Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of entanglement in quantum communication, and analysis of the detection loophole

Entanglement is a feature at the heart of quantum information. Its enablement of unusual correlations between particles drives a new wave of communication and computation. This thesis explores some of the ways in which the tools for studying entanglement can be used to quantify the transmission of quantum information, and compares the use of different techniques. We begin this thesis by expanding the technique of teleportation simulation, which adds noise to the entangled resource state to mimic channel effects. By introducing classical noise in the communication step, we show it is possible to simulate more than just Pauli channels using teleportation. This new class is characterised, and studied in detail for a particular resource state, leading to a family of simulable channels named “Pauli-Damping channels” whose properties are analysed. Also introduced are a new family of quantum states, “phase Werner” states, whose entanglement properties relate to the interesting conjecture of bound entangled states with a negative partial transpose. Holevo-Werner channels, to which these states are connected, are shown to be teleportation covariant. We exploit this to present several interesting results, including the optimal estimation of the channel-defining parameter. The minimal binary-discrimination error for Holevo-Werner channels is bounded for the first time with the analytical form of the quantum Chernoff bound. We also consider the secret key capacity of these channels, showing how different entanglement measures provide a better upper bound for different regions of these channels. Finally, a method for generating new Bell inequalities is presented, exploiting nonphysical probability distributions to obtain new inequalities. Tens of thousands of new inequivalent inequalities are generated, and their usefulness in closing the detection loophole for imperfect detectors is examined, with comparison to the current optimal construction. Two candidate Bell inequalities which may equal or beat the best construction are presented.
Determination of the Cosmic Infrared Background from COBE/FIRAS and Planck HFI Observations

New determinations are presented of the cosmic infrared background monopole brightness in the Planck HFI bands from 100 to 857 GHz. Planck was not designed to measure the monopole component of sky brightness, so cross-correlation of the 2015 HFI maps with COBE/FIRAS data is used to recalibrate the zero level of the HFI maps. For the HFI 545 and 857 GHz maps, the brightness scale is also recalibrated. Correlation of the recalibrated HFI maps with a linear combination of Galactic H i and Hα data is used to separate the Galactic foreground emission and determine the cosmic infrared background brightness in each of the HFI bands. We obtain CIB values of 0.007 ± 0.014, 0.010 ± 0.019, 0.060 ± 0.023, 0.149 ± 0.017, 0.371 ± 0.018, and 0.576 ± 0.034 MJy sr−1 at 100, 143, 217, 353, 545, and 857 GHz, respectively. The estimated uncertainties for the 353–857 GHz bands are about 3–6 times smaller than those of previous direct CIB determinations at these frequencies. Our results are compared with integrated source brightness results from selected recent submillimeter and millimeter wavelength imaging surveys.
Nitrogen fractionation in external galaxies

In star forming regions in our own Galaxy, the 14N/15N ratio is found to vary from $\sim$ 100 in meteorites, comets and protoplanetary disks up to $\sim$ 1000 in pre-stellar and star forming cores, while in external galaxies the very few single-dish large scale measurements of this ratio lead to values of 100-450. The extent of the contribution of isotopic fractionation to these variations is, to date, unknown. In this paper we present a theoretical chemical study of nitrogen fractionation in external galaxies in order to determine the physical conditions that may lead to a spread of the 14N/15N ratio from the solar value of $\sim$440 and hence evaluate the contribution of chemical reactions in the ISM to nitrogen fractionation. We find that the main cause of ISM enrichment of nitrogen fractionation is high gas densities, aided by high fluxes of cosmic rays.
Presenting Static Friction Sensation at Stick-slip Transition using Pseudo-haptic Effect

Previous studies have aimed at creating a simple hardware implementation of surface friction display. In this study, we propose a new method for presenting static frictional sensation using the pseudo-haptic effect as a first attempt, which is the simplest implementation of presenting static friction sensation. We focus on the stick-slip phenomenon while users explore surfaces with an input device, such as a stylus. During the stick phase, we present users with pseudo-haptic feedback that represents static friction on the surface. In our method, users watch a virtual contact point become stuck at the contact point on screen while users freely move the input device. We hypothesize that the perceived probability and intensity of static friction sensation can be controlled by changing the static friction coefficient as a visual parameter. User studies were conducted, and results show the threshold value over which users felt the pseudo-haptic static friction sensation at 90% probability. The results also show that the perceived intensity of the sensation changed with respect to the static friction coefficient. The maximum intensity change was 23%. These results confirm the hypothesis and show that our method is a promising option for presenting static friction sensation.
PLOW: Probabilistic Logic Over the Well-Founded Semantics

