Miracle-dz/UniFAR-FTUs huggingface dataset:
Partitioning dysprosium's electronic spin to reveal entanglement in nonclassical states

Quantum spins of mesoscopic size are a well-studied playground for engineering nonclassical states. If the spin represents the collective state of an ensemble of qubits, its nonclassical behavior is linked to entanglement between the qubits. In this paper, we report on an experimental study of entanglement between two subsystems of dysprosium’s electronic spin. Its ground state, of angular momentum J = 8, can formally be viewed as a set of 2 J qubits symmetric upon exchange. To access entanglement properties, we partition the spin by optically coupling it to an excited state J (cid:2) = J − 1, which removes a pair of qubits in a state deﬁned by the light polarization. Starting with the well-known W and squeezed states, we extract the concurrence of qubit pairs, which quantiﬁes their nonclassical character. We also directly demonstrate entanglement between the 14- and 2-qubit subsystems via an increase in entropy upon partition. In a complementary set of experiments, we probe decoherence of a state prepared in the excited level J (cid:2) = J + 1 and interpret spontaneous emission as a loss of a qubit pair in a random state. This allows us to contrast the robustness of nonclassical pairwise correlations of the W state with the fragility of the coherence involved in a Schrödinger cat state. Our ﬁndings open up the possibility to engineer novel types of entangled atomic ensembles, in which entanglement occurs within each atom’s electronic spin as well as between different atoms. Qubit ensembles with large entanglement depth could then be realized with a few atoms only, facilitating the scaling up of quantum-enhanced sensors. An off-resonant optical cavity in the strong-coupling regime couples an ensemble of N atoms together. For σ + polarized cavity light, the total spin projection along z is conserved, and the cavity mediates the coherent exchange of ↑ qubit excitations between atoms. Such couplings can be used to stabilize a W state, with a single ↑ excitation symmetrically shared between the N × (2 J ) qubits.
Requirements Contracts: Definition, Design, and Analysis

What are the necessary and sufficient conditions for a proposition to be called a requirement? In Requirements Engineering research, a proposition is a requirement if and only if specific grammatical and/or communication conditions hold. I offer an alternative, that a proposition is a requirement if and only if specific contractual, economic, and engineering relationships hold. I introduce and define the concept of"Requirements Contract"which defines these conditions. I argue that seeing requirements as propositions governed by specific types of contracts leads to new and interesting questions for the field, and relates requirements engineering to such topics as economic incentives, interest alignment, principal agent problem, and decision-making with incomplete information.
A Novel Transmission Policy for Intelligent Reflecting Surface Assisted Wireless Powered Sensor Networks

This paper proposes a novel transmission policy for a wireless powered sensor network (WPSN) assisted by an intelligent reflecting surface (IRS) to enhance the performance of wireless energy transfer (WET) and wireless information transfer (WIT) with certain phase shifts. To achieve its self-sustainability, IRS collects energy from the energy station to support its own control circuit operation. Our proposed policy for the considered WPSN is called IRS assisted harvest-then-transmit time switching (IRS-HTT-TS), which is capable to schedule the transmission time slots by switching between energy collection and energy reflection modes. We provide a joint design of the transmission time slots, the power allocation as well as the discrete phase shifts of the WET and WIT, aiming to maximize the system sum throughput. This formulates a problem as a mixed-integer non-linear program (MINLP), which is NP-hard and non-convex. To effectively solve this problem, we first relax it to the one with continuous phase shifts, and then propose a two-step approach and decompose the original problem into two sub-problems. We address the first sub-problem in closed form with respect to the phase shifts of the WIT. For the second sub-problem, we apply the Lagrange dual method and Karush-Kuhn-Tucker conditions to derive the optimal closed-form transmission time slots, power allocation, and phase shift of the WET, respectively. The optimal discrete phase shifts can be obtained by quantizing the continuous values. Numerical results demonstrate the effectiveness of the proposed policy and validate the beneficial role of the IRS in comparison to the benchmark schemes.
Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting

