Miracle-dz/UniFAR-FTUs huggingface dataset:
Augmented reality as a novel approach for addiction treatment: development of a smoking cessation app

Abstract Objective Augmented reality (AR) is a rapidly developing technology that has substantial potential as a novel approach for addiction treatment, including tobacco use. AR can facilitate the delivery of cue exposure therapy (CET) such that individuals can experience the treatment in their natural environments as viewed via a smartphone screen, addressing the limited generalizbility of extinction learning. Previously, our team developed a basic AR app for smoking cessation and demonstrated the necessary mechanisms for CET. Specifically, we showed that the AR smoking cues, compared to neutral cues, elicited substantial cue reactivity (i.e. increased urge) and that repeated exposure to the AR smoking cues reduced urge (i.e. extinction) in a laboratory setting. Here we report the next step in the systematic development of the AR app, in which we assessed the usability and acceptability of the app among daily smokers in their natural environments. Method Daily smokers (N = 23, 78.3% female, Mean Age = 43.4, Mean Cigarettes/Day = 14.9), not actively quitting, were instructed to use the AR app in locations and situations where they smoke (e.g. home, bar) at least 5 times per day over one week. The study is registered in clinicaltrials.gov (NCT04101422). Results Results indicated high usability and acceptability. Most of the participants (73.9%) used the AR app on at least 5 days. Participants found the AR cues realistic and well-integrated in their natural environments. The AR app was perceived as easy to use (Mean = 4.1/5) and learn (mean of 2 days to learn). Overall satisfaction with the app was also high. Secondary analyses found that 56.5% reported reduced smoking, with an average 26% reduction in cigarettes per day at follow-up. Conclusions These findings set the stage for a randomized controlled trial testing the AR app as an adjuvant therapy for treating tobacco dependence, with potential applicability to other substances. KEY MESSAGE This study found that the augmented reality (AR) smartphone application that utlized cue exposure treatment for smoking cessation was perceived as easy to use and learn in the natural, day-to-day environment of daily smokers. Findings set the stage for a larger clinical trial testing the AR app as an adjuvant therapy for treating tobacco dependence, with potential applicability to other addictive behaviors.
Feasibility of approximating spatial and local entanglement in long-range interacting systems using the extended Hubbard model

We investigate the extended Hubbard model as an approximation to the local and spatial entanglement of a one-dimensional chain of nanostructures where the particles interact via a long-range interaction represented by a “soft” Coulomb potential. In the process we design a protocol to calculate the particle-particle spatial entanglement for the Hubbard model and show that, in striking contrast with the loss of spatial degrees of freedom, the predictions are reasonably accurate. We also compare results for the local entanglement with previous results found using a contact interaction (Coe J. P. et al., Phys. Rev. A, 81 (2010) 052321) and show that while the extended Hubbard model recovers a better agreement with the entanglement of a long-range interacting system, there remain realistic parameter regions where it fails to predict the quantitative and qualitative behaviour of the entanglement in the nanostructure system.
Optimality of refraction strategies for a constrained dividend problem

Abstract We consider de Finetti’s problem for spectrally one-sided Lévy risk models with control strategies that are absolutely continuous with respect to the Lebesgue measure. Furthermore, we consider the version with a constraint on the time of ruin. To characterize the solution to the aforementioned models, we first solve the optimal dividend problem with a terminal value at ruin and show the optimality of threshold strategies. Next, we introduce the dual Lagrangian problem and show that the complementary slackness conditions are satisfied, characterizing the optimal Lagrange multiplier. Finally, we illustrate our findings with a series of numerical examples.
Local laws for non-Hermitian random matrices and their products

We consider products of independent [Formula: see text] non-Hermitian random matrices [Formula: see text]. Assume that their entries, [Formula: see text], are independent identically distributed random variables with zero mean, unit variance. Götze and Tikhomirov [On the asymptotic spectrum of products of independent random matrices, preprint (2010), arXiv:1012.2710] and O’Rourke and Sochnikov [Products of independent non-Hermitian random matrices, Electron. J. Probab. 16 (2011) 2219–2245] proved that under these assumptions the empirical spectral distribution (ESD) of [Formula: see text] converges to the limiting distribution which coincides with the distribution of the [Formula: see text]th power of random variable uniformly distributed in the unit circle. In this paper, we provide a local version of this result. More precisely, assuming additionally that [Formula: see text] for some [Formula: see text], we prove that ESD of [Formula: see text] converges to the limiting distribution on the optimal scale up to [Formula: see text] (up to some logarithmic factor). Our results generalize the recent results of Bourgade et al. [Local circular law for random matrices, Probab. Theory Related Fields 159 (2014) 545–595], Tao and Vu [Random matrices: Universality of local spectral statistics of non-Hermitian matrices, Ann. Probab. 43 (2015) 782–874] and Nemish [Local law for the product of independent non-hermitian random matrices with independent entries, Electron. J. Probab. 22 (2017) 1–35]. We also give further development of Stein’s type approach to estimate the Stieltjes transform of ESD.
Rational homology and homotopy of high-dimensional string links

