Reviewerly/exHarmony huggingface dataset:
Few-photon Fock-state wave packet interacting with a cavity-atom system in a waveguide: Exact quantum state dynamics

In the paper, we employ a wave-function approach to investigate the evolution of a two-photon wave packet propagating in a one-dimensional waveguide coupled to the Jaynes-Cummings (JC) system. We derive and solve, both analytically and numerically, a set of equations of motion governing the quantum state of the system. That allows us to provide real-time analysis of the evolution of the wave packet two-photon joint spectrum (2PJS) and the excitation dynamics of the JC system in the course of its interaction with the two-photon pulse. We demonstrate that the 2PJS and the spectrum of the wave packet scattered from the JC system experience transformation for nonzero atom-cavity couplings. Moreover, using Schmidt decomposition, we show that the scattered photons feature frequency entanglement contrary to the incident ones which are not entangled.
Learning target-aware correlation filters for visual tracking

Discriminative Correlation Filters (DCF) have achieved enormous popularity in the tracking community. Generally, DCF based trackers assume that the target can be well shaped by an axis-aligned bounding box. Therefore, in terms of irregularly shaped objects, the learned correlation filter is unavoidably deteriorated by the background pixels inside the bounding box. To tackle this problem, we propose Target-Aware Correlation Filters (TACF) for visual tracking. A target likelihood map is introduced to impose discriminative weight on filter values according to the probability of this location belonging to the foreground target. According to the TACF formulation, we further propose an optimization strategy based on the Preconditioned Conjugate Gradient method for efficient filter learning. With hand-crafted features (HOG), our approach achieves state-of-the-art performance (62.8% AUC) on OTB100 while running in real-time (24 fps) on a single CPU. With shallow convolutional features, our approach achieves 66.7% AUC on OTB100 and the top rank in EAO on the VOT2016 challenge.
Speaker Diarization with Lexical Information

This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition. We propose a speaker diarization system that can incorporate word-level speaker turn probabilities with speaker embeddings into a speaker clustering process to improve the overall diarization accuracy. To integrate lexical and acoustic information in a comprehensive way during clustering, we introduce an adjacency matrix integration for spectral clustering. Since words and word boundary information for word-level speaker turn probability estimation are provided by a speech recognition system, our proposed method works without any human intervention for manual transcriptions. We show that the proposed method improves diarization performance on various evaluation datasets compared to the baseline diarization system using acoustic information only in speaker embeddings.
Approximating LCS in Linear Time: Beating the √<i>n</i> Barrier

Longest common subsequence (LCS) is one of the most fundamental problems in combinatorial optimization. Apart from theoretical importance, LCS has enormous applications in bioinformatics, revision control systems, and data comparison programs1. Although a simple dynamic program computes LCS in quadratic time, it has been recently proven that the problem admits a conditional lower bound and may not be solved in truly subquadratic time [2]. In addition to this, LCS is notoriously hard with respect to approximation algorithms. Apart from a trivial sampling technique that obtains a nx approximation solution in time O(n2–2x) nothing else is known for LCS. This is in sharp contrast to its dual problem edit distance for which several linear time solutions are obtained in the past two decades [4, 5, 9, 10, 16].In this work, we present the first nontrivial algorithm for approximating LCS in linear time. Our main result is a linear time algorithm for the longest common subsequence which has an approximation factor of O(n0.497956). This beats the barrier for approximating LCS in linear time.
Leadership Gap in Agile Teams: How Teams and Scrum Masters Mature

Motivation: How immature teams can become agile is a question that puzzles practitioners and researchers alike. Scrum is one method that supports agile working. Empirical research on the Scrum Master role remains scarce and reveals contradicting results. While the Scrum Master role is often centred on one person in rather immature teams, the role is expected to be shared among multiple members in mature teams. Objective: Therefore, we aim to understand how the Scrum Master role changes while the team matures. Method: We applied Grounded Theory and conducted qualitative interviews with 53 practitioners of 29 software and non-software project teams from Robert Bosch GmbH. Results: We discovered that Scrum Masters initially plays nine leadership roles which they transfer to the team while it matures. Roles can be transferred by providing a leadership gap, which allows team members to take on a leadership role, and by providing an internal team environment with communication on equal terms, psychological safety, transparency, shared understanding, shared purpose and self-efficacy. Conclusion: The Scrum Master role changes while the team matures. Trust and freedom to take over a leadership role in teams are essential enablers. Our results support practitioners in implementing agile teams in established companies.
NAIRS

