Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting mobile interruptibility

The scope of users receiving interruptions has expanded dramatically with the widespread use of mobile devices, such as smartphones and smartwatches. Generally, mobile devices notify users of newly available information frequently via notifications. It can be very disruptive when these notifications interrupt users at an inappropriate time. For example, irrelevant notifications that arrive when you are in an important meeting could be obtrusive and annoying. Therefore, when to interrupt user becomes a defining problem in the interaction between users and mobile devices. In practice, as our work shows, people usually express different levels of interruptibility at different contexts. Previous work has classified interruptibility as a binary status, interruptible or not interruptible. Our work shows that this is not sufficient to accurately measure users availability towards interruptions. We present the design, implementation and evaluation of a personality-dependent two-stage hierarchical model to predict users fine-grained Interruptibility. To the best of our knowledge, our work is the first to introduce personality variables into the interruptibility prediction model. We also solve the important problem in ubiquitous computing, how to enabled predictions before individually training on the user? Our model uses the data of people who share similar personality with the user to predict before training to the particular user. To investigate the effects of different factors on interruptibility in the model, we applied hierarchical Bayesian approach to analyze the data extensively. Defense Committee: Prof. Janne Lindqvist (Chair), Prof. Wade Trappe, Prof. Richard Martin, Prof. Guiling
Tawki: Towards Self-Sovereign Social Communication

As of today, web-based social communication platforms, such as WhatsApp, Twitter, or Facebook, are almost exclusively realized via centralized platforms, based on proprietary interfaces, protocols, and data formats. In consequence, even though social communication being a decentralized, peer-to-peer phenomenon, web-based communication today is implemented via closed, proprietary data silos, which not only lock-in users into their service platforms, but also control and own exchange information and data, including personal and sensitive data such as photos, messages, or contact information. In this paper we present Tawki, a decentralized service architecture for social communication. Using Tawki, users remain in full control of their personal data, which is stored and managed by personal data storages. Each data storage is accessible via a unified Tawki API, which allows users to send and request data to and from other users' personal data storages. Following this approach, social communication is again peer-to-peer without involving a third party controlling and monitoring the process. Tawki uses the Ethereum Name Service (ENS) for both the management of user identities and resolving identifiers to the respective user's personal storage location. Leveraging the immutability of the Ethereum Blockchain, identity management and discovery of personal data storages is secured against censorship and control through any third party.
Integrable systems connected with black holes

We studied some important questions in general relativity and mathematical physics mainly related to the two most important solutions of the theory of relativity - gravitational waves and black holes. In particular, the work is related to astrophysical shock waves, gravitational waves, black holes, integrable systems associated with them as well as their quantum equivalents. We studied the effects of null shells on geodesic congruences and suggested a general covariant definition of the gravitational memory effect. Thus, we studied observable effects that astrophysical shock waves can have on test particles after cataclysmic astrophysical events. We studied the geodesics of massive particles in Near Horizon Extremal Myers-Perry (NHEMP) black hole geometries. This is the space-time in the vicinity of the horizon of higher dimensional rotating black holes. Thus, this work can have applications for studying accretions of black holes. The system is also important in mathematical physics as it describes integrable (in special cases superintegrable) system, where the constants of motion are fully studied. On the other hand, the quantum counterparts of this and other integrable systems are studied as well and a new technique is suggested for geometrization of these systems.
Allowed permutation symmetry in atomic and molecular wavefunctions: simple examples

It is well known that the allowed wavefunctions for an N-electron system should be antisymmetric with respect to the permutation of any pair of electron labels. On the other hand, the Hamiltonian for such a system is invariant under any permutation of electron labels and, consequently, its eigenfunctions are the basis for the irreducible representations of the symmetric group SN. Here, we investigate which symmetry species of the SN group are compatible with the antisymmetry principle. We illustrate the conclusions by means of simple N-particle one-dimensional models with harmonic interactions.
Conformal transformations of the pseudo‐Riemannian metric of a homogeneous pair

We introduce a new notion of a homogeneous pair for a pseudo‐Riemannian metric g and a positive function f on a manifold M admitting a free R>0 ‐action. There are many examples admitting this structure. For example, (a) a class of pseudo‐Hessian manifolds admitting a free R>0 ‐action and a homogeneous potential function such as the moduli space of torsion‐free G2 ‐structures, (b) the space of Riemannian metrics on a compact manifold and (c) many moduli spaces of geometric structures such as torsion‐free Spin(7) ‐structures admit this structure. Hence we provide the unified method for the study of these geometric structures.
ReadsMap: a new tool for high precision mapping of DNAseq and RNAseq read sequences