We study video crowd counting, which is to estimate the number of objects (people in this paper) in all the frames of a video sequence. Previous work on crowd counting is mostly on still images. There has been little work on how to properly extract and take advantage of the spatial-temporal correlation between neighboring frames in both short and long ranges to achieve high estimation accuracy for a video sequence. In this work, we propose Monet, a novel and highly accurate motion-guided non-local spatial-temporal network for video crowd counting. Monet first takes people flow (motion information) as guidance to coarsely segment the regions of pixels where a person may be. Given these regions, Monet then uses a non-local spatial-temporal network to extract spatial-temporally both short and long-range contextual information. The whole network is finally trained end-to-end with a fused loss to generate a high-quality density map. Noting the scarcity and low quality (in terms of resolution and scene diversity) of the publicly available video crowd datasets, we have collected and built a large-scale video crowd counting datasets, VidCrowd, to contribute to the community. VidCrowd contains 9,000 frames of high resolution (2560 x 1440), with 1,150,239 head annotations captured in different scenes, crowd density and lighting in two cities. We have conducted extensive experiments on the challenging VideoCrowd and two public video crowd counting datasets: UCSD and Mall. Our approach achieves substantially better performance in terms of MAE and MSE as compared with other state-of-the-art approaches.
Theory of Magnetic-Texture-Induced Anomalous Hall Effect on the Surface of Topological Insulators

The anomalous Hall effect is caused by magnetic textures such as skyrmions. We derive an analytical formula of the Hall conductivity on the surface of a topological insulator up to third order in magnetization, $\boldsymbol{M}(\boldsymbol{x})$, based on a perturbative approach. We identify the magnetic textures that contribute to the Hall conductivity up to third order in magnetization and second order in spatial differentiation. We treat magnetization as a perturbation to calculate the Hall conductivity for each magnetic texture based on the linear response theory. Furthermore, we estimate the skyrmion-induced Hall conductivity and confirm that it depends on the shape of skyrmions, such as Bloch-type or N\'eel-type skyrmions. The results of this study can be applied not only to conventional skyrmion systems but also to more general magnetic structures.
Online certification of preference-based fairness for personalized recommender systems

Recommender systems are facing scrutiny because of their growing impact on the opportunities we have access to. Current audits for fairness are limited to coarse-grained parity assessments at the level of sensitive groups. We propose to audit for envy-freeness, a more granular criterion aligned with individual preferences: every user should prefer their recommendations to those of other users. Since auditing for envy requires to estimate the preferences of users beyond their existing recommendations, we cast the audit as a new pure exploration problem in multi-armed bandits. We propose a sample-efficient algorithm with theoretical guarantees that it does not deteriorate user experience. We also study the trade-offs achieved on real-world recommendation datasets.
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathology Image Classification

For deep learning methods applied to the diagnosis of gastric cancer intelligently, existing methods concentrate more on Convolutional Neural Networks (CNN) but no approaches are available using Visual Transformer (VT). VT’s eﬃcient and stable deep learning models with the most recent application in the ﬁeld of computer vision, which is capable of improving the recognition of global information in images. In this paper, a multi-scale visual transformer model (GasHis-Transformer) is proposed for a gastric histopathology image classiﬁcation (GHIC) task, which enables the automatic classiﬁcation of gastric histological images of abnormal and normal cancer by obtained by optical microscopy to facilitate the medical work of histopathologists. This GasHis-Transformer model is built on two fundamental modules, including a global information module (GIM) and a local infor-mationmodule(LIM). Intheexperiment, anopensourcehematoxylinandeosin(H&E)stainedgastric histopathology dataset with 280 abnormal or normal images are divided into training, validation, and test sets at a ratio of 1:1:2 ﬁrst. Then, GasHis-Transformer obtains precision, recall, F1-score, and accuracy on the testing set of 98.0%, 100.0%, 96.0%, and 98.0%. Furthermore, a contrast experiment also tests the generalization ability of the proposed GatHis-Transformer model with a lymphoma image dataset including 374 images and a breast cancer dataset including 1390 images in two extended experiments and achieves an accuracy of 83.9% and 89.4%, respectively. Finally, GasHis-Transformer model demonstrates high classiﬁcation performance and shows its eﬀectiveness and enormous potential in GHIC tasks.
Preparing Dicke states in a spin ensemble using phase estimation

We present a Dicke state preparation scheme which uses global control of N spin qubits: our scheme is based on the standard phase estimation algorithm, which estimates the eigenvalue of a unitary operator. The scheme prepares a Dicke state non-deterministically by collectively coupling the spins to an ancilla qubit via a ZZ-interaction, using dlog2Ne + 1 ancilla qubit measurements. The preparation of such Dicke states can be useful if the spins in the ensemble are used for magnetic sensing: we discuss a possible realization using an ensemble of electronic spins located at diamond Nitrogen-Vacancy (NV) centers coupled to a single superconducting flux qubit. We also analyze the effect of noise and limitations in our scheme.
What Did It Look Like: A service for creating website timelapses using the Memento framework

