Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies of intrinsic properties of gamma ray bursts detected by the HETE-II satellite

Analysis of HETE-II data is discussed with the aim of understanding the intrinsic properties of gamma-ray bursts (GRBs). A technique is developed that allows the simultaneous estimation of source and background counts during a burst with coded aperture instruments such as the Wide-field X-ray Monitor (WXM) on HETE-II. A closely related photon-byphoton statistical bootstrap analysis is then described that can be used to compute the non-Gaussian error distribution of GRB temporal statistics. This is applied to the T90 and To.45 duration measures. The distribution of T90 has been extensively studied since the availability of the BATSE gamma-ray burst catalogs and is widely believed to be lognormally distributed. It is shown that intrinsically, GRBs may in fact have a much narrower distribution, and the wide log-normal may primarily be due to measurement artifacts. Computation of the T0. 45 parameter enables the inference of redshifts through the recently discovered Liso-Epk-TO.45 relation. This in turn allows the compilation of a fluxlimited sample of bursts with redshifts that is free of the observational selection effects inherent in spectroscopic catalogs. This analysis is performed for 31 WXM bursts and redshift-corrected distributions of T90 and T0.45 are computed. It is shown for the first time that the distribution of T0.45 can be modeled by an exponential distribution. The redshifts calculated through the Liso-Epk-TO.45 relation are also used to calculate the implied isotropic luminosities. The normalized luminosity function and redshift distribution of gamma-ray bursts are derived using the non-parametric methods of Lynden-Bell and Efron & Petrosian. The results imply strong evidence for luminosity evolution with redshift and are consistent with prior studies based on BATSE bursts. Concordance cosmology (A -0.7, QM = 0.3, Ho=70 km s1 Mpc 1) is assumed throughout the analysis. Effects of log-normal errors in the redshifts are estimated using Monte-Carlo methods. Results indicate that a fraction close to 10% of GRBs are to be expected at high redshifts (> 5) in consonance with theoretical predictions of high-redshift Swift detections. Thesis Supervisor: George R. Ricker Title: Senior Research Scientist Thesis Supervisor: Saul Rappaport Title: Professor
Non-Binary Protograph-Based LDPC Codes: Analysis, Enumerators and Designs

Non-binary LDPC codes can outperform binary LDPC codes using sum-product algorithm with higher computation complexity. Non-binary LDPC codes based on protographs have the advantage of simple hardware architecture. In the first part of this thesis, we will use EXIT chart analysis to compute the thresholds of different protographs over GF(q). Based on threshold computation, some non-binary protograph-based LDPC codes are designed and their frame error rates are compared with binary LDPC codes. For maximum-likelihood decoder, weight enumerator can predict frame error rate of an LDPC code. In the second part of this thesis, we calculate weight enumerators of protograph-based non-binary LDPC code ensembles both for finite length case and asymptotic case. In addition, the trapping set and stopping set enumerators are presented.
Analytical techniques for acoustic scattering by arrays of cylinders

The problem of two-dimensional acoustic scattering of an incident plane wave by a semi-infinite lattice is solved. The problem is first considered for sound-soft cylinders whose size is small compared to the wavelength of the incident field. In this case the formulation leads to a scalar Wiener--Hopf equation, and this in turn is solved via the discrete Wiener--Hopf technique. We then deal with a more complex case which arises either by imposing Neumann boundary condition on the cylinders' surface or by increasing their radii. This gives rise to a matrix Wiener--Hopf equation, and we present a method of solution that does not require the explicit factorisation of the kernel. In both situations, a complete description of the far field is given and a conservation of energy condition is obtained. For certain sets of parameters (`pass bands'), a portion of the incident energy propagates through the lattice in the form of a Bloch wave. For other parameters (`stop bands' or `band gaps'), no such transmission is possible, and all of the incident field energy is reflected away from the lattice.
Energy band alignment of HfO2 on Ge