There are currently plenty of programs available for mapping short sequences (reads) to a genome. Most of them, however, including such popular and actively developed programs as Bowtie, BWA, TopHat and many others, are based on Burrows-Wheeler Transform (BWT) algorithm. This approach is very effective for mapping high-homology reads, but runs into problems when mapping reads with high level of errors or SNP. Also it has problems with mapping RNASeq spliced reads (such as reads that aligning with gaps corresponding intron sequences), the kind that is essential for finding introns and alternative splicing gene isoforms. Meanwhile, finding intron positions is the most important task for determining the gene structure, and especially alternatively spliced variants of genes. In this paper, we propose a new algorithm that involves hashing reference genome. ReadsMap program, implementing such algorithm, demonstrate very high-accuracy mapping of large number of short reads to one or more genomic contigs. It is achieved mostly by better alignment of very short parts of reads separated by long introns with accounting information from mapping other reads containing the same intron inserted between bigger blocks. Availability and implementation: ReadsMap is implemented in C. It is incorporated in Fgenesh++ gene identification pipeline and is freely available to academic users at Softberry web server this http URL.
Reconstruction of the magnetic field for a Schr\"odinger operator in a cylindrical setting.

In this thesis we consider a magnetic Schrodinger inverse problem over a compact domain contained in an infinite cylindrical manifold. We show that, under certain conditions on the electromagnetic potentials, we can recover the magnetic field from boundary measurements in a constructive way. A fundamental tool for this procedure is a global Carleman estimate for the magnetic Schrodinger operator. We prove this by conjugating the magnetic operator essentially into the Laplacian, and using the Carleman estimates for it proven by Kenig-Salo-Uhlmann in the anisotropic setting, see [KSU11a]. The conjugation is achieved through pseudodifferential operators over the cylinder, for which we develop the necessary results. The main motivations to attempt this question are the following results concerning the magnetic Schrodinger operator: first, the solution to the uniqueness problem in the cylindrical setting in [DSFKSU09], and, second, the reconstruction algorithm in the Euclidean setting from [Sal06]. We will also borrow ideas from the reconstruction of the electric potential in the cylindrical setting from [KSU11b]. These two new results answer partially the Carleman estimate problem (Question 4.3.) proposed in [Sal13] and the reconstruction for the magnetic Schrodinger operator mentioned in the introduction of [KSU11b]. To our knowledge, these are the first global Carleman estimates and reconstruction procedure for the magnetic Schrodinger operator available in the cylindrical setting.
Systems of difference equations on a vector valued function that admit 3D space of scalar potentials

For some involutive maps $\Phi:{\mathbb C}P^1 \times {\mathbb C}P^1 \to {\mathbb C}P^1 \times {\mathbb C}P^1$ we find all invariants with separated variables. We investigate a link of the maps and their invariants with separated variables to discrete integrable systems. Maps correspond to integrable systems on edges (bond systems), while their invariants with separated variables yields potentials of the bond systems, that allows us to rewrite the integrable sytems as models on vertices. Among the latter ones one can find well known integrable difference equations as well as difference relations, which in contrast to the equations give non-single-valued evolution of the dependent variable. However, the non-single-valuedness can be resolved by the link with the bond system.
Asymptotic Enumeration of Orientations of a Graph as a Function of the Out-Degree Sequence

We prove an asymptotic formula for the number of orientations with given out-degree (score) sequence for a graph $G$. The graph $G$ is assumed to have average degrees at least $n^{1/3 + \epsilon}$ for some $\epsilon > 0$, and to have strong mixing properties, while the maximum imbalance (out-degree minus in-degree) of the orientation should be not too large. Our enumeration results have applications to the study of subdigraph occurrences in random orientations with given imbalance sequence. As one step of our calculation, we obtain new bounds for the maximum likelihood estimators for the Bradley-Terry model of paired comparisons.
Repair Pipelining for Erasure-coded Storage: Algorithms and Evaluation

We propose repair pipelining, a technique that speeds up the repair performance in general erasure-coded storage. By carefully scheduling the repair of failed data in small-size units across storage nodes in a pipelined manner, repair pipelining reduces the single-block repair time to approximately the same as the normal read time for a single block in homogeneous environments. We further design different extensions of repair pipelining algorithms for heterogeneous environments and multi-block repair operations. We implement a repair pipelining prototype, called ECPipe, and integrate it as a middleware system into two versions of Hadoop Distributed File System (HDFS) (namely, HDFS-RAID and HDFS-3) as well as Quantcast File System. Experiments on a local testbed and Amazon EC2 show that repair pipelining significantly improves the performance of degraded reads and full-node recovery over existing repair techniques.
Restricted Linearized Augmented Lagrangian Method for Euler's Elastica Model