Motivations: A central challenge for AI is how to combine most effectively two core areas: knowledge representation & reasoning (KRR) and machine learning (ML). ML can provide capabilities for acquiring knowledge from data at relatively low cost in human labor. Adding KRR to ML can provide the ability to combine heterogeneous forms and/or multiple sources of knowledge (both human crafted and machine learned), and then use this combination to recommend or make decisions. When the form of KRR is highly explainable, it can provide significantly more trustability to AI systems, helping to meet the rapidly rising demands of users. KRR also offers further advantages in interpreting contextspecific ML results. Significantly, KRR can be essential for complying with organizational policies, ethics, and pertinent legal regulations; and for applying human social and experiential knowledge. Problem Addressed: KRR systems of various kinds have advanced rapidly in the past 5–10 years. In particular, a class of recent logic programming systems, called Rulelog systems, semantically extend database logic and provide high expressiveness together with strong explainability (Grosof 2013; Andersen et al. 2013). These systems support logical functions and higher-order syntax, defeasibility (a.k.a. exceptions, argumentation, and defaults), constraint-based reasoning, and reactivity. Further, they provide a variety of semantically meaningful forms of bounded rationality to enforce query termination. Unlike logic programming systems based on answer set programming (ASP), these Rulelog systems are based on the three-valued well-founded semantics, which offers much better scalability than ASP. Notable Rulelog systems include ErgoAI, Flora-2, and XSB.1 However, until recently Rulelog systems such as these have lacked the sort of quantitative uncertainty reasoning that is needed to reason productively and efficiently using results from a wide variety of ML approaches.
Temporal Unet: Sample Level Human Action Recognition using WiFi

Human doing actions will result in WiFi distortion, which is widely explored for action recognition, such as the elderly fallen detection, hand sign language recognition, and keystroke estimation. As our best survey, past work recognizes human action by categorizing one complete distortion series into one action, which we term as series-level action recognition. In this paper, we introduce a much more fine-grained and challenging action recognition task into WiFi sensing domain, i.e., sample-level action recognition. In this task, every WiFi distortion sample in the whole series should be categorized into one action, which is a critical technique in precise action localization, continuous action segmentation, and real-time action recognition. To achieve WiFi-based sample-level action recognition, we fully analyze approaches in image-based semantic segmentation as well as in video-based frame-level action recognition, then propose a simple yet efficient deep convolutional neural network, i.e., Temporal Unet. Experimental results show that Temporal Unet achieves this novel task well. Codes have been made publicly available at this https URL.
Second Di erentials in Arbitrary Feed-Forward Neural Networks

We extend here a general mathematical model for feed-forward neural networks. Such a network is represented as a vectorial function f of two variables, x (the input of the network) and w (the weight vector). We have already shown that the diierential of f can be computed with an extended back-propagation algorithm as well as with a direct method. In this paper, we show that the second diierentials of f can also be computed with several diierent algorithms. Evaluating the theoretical complexities of these methods allow to choose the fastest algorithm for a particular architecture. This will allow us to handle arbitrary feed-forward neural network learning with the help of recent training and analysis techniques based on the Hessian matrix of the error.
DPVis: Visual Exploration of Disease Progression Pathways

Clinical researchers use disease progression modeling algorithms to predict future patient status and characterize progression patterns. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make predictions concerning future states for new patients. HMMs can be trained using longitudinal observations of subjects from large-scale cohort studies, clinical trials, and electronic health records. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, complex modeling parameters, and clinically make sense of the patterns. To tackle this problem, we conducted a design study with physician scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of certain chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable, and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and designing and comparing clinically relevant subgroup cohorts by introducing a case study on observation data from clinical studies of T1D.
Characterization of the nuclear protein import mechanism using Ran mutants with altered nucleotide binding specificities.

The small nuclear GTP binding protein Ran is required for transport of nuclear proteins through the nuclear pore complex (NPC). Although it is known that GTP hydrolysis by Ran is essential for this reaction, it has been unclear whether additional energy‐consuming steps are also required. To uncouple the energy requirements for Ran from other nucleoside triphosphatases, we constructed a mutant derivative of Ran that has an altered nucleotide specificity from GTP to xanthosine 5′ triphosphate. Using this Ran mutant, we demonstrate that nucleotide hydrolysis by Ran is sufficient to promote efficient nuclear protein import in vitro. Under these conditions, protein import could no longer be inhibited with non‐hydrolysable nucleotide analogues, indicating that no Ran‐independent energy‐requiring steps are essential for the protein translocation reaction through the NPC. We further provide evidence that nuclear protein import requires Ran in the GDP form in the cytoplasm. This suggests that a coordinated exchange reaction from Ran‐GDP to Ran‐GTP at the pore is necessary for translocation into the nucleus.
Explainable Software for Cyber-Physical Systems (ES4CPS): Report from the GI Dagstuhl Seminar 19023, January 06-11 2019, Schloss Dagstuhl

