Miracle-dz/UniFAR-FTUs huggingface dataset:
From Timing Variations to Performance Degradation: Understanding and Mitigating the Impact of Software Execution Timing in SLAM

Timing is an important property for robotic systems that continuously interact with our physical world. Variation in program execution time caused by limited computational resources or system resource contention can lead to significant impact on algorithmic result accuracy. Even though recent work has found Simultaneous Localization And Mapping (SLAM) to be timing-sensitive, little exists in understanding the interactions between the timing variations in SLAM systems and the corresponding degradation. In this paper we conduct a systematic analysis of nine state-of-the-art SLAM systems and dissect the root causes of their degradation. We discovered that timing-induced errors are generated either from delayed execution in certain critical tasks, or from desynchronization in sensor fusion. Based on the insights from our analysis, we propose a solution that combines selective fusion on data in the front end and temporal budget optimization on bundle adjust-ment in the backend to mitigate the impacts of unexpected timing variation adaptively. Experimental results show that our proposed method makes it possible to migrate expensive algorithms to low-cost platforms without laborious tuning, while making the SLAM system robust against the effects of abnormal timing.
Imatinib disassembles the regulatory core of Abelson kinase by binding to its ATP site and not by binding to its myristoyl pocket

Abstract It was recently reported (Xie et al., 2022) that the Abelson tyrosine kinase (Abl) ATP-site inhibitor imatinib also binds to Abl's myristoyl binding pocket, which is the target of allosteric Abl inhibitors. This was based on a crystal structure of a truncated Abl kinase domain construct in complex with imatinib bound to the allosteric site as well as further isothermal titration calorimetry (ITC), NMR, and kinase activity data. Although imatinib's affinity for the allosteric site is significantly weaker (10 µ M) than for the ATP site (10 nM), imatinib binding to the allosteric site may disassemble the regulatory core of Abl, thereby stimulating kinase activity, in particular for Abl mutants with reduced imatinib ATP-site affinity. It was argued that the previously observed imatinib-induced opening of the Abl regulatory core (Skora et al., 2013; Sonti et al., 2018) may be caused by the binding of imatinib to the allosteric site and not to the ATP site. We show here that this is not the case but that indeed imatinib binding to the ATP site induces the opening of the regulatory core at nanomolar concentrations. This agrees with findings that other type-II ATP-site inhibitors (nilotinib, ponatinib) disassemble the regulatory core despite demonstrated negligible binding to the allosteric site.
Benefits of an Interactive Robot Character in Immersive Puzzle Games

Robots are becoming increasingly prominent in the entertainment sphere, where they interact with guests in themed environments to tell stories, often in place of human characters. To evaluate the potential benefits of robots in these contexts compared to humans, we created an interactive puzzle game where either a robot or a human actor serves as a diegetic "game guide" character that is both a cooperative partner and an omniscient game master. In the game, participants solve a crime mystery by asking the game guide for information to complete tasks and for hints to solve puzzles. We conducted a between-subjects study (n = 42) to investigate how players’ game experiences differed when the game guide was a human compared to an embodied robot. Our results show that participants playing with a robot had more fun, felt less judged, and felt more connected with the robot while solving tasks compared to those playing with a human. These results suggest that robots can be effective alternatives to human actors in broader immersive entertainment contexts such as escape rooms to provide greater enjoyment and promote more social interaction with in-game characters.
The Essence of Q#: Toward Safe and Certified Quantum Programs

Q# is one of the only industry-supported standalone quantum programming languages, but it lacks precision in its specification. I propose to provide a formal language definition for Q# through a translation to λ Q # , a small core language for quantum computation. This definition leads to a clearer understanding of the Q# language semantics. Using λ Q # as a testbed, I propose the design of a stronger type system that will lead to safer Q# programs. I also propose a verification architecture for λ Q # that can serve as a unified framework for formally specifying, programming, and reasoning about quantum programs.
On-Orbit Performance of the Far Ultraviolet Spectroscopic Explorer Satellite

The launch of the Far Ultraviolet Spectroscopic Explorer (FUSE) has been followed by an extensive period of calibration and characterization as part of the preparation for normal satellite operations. Major tasks carried out during this period include the initial coalignment, focusing, and characterization of the four instrument channels and a preliminary measurement of the resolution and throughput performance of the instrument. We describe the results from this test program and present preliminary estimates of the on-orbit performance of the FUSE satellite based on a combination of these data and prelaunch laboratory measurements.
Privacy-aware Adaptive Scheduling for Coalition Operations