Euler's elastica model has been extensively studied and applied to image processing tasks. However, due to the high nonlinearity and nonconvexity of the involved curvature term, conventional algorithms suffer from slow convergence and high computational cost. Various fast algorithms have been proposed, among which, the augmented Lagrangian based ones are very popular in the community. However, parameter tuning might be very challenging for these methods. In this paper, a simple cutting-off strategy is introduced into the augmented Lagrangian based algorithms for minimizing the Euler's elastica energy, which leads to easy parameter tuning and fast convergence. The cutting-off strategy is based on an observation of inconsistency inside the augmented Lagrangian based algorithms. When the weighting parameter of the curvature term goes to zero, the energy functional boils down to the ROF model. So, a natural requirement is that its augmented Lagrangian based algorithms should also approach the augmented Lagrangian based algorithms formulated directly for solving the ROF model from the very beginning. Unfortunately, this is not the case for certain existing augmented Lagrangian based algorithms. The proposed cutting-off strategy helps to decouple the tricky dependence between the auxiliary splitting variables, so as to remove the observed inconsistency. Numerical experiments suggest that the proposed algorithm enjoys easier parameter-tuning, faster convergence and even higher quality of image restorations.
Spams meet Cryptocurrencies: Sextortion in the Bitcoin Ecosystem

In the past year, a new spamming scheme has emerged: sexual extortion messages requiring payments in the cryptocurrency Bitcoin, also known as sextortion. This scheme represents a first integration of the use of cryptocurrencies by members of the spamming industry. Using a dataset of 4,340,736 sextortion spams, this research aims at understanding such new amalgamation by uncovering spammers' operations. To do so, a simple, yet effective method for projecting Bitcoin addresses mentioned in sextortion spams onto transaction graph abstractions is computed over the entire Bitcoin blockchain. This allows us to track and investigate monetary flows between involved actors and gain insights into the financial structure of sextortion campaigns. We find that sextortion spammers are somewhat sophisticated, following pricing strategies and benefiting from cost reductions as their operations cut the upper-tail of the spamming supply chain. We discover that one single entity is likely controlling the financial backbone of the majority of the sextortion campaigns and that the 11-month operation studied yielded a lower-bound revenue between $1,300,620 and $1,352,266. We conclude that sextortion spamming is a lucrative business and spammers will likely continue to send bulk emails that try to extort money through cryptocurrencies.
Beam Codebook Design for 5G mmWave Terminals

A beam codebook of 5G millimeter wave (mmWave) for data communication consists of multiple high-peak-gain beams to compensate the high pathloss at the mmWave bands. These beams also have to point to different angular directions, such that by performing beam searching over the codebook, a good mmWave signal coverage over the full sphere around the terminal (spherical coverage) can be achieved. A model-based beam codebook design that assumes ideal omni-directional antenna pattern, and neglects the impact of terminal housing around the antenna, does not work well because the radiation pattern of a practical mmWave antenna combined with the impact of terminal housing is highly irregular. In this paper, we propose a novel and efficient data-driven method to generate a beam codebook to boost the spherical coverage of mmWave terminals. The method takes as inputs the measured or simulated electric field response data of each antenna and provides the codebook according to the requirements on the codebook size, spherical coverage, and so on. The method can be applied in a straightforward manner to different antenna type, antenna array configuration, placement, and terminal housing design. Our simulation results show that the proposed method generates a codebook better than the benchmark and 802.15. 3c codebooks in terms of the spherical coverage.
SANTLR: Speech Annotation Toolkit for Low Resource Languages

While low resource speech recognition has attracted a lot of attention from the speech community, there are a few tools available to facilitate low resource speech collection. In this work, we present SANTLR: Speech Annotation Toolkit for Low Resource Languages. It is a web-based toolkit which allows researchers to easily collect and annotate a corpus of speech in a low resource language. Annotators may use this toolkit for two purposes: transcription or recording. In transcription, annotators would transcribe audio files provided by the researchers; in recording, annotators would record their voice by reading provided texts. We highlight two properties of this toolkit. First, SANTLR has a very user-friendly User Interface (UI). Both researchers and annotators may use this simple web interface to interact. There is no requirement for the annotators to have any expertise in audio or text processing. The toolkit would handle all preprocessing and postprocessing steps. Second, we employ a multi-step ranking mechanism facilitate the annotation process. In particular, the toolkit would give higher priority to utterances which are easier to annotate and are more beneficial to achieving the goal of the annotation, e.g. quickly training an acoustic model.
First-principles calculations of charge carrier mobility and conductivity in bulk semiconductors and two-dimensional materials

