Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying and Alleviating the Language Prior Problem in Visual Question Answering

Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past few years. Although some progress has been achieved so far, several studies have pointed out that current VQA models are heavily affected by the language prior problem, which means they tend to answer questions based on the co-occurrence patterns of question keywords (e.g., how many) and answers (e.g., 2) instead of understanding images and questions. Existing methods attempt to solve this problem by either balancing the biased datasets or forcing models to better understand images. However, only marginal effects and even performance deterioration are observed for the first and second solution, respectively. In addition, another important issue is the lack of measurement to quantitatively measure the extent of the language prior effect, which severely hinders the advancement of related techniques. In this paper, we make contributions to solve the above problems from two perspectives. Firstly, we design a metric to quantitatively measure the language prior effect of VQA models. The proposed metric has been demonstrated to be effective in our empirical studies. Secondly, we propose a regularization method (i.e., score regularization module) to enhance current VQA models by alleviating the language prior problem as well as boosting the backbone model performance. The proposed score regularization module adopts a pair-wise learning strategy, which makes the VQA models answer the question based on the reasoning of the image (upon this question) instead of basing on question-answer patterns observed in the biased training set. The score regularization module is flexible to be integrated into various VQA models. We conducted extensive experiments over two popular VQA datasets (i.e., VQA 1.0 and VQA 2.0) and integrated the score regularization module into three state-of-the-art VQA models. Experimental results show that the score regularization module can not only effectively reduce the language prior problem of these VQA models but also consistently improve their question answering accuracy.
Pressure dependence of ferroelectric quantum critical fluctuations

The effect of ferroelectric fluctuations on the temperature dependent dielectric constant of SrTiO$_3$ has been long studied. Those fluctuations have been shown in recent years to be quantum critical and STO demonstrated to form the archetypal quantum critical paraelectric. The effect of those same fluctuations on the pressure and temperature dependence of the ferroelectric soft phonon mode as the system is tuned away from criticality are reported for the first time in this paper. We show that the mean field approximation is confirmed experimentally. Furthermore, using a self-consistent model of the quantum critical excitations including coupling to the volume strain and without adjustable parameters, we determine logarithmic corrections that would be observable only very close to the quantum critical point. Thus, the mean-field character of the pressure dependence is much more robust to the fluctuations than is the temperature dependence. We predict stronger corrections for lower dimensionalities however. The same calculation confirms that the Lydanne-Sachs-Teller relation is valid over the whole pressure and temperature range considered. Therefore, the measured dielectric constant can be used to extract the frequency of the soft mode down to 1.5 K and up to 20 kbar of applied pressure. The soft mode is observed to stiffen further, raising the low-temperature energy gap and returning towards the expected shallow temperature dependence of an optical mode. This behavior is consistent with the existence of a quantum critical point on the pressure-temperature phase diagram of STO, which applied pressure tunes the system away from. This work represents the first experimental measurement of the stiffening of a soft phonon mode as a system is tuned away from criticality, a potentially universal phenomenon across a variety of phase transitions and systems in condensed matter physics.
Using convolutional neural networks to identify gravitational lenses in astronomical images

The Euclid telescope, due for launch in 2021, will perform an imaging and slitless spectroscopy survey over half the sky, to map baryon wiggles and weak lensing. During the survey, Euclid is expected to resolve 100 000 strong gravitational lens systems. This is ideal to find rare lens configurations, provided they can be identified reliably and on a reasonable time-scale. For this reason, we have developed a convolutional neural network (CNN) that can be used to identify images containing lensing systems. CNNs have already been used for image and digit classification as well as being used in astronomy for star-galaxy classification. Here, our CNN is trained and tested on Euclid-like and KiDS (Kilo-Degree Survey)-like simulations from the Euclid Strong Lensing Group, successfully classifying 77 per cent of lenses, with an area under the ROC curve of up to 0.96. Our CNN also attempts to classify the lenses in COSMOS Hubble Space Telescope F814W-band images. After convolution to the Euclid resolution, we find we can recover most systems that are identifiable by eye. The python code is available on Github.
Stochastic String Stability of Vehicle Platoons via Cooperative Adaptive Cruise Control With Lossy Communication

This paper is about obtaining stable vehicle platooning by using Cooperative Adaptive Cruise Control when the communication is unreliable and suffers from message losses. We model communication losses as independent random events and we propose an original design for the cooperative controller, which effectively mitigates the effects of the losses. Our design explicitly takes into account the stochastic nature of the losses by considering both the average evolution of the system and the stochastic variations around it. The control design promotes both plant stability and string stability of the average error dynamics by an $\mathscr {H}_\infty $ approach, while minimizing the variance of the trajectories around their average. We show by simulations that the proposed controller is able to compensate losses even for high loss probabilities.
Labor Market Imperfections and the Dynamics of Postwar Business Cycles

