Continual Improvement of Threshold-Based Novelty Detection

When evaluated in dynamic, open-world situations, neural networks struggle to detect unseen classes. This issue complicates the deployment of continual learners in realistic environments where agents are not explicitly informed when novel categories are encountered. A common family of techniques for detecting novelty relies on thresholds of similarity between observed data points and the data used for training. However, these methods often require manually specifying (ahead of time) the value of these thresholds, and are therefore incapable of adapting to the nature of the data. We propose a new method for automatically selecting these thresholds utilizing a linear search and leave-one-out cross-validation on the ID classes. We demonstrate that this novel method for selecting thresholds results in improved total accuracy on MNIST, Fashion MNIST, and CIFAR-10.Comment: Presented in the workshop track at CoLLAs 202

Analysis of the Bayesian Cramer-Rao lower bound in astrometry: Studying the impact of prior information in the location of an object

Context. The best precision that can be achieved to estimate the location of a stellar-like object is a topic of permanent interest in the astrometric community. Aims. We analyse bounds for the best position estimation of a stellar-like object on a CCD detector array in a Bayesian setting where the position is unknown, but where we have access to a prior distribution. In contrast to a parametric setting where we estimate a parameter from observations, the Bayesian approach estimates a random object (i.e., the position is a random variable) from observations that are statistically dependent on the position. Methods. We characterize the Bayesian Cramer-Rao (CR) that bounds the minimum mean square error (MMSE) of the best estimator of the position of a point source on a linear CCD-like detector, as a function of the properties of detector, the source, and the background. Results. We quantify and analyse the increase in astrometric performance from the use of a prior distribution of the object position, which is not available in the classical parametric setting. This gain is shown to be significant for various observational regimes, in particular in the case of faint objects or when the observations are taken under poor conditions. Furthermore, we present numerical evidence that the MMSE estimator of this problem tightly achieves the Bayesian CR bound. This is a remarkable result, demonstrating that all the performance gains presented in our analysis can be achieved with the MMSE estimator. Conclusions The Bayesian CR bound can be used as a benchmark indicator of the expected maximum positional precision of a set of astrometric measurements in which prior information can be incorporated. This bound can be achieved through the conditional mean estimator, in contrast to the parametric case where no unbiased estimator precisely reaches the CR bound.Comment: 17 pages, 12 figures. Accepted for publication on Astronomy & Astrophysic

Litigation and Settlement: New Evidence from Labor Courts in Mexico

Using a newly assembled data set on procedures filed in Mexican labor tribunals, we study the determinants of final awards to workers. On average, workers recover less than 30% of their claim. Our strongest result is that workers receive higher percentages of their claims in settlements than in trial judgments. We also find that cases with multiple claimants against a single firm are less likely to be settled, which partially explains why workers involved in these procedures receive lower percentages of their claims. Finally, we find evidence that a worker who exaggerates her claim is less likely to settle.

Performance analysis of the Least-Squares estimator in Astrometry

We characterize the performance of the widely-used least-squares estimator in astrometry in terms of a comparison with the Cramer-Rao lower variance bound. In this inference context the performance of the least-squares estimator does not offer a closed-form expression, but a new result is presented (Theorem 1) where both the bias and the mean-square-error of the least-squares estimator are bounded and approximated analytically, in the latter case in terms of a nominal value and an interval around it. From the predicted nominal value we analyze how efficient is the least-squares estimator in comparison with the minimum variance Cramer-Rao bound. Based on our results, we show that, for the high signal-to-noise ratio regime, the performance of the least-squares estimator is significantly poorer than the Cramer-Rao bound, and we characterize this gap analytically. On the positive side, we show that for the challenging low signal-to-noise regime (attributed to either a weak astronomical signal or a noise-dominated condition) the least-squares estimator is near optimal, as its performance asymptotically approaches the Cramer-Rao bound. However, we also demonstrate that, in general, there is no unbiased estimator for the astrometric position that can precisely reach the Cramer-Rao bound. We validate our theoretical analysis through simulated digital-detector observations under typical observing conditions. We show that the nominal value for the mean-square-error of the least-squares estimator (obtained from our theorem) can be used as a benchmark indicator of the expected statistical performance of the least-squares method under a wide range of conditions. Our results are valid for an idealized linear (one-dimensional) array detector where intra-pixel response changes are neglected, and where flat-fielding is achieved with very high accuracy.Comment: 35 pages, 8 figures. Accepted for publication by PAS