One of the fundamental properties of semiconductors is their ability to support highly tunable electric currents in the presence of electric fields or carrier concentration gradients. These properties are described by transport coefficients such as electron and hole mobilities. Over the last decades, our understanding of carrier mobilities has largely been shaped by experimental investigations and empirical models. Recently, advances in electronic structure methods for real materials have made it possible to study these properties with predictive accuracy and without resorting to empirical parameters. These new developments are unlocking exciting new opportunities, from exploring carrier transport in quantum matter to in silico designing new semiconductors with tailored transport properties. In this article, we review the most recent developments in the area of ab initio calculations of carrier mobilities of semiconductors. Our aim is threefold: to make this rapidly-growing research area accessible to a broad community of condensed-matter theorists and materials scientists; to identify key challenges that need to be addressed in order to increase the predictive power of these methods; and to identify new opportunities for increasing the impact of these computational methods on the science and technology of advanced materials. The review is organized in three parts. In the first part, we offer a brief historical overview of approaches to the calculation of carrier mobilities, and we establish the conceptual framework underlying modern ab initio approaches. We summarize the Boltzmann theory of carrier transport and we discuss its scope of applicability, merits, and limitations in the broader context of many-body Green’s function approaches. We discuss recent implementations of the Boltzmann formalism within the context of density functional theory and many-body perturbation theory calculations, placing an emphasis on the key computational challenges and suggested solutions. In the second part of the article, we review applications of these methods to materials of current interest, from three-dimensional semiconductors to layered and two-dimensional materials. In particular, we discuss in detail recent investigations of classic materials such as silicon, diamond, gallium arsenide, gallium nitride, gallium oxide, and lead halide perovskites as well as low-dimensional semiconductors such as graphene, silicene, phosphorene, molybdenum disulfide, and indium selenide. We also review recent efforts toward high-throughput calculations of carrier transport. In the last part, we identify important classes of materials for which an ab initio study of carrier mobilities is warranted. We discuss the extension of the methodology to study topological quantum matter and materials for spintronics and we comment on the possibility of incorporating Berry-phase effects and many-body correlations beyond the standard Boltzmann formalism.
Constraining ν-process production of fluorine through cosmic ray nucleosynthesis

Fluorine is massive enough that it is not considered to be a light (Z ≤ 5) element, yet compared to its near neighbours, C, N, O, and Ne, it is far underproduced in the course of stellar evolution, making its origin more complex. In fact, the abundance of fluorine is the lowest among all elements between Z = 5 and 21 and is roughly 3–4 orders of magnitude below that of C, N, O, and Ne. There are several plausible sources for F beyond standard stellar evolution. These include the production in the asymptotic giant branch phase (AGB) in intermediate-mass stars, production in Wolf–Rayet stars, and the production through neutrino spallation in supernovae. The latter, known as the ν-process, is an important source for 11B, and may contribute to the abundance of 7Li as well. We combine a simple model of Galactic chemical evolution with a standard Galactic cosmic ray nucleosynthesis model to treat self-consistently the evolution of the Li, Be, and B isotopes. We include massive star production of F, as well as contributions from AGB stars, and the ν-process. Given the uncertainties in neutrino energies in supernovae, we normalize the ν-process using the observed 11B/10B ratio as a constraint. As a consequence, we are able to determine the relative importance of each contribution to the F abundance. We find that although the ν-process dominates at early times (low metallicity), the present-day F abundance is found to originate primarily from AGB stars.
Learning Compact Target-Oriented Feature Representations for Visual Tracking

Many state-of-the-art trackers usually resort to the pretrained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected. To handle this problem, we propose a novel approach, which takes both advantages of good generalization of generative models and excellent discrimination of discriminative models, for visual tracking. In particular, we learn compact, discriminative and target-oriented feature representations using the Laplacian coding algorithm that exploits the dependence among the input local features in a discriminative correlation filter framework. The feature representations and the correlation filter are jointly learnt to enhance to each other via a fast solver which only has very slight computational burden on the tracking speed. Extensive experiments on three benchmark datasets demonstrate that this proposed framework clearly outperforms baseline trackers with a modest impact on the frame rate, and performs comparably against the state-of-the-art methods.