Coalition operations are essential for responding to the increasing number of world-wide incidents that require large-scale humanitarian assistance. Many nations and nongovernmental organizations regularly coordinate to address such problems but their cooperation is often impeded by limits on what information they are able to share. In this paper, we consider the use of an advanced cryptographic technique called secure multi-party computation to enable coalition members to achieve joint objectives while still meeting privacy requirements. Our particular focus is on a multination aid delivery scheduling task that involves coordinating when and where various aid provider nations will deliver relief materials after the occurrence of a natural disaster. Even with the use of secure multi-party computation technology, information about private data can leak. We describe how the emerging field of quantitative information flow can be used to help data owners understand the extent to which private data might become vulnerable as the result of possible or actual scheduling operations, and to enable automated adjustments of the scheduling process to ensure privacy requirements.
SOME ASPECTS OF PION PHYSICS IN A DYNAMICALLY BROKEN ABELIAN GAUGE THEORY

We show how an Abelian spontaneously broken gauge theory of fermions endowed with a composite scalar multiplet becomes naturally anomaly-free, and yet correctly describes the couplings of a neutral isoscalar pion to two gauge fields and to leptons: the first coupling is the same as that computed from the chiral anomaly, and the second is identical with that obtained from the 'Partially Conserved Axial Current' hypothesis. The general (non-Abelian) case of the standard model is only mentioned and will be the subject of another work.
Analysis of belief-propagation decoding of LDPC codes over the biAWGN channel using improved Gaussian approximation based on the mutual information measure

Summary form only given. The capacity of the message-passing decoder for LDPC codes can be computed by the density evolution algorithm by iteratively computing message densities. The infinite-dimensional problem of iteratively calculating the message densities in the case of the binary input AWGN channel and the belief propagation decoder can be simplified to a one-dimensional problem by using a Gaussian approximation. By assuming Gaussian densities the density evolution simplifies to updating means of Gaussian densities. An improved Gaussian approximation algorithm is suggested for computing the capacity of the BP decoder based on the mutual information measure. An analytical method for computing the mutual information of a transmitted bit with the message corresponding to it is proposed. Using this method we can approximate the non-Gaussian message by a Gaussian message that has the same mutual information with the transmitted bit. Computationally, the algorithm is similar to the Gaussian approximation algorithm proposed in Chung (2001). For various regular LDPC codes that were examined, the algorithm computed threshold values within 0.01dB or less from the exact threshold which is an improvement over the Gaussian approximation. Furthermore, additional insight on the convergence process can be gained from EXIT charts that can be derived from the algorithm. This can assist in designing better irregular LDPC codes.
Interaction between dual cavity modes in a planar photonic microcavity

Abstract We theoretically study the interaction between dual cavity modes in a planar photonic microcavity structure in the optical communication wavelength range. The merging and splitting of cavity mode is analysed with realistic microcavity structures. The merging of dual cavity resonance into a single cavity resonance is achieved by changing the number of layers between the two cavities. The splitting of single cavity resonance into dual cavity resonance is obtained with an increase in the reflectivity of mirrors in the front and rear side of the microcavity structure. The threshold condition for the merging and splitting of cavity mode is established in terms of structural parameters. The physical origin of the merging of dual cavity modes into a single cavity resonance is discussed in terms of the electric field intensity distribution in the microcavity structure. The microcavity structure with dual cavity modes is useful for the generation of entangled photon pairs, for achieving the strong-coupling regime between exciton and photon and for high-resolution multi-wavelength filters in optical communication.
Edge structure of graphene monolayers in the ν = 0 quantum Hall state

Monolayer graphene at neutrality in the quantum Hall regime has many competing ground states with various types of ordering. The outcome of this competition is modified by the presence of the sample boundaries. In this paper we use a Hartree-Fock treatment of the electronic correlations allowing for space-dependent ordering. The armchair edge influence is modeled by a simple perturbative effective magnetic field in valley space. We find that all phases found in the bulk of the sample, ferromagnetic, canted antiferromagnetic, charge-density wave, and Kekul\'e distortion, are smoothly connected to a Kekul\'e-distorted edge. The single-particle excitations are computed taking into account the spatial variation of the order parameters. An eventual metal-insulator transition as a function of the Zeeman energy is not simply related to the type of bulk order.
On the Stability of Quadratic (∗-) Derivations on (∗-) Banach Algebras

The stability of quadratic derivations on a Banach algebra is established. The stability and the superstability of quadratic ∗-derivations on Banach ∗ -algebras are proved as well. In fact, it is modified the result of Jang and Park [1] on a quadratic ∗-derivation.
H-alpha survey of nearby dwarf galaxies

We present the H-alpha imaging data and flux measurements for 30 dwarf galaxies in the Local volume. The H-alpha fluxes are used to derive the galaxy star formation rate, SFR. The sample of observed galaxies is characterized by the following parameters: the median distance of 7.5 Mpc, the median blue absolute magnitude of -14.8 mag, and median SFR of -2.0 dex. Two dSph members of the Local Group: Cetus and Leo IV do not show signs of star formation on the rate of -5.4 dex and -7.0 dex, respectively. The BCD galaxy ESO 553-46 has one of the highest specific SFR among the Local volume galaxies.
Nanoscale challenges of fluid mechanics