Arone and the second author showed that when the dimensions are in the stable range, the rational homology and homotopy of the high-dimensional analogues of spaces of long knots can be calculated as the homology of a direct sum of finite graph-complexes that they described explicitly. They also showed that these homology and homotopy groups can be interpreted as the higher-order Hochschild homology, also called Hochschild–Pirashvili homology. In this paper, we generalize all these results to high-dimensional analogues of spaces of string links. The methods of our paper are applicable in the range when the ambient dimension is at least twice the maximal dimension of a link component plus two, which in particular guarantees that the spaces under study are connected. However, we conjecture that our homotopy graph-complex computes the rational homotopy groups of link spaces always when the codimension is greater than two, i.e. always when the Goodwillie–Weiss calculus is applicable. Using Haefliger’s approach to calculate the groups of isotopy classes of higher-dimensional links, we confirm our conjecture at the level of {\pi_{0}}.
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System

Network intrusion detection systems (NIDS) to detect malicious attacks continue to meet challenges. NIDS are often developed offline while they face auto-generated port scan infiltration attempts, resulting in a significant time lag from adversarial adaption to NIDS response. To address these challenges, we use hypergraphs focused on internet protocol addresses and destination ports to capture evolving patterns of port scan attacks. The derived set of hypergraph-based metrics are then used to train an ensemble machine learning (ML) based NIDS that allows for real-time adaption in monitoring and detecting port scanning activities, other types of attacks, and adversarial intrusions at high accuracy, precision and recall performances. This ML adapting NIDS was developed through the combination of (1) intrusion examples, (2) NIDS update rules, (3) attack threshold choices to trigger NIDS retraining requests, and (4) a production environment with no prior knowledge of the nature of network traffic. 40 scenarios were auto-generated to evaluate the ML ensemble NIDS comprising three tree-based models. The resulting ML Ensemble NIDS was extended and evaluated with the CIC-IDS2017 dataset. Results show that under the model settings of an Update-ALL-NIDS rule (specifically retrain and update all the three models upon the same NIDS retraining request) the proposed ML ensemble NIDS evolved intelligently and produced the best results with nearly 100% detection performance throughout the simulation.
Non-extremal near-horizon geometries

When Gaussian null coordinates are adapted to a Killing horizon, the near-horizon limit is defined by a coordinate rescaling and then by taking the regulator parameter ɛ to be small, as a way of zooming into the horizon hypersurface. In this coordinate setting, it is known that the metric of a non-extremal Killing horizon in the near-horizon limit is divergent, and it has been a common practice to impose extremality in order to set the divergent term to zero. Although the metric is divergent, we show for a class of Killing horizons that the vacuum Einstein’s equations can be separated into a divergent and a finite part, leading to a well-defined minimal set of Einstein’s equations one needs to solve. We extend the result to Einstein gravity minimally coupled to a massless scalar field. We also discuss the case of Einstein gravity coupled to a Maxwell field, in which case the separability holds if the Maxwell potential has non-vanishing components only in the directions of the horizon spatial cross section.
THE PAN-STARRS 1 PHOTOMETRIC REFERENCE LADDER, RELEASE 12.01

As of 2012 January 21, the Pan-STARRS 1 3π Survey has observed the 3/4 of the sky visible from Hawaii with a minimum of 2 and mean of 7.6 observations in five filters, gP1, rP1, iP1, zP1, yP1. Now at the end of the second year of the mission, we are in a position to make an initial public release of a portion of this unprecedented data set. This article describes the PS1 Photometric Ladder, Release 12.01. This is the first of a series of data releases to be generated as the survey coverage increases and the data analysis improves. The Photometric Ladder has rungs every hour in right ascension and at four intervals in declination. We will release updates with increased area coverage (more rungs) from the latest data set until the PS1 survey and the final re-reduction are completed. The currently released catalog presents photometry of ∼1000 objects per square degree in the rungs of the ladder. Saturation occurs at gP1, rP1, iP1 ∼ 13.5; zP1 ∼ 13.0; and yP1 ∼ 12.0. Photometry is provided for stars down to gP1, rP1, iP1 ∼ 19.1 in the AB system. This data release depends on the rigid “Ubercal” photometric calibration using only the photometric nights, with systematic uncertainties of (8.0, 7.0, 9.0, 10.7, 12.4) mmag in (gP1, rP1, iP1, zP1, yP1). Areas covered only with lower quality nights are also included, and have been tied to the Ubercal solution via relative photometry; photometric accuracy of the non-photometric regions is lower and should be used with caution.
Pretraining in Deep Reinforcement Learning: A Survey

