Miracle-dz/UniFAR-FTUs huggingface dataset:
University of Birmingham Structure from Randomness in Halfspace Learning with the Zero-One Loss

We prove risk bounds for halfspace learning when the data dimensionality is allowed to be larger than the sample size, using a notion of compressibility by random projection. In particular, we give upper bounds for the empirical risk minimizer learned eﬃciently from randomly projected data, as well as uniform upper bounds in the full high-dimensional space. Our main ﬁndings are the following: i) In both settings, the obtained bounds are able to discover and take advantage of benign geometric structure, which turns out to depend on the cosine similarities between the classiﬁer and points of the input space, and provide a new interpretation of margin distribution type arguments. ii) Furthermore our bounds allow us to draw new connections between several existing successful classiﬁcation algorithms, and we also demonstrate that our theory is predictive of empirically observed performance in numerical simulations and experiments. iii) Taken together, these results suggest that the study of compressive learning can improve our understanding of which benign structural traits – if they are possessed by the data generator – make it easier to learn an eﬀective classiﬁer from a sample.
Condition Monitoring of a Paper Feeding Mechanism Using Model-of-Signals as Machine Learning Features

Prognostics and Health Management of machine devices and parts is a hot topic in the Industry 4.0 era. In this fashion, automated procedures to evaluate machinery working conditions are essential to minimize downtime and maintenance costs. In this work, we study how to monitor the decrease in performance of a paper sheet feeder for the packaging industry under heavy-duty cycle operations. The main measurable outcome of such degradation is the increase in backlash among the device moving components. A wide variety of methods and procedures is available to tackle this monitoring problem. In this paper, we analyze the use of a simple yet efficient diagnosis methodology that can exploit machinery controllers (i.e., Programmable Logic Controllers) edge-computing capabilities. Vibration measurements are known in the literature to retain information about the system's mechanics. Model-of Signals, a data-driven approach based on black box system identification, allows to extract that information reliably during machinery working cycle. The refinement of those data using machine learning allows the retrieval of knowledge about the health state of the machine. In this study, the feeder mechanism is run to failure with its parts backlash measured at given time intervals. Accelerometer signals are modelled as AutoRegressive processes whose coefficients are then considered as features to feed to machine learning algorithms, which are employed to perform severity evaluation of the ongoing degradation. Estimation and prediction are both implementable on-board the controller, while the learning task can be carried out remotely, in a cloud computing perspective. The exploitation of AutoRegressive modelling gives a simple and inherent methodology for feature selection, serving as a foundation of the machine learning stage. We make use of a Support Vector Machine algorithm to analyze how obtained models represent the various levels of backlash in the device and develop a suitable predictor of the degradation severity. Finally, the results of the application of the methodology to the case study are shown.
MODELLING AND CONTROL OF A LONG FLEXIBLE GUYED STRUCTURE

The objective of this work is to determine a dynamic model to represent a long, flexible deployable structure, aiming its vibration control. We propose a modeling strategy in order to overcome the problems related to the construction of numerical parametric models of exploration probes, shaped as long deformed beams with variable cross section, subject to self-weight and concentrated loads The proposed modeling strategy consists in finding the static large deformation curve of the beam subjected to the pulling cable force. Then, a coarse finite elements mesh is adapted to the static deformed shape of the pulled beam, yielding a linear model of the dynamic parameters. The modeling strategy is also applied to characterize the static and dynamic behaviors of a real beam. The H∞ control methodology is applied to the numerical dynamic model in order to suppress the vibration of the flexible beam with large deformations. The control strategy design is based on the modal characteristics of the structure. The results show that the proposed strategy of combining an analytical model of static deformation with a low order finite element model of the solid is an efficient way to obtain a convenient dynamic description of the structure.
Analysis of Developer Information Needs on Collaborative Platforms

Developer information needs are diverse, and so are the sources to satisfy those needs. Besides internal sources, developers frequently use external sources of information (e.g., Q&A sites, mailing lists). Researchers use data from these sources to gain insights into developer information needs and build solutions to support the development process. However, along with the opportunities, the diversity of external sources poses challenges for research. In this thesis, we identify and describe various external sources used by researchers to investigate developer information needs. We analyze the methodology of relevant literature to extract common practices, identify challenges, and assess data extraction and preprocessing reproducibility. To further increase knowledge about developer information needs and the process of analyzing these information needs, we conduct a case study about Code Comment Convention questions using data from multiple sources. The case study follows best practices identified in the relevant literature and is conducted with the help of a self-developed prototype tool. The prototype tool is designed to help researchers extract, manage, and preprocess a dataset from multiple sources in a documented and reproducible way.
An asynchronous steady-state NSGA-II algorithm for multi-objective optimization of Diesel combustion