The band alignment of hafnium oxide films grown on Ge (100) by atomic layer deposition has been investigated by x-ray photoelectron spectroscopy (XPS) and internal photoemission (IPE) spectroscopy. HfO2 films have been grown using HfCl4 as hafnium precursor while O3 or H2O have been used as oxygen precursors. The valence-band offset (VBO) values, determined by XPS, are 3.0±0.1 eV and 3.1±0.1 eV for the samples grown using O3 and H2O, respectively. A conduction-band offset (CBO) value of 2.0±0.1 eV has been obtained by IPE for all the samples. Considering a band gap of 5.6±0.1 eV, as obtained by photoconductivity measurements, XPS and IPE results have been found to be in excellent agreement. The CBO and VBO values are the same in all the samples within the experimental error. The presence of a thick GeOx interfacial layer in the samples grown using O3 is not affecting the band alignment of the HfO2∕Ge heterojunction.
Alteration in regulation of inﬂammatory response to inﬂuenza a virus and endotoxin in suckling rat pups: a potential relationship to sudden infant death syndrome

Data increasingly implicate a possible role of immune and inﬂammatory responses to infection in sudden infant death syndrome (SIDS). We have previously described a dual challenge model that results in pathology, organ damage, vascular collapse and unexplained death similar to that seen in SIDS. In this study, we examined changes in inﬂammatory cytokine mRNA in the lung and liver and regulation of pathways associated with nitric oxide production. Our data suggest that priming of the immune system by mild viral infection disturbs normal inﬂammatory response to endotoxin. This results in an increased nitric oxide synthase production, most likely the cause of liver pathology and clotting abnormalities.
Canonical Training is Bad for Reconfigurable Intelligent Surfaces

Channel training in reconfigurable intelligent surfaces (RIS) is different from MIMO; in addition to pilots, it also requires setting RIS training states. Several RIS training schemes are studied in the literature, but the effect of training overhead and accuracy on capacity has not received the deserved attention. This is necessary for guiding the choice of RIS dimensionality and the parameters of modulation and coding. The present work fills this gap and shows that the spectral efficiency of RIS with DFT training (that uses the columns of DFT matrix for the RIS training states) is higher than that with the canonical training (that uses the canonical basis for RIS training states). Specifically, with 32 RIS elements, DFT training achieves a 2 bits/s/Hz gain compared to canonical training when the coherence interval is 150 time slots. Our results also reveal that beyond a certain critical RIS array size, further increase in size is not beneficial and that the optimal array size goes down with increase in SNR.
Predictive preservation of historic buildings through IoT-based system

Cultural Heritage conservation is the set of interventions and actions to protect artworks, ensuring their values accessibility to present and future generations. The advent of new technologies introduced many possibilities and strategies to monitor environments, including those with Cultural Value. This paper introduces effective preventive conservation and details the potential contributions of active environmental control and Internet of Things (IoT) technologies. This strategy is applied through the case study of the Farina tobacco factory in Battipaglia (Italy). The monitoring of environmental parameters was implemented through a remote control system, installing digital sensors inside the structure. Data were collected continuously through integrated supervision, monitoring, and shared management platform, based on web-cloud-IoT technology. The application of IoT systems in climate monitoring and control facilitated the prediction of humidity within the structures being conserved.
Decentralized Updates Scheduling for Data Freshness in Mobile Edge Computing

Age of information (AoI) has been proposed to quantify data freshness. In some real-time applications such as surveillance systems, real-time analytics on source updates requires intensive computation resources and incurs high energy consumption. By providing computing resources at the network edge, mobile edge computing (MEC) can reduce update processing time and improve data freshness. In this paper, we investigate the age-optimal computation-intensive update scheduling for multiple sources in MEC-enabled IoT networks. Since the centralized algorithms may not apply due to the high computational complexity, we design an efficient decentralized scheduling mechanism for self-organized IoT networks. We provide a game-theoretic analysis and prove the existence of pure strategy Nash equilibrium. An efficient and decentralized algorithm based on the best-response dynamics is proposed to compute the equilibrium. We also provide the approximation ratio of the proposed algorithm. In particular, for the homogeneous source model, we show that the approximation ratio is at most 2.5. Extensive evaluation results show that the proposed decentralized algorithm is computationally efficient and closely approximates the centralized optimum in various settings.
Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach

