Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Optimization of Successful Symbol Transmission Rate for Grant-free Massive Access With Massive MIMO

Grant-free massive access is an important technique for supporting massive machine-type communications (mMTC) for Internet-of-Things (IoT). Two important features in grant-free massive access are low-complexity devices and short-packet data transmission, making the traditional performance metric, achievable rate, unsuitable in this case. In this letter, we investigate grant-free massive access in a massive multiple-input multiple-output (MIMO) system. We consider random access control, and adopt approximate message passing (AMP) for user activity detection and channel estimation in the pilot transmission phase and small phase-shift-keying (PSK) modulation in the data transmission phase. We propose a more reasonable performance metric, namely successful symbol transmission rate (SSTR), for grant-free massive access. We obtain closed-form approximate expressions for the asymptotic SSTR in the cases of maximal ratio combining (MRC) and zero forcing (ZF) beamforming at the base station (BS), respectively. We also maximize the asymptotic SSTR with respect to the access parameter and pilot length.
Numerical semigroups with embedding dimension three and minimal catenary degree.

We characterize numerical semigroups $S$ with embedding dimension three attaining equality in the inequality $\max\Delta(S)+2\leq \operatorname{cat}(S)$, where $\Delta(S)$ denotes the Delta set of $S$ and $\operatorname{cat}(S)$ denotes the catenary degree of $S$.
Intelligent Policing Strategy for Traffic Violation Prevention

Police officer presence at an intersection discourages a potential traffic violator from violating the law. It also alerts the motorists' consciousness to take precaution and follow the rules. However, due to the abundant intersections and shortage of human resources, it is not possible to assign a police officer to every intersection. In this paper, we propose an intelligent and optimal policing strategy for traffic violation prevention. Our model consists of a specific number of targeted intersections and two police officers with no prior knowledge on the number of the traffic violations in the designated intersections. At each time interval, the proposed strategy, assigns the two police officers to different intersections such that at the end of the time horizon, maximum traffic violation prevention is achieved. Our proposed methodology adapts the PROLA (Play and Random Observe Learning Algorithm) algorithm [1] to achieve an optimal traffic violation prevention strategy. Finally, we conduct a case study to evaluate and demonstrate the performance of the proposed method.
Mining Minimal Map-Segments for Visual Place Classifiers

In visual place recognition (VPR), map segmentation (MS) is a preprocessing technique used to partition a given view-sequence map into place classes (i.e., map segments) so that each class has good place-specific training images for a visual place classifier (VPC). Existing approaches to MS implicitly/explicitly suppose that map segments have a certain size, or individual map segments are balanced in size. However, recent VPR systems showed that very small important map segments (minimal map segments) often suffice for VPC, and the remaining large unimportant portion of the map should be discarded to minimize map maintenance cost. Here, a new MS algorithm that can mine minimal map segments from a large view-sequence map is presented. To solve the inherently NP hard problem, MS is formulated as a video-segmentation problem and the efficient point-trajectory based paradigm of video segmentation is used. The proposed map representation was implemented with three types of VPC: deep convolutional neural network, bag-of-words, and object class detector, and each was integrated into a Monte Carlo localization algorithm (MCL) within a topometric VPR framework. Experiments using the publicly available NCLT dataset thoroughly investigate the efficacy of MS in terms of VPR performance.
Locality, Statefulness, and Causality in Distributed Information Systems (Concerning the Scale Dependence Of System Promises)

Several popular best-practice manifestos for IT design and architecture use terms like `stateful', `stateless', `shared nothing', etc, and describe `fact based' or `functional' descriptions of causal evolution to describe computer processes, especially in cloud computing. The concepts are used ambiguously and sometimes in contradictory ways, which has led to many imprecise beliefs about their implications. This paper outlines the simple view of state and causation in Promise Theory, which accounts for the scaling of processes and the relativity of different observers in a natural way. It's shown that the concepts of statefulness or statelessness are artifacts of observational scale and causal bias towards functional evaluation. If we include feedback loops, recursion, and process convergence, which appear acausal to external observers, the arguments about (im)mutable state need to be modified in a scale-dependent way. In most cases the intended focus of such remarks is not terms like `statelessness' but process predictability. A simple principle may be substituted in most cases as a guide to system design: the principle the separation of dynamic scales. Understanding data reliance and the ability to keep stable promises is of crucial importance to the consistency of data pipelines, and distributed client-server interactions, albeit in different ways. With increasingly data intensive processes over widely separated distributed deployments, e.g. in the Internet of Things and AI applications, the effects of instability need a more careful treatment. These notes are part of an initiative to engage with thinkers and practitioners towards a more rational and disciplined language for systems engineering for era of ubiquitous extended-cloud computing.
Thermodynamics of fluctuations in small systems interacting with the environment