In order to comply with environmental regulations, automotive manufacturers have to develop efficient engines with low fuel consumption and low emissions. Thus, development of engine combustion systems (chamber, injector, air loop) becomes a hard task since many parameters have to be defined in order to optimize many objectives in conflict. Evolutionary Multi-objective optimization represents an efficient tool to explore the search space and find promising engine combustion systems. In this work, a phenomenological 0D model of Diesel engine combustion is coupled with a state-of-the-art multi-objective evolutionary algorithm NSGA-II. The 0D combustion model allows to compute, from the compression to the expansion strokes, the phenomena of: Injection, mixing, combustion, and major pollutants formation, thanks to a detailed kinetic scheme. Besides, it takes into account spatial heterogeneities on temperature and mixture composition inside the combustion chamber. A standard parallel version of NSGA-II based on master-slave paradigm is applied using a Grid system. In this standard version, the selection process is generational: Variation operators are applied once all individuals belonging to the same generation have been evaluated. However, the computation of different solutions can have very different durations. Thus, some processors sometimes stay idle. To tackle this issue, a steady-state version of NSGA-II is implemented: Only one individual is created per generation, and uninterrupted use of available processors in the Grid system is ensured. Comparison between both approaches is performed.
Analysis of the Global Banking Network by Random Matrix Theory

Since the financial crisis of 2008, the network analysis of financial systems has attracted a lot of attention. In this paper, we analyze the global banking network via the method of Random Matrix Theory. By applying that method on a cross border lending network, it is shown that while the connectivity between different parts of the network has risen and the profile of transactions has diversified, the role of hubs remains important in the weighted perspective. The largest eigenvalue of the transaction matrix as the leading mode of the system shows sharp growth since 2002. As well, it is observed that its growth has diminished since 2008. This indicates that the crisis of 2008 has left a long-lasting footprint on the financial system. Analyzing the mean value of the participation ratio reveals the fact that the role of countries in forming small modes, has increased since 2002. In our final analysis, we provide snapshots of the hubs in the network over time. We observe that the share of countries in total transactions is not equal to their share in shaping the eigenvector of the largest eigenvalue. In 2018 for example, while the United Kingdom leads the share of transactions, it is the United States that has the largest value in the leading eigenvector. The proposed technique in the paper can be useful for analyzing different types of interaction networks between countries.
Validation of emission-line galaxies target selection algorithms for the Dark Energy Spectroscopic Instrument using the MMT Binospec

The forthcoming Dark Energy Spectroscopic Instrument (DESI) experiment plans to measure the effects of dark energy on the expansion of the Universe and create a $3$D map of the Universe using galaxies up to $z \sim 1.6$ and QSOs up to $z \sim 3.5$. In order to create this map, DESI will obtain spectroscopic redshifts of over $30$ million objects; among them, a majority are \oii emitting star-forming galaxies known as emission-line galaxies (ELGs). These ELG targets will be pre-selected by drawing a selection region on the $g - r$ vs. $r - z$ colour-colour plot, where high redshift ELGs form a separate locus from the lower redshift ELGs and interlopers. In this paper, we study the efficiency of three ELG target selection algorithms -- the final design report (FDR) cut based on the DEEP2 photometry, Number Density Modelling and Random Forest -- to determine how the combination of these three algorithms can be best used to yield a simple selection boundary that will be best suited to meet DESI's science goals. To do this, we selected $17$ small patches in the DESI footprint where we run the three target selection algorithms to pre-select ELGs based on their photometry. We observed the pre-selected ELGs using the MMT Binospec, which is similar in functionality to the DESI instrument, to obtain their spectroscopic redshifts and fluxes of $1054$ ELGs. By analysing the redshift and fluxing distribution of these galaxies, we find that although NDM performed the best, simple changes in the FDR definition would also yield sufficient performance.
Optimal Procurement Auction for Cooperative Production of Virtual Products: Vickrey-Clarke-Groves Meet Cremer-McLean

We set up a supply-side game-theoretic model for the cooperative production of virtual products. In our model, a group of producers collaboratively produce a virtual product by contributing costly input resources to a production coalition. Producers are capacitated, i.e., they cannot contribute more resources than their capacity limits. Our model is an abstraction of emerging internet-based business models such as federated learning and crowd computing. To maintain an efficient and stable production coalition, the coordinator should share with producers the income brought by the virtual product. Besides the demand-side information asymmetry, another two sources of supply-side information asymmetry intertwined in this problem: 1) the capacity limit of each producer and 2) the cost incurred to each producer. In this paper, we rigorously prove that a supply-side mechanism from the VCG family, PVCG, can overcome such multiple information asymmetry and guarantee truthfulness. Furthermore, with some reasonable assumptions, PVCG simultaneously attains truthfulness, ex-post allocative efficiency, ex-post individual rationality, and ex-post weak budget balancedness on the supply side, easing the well-known tension between these four objectives in the mechanism design literature.
Boardroom Voting: Verifiable Voting with Ballot Privacy Using Low-Tech Cryptography in a Single Room