In this paper, we develop a neural attentive interpretable recommendation system, named NAIRS. A self-attention network, as a key component of the system, is designed to assign attention weights to interacted items of a user. This attention mechanism can distinguish the importance of the various interacted items in contributing to a user profile. %, and it also provides interpretable recommendations. Based on the user profiles obtained by the self-attention network, NAIRS offers personalized high-quality recommendation. Moreover, it develops visual cues to interpret recommendations. This demo application with the implementation of NAIRS enables users to interact with a recommendation system, and it persistently collects training data to improve the system. The demonstration and experimental results show the effectiveness of NAIRS.
Landau-Majorana-Stückelberg-Zener dynamics driven by coupling for two interacting qutrit systems

A time-dependent model of two interacting spin qutrits is analyzed is analyzed and solved. The two interacting qutrits are subjected to a longitudinal field linearly varying over time as in the Landau-Majorana-St\"uckelberg- Zener (LMSZ) scenario. Although a transverse field is absent, we show the occurrence of LMSZ transitions assisted by the coupling between the two spin-qutrits. Such a physical effect permits us to estimate experimentally the coupling strength between the spins and allows the generation of entangled states of the two qutrits by appropriately setting the slope of the ramp. Furthermore, the possibility of local and nonlocal control as well as the existence of dark states of the two qutrits have been brought to light. Effects stemming from a noisy surrounding environment are also taken into account by introducing a random fluctuating field component as well as non-Hermitian terms in the Hamiltonian model.
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?

Adversarial training is one of the strongest defenses against adversarial attacks, but it requires adversarial examples to be generated for every mini-batch during optimization. The expense of producing these examples during training often precludes adversarial training from use on complex image datasets. In this study, we explore the mechanisms by which adversarial training improves classifier robustness, and show that these mechanisms can be effectively mimicked using simple regularization methods, including label smoothing and logit squeezing. Remarkably, using these simple regularization methods in combination with Gaussian noise injection, we are able to achieve strong adversarial robustness -- often exceeding that of adversarial training -- using no adversarial examples.
On the maximum order complexity of subsequences of the Thue–Morse and Rudin–Shapiro sequence along squares

Automatic sequences such as the Thue–Morse sequence and the Rudin–Shapiro sequence are highly predictable and thus not suitable in cryptography. In particular, they have small expansion complexity. However, they still have a large maximum order complexity. Certain subsequences of automatic sequences are not automatic anymore and may be attractive candidates for applications in cryptography. In this paper we show that subsequences along the squares of certain pattern sequences including the Thue–Morse sequence and the Rudin–Shapiro sequence have also large maximum order complexity but do not suffer a small expansion complexity anymore.
Robust Learning from Untrusted Sources

Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from external sources, e.g. via crowdsourcing. Unfortunately, the quality of these sources is not always guaranteed. As additional complications, the data might be stored in a distributed way, or might even have to remain private. In this work, we address the question of how to learn robustly in such scenarios. Studying the problem through the lens of statistical learning theory, we derive a procedure that allows for learning from all available sources, yet automatically suppresses irrelevant or corrupted data. We show by extensive experiments that our method provides significant improvements over alternative approaches from robust statistics and distributed optimization.
Collaboration by academic libraries: What are the benefits, what are the constraints, and what do you need to do to be successful?

For academic libraries, effective collaboration is no longer an option but a necessity. The world around academic libraries is changing rapidly (Pinfield, Cox, & Rutter, 2017). In universities ther...
Simultaneously Learning Vision and Feature-Based Control Policies for Real-World Ball-In-A-Cup

We present a method for fast training of vision based control policies on real robots.The key idea behind our method is to perform multi-task Reinforcement Learning with auxiliary tasks that differ not only in the reward to be optimized but also in the state-space in which they operate.In particular, we allow auxiliary task policies to utilize task features that are available only at training-time.This allows for fast learning of auxiliary policies, which subsequently generate good data for training the main, vision-based control policies.This method can be seen as an extension of the Scheduled Auxiliary Control (SAC-X) framework.We demonstrate the efficacy of our method by using both a simulated and real-world Ball-in-a-Cup game controlled by a robot arm.In simulation, our approach leads to significant learning speed-ups when compared to standard SAC-X.On the real robot we show that the task can be learned from-scratch, i.e., with no transfer from simulation and no imitation learning.
Probabilistic Modeling with Matrix Product States

Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models. The gradient-free algorithm, presented as a sequence of exactly solvable effective models, is a modification of the density matrix renormalization group procedure adapted for learning a probability distribution. The conclusion that circuit-based models offer a useful inductive bias for classical datasets is supported by experimental results on the parity learning problem.
TableBank: Table Benchmark for Image-based Table Detection and Recognition

We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand human-labeled examples, which is difficult to generalize on real-world applications. With TableBank that contains 417K high quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks. We make TableBank publicly available and hope it will empower more deep learning approaches in the table detection and recognition task. The dataset and models are available at \url{this https URL}.
Parameter Identification of Reed-Solomon Codes Based on Probability Statistics and Galois Field Fourier Transform

The parameter identification of channel codes plays a significant role in the fields of adaptive modulation and coding (AMC) as well as non-cooperative communications.In this paper, an algorithm based on probability statistics and Galois field Fourier transform (PS-GFFT) is proposed to identify the parameters of the Reed-Solomon (RS) codes.A threshold obtained by the probability statistics is used to skip wrong parameters within a candidate set, while GFFT is applied to reduce the error identification probability.Meanwhile, the upper bound on correct recognition rate of RS codes has been derived and proved, which quantifies the influence of the received codewords' length, the bit-error-rate of codewords, and the number of bits per symbol on the accuracy of parameters estimation.To the best of our knowledge, the upper bound, which is of great significance in evaluating the performance of recognition algorithms, is provided in this paper for the first time.The numerous simulation results illustrate that the proposed algorithm has better recognition performance than the existing RS codes recognition algorithms.Specifically, the correct recognition probability of the RS codes whose length is no more than 255 can be over 90% when the bit error rate of codewords is below 3 * 10 -3 , while the conventional algorithms have the best correct recognition probability of 10%.Furthermore, it is observed that the correct recognition rate of our proposed algorithm is close to the derived upper bound, especially for long code length, which further verifies the superiority of our proposed algorithm.
Hadoop Perfect File: A fast and memory-efficient metadata access archive file to face small files problem in HDFS

Storing and processing massive small files is one of the major challenges for the Hadoop Distributed File System (HDFS). In order to provide fast data access, the NameNode (NN) in HDFS maintains the metadata of all files in its main-memory. Hadoop performs well with a small number of large files that require relatively little metadata in the NN s memory. But for a large number of small files, Hadoop has problems such as NN memory overload caused by the huge metadata size of these small files. We present a new type of archive file, Hadoop Perfect File (HPF), to solve HDFS s small files problem by merging small files into a large file on HDFS. Existing archive files offer limited functionality and have poor performance when accessing a file in the merged file due to the fact that during metadata lookup it is necessary to read and process the entire index file(s). In contrast, HPF file can directly access the metadata of a particular file from its index file without having to process it entirely. The HPF index system uses two hash functions: file s metadata are distributed through index files by using a dynamic hash function and, for each index file, we build an order preserving perfect hash function that preserves the position of each file s metadata in the index file. The HPF design will only read the part of the index file that contains the metadata of the searched file during its access. HPF file also supports the file appending functionality after its creation. Our experiments show that HPF can be more than 40% faster file s access from the original HDFS. If we don t consider the caching effect, HPF s file access is around 179% faster than MapFile and 11294% faster than HAR file. If we consider caching effect, HPF is around 35% faster than MapFile and 105% faster than HAR file.
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness

Adversarial examples are malicious inputs crafted to cause a model to misclassify them. Their most common instantiation, adversarial examples introduce changes to the input that leave its true label unchanged, yet result in a different model prediction. Conversely, adversarial examples insert changes to the input that leave the model's prediction unaffected despite the underlying input's label having changed. In this paper, we demonstrate that robustness to perturbation-based adversarial examples is not only insufficient for general robustness, but worse, it can also increase vulnerability of the model to invariance-based adversarial examples. In addition to analytical constructions, we empirically study vision classifiers with state-of-the-art robustness to perturbation-based adversaries constrained by an $\ell_p$ norm. We mount attacks that exploit excessive model invariance in directions relevant to the task, which are able to find adversarial examples within the $\ell_p$ ball. In fact, we find that classifiers trained to be $\ell_p$-norm robust are more vulnerable to invariance-based adversarial examples than their undefended counterparts. Excessive invariance is not limited to models trained to be robust to perturbation-based $\ell_p$-norm adversaries. In fact, we argue that the term adversarial example is used to capture a series of model limitations, some of which may not have been discovered yet. Accordingly, we call for a set of precise definitions that taxonomize and address each of these shortcomings in learning.
Development of critical thinking of master’s degree students using STEM cases