Popular web pages are archived frequently, which makes it difficult to visualize the progression of the site through the years at web archives. The What Did It Look Like (WDILL) Twitter bot shows web page transitions by creating a timelapse of a given website using one archived copy from each calendar year. We recently added new features to WDILL, such as date range requests, diversified memento selection, updated visualizations, and sharing visualizations to Instagram. This would allow scholars and the general public to explore the temporal nature of web archives.
Towards Phytopia; A framework for reflection on phytosanitary policy

What is the ideal phytosanitary policy? The Ministry of Economic Affairs, Agriculture & Innovation has asked LEI to develop a framework based on which the government can review its role in phytosanitary policy. The framework should contain a step by step plan which can be used with regard to new developments and for a range of phytosanitary problems. This report contains the theoretical framework that can be used for this purpose. The 'Towards Phytopia' framework has been tested several times with representatives from the Ministry of EL&I. The framework has already played a role in the dialogue about phytosanitary responsibilities which the government is conducting with industry and social parties.
MinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization

One of the most common problems of weakly supervised object localization is that of inaccurate object coverage. In the context of state-of-the-art methods based on Class Activation Mapping, this is caused either by localization maps which focus, exclusively, on the most discriminative region of the objects of interest or by activations occurring in background regions. To address these two problems, we propose two representation regularization mechanisms: Full Region Regularizationwhich tries to maximize the coverage of the localization map inside the object region, and Common Region Regularization which minimizes the activations occurring in background regions. We evaluate the two regularizations on the ImageNet, CUB-200-2011 and OpenImages-segmentation datasets, and show that the proposed regularizations tackle both problems, outperforming the state-of-the-art by a significant margin.
On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning

Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on simplified physical models and hard-coded strategies. Although successful in specific scenarios, this approach requires demanding tuning of parameters and switching logic between specifically-designed controllers for handling more general perturbations. We apply model-free Deep Reinforcement Learning for training a general and robust humanoid push-recovery policy in a simulation environment. Our method targets high-dimensional whole-body humanoid control and is validated on the iCub humanoid. Reward components incorporating expert knowledge on humanoid control enable fast learning of several robust behaviors by the same policy, spanning the entire body. We validate our method with extensive quantitative analyses in simulation, including out-of-sample tasks which demonstrate policy robustness and generalization, both key requirements towards real-world robot deployment.
Bridging the gap between streaming and non-streaming ASR systems bydistilling ensembles of CTC and RNN-T models

Streaming end-to-end automatic speech recognition (ASR) systems are widely used in everyday applications that require transcribing speech to text in real-time. Their minimal latency makes them suitable for such tasks. Unlike their non-streaming counterparts, streaming models are constrained to be causal with no future context and suffer from higher word error rates (WER). To improve streaming models, a recent study [1] proposed to distill a non-streaming teacher model on unsupervised utterances, and then train a streaming student using the teachers' predictions. However, the performance gap between teacher and student WERs remains high. In this paper, we aim to close this gap by using a diversified set of non-streaming teacher models and combining them using Recognizer Output Voting Error Reduction (ROVER). In particular, we show that, despite being weaker than RNN-T models, CTC models are remarkable teachers. Further, by fusing RNN-T and CTC models together, we build the strongest teachers. The resulting student models drastically improve upon streaming models of previous work [1]: the WER decreases by 41% on Spanish, 27% on Portuguese, and 13% on French.
Assuming Just Enough Fairness to make Session Types Complete for Lock-freedom

We investigate how different fairness assumptions affect results concerning lock-freedom, a typical liveness property targeted by session type systems. We fix a minimal session calculus and systematically take into account all known fairness assumptions, thereby identifying precisely three interesting and semantically distinct notions of lock-freedom, all of which having a sound session type system. We then show that, by using a general merge operator in an otherwise standard approach to global session types, we obtain a session type system complete for the strongest amongst those notions of lock-freedom, which assumes only justness of execution paths, a minimal fairness assumption for concurrent systems.
Analysis of Legal Documents via Non-negative Matrix Factorization Methods