A boardroom election is an election that takes place in a single room-the boardroom-in which all voters can see and hear each other. We present an initial exploration of boardroom elections with ballot privacy and voter verifiability that use only "low-tech cryptography" without using computers to mark or collect ballots. Specifically, we define the problem, introduce several building blocks, and propose a new protocol that combines these blocks in novel ways. Our new building blocks include "foldable ballots" that can be rotated to hide the alignment of ballot choices with voting marks, and "visual secrets" that are easy to remember and use but hard to describe. Although closely seated participants in a boardroom election have limited privacy, the protocol ensures that no one can determine how others voted. Moreover, each voter can verify that their ballot was correctly cast, collected, and counted, without being able to prove how they voted, providing assurance against undue influence. Low-tech cryptography is useful in situations where constituents do not trust computer technology, and it avoids the complex auditing requirements of end-to-end cryptographic voting systems such as Pret-a-Voter. This paper's building blocks and protocol are meant to be a proof of concept that might be tested for usability and improved.
Maternal Characteristics and the Risk of Cleft Lip and Palate in the United States

Maternal Characteristics and the Risk of Cleft Lip and Palate in the United States by Letha Thomas MSN, University of Manchester, 2004 BSN, Armed Forces Medical College, Pune University, 1989 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Health ServicesHealth Care Administration
Deterministic Distributed Expander Decomposition and Routing with Applications in Distributed Derandomization

There is a recent exciting line of work in distributed graph algorithms in the CONGEST model that exploit expanders. All these algorithms so far are based on two tools: expander decomposition and expander routing. An (<tex>$\epsilon, \phi$</tex>)-expander decomposition removes <tex>$\epsilon$</tex>-fraction of the edges so that the remaining connected components have conductance at least <tex>$\phi$</tex>, i.e., they are <tex>$\phi$</tex>-expanders, and expander routing allows each vertex <tex>$v$</tex> in a <tex>$\phi$</tex>-expander to very quickly exchange <tex>$\deg(v)$</tex> messages with any other vertices, not just its local neighbors. In this paper, we give the first efficient deterministic distributed algorithms for both tools. We show that an (<tex>$\epsilon, \phi$</tex>) -expander decomposition can be deterministically computed in <tex>$\text{poly} (\epsilon^{-1})n^{o(1)}$</tex> rounds for <tex>$\phi= \text{poly} (\epsilon)n^{-o(1)}$</tex>, and that expander routing can be performed deterministically in <tex>$\text{poly} (\phi^{-1})n^{o(1)}$</tex> rounds. Both results match previous bounds of randomized algorithms by [Chang and Saranurak, PODC 2019] and [Ghaffari, Kuhn, and Su, PODC 2017] up to subpolynomial factors. Consequently, we derandomize existing distributed algorithms that exploit expanders. We show that a minimum spanning tree on <tex>$n^{-o(1)}$</tex>-expanders can be constructed deterministically in <tex>$n^{o(1)}$</tex> rounds, and triangle detection and enumeration on general graphs can be solved deterministically in <tex>$O(n^{0.58})$</tex> and <tex>$n^{2/3+o(1)}$</tex> rounds, respectively. Using similar techniques, we also give the first polylogarithmic-round randomized algorithm for constructing an (<tex>$\epsilon, \phi$</tex>) -expander decomposition in <tex>$\text{poly} (\epsilon^{-1}, \log n)$</tex> rounds for <tex>$\phi=1/\text{poly}(\epsilon^{-1}, \log n)$</tex>. This algorithm is faster than the previous algorithm by [Chang and Saranurak, PODC 2019] in all regimes of parameters. The previous algorithm needs <tex>$n^{\Omega(1)}$</tex> rounds for any <tex>$\phi\geq 1/\text{poly}\log n$</tex>.
Subbands in the doped two-orbital Kanamori-Hubbard model

We calculate and resolve with unprecedented detail the local density of states (DOS) and momentum-dependent spectral functions at zero temperature of one of the key models for strongly correlated electron materials, the degenerate two-orbital Kanamori-Hubbard model, by means of a highly optimized Dynamical Mean Field Theory which uses the Density Matrix Renormalization Group as the impurity solver. When the system is hole doped, and in the presence of a finite interorbital Coulomb interaction we find the emergence of a novel holon-doublon in-gap subband which is split by the Hund's coupling. We also observe new interesting features in the DOS like the splitting of the lower Hubbard band into a coherent narrowly dispersing peak around the Fermi energy, and another subband which evolves with the chemical potential. We characterize the main transitions giving rise to each subband by calculating the response functions of specific projected operators and comparing with the energies in the atomic limit, obtaining excellent agreement. The detailed results for the spectral functions found in this work pave the way to study with great precision the microscopic quantum behavior in correlated materials.
Optimal Power Allocation Strategies in Two-Hop X-duplex Multi-source Relay Channel