In this talk we would like to tackle general question of contemporary fluid dynamics, how far its assumption of a continuous, smooth medium remains useful when size and time scales start to approach molecular ones. The question is not trivial and seems to depend on several additional factors usually minored. For example, when full Navier-Stokes equations are replaced by their linear approximation we are loosing basic characteristics of convective motion, and still we use such approach. Once our fluid becomes granular matter with its own internal properties, proper interpretation of flow interactions with other molecular structures probably needs deeper physics. But still we try to convert such problem to the classical macro/micro scale description. Hence a general question arises, how small does a fluid have to be before it is not a fluid anymore?
Interactions between hot and dry fuel conditions and vegetation dynamics in the 2017 fire season in Portugal

Wildfires are a serious threat to ecosystems and human. In Portugal, during 2017, a catastrophic fire season burned more than 500 000 hectares and caused the death of more than 100 people. Previous studies have shown that hot and dry fuel conditions promoted widespread propagation of wildfires. However, burned area (BA) and mega-fires, such as the 2017 ones, depend not just on favourable meteorological conditions, but also on fuel accumulation and dryness. In this study, we will assess the influence of spring meteorological conditions on fire season BA, through their effects on fuel accumulation and dryness. Using satellite-based data, we show that the association of higher temperatures and water availability in spring can increase the risk of summer wildfires propagation, flammability and intensity through their influence on vegetation gross productivity. This study highlights the important role of fuel accumulation during the growing season in fire-prone regions like Portugal. Our results imply that fuel management may be an effective way to mitigate extreme fire seasons associated with warmer and drier conditions in the future.
Utilization of a Wireless Network Performing CSMA/CA with Random Backoff Algorithm

Optimizing WLAN performance in IEEE 802.11 network with extremely different working environments is a difficult task. We must use a network that can provide the most benefits to its end-users to improve network strength and data transmission. To increase performance in this congested situation, we construct an adaptive backoff algorithm for the IEEE 802.11 DCF that enhances in congested network conditions, system throughput is improved, the collision probability is reduced, and good fairness is maintained. This paper deals with the utilization of networks for different backoff strategies for different network conditions. The results obtained were comparatively much better than the existing methods.
A Low Cost all Optical Photonic Technique Based on Brillouin Scattering for mm-Wave Generation

Generation of millimeter wave (mm-wave) in radio over fiber (ROF) systems is suffered from nonlinearity effects in optical fiber communication. In order to mitigate the degrading effects, a low cost all optical photonic method for the generation of mm-wave is proposed in this work. Therefore, work is accentuated on the enhanced frequency shift based on stimulated Brillouin scattering (SBS) by utilizing only one pump source. Effect of length of optical fiber, launched power and photo detector quantum efficiency is investigated over proposed architecture in order to observe power fading effects. Photonically stable, mm-wave is realized productively in the cost effective way using SBS incorporating different frequencies from 20 GHz-80 GHz. Results reveals that USB (upper sidebands) and LSB (lower sidebands) generated with demonstrated system have high power and good SNR. This makes system appropriate for radio over fiber system.
Discrete-time construction of nonequilibrium path integrals on the Kostantinov–Perel’ time contour

Rigorous nonequilibrium actions for the many-body problem are usually derived by means of path integrals combined with a discrete temporal mesh on the Schwinger–Keldysh time contour. The latter suffers from a fundamental limitation: the initial state on this contour cannot be arbitrary, but necessarily needs to be described by a non-interacting density matrix, while interactions are switched on adiabatically. The Kostantinov–Perel’ contour overcomes these and other limitations, allowing generic initial-state preparations. In this article, we apply the technique of the discrete temporal mesh to rigorously build the nonequilibrium path integral on the Kostantinov–Perel’ time contour.
Circuit Complexity in Interacting Quenched Quantum Field Theory

In this work, we explore the effects of quantum quenching on the circuit complexity of quenched quantum field theory with weakly coupled quartic interactions. We use the invariant operator method under a perturbative framework to compute the ground state of this system. We give the analytical expressions for specific reference and target states using the ground state of the system. Using a particular cost functional, we show the analytical computation of circuit complexity for the quenched and interacting field theory. Furthermore, we give a numerical estimate of circuit complexity with respect to the quench rate, δt, for two coupled oscillators. The parametric variation in the unambiguous contribution of the circuit complexity for an arbitrary number of oscillators has been studied with respect to the dimensionless parameter (t/δt). We comment on the variation in the circuit complexity for different values of coupling strength, different numbers of oscillators and even in different dimensions.
Tube-Based Zonotopic Data-Driven Predictive Control