The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files. Processing and interpreting this large amount of information presents a significant challenge for CIP officials, which can be successfully aided by topic modeling techniques.In this paper, we apply Non-negative Matrix Factorization (NMF) method and implement various offshoots of it to the important and previously unstudied data set compiled by CIP. We identify underlying topics of existing case files and classify request files by crime type and case status (decision type). The results uncover the semantic structure of current case files and can provide CIP officials with a general understanding of newly received case files before further examinations. We also provide an exposition of popular variants of NMF with their experimental results and discuss the benefits and drawbacks of each variant through the real-world application.
Hypernetwork Dismantling via Deep Reinforcement Learning

Network dismantling aims to degrade the connectivity of a network by removing an optimal set of nodes. It has been widely adopted in many real-world applications such as epidemic control and rumor containment. However, conventional methods usually focus on simple network modeling with only pairwise interactions, while group-wise interactions modeled by hypernetwork are ubiquitous and critical. In this work, we formulate the hypernetwork dismantling problem as a node sequence decision problem and propose a deep reinforcement learning (DRL)-based hypernetwork dismantling framework. Besides, we design a novel inductive hypernetwork embedding method to ensure the transferability to various real-world hypernetworks. Our framework first generates small-scale synthetic hypernetworks and embeds the nodes and hypernetworks into a low dimensional vector space to represent the action and state space in DRL, respectively. Then trial-and-error dismantling tasks are conducted by an agent on these synthetic hypernetworks, and the dismantling strategy is continuously optimized. Finally, the well-optimized strategy is applied to real-world hypernetwork dismantling tasks. Experimental results on five real-world hypernetworks demonstrate the effectiveness of our proposed framework.
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning

Autonomous driving is rapidly advancing, and Level 2 functions are becoming a standard feature. One of the foremost outstanding hurdles is to obtain robust visual perception in harsh weather and low light conditions where accuracy degradation is severe. It is critical to have a weather classification model to decrease visual perception confidence during these scenarios. Thus, we have built a new dataset for weather (fog, rain, and snow) classification and light level (bright, moderate, and low) classification. Furthermore, we provide street type (asphalt, grass, and cobblestone) classification, leading to 9 labels. Each image has three labels corresponding to weather, light level, and street type. We recorded the data utilizing an industrial front camera of RCCC (red/clear) format with a resolution of $1024 \times 1084$, We collected 15k video sequences and sampled 60k images. We implement an active learning framework to reduce the dataset's redundancy and find the optimal set of frames for training a model. We distilled the 60k images further to 1.1k images, which will be shared publicly after privacy anonymization. There is no public dataset for weather and light level classification focused on autonomous driving to the best of our knowledge. The baseline ResNet18 network used for weather classification achieves state-of-the-art results in two non-automotive weather classification public datasets but significantly lower accuracy on our proposed dataset, demonstrating it is not saturated and needs further research.
Application Traffic Modelling & Optimization for NoC Communication Submitted in fulfillment of the requirement of the degree of Doctor of Philosophy by Amit Chaurasia

The present study aims to develop a simplified methodology for the generation of Synthetic traffic for multicore architecture. To begin with, self-similarity processes are studied for the generation of synthetic traffic. The exponent known as the Hurst parameter is calculated by various method from Rescaled-range method to Whittle’s Estimator used to represent the self-similar process. The synthetic traffic is used to simulate on the Mesh architecture of different scalable sizes to decrease the simulation time and it provides the flexibility to network designers. The synthetic traffic is generated for the advanced class of MPEG-4 videos with HEVC. The algorithm presented here requires less time for its generation as its complexity is less as compared with other existing algorithms for the generation of Synthetic traffic. The synthetic traffic is simulated on different classes of traffic patterns such as Complement,Neighbourhood and Uniform patterns which constitute the selection source and destination pair. Some of the important parameters such as end-to-end latency, link utilization, packet loss probability are calculated based on the simulation outcomes helps in understanding the communication paradigm and helps the network designer for a better selection of communication resources in the early design process of Networks-on-chip architecture. Subsequently, the role of non-Gaussian processes is studied with the Hermite process. In this study, the role and requirement of Non-Gaussian process are identified, various class of processes identified in Hermite process, the Rosenblatt process is one of them whose distribution is non-Gaussian. The algorithm designed for generating Non-Gaussian process based on Rosenblatt process whose function is the integral of time and space domain concerning Fractional Brownian Motion. The generation of Gaussian and Non-Gaussian process are compared with Rosenblatt Non-Gaussian process shows the burstiness in dataflow is low in Gaussian and NonGaussian process as compared with Rosenblatt Non-Gaussian process. The results show the Rosenblatt Non-Gaussian process are better to simulation and representing the actual dataflow.