PREFACE xiii 1 OVERVIEW OF MULTIVARIATE AND REGRESSION METHODS 1 2 THE SEVEN HABITS OF HIGHLY EFFECTIVE QUANTS: A REVIEW OF ELEMENTARY STATISTICS USING MATRIX ALGEBRA 20 3 FUNDAMENTALS OF MATRIX ALGEBRA 88 4 FACTOR ANALYSIS AND RELATED METHODS: QUINTESSENTIALLY MULTIVARIATE 139 5 MULTIVARIATE GRAPHICS 227 6 CANONICAL CORRELATION: THE UNDERUSED METHOD 283 7 HOTELLING S T 2 AS THE SIMPLEST CASE OF MULTIVARIATE INFERENCE 333 8 MULTIVARIATE ANALYSIS OF VARIANCE 351 9 MULTIPLE REGRESSION AND THE GENERAL LINEAR MODEL 373 APPENDICES: STATISTICAL TABLES 443 NAME INDEX 456 SUBJECT INDEX 460
Fused Quartz Dual-Shell Resonator Gyroscope

This paper introduces a 3-D fused quartz dual-shell microstructure, discussing its design, simulation, fabrication, and instrumentation as a Microelectromechanical System (MEMS) gyroscope. The dual-shell was realized from plastic deformation of fused quartz using triple-stack wafer bonding and high-temperature micro-glassblowing processes. The fabrication process was developed and dual-shell prototypes with different design parameters were fabricated. A Finite Element (FE) model was presented to simulate the deformation of viscous fused quartz during the micro-glassblowing process. The model was utilized to predict the final 3-D geometry from the design process parameters. A modeling framework based on FE simulations was presented for designing the resonators as well as predicting the resonator’s response to transient excitation, such as shock and vibrations. Fused quartz dual-shell resonator prototype with Q-factors as high as 1.83 million and amplitude ringdown time of 120 seconds was demonstrated. An electrode substrate was designed and integrated with the dual-shell resonator for electrostatic excitation and detection of the vibration modes, as well as 3-D packaging and vacuum encapsulation of the sensing element. An open-loop angular rate gyro operation with electrostatic excitation and capacitive detection was demonstrated on the dual-shell resonator. The Allan deviation of zero rate output bias revealed Angle Random Walk (ARW) of $0.058 \deg /\sqrt {hr}$ and in-run bias instability of $0.4 \deg /hr$ without temperature control on the dual-shell resonator gyroscope. The proposed structure can be instrumented to operate as a resonator, a gyroscope, or other vibratory sensors, and is anticipated to have advantages for precision operation in a harsh environment.
Bandwidth Expansion for Over-the-Air Computation with One-Sided CSI

We consider a distributed computation problem over a multiple access channel (MAC), with N devices. It is known that over-the-air computation (OAC) can provide significant gains for this problem, but existing works are limited to the scenario with matched source and channel bandwidths. We propose OAC schemes for block-fading MACs that modulate the source to fit the available channel bandwidth in a wideband channel, while having channel state information (CSI) only at the transmitter or the receiver. Our results show that the proposed OAC scheme outperforms even ideal capacity-achieving digital schemes when the CSI is available only at the transmitter, and the distortion does not scale with the number of participating devices. We demonstrate the effectiveness of our proposed scheme in federated edge learning (FEEL), where OAC is used to aggregate model updates from the participating devices.
A Framework for Shannon Ciphers under Side-Channel Attacks: a Strong Converse and More

