other side, are different from the classical Property (Pq) and Property (P̃q). As an application of our result, we show that if the Hausdorff (2n−2)-dimensional measure of the weakly pseudoconvex points on the boundary of a smooth bounded pseudoconvex domain is zero, then the ∂-Neumann operator Nn−1 is L -compact on (0, n − 1)-level forms. This result generalizes Boas and Sibony’s results on (0, 1)-level forms.
Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection

—In real-world clinical practice, overlooking unan- ticipated ﬁndings can result in serious consequences. However, supervised learning , which is the foundation for the current success of deep learning, only encourages models to identify abnormalities that are deﬁned in datasets in advance. Therefore, abnormality detection must be implemented in medical images that are not limited to a speciﬁc disease category. In this study, we demonstrate an unsupervised learning framework for pixel-wise abnormality detection in brain magnetic resonance imaging captured from a patient population with metastatic brain tumor. Our concept is as follows: If an image reconstruction network can faithfully reproduce the global features of “normal” anatomy, then the “abnormal” lesions in unseen images can be identiﬁed based on the local difference from those reconstructed as “normal” by a discriminative network. Both networks are trained on a dataset comprising only normal images without labels. In addition, we devise a metric to evaluate the anatomical ﬁdelity of the reconstructed images and conﬁrm that the overall detection performance is improved when the image reconstruction network achieves a higher score. For evaluation, clinically signiﬁcant abnormalities are comprehensively segmented. The results show that the area under the receiver operating characteristics curve values for metastatic brain tumors, extracranial metastatic tu- mors, postoperative cavities, and structural changes are 0.78, 0.61, 0.91, and 0.60, respectively.
Recovering shared structure from multiple networks with unknown edge distributions

In increasingly many settings, data sets consist of multiple samples from a population of networks, with vertices aligned across these networks. For example, brain connectivity networks in neuroscience consist of measures of interaction between brain regions that have been aligned to a common template. We consider the setting where the observed networks have a shared expectation, but may differ in the noise structure on their edges. Our approach exploits the shared mean structure to denoise edge-level measurements of the observed networks and estimate the underlying population-level parameters. We also explore the extent to which edge-level errors influence estimation and downstream inference. We establish a finite-sample concentration inequality for the low-rank eigenvalue truncation of a random weighted adjacency matrix that may be of independent interest. The proposed approach is illustrated on synthetic networks and on data from an fMRI study of schizophrenia.
On the Convergence of SGD with Biased Gradients

We analyze the complexity of biased stochastic gradient methods (SGD), where individual updates are corrupted by deterministic, i.e. biased error terms. We derive convergence results for smooth (non-convex) functions and give improved rates under the Polyak-Łojasiewicz condition. We quantify how the magnitude of the bias impacts the attainable accuracy and the convergence rates (sometimes leading to divergence). Our framework covers many applications where either only biased gradient updates are available, or preferred, over unbiased ones for performance reasons. For instance, in the domain of distributed learning, biased gradient compression techniques such as top- k compression have been proposed as a tool to alleviate the communication bottleneck and in derivative-free optimization, only biased gradient estimators can be queried. We discuss a few guiding examples that show the broad appli-cability of our analysis.
A multidisciplinary definition of privacy labels

Purpose This paper aims to present arguments about how a complex concept of privacy labeling can be a solution to the current state of privacy. Design/methodology/approach The authors give a precise definition of Privacy Labeling (PL), painting a panoptic portrait from seven different perspectives: Business, Legal, Regulatory, Usability and Human Factors, Educative, Technological and Multidisciplinary. They describe a common vision, proposing several important “traits of character” of PL as well as identifying “undeveloped potentialities”, i.e. open problems on which the community can focus. Findings This position paper identifies the stakeholders of the PL and their needs with regard to privacy, describing how PL should be and look like to address these needs. Main aspects considered are the PL’s educational power to change people’s knowledge of privacy, tools useful for constructing PL and the possible visual appearances of PL. They also identify how the present landscape of privacy certifications could be improved by PL. Originality/value The authors adopt a multidisciplinary approach to defining PL as well as give guidelines in the form of goals, characteristics, open problems, starting points and a roadmap for creating the ideal PL.
Profile-Guided, Multi-Version Binary Rewriting

The static instrumentation of machine code, also known as binary rewriting, is a power technique, but suffers from high runtime overhead compared to compiler-level instrumentation. Recent research has shown that tools can achieve near-to-zero overhead when rewriting binaries (excluding the overhead from the application specific instrumentation). However, the users of binary rewriting tools often have diffi-culties in understanding why their instrumentation is slow and how to optimize their instrumentation. We are inspired by a traditional program optimization workflow, where one can profile the program execution to identify performance hot spots, modify the source code or apply suitable compiler optimizations, and even apply profile-guided optimization. We present profile-guided, Multi-Version Binary Rewriting to enable this optimization workflow for static binary instrumentation. Our new techniques include three components. First, we augment existing binary rewriting to support call path profiling; one can interactively view instrumentation costs and understand the calling contexts where the costs incur. Second, we present Versioned Structure Binary Editing , which is a general binary transformation technique. Third, we use call path profiles to guide the application of binary transformation. We apply our new techniques to shadow stack and basic block code coverage. Our instrumentation optimization workflow helps us identify several opportunities with regard to code transformation and instrumentation data layout. Our evaluation on SPEC CPU 2017 shows that the geometric overhead of shadow stack and block coverage is reduced from 7.6% and 161.3% to 1.4% and 4.0%, respectively. We also achieve promising results on Apache HTTP Server, where the shadow stack overhead is reduced from about 20% to 3.5%.
Rate of Orientation Change as a New Metric for Robot-Assisted and Open Surgical Skill Evaluation

Surgeons’ technical skill directly impacts patient outcomes. To date, the angular motion of the instruments has been largely overlooked in objective skill evaluation. To fill this gap, we have developed metrics for surgical skill evaluation that are based on the orientation of surgical instruments. We tested our new metrics on two datasets with different conditions: (1) a dataset of experienced robotic surgeons and nonmedical users performing needle-driving on a dry lab model, and (2) a small dataset of suturing movements performed by surgeons training on a porcine model. We evaluated the performance of our new metrics (angular displacement and the rate of orientation change) alongside the performances of classical metrics (task time and path length). We calculated each metric on different segments of the movement. Our results highlighted the importance of segmentation rather than calculating the metrics on the entire movement. Our new metric, the rate of orientation change, showed statistically significant differences between experienced surgeons and nonmedical users / novice surgeons, which were consistent with the classical task time metric. The rate of orientation change captures technical aspects that are taught during surgeons’ training, and together with classical metrics can lead to a more comprehensive discrimination of skills.