The aim of this thesis is to find an optimal power allocation strategy for the transmitting nodes in a network scenario which comprises two sources, a single relay and a single destination. The main problem which is present in such network configurations is the self-interference phenomenon created by the relay, which attempts to transmit and receive at the same time on the same frequency band. Finding the correct power level (both at sources and relay) which allows the overall rate maximization is a theme of great interest, since many wireless applications present this problem and the use of relays is expected to increase in the near future. The strategy found in this work contemplates the use of Half-Duplex or Full-Duplex transmission modes at the relay. Moreover it is proved that such strategy has interesting performances with respect to other solutions provided in similar scenarios, but which require more precise information about the relay to be known at the sources. \par The novelty introduced here is the presence of multiple sources, which raises issues of fairness. The fairness concern is addressed and integrated into the system model provided. All these topics are mathematically described and solved, and the theoretical part is supported with numerical results which confirm the validity of the provided model.
SplitFlyer: a Modular Quadcoptor that Disassembles into Two Flying Robots

We introduce SplitFlyer–a novel quadcopter with an ability to disassemble into two self-contained bicopters through human assistance. As a subunit, the bicopter is a severely underactuated aerial vehicle equipped with only two propellers. Still, each bicopter is capable of independent flight. To achieve this, we provide an analysis of the system dynamics by relaxing the control over the yaw rotation, allowing the bicopter to maintain its large spinning rate in flight. Taking into account the gyroscopic motion, the dynamics are described and a cascaded control strategy is developed. We constructed a transformable prototype to demonstrate consecutive flights in both configurations. The results verify the proposed control strategy and show the potential of the platform for future research in modular aerial swarm robotics.
Robustness of the Floquet many-body localized phase in the presence of a smooth and a non-smooth drive

In this work, we investigate how the critical driving amplitude at the Floquet MBL-to-ergodic phase transition differs between smooth and non-smooth driving over a wide range of driving frequencies. To this end, we study numerically a disordered spin-1/2 chain which is periodically driven by a sine or a square-wave drive, respectively. In both cases, the critical driving amplitude increases monotonically with the frequency, and at large frequencies, it is identical for the two drives in the appropriate normalization. However, at low and intermediate frequencies the critical amplitude of the square-wave drive depends strongly on the frequency, while the one of the cosine drive is almost constant in a wide frequency range. By analyzing the density of drive-induced resonance in a Fourier space perspective, we conclude that this difference is due to resonances induced by the higher harmonics which are present (absent) in the Fourier spectrum of the square-wave (sine) drive. Furthermore, we suggest a numerically efficient method to estimate the frequency dependence of the critical driving amplitudes for different drives, based on measuring the density of drive-induced resonances.
A regularized deep matrix factorized model of matrix completion for image restoration

It has been an important approach of using matrix completion to perform image restoration. Most previous works on matrix completion focus on the low-rank property by imposing explicit constraints on the recovered matrix, such as the constraint of the nuclear norm or limiting the dimension of the matrix factorization component. Recently, theoretical works suggest that deep linear neural network has an implicit bias towards low rank on matrix completion. However, low rank is not adequate to reflect the intrinsic characteristics of a natural image. Thus, algorithms with only the constraint of low rank are insufficient to perform image restoration well. In this work, we propose a Regularized Deep Matrix Factorized (RDMF) model for image restoration, which utilizes the implicit bias of the low rank of deep neural networks and the explicit bias of total variation. We demonstrate the effectiveness of our RDMF model with extensive experiments, in which our method surpasses the state of art models in common examples, especially for the restoration from very few observations. Our work sheds light on a more general framework for solving other inverse problems by combining the implicit bias of deep learning with explicit regularization.
Lifelong Navigation

This paper presents a continually self-improving lifelong learning framework for a mobile robot navigating in different environments. Classical static navigation methods require environment-specific in-situ system adjustment, e.g. from human experts, or may repeat their mistakes regardless of how many times they have navigated in the same environment. Having the potential to improve with experience, learning-based navigation is highly dependent on access to training resources, e.g. sufficient memory and fast computation, and is prone to forgetting previously learned capability, especially when facing different environments. In this work, we propose a lifelong learning framework for mobile robot navigation which (1) improves from its own experience without expert demonstrations, and (2) retains capability to navigate in previous environments after learning in new ones. This framework is implemented and tested entirely onboard a physical robot with a limited memory and computation budget.