A few decades after Hill's work on nano-thermodynamics, the development of a thermodynamic framework, to account consistently for the fluctuations of small systems due to their interactions with the surrounding environment, is still underway. Here we discuss how, in a small system, the interaction energy with the environment may be described through a conjugate pair of intensive and extensive variables, giving rise to a Gibbs thermodynamics with an additional thermodynamic degree of freedom. The relevant thermodynamic potentials help describe the equilibrium conditions and the material properties that measure the susceptibility of the system to the interaction with the environment. The resulting generalized ensembles, which describe the non-negligible, small system fluctuations, are shown to be equivalent. Away from the average thermodynamic state, the availability describes the distance between system and the environment, in terms of the maximum work extractable from the fluctuation, and a proper definition of entropy extends the thermodynamics to the generic fluctuating state. From the latter state, entropic forces arise to restore the average thermodynamic state. Our framework unifies and extends the ensemble thermodynamics by Hill and the recent advances in statistical mechanics under strong coupling and reduces to classical macroscopic thermodynamics when the system is large. The particular case of a small ideal gas system is discussed in detail and the example of a single particle immersed in a bath is revisited in the light of the new formalism.
Defining shapes of 2D-crystals with undefinable edge-energies

The equilibrium shape of crystals is a fundamental property of both aesthetic appeal and practical import. It is also a visible macro-manifestation of the underlying atomic-scale forces and chemical makeup, most conspicuous in two-dimensional (2D) materials of keen current interest. If the crystal surface/edge energy is known for different directions, its shape can be obtained by geometric Wulff construction, a tenet of crystal physics. However, if symmetry is lacking, the crystal edge energy cannot be defined or calculated, so its shape becomes elusive, presenting an insurmountable problem for theory. Here we show how, in fact, one can proceed with "latent edge energies" towards constructive prediction of a unique crystal shape, and demonstrate it for challenging material-examples, like ${\rm SnS}$ of ${\rm C_{2v}}$ symmetry and even ${\rm AgNO_2}$ of ${\rm C_1}$-no symmetry at all.
Forecasting Fertility with Parametric Mixture Models

This paper sets out a forecasting method that employs a mixture of parametric functions to capture the pattern of fertility with respect to age. The overall level of cohort fertility is decomposed over the range of fertile ages using a mixture of parametric density functions. The level of fertility and the parameters describing the shape of the fertility curve are projected foward using time series methods. The model is estimated within a Bayesian framework, allowing predictive distributions of future fertility rates to be produced that naturally incorporate both time series and parametric uncertainty. A number of choices are possible for the precise form of the functions used in the two-component mixtures. The performance of several model variants is tested on data from four countries; England and Wales, the USA, Sweden and France. The former two countries exhibit multi-modality in their fertility rate curves as a function of age, while the latter two are largely uni-modal. The models are estimated using Hamiltonian Monte Carlo and the `stan` software package on data covering the period up to 2006, with the period 2007-2016 held back for assessment purposes. Forecasting performance is found to be comparable to other models identified as producing accurate fertility forecasts in the literature.
Pole skipping with finite-coupling corrections

Recently, it has been shown that many Green’s functions are not unique at special points in complex momentum space using AdS/CFT. This phenomenon is similar to the pole skipping in holographic chaos, and the special points are typically located at ωn=-(2πT)ni with appropriate values of complex wave number qn. We study finite-coupling corrections to special points. As examples, we consider four-derivative corrections to gravitational perturbations and four-dimensional Maxwell perturbations. While ωn is uncorrected, qn is corrected at finite coupling. Some special points disappear at particular values of higher-derivative couplings. Special point locations of the Maxwell scalar and vector modes are related to each other by the electromagnetic duality.
Massive Warm/Hot Galaxy Coronae. II. Isentropic Model