The past few years have seen rapid progress in combining reinforcement learning (RL) with deep learning. Various breakthroughs ranging from games to robotics have spurred the interest in designing sophisticated RL algorithms and systems. However, the prevailing workflow in RL is to learn tabula rasa, which may incur computational inefficiency. This precludes continuous deployment of RL algorithms and potentially excludes researchers without large-scale computing resources. In many other areas of machine learning, the pretraining paradigm has shown to be effective in acquiring transferable knowledge, which can be utilized for a variety of downstream tasks. Recently, we saw a surge of interest in Pretraining for Deep RL with promising results. However, much of the research has been based on different experimental settings. Due to the nature of RL, pretraining in this field is faced with unique challenges and hence requires new design principles. In this survey, we seek to systematically review existing works in pretraining for deep reinforcement learning, provide a taxonomy of these methods, discuss each sub-field, and bring attention to open problems and future directions.
INCLINATION-DEPENDENT ACTIVE GALACTIC NUCLEUS FLUX PROFILES FROM STRONG LENSING OF THE KERR SPACETIME

Recent quasar microlensing observations have constrained the X-ray emission sizes of quasars to be about 10 gravitational radii, one order of magnitude smaller than the optical emission sizes. Using a new ray-tracing code for the Kerr spacetime, we find that the observed X-ray flux is strongly influenced by the gravity field of the central black hole, even for observers at moderate inclination angles. We calculate inclination-dependent flux profiles of active galactic nuclei in the optical and X-ray bands by combining the Kerr lensing and projection effects for future reference. We further study the dependence of the X-ray-to-optical flux ratio on the inclination angle caused by differential lensing distortion of the X-ray and optical emission, assuming several corona geometries. The strong lensing X-ray-to-optical magnification ratio can change by a factor of ∼10 for normal quasars in some cases, and a further factor of ∼10 for broad absorption line (BAL) quasars and obscured quasars. Comparing our results with the observed distributions in normal and BAL quasars, we find that the inclination angle dependence of the magnification ratios can significantly change the X-ray-to-optical flux ratio distributions. In particular, the mean value of the spectrum slope parameter αox, 0.3838log F2 keV/F2500 Å, can differ by ∼0.1–0.2 between normal and BAL quasars, depending on corona geometries, suggesting larger intrinsic absorptions in BAL quasars.
Interoperability in encoded quantum repeater networks

The future of quantum repeater networking will require interoperability between various error correcting codes. A few specific code conversions and even a generalized method are known, however, no detailed analysis of these techniques in the context of quantum networking has been performed. In this paper, we analyze a generalized procedure to create Bell pairs encoded heterogeneously between two separate codes used often in error corrected quantum repeater network designs. We begin with a physical Bell pair, then encode each qubit in a different error correcting code, using entanglement purification to increase the fidelity. We investigate three separate protocols for preparing the purified encoded Bell pair. We calculate the error probability of those schemes between the Steane [[7,1,3]] code, a distance three surface code and single physical qubits by Monte Carlo simulation under a standard Pauli error model, and estimate the resource efficiency of the procedures. A local gate error rate of $10^{-3}$ allows us to create high-fidelity logical Bell pairs between any of our chosen codes. We find that a postselected model, where any detected parity flips in code stabilizers result in a restart of the protocol, performs the best.
From approximate to exact integer programming

Approximate integer programming is the following: For a convex body $K \subseteq \mathbb{R}^n$, either determine whether $K \cap \mathbb{Z}^n$ is empty, or find an integer point in the convex body scaled by $2$ from its center of gravity $c$. Approximate integer programming can be solved in time $2^{O(n)}$ while the fastest known methods for exact integer programming run in time $2^{O(n)} \cdot n^n$. So far, there are no efficient methods for integer programming known that are based on approximate integer programming. Our main contribution are two such methods, each yielding novel complexity results. First, we show that an integer point $x^* \in (K \cap \mathbb{Z}^n)$ can be found in time $2^{O(n)}$, provided that the remainders of each component $x_i^* \mod{\ell}$ for some arbitrarily fixed $\ell \geq 5(n+1)$ of $x^*$ are given. The algorithm is based on a cutting-plane technique, iteratively halving the volume of the feasible set. The cutting planes are determined via approximate integer programming. Enumeration of the possible remainders gives a $2^{O(n)}n^n$ algorithm for general integer programming. This matches the current best bound of an algorithm by Dadush (2012) that is considerably more involved. Our algorithm also relies on a new asymmetric approximate Carath\'eodory theorem that might be of interest on its own. Our second method concerns integer programming problems in equation-standard form $Ax = b, 0 \leq x \leq u, \, x \in \mathbb{Z}^n$ . Such a problem can be reduced to the solution of $\prod_i O(\log u_i +1)$ approximate integer programming problems. This implies, for example that knapsack or subset-sum problems with polynomial variable range $0 \leq x_i \leq p(n)$ can be solved in time $(\log n)^{O(n)}$. For these problems, the best running time so far was $n^n \cdot 2^{O(n)}$.
Many-Server Queueing Systems with Heterogeneous Strategic Servers in Heavy Traffic