We are interested in investigating the security of source encryption with a symmetric key under side-channel attacks. In this paper, we propose a general framework of source encryption with a symmetric key under the side-channel attacks, which applies to any source encryption with a symmetric key and any kind of side-channel attacks targeting the secret key. We also propose a new security criterion for strong secrecy under side-channel attacks, which is a natural extension of mutual information, i.e., the maximum conditional mutual information between the plaintext and the ciphertext given the adversarial key leakage, where the maximum is taken over all possible plaintext distribution. Under this new criterion, we successfully formulate the rate region, which serves as both necessary and sufficient conditions to have secure transmission even under side-channel attacks. Furthermore, we also prove another theoretical result on our new security criterion, which might be interesting in its own right: in the case of the discrete memoryless source, no perfect secrecy under side-channel attacks in the standard security criterion, i.e., the ordinary mutual information, is achievable without achieving perfect secrecy in this new security criterion, although our new security criterion is more strict than the standard security criterion.
Query Age of Information: Optimizing AoI at the Right Time

This paper is eligible for the Jack Keil Wolf ISIT Student Paper Award. We study a communication system in which a source node is to send status updates over a channel to a remote destination. The update packets will be received error-free, however the channel imposes independent and identically distributed transmission delay on each transmission. The destination node will utilize the upcoming update packets at certain instants that are referred to as query instants. The Query Age of Information (QAoI) at the destination is defined to be the time-average Age of Information (AoI) measured at query instants. We define the problem where the receiver decides when to send requests to "pull" data from the source, to minimize the QAoI, as the pull-or-wait (PoW) problem. We contrast the solution of the PoW problem with that of the update-or-wait (UoW) problem, where the source decides when to generate updates to minimize the time-average AoI. We show that when the query instants occur according to a Poisson process, the solution of the PoW problem is equivalent to that of the UoW problem; however, under periodic query arrivals, the optimal QAoI is always less than or equal to the time average AoI under the same power constraint. We believe this motivates using QAoI as an objective instead of plain AoI in many applications requiring time-sensitive updates.
Generalizing Nesterov’s Acceleration Framework by Embedding Momentum Into Estimating Sequences: New Algorithm and Bounds

We present a new type of heavy-ball momentum term, which is used to construct a class of generalized estimating sequences. These allow for accelerating the minimization process by exploiting the information accumulated in the previous iterates. Combining a newly introduced momentum term with the estimating sequences framework, we devise, as an example, a new black-box accelerated first-order method for solving smooth unconstrained optimization problems. We prove that the proposed method exhibits an improvement over the rate of the celebrated fast gradient method by at least a factor of $\frac{1}{{\sqrt 2 }}$, and establish that lower bound on the number of iterations carried through until convergence is $\mathcal{O}\left( {\sqrt {\frac{\kappa }{2}} } \right)$. Finally, the practical performance benefits of the proposed method are demonstrated by numerical experiments.
A Parameter Optimization Method Based on Eigenvalue Analysis for Microgrid With Multiple VSG Units

Most of distributed generations in microgrid are lack of inertia. To solve this problem, virtual synchronous generator (VSG) control strategy is proposed to simulate the characteristics of synchronous generator. However, static stability problem is introduced under basic VSG control, which would be more serious when multiple VSGs operate in parallel. Therefore, the parameters of micro grid with multiple VSG units must be optimized. In this paper, firstly, the state space model of microgrid is used to propose the method of solving and analyzing eigenvalues, and the parameter optimization objective based on eigenvalue analysis is proposed. For the optimization objective, then the preliminary parameter optimization method based on eigenvalue root locus and the advanced parameter optimization method based on improved particle swarm optimization(PSO) algorithm are proposed. Finally, the correctness and feasibility of the parameter optimization method are verified by MATLAB/Simulink simulation experiment.
Depth camera based row-end detection and headland manuvering in orchard navigation without GNSS

A robust navigation system is a prerequisite for a mobile robotic platform to carry out precision agriculture tasks in a modern orchard. In contrast to open fields, navigation based solely on the Global Navigation Satellite System (GNSS) is not stable in many orchards, where tree canopies may block the GNSS signal or introduce multipath. Many works have been done to localize the robot while traversing the inside a row, but navigating the robot to the next row on headland still relies on a reference map or artificial landmarks. In this work, we developed a row end detection method by exploiting drastic changes in the statistical distribution of points sensed by a depth camera compared to the points inside the row. Also, a robust way of row entry method is implemented by building a local environment map and reactive path tracker. The whole navigation system is tested and evaluated on a mobile robot in a vineyard. The experiment results show the robot can detect the tree row-end accurately and maneuver a U-turn to the next row safely.
A Novel Set of Directives for Multi-device Programming with OpenMP