An estimated dynamic general equilibrium model which features imperfectly competititve households, sticky nominal wages and costly labor input adjustment is shown to be consistent with several stylized aspects of U.S. postwar business cycle dynamics including the positive serial correlation of output, consumption, investment and employment growth over short horizons and the persistent, hump-shaped response of output to innovations in the temporary component.
On the geometric mean method for incomplete pairwise comparisons

One of the most popular methods of calculating priorities based on the pairwise comparisons matrices (PCM) is the geometric mean method (GMM). It is equivalent to the logarithmic least squares method (LLSM), so some use both names interchangeably, treating it as the same approach. The main difference, however, is in the way the calculations are done. It turns out, however, that a similar relationship holds for incomplete matrices. Based on Harker’s method for the incomplete PCM, and using the same substitution for the missing entries, it is possible to construct the geometric mean solution for the incomplete PCM, which is fully compatible with the existing LLSM for the incomplete PCM. Again, both approaches lead to the same results, but the difference is how the final solution is computed. The aim of this work is to present in a concise form, the computational method behind the geometric mean method (GMM) for an incomplete PCM. The computational method is presented to emphasize the relationship between the original GMM and the proposed solution. Hence, everyone who knows the GMM for a complete PCM should easily understand its proposed extension. Theoretical considerations are accompanied by a numerical example, allowing the reader to follow the calculations step by step.
CHANG-ES. XX. High-resolution Radio Continuum Images of Edge-on Galaxies and Their AGNs: Data Release 3

The CHANG-ES galaxy sample consists of 35 nearby edge-on galaxies that have been observed using the VLA at 1.6 and 6.0 GHz. Here we present the third data release of our sample, namely, the B configuration 1.6 GHz sample. In addition, we make available the band-to-band spectral index maps between 1.6 and 6.0 GHz, the latter taken in the matching resolution C configuration. The images can be downloaded from https://www.queensu. ca/changes. These are our highest-resolution images (≈3″), and we examine the possible presence of low-luminosity active galactic nuclei (AGNs) in the sample, as well as some in-disk structure. New features can be seen in the spectral index maps that are masked in the total intensity emission, including hidden spiral arms in NGC 3448 and two previously unknown radio lobes on either side of the nucleus of NGC 3628. Our AGN detection rate, using only radio criteria, is 55%, which we take as a lower limit because some weaker embedded AGNs are likely present that could be revealed at higher resolution. Archival XMM-Newton data were used to search for further fingerprints of the AGNs in the studied sample. In galaxy disks, discrete regions of flat spectral index are seen, likely due to a thermal emission fraction that is higher than the global average.
Exact high-dimensional asymptotics for support vector machine

The Support Vector Machine (SVM) is one of the most widely used classification methods. In this paper, we consider the soft-margin SVM used on data points with independent features, where the sample size $n$ and the feature dimension $p$ grows to $\infty$ in a fixed ratio $p/n\rightarrow \delta$. We propose a set of equations that exactly characterizes the asymptotic behavior of support vector machine. In particular, we give exact formulas for (1) the variability of the optimal coefficients, (2) the proportion of data points lying on the margin boundary (i.e. number of support vectors), (3) the final objective function value, and (4) the expected misclassification error on new data points, which in particular implies the exact formula for the optimal tuning parameter given a data generating mechanism. We first establish these formulas in the case where the label $y\in\{+1,-1\}$ is independent of the feature $x$. Then the results are generalized to the case where the label $y\in\{+1,-1\}$ is allowed to have a general dependence on the feature $x$ through a linear combination $a_0^Tx$. These formulas for the non-smooth hinge loss are analogous to the recent results in \citep{sur2018modern} for smooth logistic loss. Our approach is based on heuristic leave-one-out calculations.
Predictions for radio afterglows of binary neutron star mergers: a population study for O3 and beyond