This report documents the program and results of the GI-Dagstuhl Seminar 19023 on Explainable Software for Cyber-Physical Systems (ES4CPS). The seminar was concerned with the challenge that for future Cyber-Physical Systems (CPS), it will become increasingly relevant to explain their behavior (past, current, and future behavior, why a certain action was taken, how a certain goal can be achieved, etc.) to users, engineers, and other stakeholders. In order to increase the explainability of CPS and their engineering tools, fundamental, interdisciplinary research is required; solutions from multiple disciplines within software engineering, systems engineering, and related fields have to be applied, combined, and researched further. The goal of this seminar was to serve as a starting point for an interdisciplinary coordination of research activities targeting ES4CPS and an incubator of a new research community around this topic.
Hardware/software optimization of error detection implementation for real-time embedded systems

This paper presents an approach to system-level optimization of error detection implementation in the context of fault-tolerant real-time distributed embedded systems used for safety-critical applications. An application is modeled as a set of processes communicating by messages. Processes are mapped on computation nodes connected to the communication infrastructure. To provide resiliency against transient faults, efficient error detection and recovery techniques have to be employed. Our main focus in this paper is on the efficient implementation of the error detection mechanisms. We have developed techniques to optimize the hardware/software implementation of error detection, in order to minimize the global worst-case schedule length, while meeting the imposed hardware cost constraints and tolerating multiple transient faults. We present two design optimization algorithms which are able to find feasible solutions given a limited amount of resources: the first one assumes that, when implemented in hardware, error detection is deployed on static reconfigurable FPGAs, while the second one considers partial dynamic reconfiguration capabilities of the FPGAs.
From Predictions to Prescriptions in Multistage Optimization Problems

In this paper, we introduce a framework for solving finite-horizon multistage optimization problems under uncertainty in the presence of auxiliary data. We assume the joint distribution of the uncertain quantities is unknown, but noisy observations, along with observations of auxiliary covariates, are available. We utilize effective predictive methods from machine learning (ML), including $k$-nearest neighbors regression ($k$NN), classification and regression trees (CART), and random forests (RF), to develop specific methods that are applicable to a wide variety of problems. We demonstrate that our solution methods are asymptotically optimal under mild conditions. Additionally, we establish finite sample guarantees for the optimality of our method with $k$NN weight functions. Finally, we demonstrate the practicality of our approach with computational examples. We see a significant decrease in cost by taking into account the auxiliary data in the multistage setting.
Protein Kinase C-Delta Mediates Adventitial Cell Migration Through Regulation of Monocyte Chemoattractant Protein-1 Expression in a Rat Angioplasty Model

Objective—The adventitia is increasingly recognized as an important player during the development of intimal hyperplasia. However, the mechanism of adventitial cell recruitment to the subintimal space remains largely undefined. We have shown previously that gene transfer of protein kinase C-delta (PKC&dgr;) increases apoptosis of smooth muscle cells following balloon injury. In the current study, we investigated a potential role of PKC&dgr; in regulating the recruitment of adventitial cells. Methods and Results—Conditioned media from PKC&dgr;-overexpressing smooth muscle cells stimulated migration and CCR2 expression of adventitial fibroblasts through a MCP-1 dependent mechanism. Following balloon injury of rat carotid arteries, overexpression of PKC&dgr; in smooth muscle cells significantly increased MCP-1 and CCR2 expression and the number of adventitia-originated cells detected in the neointima. Administration of an anti-MCP–1 antibody markedly diminished the recruitment of adventitial cells. Combined PKC&dgr; overexpression and anti-MCP–1 inhibited intimal hyperplasia more effectively than either approach alone. Conclusion—Our data suggest that PKC&dgr; regulates recruitment of adventitial cells to the neointima via a mechanism involving upregulation of the MCP-1/CCR2 signaling axis in injured arteries. Blockage of MCP-1 while enhancing apoptosis may serve as a potential therapeutic strategy to attenuate intimal hyperplasia.
1 50 8 . 03 68 3 v 1 [ nu clt h ] 1 5 A ug 2 01 5 Nuclear Level Density : Shell Model vs Mean Field