Introduction. Today, a modern state needs people capable to regular intensive implementation of innovative activity in order to keep competitiveness in a dynamically changing world that is undergoing a significant increase in the amount of new information and its fast obsolescence. The need of modern economy for innovators inevitably leads to the shift of priorities in education from memorising and reproducing of a specified amount of knowledge to the development of students' independent thinking, which, accordingly, requires teachers' certain qualities and critical mentality. The aim of the research was to identify the components of critical thinking important for innovative activity and search for the ways of development of these components in young teachers, entering a master's programme Modern Natural Science. Methodology and research methods. In the course of the research, the methods of the analysis and synthesis of scientific and scientific-methodological publications devoted to the development of higher order thinking skills and the concept of STEM education (Science, Technology, Engineering and Mathematics) were employed. The assessment of the level of students' thinking and its changes was carried out through diagnostic tasks and empirical observation of educational and project-based activity of undergraduates and school students. Results and scientific novelty. The key components of critical thinking for the implementation and realisation of innovative activities were identified: creative, rational, logical, reflexive and metacognitive. To assess the development of components, a set of problem-situational tasks (STEM cases) was developed. Diagnostic tools were introduced to measure the level of critical thinking skills formation. The authors described the methodology for working with STEM cases through the example of the development and presentation of hybrid technology of lifting the sunken ship with a damaged hull, based on the students' understanding of characteristics of the aggregate state of water. The results of approbation of the presented methodology used in small groups at various educational levels prove that its implementation contributes to the development of critical thinking in students, if the plots of cues are based on real problems of science and technology, and the solution requires contextual use of educational material, building a mathematical model, generating rational ideas of new or improved technologies. Practical significance. The research demonstrates the possibilities of acquisition by students of higher order thinking skills within the studied academic disciplines in the natural-science cycle without introduction of a special course in educational programmes.
A Provable Defense for Deep Residual Networks

We present a training system, which can provably defend significantly larger neural networks than previously possible, including ResNet-34 and DenseNet-100. Our approach is based on differentiable abstract interpretation and introduces two novel concepts: (i) abstract layers for fine-tuning the precision and scalability of the abstraction, (ii) a flexible domain specific language (DSL) for describing training objectives that combine abstract and concrete losses with arbitrary specifications. Our training method is implemented in the DiffAI system.
A Systematic Review of Domains, Techniques, Delivery Modes and Validation Methods for Intelligent Tutoring Systems

An Intelligent Tutoring System (ITS) is a computer software that help students in learning educational or academics concepts in customized environment.ITSs are instructional systems that have capability to facilitate user by providing instantaneous feedback and instructions without any human intervention.The advancement of new technologies has integrated computer based learning with artificial intelligence methods with aim to develop better custom-made education systems that referred as ITS.One of the important factors that affect students learning process is self-learning; all students cannot have similar experience of learning scholastic concepts from same educational material.Because students have individual differences that make some topics difficult or easy to understand regarding taken subjects.These systems have capability to improve teaching and learning process in different educational domains while respecting individual learning needs.In this study an attempt is made to review the research in field of ITSs and highlight the educational areas or domains in which ITSs have been introduced.Techniques, delivering modes and evaluation methodologies that have been used in developed ITSs have also been discussed in this work.This work will be helpful for both academia and new comers in the field of ITSs to further strengthen basis of tutoring systems in educational domains.
Pedestrian Flow Tracking and Statistics of Monocular Camera Based on Convolutional Neural Network and Kalman Filter

Pedestrian flow statistics and analysis in public places is an important means to ensure urban safety. However, in recent years, a video-based pedestrian flow statistics algorithm mainly relies on binocular vision or a vertical downward camera, which has serious limitations on the application scene and counting area, and cannot make use of the large number of monocular cameras in the city. To solve this problem, we propose a pedestrian flow statistics algorithm based on monocular camera. Firstly, a convolution neural network is used to detect the pedestrian targets. Then, with a Kalman filter, the motion models for the targets are established. Based on these motion models, data association algorithm completes target tracking. Finally, the pedestrian flow is counted by the pedestrian counting method based on virtual blocks. The algorithm is tested on real scenes and public data sets. The experimental results show that the algorithm has high accuracy and strong real-time performance, which verifies the reliability of the algorithm.