In most service systems, the servers are humans who desire to experience a certain level of idleness. In call centers, this manifests itself as the call avoidance behavior, where servers strategically adjust their service rate to strike a balance between the idleness they receive and effort to work harder. Moreover, being humans, each server values this trade-off differently and has different capabilities. Drawing ideas on mean-ﬁeld games we develop a novel framework relying on measure-valued processes to simultaneously address strategic server behavior and inherent server heterogeneity in service systems. This framework enables us to extend the recent literature on strategic servers in four new directions by: (i) incorporating individual choices of servers, (ii) incorporating individual abilities of servers, (iii) modeling the discomfort experienced by servers due to low levels of idleness, and (iv) considering more general routing policies. Using our framework, we are able to asymptotically characterize asymmetric Nash equilibria for many-server systems with strategic servers. In simpler cases, it has been shown that the purely quality-driven regime is asymptotically optimal. However, we show that if the discomfort increases fast enough as the idleness approaches zero, the quality-and-efﬁciency-driven regime and other quality driven regimes can be optimal. This is the ﬁrst time this conclusion appears in the literature. characterization Nash equilibria under an h -Random policy in purely-quality driven regime Numerical
Towards Extending the Range of Bugs That Automated Program Repair Can Handle

Modern automated program repair (APR) is well-tuned to finding and repairing bugs that introduce observable erroneous behavior to a program. However, a significant class of bugs does not lead to such observable behavior (e.g., liveness/termination bugs, non-functional bugs, and information flow bugs). Such bugs can generally not be handled with current APR approaches, so, as a community, we need to develop complementary techniques.To stimulate the systematic study of alternative APR approaches and hybrid APR combinations, we devise a novel bug classification system that enables methodical analysis of their bug detection power and bug repair capabilities. To demonstrate the benefits, we analyze the repair of termination bugs in sequential and concurrent programs. The study shows that integrating dynamic APR with formal analysis techniques, such as termination provers and software model checkers, reduces complexity and improves the overall reliability of these repairs.
S U ( 6 ) GUT origin of the TeV-scale vectorlike particles associated with the 750 GeV diphoton resonance

We consider the SU(6) grand unified theory (GUT) model as an explanation for the diphoton final state excess, where the masses of all associated particles are linked with a new symmetry breaking scale. In this model, the diphoton final states arise due to loops involving three pairs of new vectorlike particles having the same quantum numbers as down-type quarks and lepton doublets. These new vectorlike fermions arc embedded alongside the standard model (SM) fermions into minimal anomaly-free representations of the SU(6) gauge symmetry. The SU(6) symmetry is broken to the standard model times U(1)(x) at the GUT scale, and masses for the vectorlike fermions arise at the TeV scale only after the residual U(1)(x) symmetry is broken. The vectorlike fermions do not acquire masses via breaking of the SM symmetry at the electroweak scale. The field which is responsible for the newly observed resonance belongs to the 6(H) representation. The dark matter arises from the SM singlet fermion residing in 6 and is of Majorana type. We explicitly demonstrate gauge coupling unification in this model, and also discuss the origin of neutrino masses. In addition to the diphoton final states, we make distinctive predictions for other final states which are likewise accessible to the ongoing LHC experimental effort.
The free algebra in R

The free algebra is an interesting and useful algebraic object. Here I introduce freealg , an R package which furnishes computational support for free algebras. The package uses the standard template library’s map class for eﬃciency, which uses the fact that the order of the terms is algebraically immaterial. The package follows disordR discipline. I demonstrate some properties of free algebra using the package, and showcase package idiom. The package is available on CRAN at https://CRAN.R-project.org/package=freealg .
Estimating Treatment Effects using Neurosymbolic Program Synthesis

Estimating treatment effects from observational data is a central problem in causal inference. Methods to solve this problem exploit inductive biases and heuristics from causal inference to design multi-head neural network architectures and reg-ularizers. In this work, we propose to use neurosymbolic program synthesis, a data-efﬁcient, and interpretable technique, to solve the treatment effect estimation problem. We theoretically show that neurosymbolic programming can solve the treatment effect estimation problem. By designing a Domain Speciﬁc Language (DSL) for treatment effect estimation problem based on the inductive biases used in literature, we argue that neurosymbolic programming is a better alternative to treatment effect estimation than traditional methods. Our empirical study reveals that our method, which implicitly encodes inductive biases in a DSL, achieves better performance on benchmark datasets than the state-of-the-art methods.