The knowledge of the nuclear level density is necessary for understanding various reactions including those in the stellar environment. Usually the combinatorics of Fermi-gas plus pairing is used for finding the level density. Recently a practical algorithm avoiding diagonalization of huge matrices was developed for calculating the density of many-body nuclear energy levels with certain quantum numbers for a full shell-model Hamiltonian. The underlying physics is that of quantum chaos and intrinsic thermalization in a closed system of interacting particles. We briefly explain this algorithm and, when possible, demonstrate the agreement of the results with those derived from exact diagonalization. The resulting level density is much smoother than that coming from the conventional mean-field combinatorics. We study the role of various components of residual interactions in the process of thermalization, stressing the influence of incoherent collision-like processes. The shell-model results for the traditionally used parameters are also compared with standard phenomenological approaches.
Neural Network Based Model for Forecasting Reservoir Storage for Hydropower Dam Operation

Reservoirs are constructed to accommodate unregulated excess random water flows in the periods of high flows for use in low-flow periods. In most cases, these reservoirs are meant to perform multiple objectives. As a result of high variability of annual rainfall and conflicting demand for water resources, the study of reservoir operation requires accurate forecasting of available water in the reservoir, which helps in planning and management of multi-objective reservoirs. This paper therefore, presents the operation of hydropower reservoirs in Nigeria by forecasting their future storage using Neural Network (NN) model. The networks were created and trained with monthly historical data such as reservoir inflow, turbine releases, reservoir storage and evaporation losses for Jebba, Kainji and Shiroro hydropower dams. The trained networks yielded 95% & 97% of goodness of fit respectively for training and testing of data at Jebba, 69% &75% at Kainji and 98% & 97% at Shiroro. The correlation coefficients between the forecast and observed reservoir storage of 0.64, 0.79 and 0.84 were obtained for Jebba, Kainji and Shiroro reservoirs respectively. The values of correlation coefficient suggested that the model fairly fit the variables and can subsequently be used for prediction of reservoir storage for operational performance.
UNIFORM MINIMUM RISK EQUIVARIANT ESTIMATES FOR MOMENT CONDITION MODELS

We consider semiparametric moment condition models invariant to transformation groups. The parameter of interest is estimated by minimum empirical divergence approach, introduced by Broniatowski and Keziou (2012). It is shown that the minimum empirical divergence estimates, including the empirical likelihood one, are equivariants. The minimum risk equivariant estimate is then identied to be any one of the minimum empirical divergence estimates minus its expectation conditionally to maximal invariant statistic of the considered group of transformations. An asymptotic approximation to the conditional expectation, is obtained, using the result of Jureckova and Picek (2009).
Cell wall development in maize coleoptiles.

The physical bases for enhancement of growth rates induced by auxin involve changes in cell wall structure. Changes in the chemical composition of the primary walls during maize (Zea mays L. cv WF9 x Bear 38) coleoptile development were examined to provide a framework to study the nature of auxin action. This report documents that the primary walls of maize cells vary markedly depending on developmental state; polymers synthesized and deposited in the primary wall during cell division are substantially different from those formed during cell elongation.The embryonal coleoptile wall is comprised of mostly glucuronoarabinoxylan (GAX), xyloglucan, and polymers enriched in 5-arabinosyl linkages. During development, both GAX and xyloglucan are synthesized, but the 5-arabinosyls are not. Rapid coleoptile elongation is accompanied by synthesis of a mixed-linked glucan that is nearly absent from the embryonal wall. A GAX highly substituted with mostly terminal arabinofuranosyl units is also synthesized during elongation and, based on pulse-chase studies, exhibits turnover possibly to xylans with less substitution via loss of the arabinosyl and glucuronosyl linkages.
Effect of the High Temperature Resistant Nano-Coolant on Automotive Engine Operation

An engine nano-coolant without agglomeration was developed, which was heated to 120 degrees and kept for 15 consecutive days, and has good anti-corrosion, and better thermal conductivity in flow compared with the conventional engine coolant. The results of engine bench scale test, vehicle driving and construction machinery test and exhaust emission test show that the fuel saving rate of the car on the expressway is 5-15%, the fuel consumption of construction machinery decreases by 12.77%, and the temperature of the water tank decreases by 8.17°C on average in summer without the help of natural wind. In addition, the emission of CO and HC during the driving process also decreases by 7.8-13% and 0-19% respectively, which proves that nano-fluids can significantly increase engine combustion efficiency, prevent engine from overheating at high temperature and reduce exhaust emissions, and will replace the ordinary ethylene glycol-water products as a new type of engine coolant with high thermal conductivity.
Model reduction by Kautz filters

A method is presented for model reduction. It is based on the representation of the original model in an (exact) Kautz series. The Kautz series is an orthonormal model and is non-unique: it depends on the ordering of the poles. The ordering of the poles can be chosen such that the last sections contribute least or the first sections contribute most to the overall impulse response of the originalsystem (in a quadratic sense). Having a specific ordering, the reduced model order, say n, can be chosen by considering the energy contained in a truncated representation. The resulting reduced order model is obtained simply by truncation of the Kautz series at the nth term.