We present a novel tube-based data-driven predictive control method for linear systems affected by a bounded addictive disturbance. Our method leverages recent results in the reachability analysis of unknown linear systems to formulate and solve a robust tube-based predictive control problem. More precisely, our approach consists in deriving, from the collected data, a zonotope that includes the true state error set. We show how to guarantee the stability of the resulting error zonotope, which can be exploited to increase the computational efficiency of existing zonotopic data-driven MPC formulations. Results on a double-integrator affected by strong adversarial noise demonstrate the effectiveness of the proposed control approach.
Knowledge Based Template Machine Translation In Low-Resource Setting

Incorporating tagging into neural machine translation (NMT) systems has shown promising results in helping translate rare words such as named entities (NE). However, translating NE in low-resource setting remains a challenge. In this work, we investigate the effect of using tags and NE hypernyms from knowledge graphs (KGs) in parallel corpus in different levels of resource conditions. We find the tag-and-copy mechanism (tag the NEs in the source sentence and copy them to the target sentence) improves translation in high-resource settings only. Introducing copying also results in polarizing effects in translating different parts-of-speech (POS). Interestingly, we find that copy accuracy for hypernyms is consistently higher than that of entities. As a way of avoiding"hard"copying and utilizing hypernym in bootstrapping rare entities, we introduced a"soft"tagging mechanism and found consistent improvement in high and low-resource settings.
Developing a multi-variate prediction model for the detection of COVID-19 from Crowd-sourced Respiratory Voice Data

COVID-19 has affected more than 223 countries worldwide. There is a pressing need for non invasive, low costs and highly scalable solutions to detect COVID-19, especially in low-resource countries where PCR testing is not ubiquitously available. Our aim is to develop a deep learning model identifying COVID-19 using voice data recordings spontaneously provided by the general population (voice recordings and a short questionnaire) via their personal devices. The novelty of this work is in the development of a deep learning model for the identification of COVID-19 patients from voice recordings. Methods: We used the Cambridge University dataset consisting of 893 audio samples, crowd-sourced from 4352 participants that used a COVID-19 Sounds app. Voice features were extracted using a Mel-spectrogram analysis. Based on the voice data, we developed deep learning classification models to detect positive COVID-19 cases. These models included Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN). We compared their predictive power to baseline classification models, namely Logistic Regression and Support Vector Machine. Results: LSTM based on a Mel-frequency cepstral coefficients (MFCC) features achieved the highest accuracy (89%,) with a sensitivity and specificity of respectively 89% and 89%, The results achieved with the proposed model suggest a significant improvement in the prediction accuracy of COVID-19 diagnosis compared to the results obtained in the state of the art. Conclusion: Deep learning can detect subtle changes in the voice of COVID-19 patients with promising results. As an addition to the current testing techniques this model may aid health professionals in fast diagnosis and tracing of COVID-19 cases using simple voice analysis
Bayes factors for longitudinal model assessment via power posteriors

Bayes factor, defined as the ratio of the marginal likelihood functions of two competing models, is the natural Bayesian procedure for model selection. Marginal likelihoods are usually computationally demanding and complex. This scenario is particularly cumbersome in linear mixed models (LMMs) because marginal likelihood functions involve integrals of large dimensions determined by the number of parameters and the number of random effects, which in turn increase with the number of individuals in the sample. The power posterior is an attractive proposal in the context of the Markov chain Monte Carlo algorithms that allows expressing marginal likelihoods as one-dimensional integrals over the unit range. This paper explores the use of power posteriors in LMMs and discusses their behaviour through two simulation studies and a real data set on European sardine landings in the Mediterranean Sea.
An Independent Measurement of the Total Active 8B Solar Neutrino Flux Using an Array of 3He Proportional Counters at the Sudbury Neutrino Observatory

The Sudbury Neutrino Observatory (SNO) used an array of {sup 3}He proportional counters to measure the rate of neutral-current interactions in heavy water and precisely determined the total active ({nu}{sub x}) {sup 8}B solar neutrino flux. This technique is independent of previous methods employed by SNO. The total flux is found to be 5.54{sub -0.31}{sup +0.33}(stat){sub -0.34}{sup +0.36}(syst) x 10{sup 6} cm{sup -2}s{sup -1}, in agreement with previous measurements and standard solar models. A global analysis of solar and reactor neutrino results yields {Delta}m{sup 2} = 7.94{sub -0.26}{sup +0.42} x 10{sup -5} eV{sup 2} and {theta} = 33.8{sub -1.3}{sup +1.4} degrees. The uncertainty on the mixing angle has been reduced from SNO's previous results.