Following the historical observations of GW170817 and its multi-wavelength afterglow, more radio afterglows from neutron star mergers are expected in the future as counterparts to gravitational wave inspiral signals. We wish to describe these events using our current knowledge of the intrinsic population of neutron star mergers coming from gamma-ray burst science, and taking into account the sensitivities of current and future gravitational wave and radio detectors. This is done by coupling a semi-analytical model for the jet-dominated radio afterglow from a neutron star merger, an analytical simplified model for the gravitational wave signal from such events, and a population model prescribing the energetics, external density and other relevant parameters of the mergers. We report the expected distributions of observables (distance, orientation, afterglow peak time and flux, etc.) from future events and study how these can be used to further probe the population of binary neutron stars, their mergers and related outflows during future observing campaigns. In the case of the O3 run of the LIGO-Virgo Collaboration, the radio afterglow of one third of gravitational-wave-detected mergers should be detectable--and detected if the source is localized thanks to the kilonova counterpart--by the Very Large Array, and these events should have viewing angles similar to that of GW170817. These findings confirm the radio afterglow as a powerful insight on these events, though some key afterglow-related techniques--such as Very Long Base Interferometry imaging of the merger remnant--may no longer be possible as the gravitational wave horizon increases.
Separating the stratospheric and tropospheric pathways of El Niño–Southern Oscillation teleconnections

The El Niño–Southern Oscillation (ENSO) is a major driver of Northern Hemisphere wintertime variability and, generally, the key ingredient used in seasonal forecasts of wintertime surface climate. Modeling studies have recently suggested that ENSO teleconnections might involve both a tropospheric pathway and a stratospheric one. Here, using reanalysis data, we carefully distinguish between the two. We first note that the temperature and circulation anomalies associated with the tropospheric pathway are nearly equal and opposite during the warm (El Niño) and cold (La Niña) phases of ENSO, whereas those associated with the stratospheric pathway are of the same sign, irrespective of the ENSO phase. We then exploit this fact to isolate the two pathways. Our decomposition reveals that ENSOs climate impacts over North America are largely associated with the tropospheric pathway, whereas ENSOs climate impacts over the North Atlantic and Eurasia are greatly affected by the stratospheric pathway. The stratospheric pathway, which we here define on the basis of the occurrence of one or more sudden stratospheric warmings in a given winter, and whose signature projects very strongly on the North Atlantic Oscillation, is found to be present 60% of the time during ENSO winters (of either phase): it therefore likely plays an important role in improving seasonal forecasts, notably over the North Atlantic and the Eurasian continent.
Neural Correlates of Subjective Awareness and Unconscious Processing: An ERP Study

The aim of the present study was to dissociate the ERP (Event Related Potentials) correlates of subjective awareness from those of unconscious perception. In a backward masking paradigm, participants first produced a forced-choice response to the location of a liminal target presented for an individually calibrated duration, and then reported on their subjective awareness of the target's presence. We recorded (Event-Related Potentials) ERPs and compared the ERP waves when observers reported being aware vs. unaware of the target but localized it correctly, thereby isolating the neural correlates of subjective awareness while controlling for differences in objective performance. In addition, we compared the ERPs when participants were subjectively unaware of the target's presence and localized it correctly versus incorrectly, thereby isolating the neural correlates of unconscious perception. All conditions involved stimuli that were physically identical and were presented for the same duration. Both behavioral measures were associated with modulation of the amplitude of the P3 component of the ERP. Importantly, this modulation was widely spread across all scalp locations for subjective awareness, but was restricted to the parietal electrodes for unconscious perception. These results indicate that liminal stimuli that do not affect performance undergo considerable processing and that subjective awareness is associated with a late wave of activation with widely distributed topography.
Cut-off for sandpiles on tiling graphs

Sandpile dynamics are considered on graphs constructed from periodic plane and space tilings by assigning a growing piece of the tiling either torus or open boundary conditions. A general method of obtaining the Green's function of the tiling is given, and a total variation cut-off phenomenon is demonstrated under general conditions. It is shown that the boundary condition does not affect the mixing time for planar tilings, nor does it change the asymptotic mixing time for the cubic lattice $\zed^d$ for all sufficiently large $d$. In a companion paper, computational methods are used to demonstrate that the mixing time is altered for the $\Dfour$ lattice in dimension 4.
Stochastic Dominance and Risk Measure: A Decision-Theoretic Foundation for VAR and C-Var

Is it possible to obtain an objective and quantifiable measure of risk backed up by choices made by some specific groups of rational investors? To answer this question, in this paper we establish some behavior foundations for various types of VaR models, including VaR and conditional-VaR, as measures of downside risk. In this paper, we will establish some logical connections among VaRs, conditional-VaR, stochastic dominance, and utility maximization. Though supported to some extents with unanimous choices by some specific groups of expected or non-expected-utility investors, VaRs as profiles of risk measures at various levels of risk tolerance are not quantifiable - they can only provide partial and incomplete risk assessments for risky prospects. We also include in our discussion the relevant VaRs and several alternative risk measures for investors; these alternatives use somewhat weaker assumptions about risk-averse behavior by incorporating a mean-preserving-spread. For this latter group of investors, we provide arguments for and against the standard deviation versus VaR and conditional-VaRs as objective and quantifiable measures of risk in the context of portfolio choice.
Matrix calculations for inhomogeneous Markov reward processes, with applications to life insurance and point processes