We construct a new analytic phenomenological model for the extended circumgalactic material (CGM) of L* galaxies. Our model reproduces the O vii/O viii absorption observations of the Milky Way (MW) and the O vi measurements reported by the COS-Halos and eCGM surveys. The warm/hot gas is in hydrostatic equilibrium in an MW gravitational potential, and we adopt a barotropic equation of state, resulting in a temperature variation as a function of radius. A pressure component with an adiabatic index of is included to approximate the effects of a magnetic field and cosmic rays. We introduce a metallicity gradient motivated by the enrichment of the inner CGM by the Galaxy. We then present our fiducial model for the corona, tuned to reproduce the observed O vi–O viii column densities and with a total mass of inside . The gas densities in the CGM are low ( cm−3), and its collisional ionization state is modified by the metagalactic radiation field. We show that for O vi-bearing warm/hot gas with typical observed column densities cm−2 at large ( kpc) impact parameters from the central galaxies, the ratio of the cooling to dynamical times, / , has a model-independent upper limit of . In our model, / at large radii is . We present predictions for a wide range of future observations of the warm/hot CGM, from UV/X-ray absorption and emission spectroscopy to dispersion measure and Sunyaev–Zel’dovich cosmic microwave background measurements. We provide the model outputs in machine-readable data files for easy comparison and analysis.
An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments

The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage. In this letter, we present a new RRT*-inspired online informative path planning algorithm. Our method continuously expands a single tree of candidate trajectories and rewires nodes to maintain the tree and refine intermediate paths. This allows the algorithm to achieve global coverage and maximize the utility of a path in a global context, using a single objective function. We demonstrate the algorithm's capabilities in the applications of autonomous indoor exploration as well as accurate Truncated Signed Distance Field (TSDF)-based 3D reconstruction on-board a Micro Aerial Vehicle (MAV). We study the impact of commonly used information gain and cost formulations in these scenarios and propose a novel TSDF-based 3D reconstruction gain and cost-utility formulation. Detailed evaluation in realistic simulation environments show that our approach outperforms sampling-based state of the art methods in these tasks. Experiments on a real MAV demonstrate the ability of our method to robustly plan in real-time, exploring an indoor environment with on-board sensing and computation. We make our framework available for future research.
Determinants Of Condom Use And Hiv Prevention Among East-African Immigrants In Minnesota

ii TABLE OF CONTENTS.iv LIST OF TABLES.v LIST OF FIGURES.vi CHAPTER 1: INTRODUCTION.1 CHAPTER 2: LITERATURE REVIEW . 17 CHAPTER 3: METHODS.34 CHAPTER 4: RESULTS.60 CHAPTER 5: DISCUSSION.80 REFERENCES.90 APPENDIX A.109 APPENDIX B.111 IRB EXEMPTION . 127 CURRICULUM VITAE.128
Representation Learning for Electronic Health Records

Information in electronic health records (EHR), such as clinical narratives, examination reports, lab measurements, demographics, and other patient encounter entries, can be transformed into appropriate data representations that can be used for downstream clinical machine learning tasks using representation learning. Learning better representations is critical to improve the performance of downstream tasks. Due to the advances in machine learning, we now can learn better and meaningful representations from EHR through disentangling the underlying factors inside data and distilling large amounts of information and knowledge from heterogeneous EHR sources. In this chapter, we first introduce the background of learning representations and reasons why we need good EHR representations in machine learning for medicine and healthcare in Section 1. Next, we explain the commonly-used machine learning and evaluation methods for representation learning using a deep learning approach in Section 2. Following that, we review recent related studies of learning patient state representation from EHR for clinical machine learning tasks in Section 3. Finally, in Section 4 we discuss more techniques, studies, and challenges for learning natural language representations when free texts, such as clinical notes, examination reports, or biomedical literature are used. We also discuss challenges and opportunities in these rapidly growing research fields.
Making Code Re-randomization Practical with MARDU

Defense techniques such as Data Execution Prevention (DEP) and Address Space Layout Randomization (ASLR) were the early role models preventing primitive code injection and return-oriented programming (ROP) attacks. Notably, these techniques did so in an elegant and utilitarian manner, keeping performance and scalability in the forefront, making them one of the few widely-adopted defense techniques. As code re-use has evolved in complexity from JIT-ROP, to BROP and data-only attacks, defense techniques seem to have tunneled on defending at all costs, losing-their-way in pragmatic defense design. Some fail to provide comprehensive coverage, being too narrow in scope, while others provide unrealistic overheads leaving users willing to take their chances to maintain performance expectations. We present Mardu, an on-demand system-wide re-randomization technique that improves re-randomization and refocuses efforts to simultaneously embrace key characteristics of defense techniques: security, performance, and scalability. Our code sharing with diversification is achieved by implementing reactive and scalable, rather than continuous or one-time diversification while the use of hardware supported eXecute-only Memory (XoM) and shadow stack prevent memory disclosure; entwining and enabling code sharing further minimizes needed tracking, patching costs, and memory overhead. Mardu's evaluation shows performance and scalability to have low average overhead in both compute-intensive (5.5% on SPEC) and real-world applications (4.4% on NGINX). With this design, Mardu demonstrates that strong and scalable security guarantees are possible to achieve at a practical cost to encourage deployment.
BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation

This study mainly investigates two decoding problems in neural keyphrase generation: sequence length bias and beam diversity. We introduce an extension of beam search inference based on word-level and n-gram level attention score to adjust and constrain Seq2Seq prediction at test time. Results show that our proposed solution can overcome the algorithm bias to shorter and nearly identical sequences, resulting in a significant improvement of the decoding performance on generating keyphrases that are present and absent in source text.
CONTRIBUIÇÃO DA MANOMETRIA ANO RETAL NA AVALIAÇÃO DA CONSTIPAÇÃO INTESTINAL CRÔNICA

A constipacao intestinal cronica e um termo que representa sintomas de diferentes etiologias e fisiopatologias, com sua prevalencia variando de 2 a 30 por cento da populacao ocidental. Nao existe uma definicao unica para constipacao, motivo este que leva a uma grande discrepância na conducao propedeutica e terapeutica desta afeccao. Recentemente, em um consenso de especialistas, foram postulados alguns criterios para a sua definicao (criterios de Roma II). Dentre varios exames para a avaliacao da constipacao, a manometria ano retal e o mais utilizado. Objetivo: o objetivo deste trabalho e de realizar uma revisao na literatura da contribuicao da manometria ano retal na avaliacao de pacientes constipados, assim como avaliar retrospectivamente os pacientes, com diagnostico de constipacao cronica, que foram submetidos a este exame. Material e metodos: foram avaliados,retrospectivamente, 49 pacientes, com queixas de constipacao, enviados para realizacao de manometria ano retal e testes de sensibilidade, capacidade e expulsao de balao intra retal. Resultados: foi obtido como resultado um amplo predominio do sexo feminino com 84,61por cento dos pacientes adultos (39). Foram avaliados 10 criancas/adolescentes (media de idade 8,8anos), com suspeita de megacolon congenito, apresentando um predominio do sexo masculino (77,77por cento). Observamos: pressao de repouso aumentada em 9 pacientes e diminuida em 2; pressao de contracao aumentada em 8 e diminuida em 3; zona de alta pressao diminuida em 4; sensibilidade ("threshold") aumentada em 8 e diminuida em 16; capacidade retal aumentada em 11 e diminuida em 5; contracao muscular paradoxal ao esforco para evacuar em 7; teste de expulsao do balao negativo em 11; reflexo inibitorio reto anal negativo em 5 pacientes e innonclusivo em 1. No grupo de pacientes com suspeita de megacolon congenito 8 tiveram a confirmacao com reflexo inibitorio negativo. Conclusao: baseados na revisao da literatura e nos nossos resultados, concluimos que os achados da manometria ano retal modificam a conduta no paciente constipado, podendo ser sugerido que esta avaliacao seja realizada inicialmente em todos estes pacientes.
Automated Verification of Integer Overflow

Integer overflow accounts for one of the major source of bugs in software. Verification systems typically assume a well defined underlying semantics for various integer operations and do not explicitly check for integer overflow in programs. In this paper we present a specification mechanism for expressing integer overflow. We develop an automated procedure for integer overflow checking during program verification. We have implemented a prototype integer overflow checker and tested it on a benchmark consisting of already verified programs (over 14k LOC). We have found 43 bugs in these programs due to integer overflow.
Learning to Conceal: A Deep Learning Based Method for Preserving Privacy and Avoiding Prejudice

In this paper, we introduce a learning model able to conceals personal information (e.g. gender, age, ethnicity, etc.) from an image, while maintaining any additional information present in the image (e.g. smile, hair-style, brightness). Our trained model is not provided the information that it is concealing, and does not try learning it either. Namely, we created a variational autoencoder (VAE) model that is trained on a dataset including labels of the information one would like to conceal (e.g. gender, ethnicity, age). These labels are directly added to the VAE's sampled latent vector. Due to the limited number of neurons in the latent vector and its appended noise, the VAE avoids learning any relation between the given images and the given labels, as those are given directly. Therefore, the encoded image lacks any of the information one wishes to conceal. The encoding may be decoded back into an image according to any provided properties (e.g. a 40 year old woman). The proposed architecture can be used as a mean for privacy preserving and can serve as an input to systems, which will become unbiased and not suffer from prejudice. We believe that privacy and discrimination are two of the most important aspects in which the community should try and develop methods to prevent misuse of technological advances.