The latest versions of OpenMP have been offering support for offloading execution to the accelerator devices present in a variety of heterogeneous architectures via the target directives. However, these directives can only refer to one device at a time, which makes multi-device programming an explicit and tedious task. In this work, we present an extension of OpenMP in the form of a new set of directives (target spread directives) which offers direct support for multiple devices and allows the distribution of data and/or workload among them without explicit programming. This results in an additional level of parallelism between the host and the devices. The target spread directives were evaluated using the Somier micro-app in a PowerPC cluster node with up to four Nvidia Tesla V100 GPUs. The results showed a speedup of approximately 2X using four GPUs and the new directive set, in comparison with the baseline implementation which used one GPU and the existing target directive set.
Upper Confidence Interval Strategies for Multi-Armed Bandits with Entropy Rewards

We introduce a multi-armed bandit problem with information-based rewards. At each round, a player chooses an arm, observes a symbol, and receives an unobserved reward in the form of the symbol’s self-information. The player aims to maximize the expected total reward associated with the entropy values of the arms played. We propose two algorithms based on upper confidence bounds (UCB) for this model. The first algorithm optimistically corrects the bias term in the entropy estimation. The second algorithm relies on data-dependent UCBs that adapt to sources with small entropy values. We provide performance guarantees by upper bounding the expected regret of each of the algorithms, and compare their asymptotic behavior to the Lai-Robbins lower bound. Finally, we provide numerical results illustrating the regret of the algorithms presented.
Exact k-way sparse matrix partitioning

To minimize the communication in parallel sparse matrix-vector multiplication while maintaining load balance, we need to partition the sparse matrix optimally into k disjoint parts, which is an NP-complete problem. We present an exact algorithm based on the branch and bound (BB) method which partitions a matrix for any k, and we explore exact sparse matrix partitioning beyond bipartitioning. The algorithm has been implemented in a software package General Matrix Partitioner (GMP). We also present an integer linear programming (ILP) model for the same problem, based on a hypergraph formulation. We used both methods to determine optimal 2,3,4-way partitionings for a subset of small matrices from the SuiteSparse Matrix Collection. For k=2, BB outperforms ILP, whereas for larger k, ILP is superior. We used the results found by these exact methods for k=4 to analyse the performance of recursive bipartitioning (RB) with exact bipartitioning. For 46 matrices of the 89 matrices in our test set of matrices with less than 250 nonzeros, the communication volume determined by RB was optimal. For the other matrices, RB is able to find 4-way partitionings with communication volume close to the optimal volume.
Ternary Convolutional LDGM Codes with Applications to Gaussian Source Compression

We present a ternary source coding scheme in this paper, which is a special class of low density generator matrix (LDGM) codes. We prove that a ternary linear block LDGM code, whose generator matrix is randomly generated with each element independent and identically distributed, is universal for source coding in terms of the symbol-error rate (SER). To circumvent the high-complex maximum likelihood decoding, we introduce a special class of convolutional LDGM codes, called block Markov superposition transmission of repetition (BMST-R) codes, which are iteratively decodable by a sliding window algorithm. Then the presented BMST-R codes are applied to construct a tandem scheme for Gaussian source compression, where a dead-zone quantizer is introduced before the ternary source coding. The main advantages of this scheme are its universality and flexibility. The dead-zone quantizer can choose a proper quantization level according to the distortion requirement, while the LDGM codes can adapt the code rate to approach the entropy of the quantized sequence. Numerical results show that the proposed scheme performs well for ternary sources over a wide range of code rates and that the distortion introduced by quantization dominates provided that the code rate is slightly greater than the discrete entropy.