A multi--state life insurance model is naturally described in terms of the intensity matrix of an underlying (time--inhomogeneous) Markov process which describes the dynamics for the states of an insured person. Between and at transitions, benefits and premiums are paid, defining a payment process, and the technical reserve is defined as the present value of all future payments of the contract. Classical methods for finding the reserve and higher order moments involve the solution of certain differential equations (Thiele and Hattendorf, respectively). In this paper we present an alternative matrix--oriented approach based on general reward considerations for Markov jump processes. The matrix approach provides a general framework for effortlessly setting up general and even complex multi--state models, where moments of all orders are then expressed explicitly in terms of so--called product integrals (matrix--exponentials) of certain matrices. As Thiele and Hattendorf type of theorems can be retrieved immediately from the matrix formulae, this methods also provides a quick and transparent approach to proving these classical results. Methods for obtaining distributions and related properties of interest (e.g. quantiles or survival functions) of the future payments are presented from both a theoretical and practical point of view (via Laplace transforms and methods involving orthogonal polynomials).
Sequence Comparison Significance and Poisson Approximation

The Chen-Stein method of Poisson approximation has been used to establish theorems about comparison of two DNA or protein sequences. The most useful result for sequence alignment applies to alignment scoring with no gaps. However, there has not been a valid method to assign statistical significance to alignment scores with gaps. In this paper we extend Poisson approximation techniques using the Aldous clumping heuristic to a practical method of estimating statistical significance.
Attachment style predicts affect, cognitive appraisals, and social functioning in daily life

The way in which attachment styles are expressed in the moment as individuals navigate their real-life settings has remained an area largely untapped by attachment research. The present study examined how adult attachment styles are expressed in daily life using experience sampling methodology (ESM) in a sample of 206 Spanish young adults. Participants were administered the Attachment Style Interview (ASI) and received personal digital assistants that signaled them randomly eight times per day for 1 week to complete questionnaires about their current experiences and social context. As hypothesized, participants’ momentary affective states, cognitive appraisals, and social functioning varied in meaningful ways as a function of their attachment style. Individuals with an anxious attachment, as compared with securely attached individuals, endorsed experiences that were congruent with hyperactivating tendencies, such as higher negative affect, stress, and perceived social rejection. By contrast, individuals with an avoidant attachment, relative to individuals with a secure attachment, endorsed experiences that were consistent with deactivating tendencies, such as decreased positive states and a decreased desire to be with others when alone. Furthermore, the expression of attachment styles in social contexts was shown to be dependent upon the subjective appraisal of the closeness of social contacts, and not merely upon the presence of social interactions. The findings support the ecological validity of the ASI and the person-by-situation character of attachment theory. Moreover, they highlight the utility of ESM for investigating how the predictions derived from attachment theory play out in the natural flow of real life.
Performance analysis of finite-source retrial queues operating in random environments

This paper is concerned with the performance analysis of finite-source retrial queues with heterogeneous sources operating in random environments. All random variables involved in the model construction are assumed to be exponentially distributed with a parameter depending on the source index and on the state of the corresponding random environment. The novelty of the investigation is the involvement of the random environments, which makes the system rather complicated. The MOSEL tool is used to formulate and solve the problem and the main performance measures are derived and graphically displayed.
Fossa: Learning ECA Rules for Adaptive Distributed Systems

The development of adaptive distributed systems is complex. Due to a large amount of interdependencies and feedback loops between network nodes and software components, distributed systems respond nonlinearly to changes in the environment and system adaptations. Although Event Condition Action (ECA) rules allow a crisp definition of the adaptive behavior and a loose coupling with the actual system implementation, defining concrete rules is nontrivial. It requires specifying the events and conditions which trigger adaptations, as well as the selection of appropriate actions leading to suitable new configurations. In this paper, we present the idea of Fossa, an ECA framework for adaptive distributed systems. Following a methodology that separates the adaptation logic from the actual application implementation, we propose learning ECA rules by automatically executing a multitude of tests. Rule sets are generated by algorithms such as genetic programming, and the results are evaluated using a utility function provided by the developer. Fossa therefore provides an automated offline learner that derives suitable ECA rules for a given utility function.