Litigation and settlement : new evidence from labor courts in Mexico

Using a newly assembled data set on procedures filed in Mexican labor tribunals, the authors of this paper study the determinants of final awards to workers. On average, workers recover less than 30 percent of their claim. The strongest result is that workers receive higher percentages of their claims in settlements than in trial judgments. It is also found that cases with multiple claimants against a single firm are less likely to be settled, which partially explains why workers involved in these procedures receive lower percentages of their claims. Finally, the authors find evidence that a worker who exaggerates his or her claim is less likely to settle.Bankruptcy and Resolution of Financial Distress,Arbitration,Information Security&Privacy,Labor Markets,Judicial System Reform

Investigation into UHI monitoring with GNSS sensor network

Radio frequency (RF) signals are used in Global Navigation Satellite Systems (GNSS) for positioning applications, however, it can also be used to monitor the atmosphere. RF signals can be affected by changes in the atmospheric refractivity index along their propagation path. This change of refractive index along the path of the signal in the troposphere causes a delay to the signal known as the Tropospheric Delay (TD). The TD in the zenith direction (ZTD) has already been used to derive the amount of precipitable water at a given site because the refractive index of air in the atmosphere is proportional to the environmental variables: temperature (T), pressure (P) and water vapour partial pressure (e). However, other environmental variables such as T have not been derived from the ZTD. Thus, this thesis presents a novel algorithm to estimate temperature from GNSS data for monitoring urban heat island intensity (UHII). An urban heat island (UHI) occurs when an urban area is warmer than its adjacent rural areas. It exacerbates heat waves, leading to increased energy consumption and adverse effects to the environment and to human health. UHIs are monitored using remote sensing techniques, which allow the monitoring of large geographical areas with low time resolution. However, the study of UHIs within a city requires better spatial and temporal resolution. It is also desired to monitor the UHI in real-time. The algorithm presented in this thesis allows UHI monitoring with higher spatial and temporal resolution using a GNSS network. The algorithm developed in this research has 6 inputs: the thickness of the troposphere, air pressure, water vapor partial pressure and the vertical profile of the refractive index obtained with radiosonde data. Another input is the ZTD obtained from the Precise Point Positioning (PPP) technique. The algorithm solves for temperature at the point where the GNSS data was collected. To validate the output of the algorithm, estimated T at 5 locations at 00:00UTC and 12:00 UTC have been compared to values of T from meteorological data near the GNSS station at the same times. Hourly data for 20 days in year 2017 has been used. An average difference of less than 1 ºC has been found for data collected during the summer. In order to measure the intensity of the UHI, it is necessary to measure the temperature at two locations simultaneously: an urban and an adjacent rural location nearby. The algorithm has been tested and validated using two publicly available datasets containing daily GNSS and meteorological data from Los Angeles, California (LA), USA and Hong Kong Special Administrative Region, China (HK). Also, the algorithm has been tested with an experimentally collected dataset containing hourly GNSS and meteorological data from Ningbo, China (NB). It has been found that an UHI with an intensity of 3.5 ºC existed in LA during the winter 2017.The UHI detected in HK during the summer 2017 had an intensity of 4 ºC and in NB had an intensity of 2 ºC

Orbits for eighteen visual binaries and two double-line spectroscopic binaries observed with HRCAM on the CTIO SOAR 4m telescope, using a new Bayesian orbit code based on Markov Chain Monte Carlo

We present orbital elements and mass sums for eighteen visual binary stars of spectral types B to K (five of which are new orbits) with periods ranging from 20 to more than 500 yr. For two double-line spectroscopic binaries with no previous orbits, the individual component masses, using combined astrometric and radial velocity data, have a formal uncertainty of ~0.1 MSun. Adopting published photometry, and trigonometric parallaxes, plus our own measurements, we place these objects on an H-R diagram, and discuss their evolutionary status. These objects are part of a survey to characterize the binary population of stars in the Southern Hemisphere, using the SOAR 4m telescope+HRCAM at CTIO. Orbital elements are computed using a newly developed Markov Chain Monte Carlo algorithm that delivers maximum likelihood estimates of the parameters, as well as posterior probability density functions that allow us to evaluate the uncertainty of our derived parameters in a robust way. For spectroscopic binaries, using our approach, it is possible to derive a self-consistent parallax for the system from the combined astrometric plus radial velocity data ("orbital parallax"), which compares well with the trigonometric parallaxes. We also present a mathematical formalism that allows a dimensionality reduction of the feature space from seven to three search parameters (or from ten to seven dimensions - including parallax - in the case of spectroscopic binaries with astrometric data), which makes it possible to explore a smaller number of parameters in each case, improving the computational efficiency of our Markov Chain Monte Carlo code.Comment: 32 pages, 9 figures, 6 tables. Detailed Appendix with methodology. Accepted by The Astronomical Journa