Search

Classical coding theory contains several techniques to obtain new codes from other codes, including puncturing and shortening. For quantum codes, a form of puncturing is known, but its description is based on the code space rather than its generators. In this work, we generalize the puncturing procedure to allow more freedom in the choice of which coded states are kept and which are removed. We describe this puncturing by focusing on the stabilizer matrix containing the generators of the code. In this way, we are able to explicitly describe the stabilizer matrix of the punctured code given the stabilizer matrix of the original stabilizer code. The additional freedom in the procedure also opens up new ways to construct new codes from old, and we present several ways to utilize this for the search of codes with good or even optimal parameters. In particular, we use the construction to obtain codes whose parameters exceed the best previously known. Lastly, we generalize the proof of the Griesmer bound from the classical setting to stabilizer codes since the proof relies heavily on the puncturing technique., Comment: Preprint

Sea ice is a key element of the global Earth system, with a major impact on global climate and regional weather. Unfortunately, accurate sea ice modeling is challenging due to the diversity and complexity of underlying physics happening there, and a relative lack of ground truth observations. This is especially true for the Marginal Ice Zone (MIZ), which is the area where sea ice is affected by incoming ocean waves. Waves contribute to making the area dynamic, and due to the low survival time of the buoys deployed there, the MIZ is challenging to monitor. In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. OMBs are affordable enough to be deployed in large number, and gather information about drift (GPS position) and waves (1-dimensional elevation spectrum). This provides data focusing on the area around Svalbard with unprecedented spatial and temporal resolution. We expect that this will allow to perform validation and calibration of ice models and remote sensing algorithms.

Dry eye disease is a common disorder of the ocular surface, leading patients to seek eye care. Clinical signs and symptoms are currently used to diagnose dry eye disease. Metabolomics, a method for analyzing biological systems, has been found helpful in identifying distinct metabolites in patients and in detecting metabolic profiles that may indicate dry eye disease at early stages. In this study, we explored using machine learning and metabolomics information to identify which cataract patients suffered from dry eye disease. As there is no one-size-fits-all machine learning model for metabolomics data, choosing the most suitable model can significantly affect the quality of predictions and subsequent metabolomics analyses. To address this challenge, we conducted a comparative analysis of nine machine learning models on three metabolomics data sets from cataract patients with and without dry eye disease. The models were evaluated and optimized using nested k-fold cross-validation. To assess the performance of these models, we selected a set of suitable evaluation metrics tailored to the data set's challenges. The logistic regression model overall performed the best, achieving the highest area under the curve score of 0.8378, balanced accuracy of 0.735, Matthew's correlation coefficient of 0.5147, an F1-score of 0.8513, and a specificity of 0.5667. Additionally, following the logistic regression, the XGBoost and Random Forest models also demonstrated good performance.

The Carbon monOxide Mapping Array Project (COMAP) Pathfinder survey continues to demonstrate the feasibility of line-intensity mapping using high-redshift carbon monoxide (CO) line emission traced at cosmological scales. The latest COMAP Pathfinder power spectrum analysis is based on observations through the end of Season 2, covering the first three years of Pathfinder operations. We use our latest constraints on the CO(1-0) line-intensity power spectrum at $z\sim3$ to update corresponding constraints on the cosmological clustering of CO line emission and thus the cosmic molecular gas content at a key epoch of galaxy assembly. We first mirror the COMAP Early Science interpretation, considering how Season 2 results translate to limits on the shot noise power of CO fluctuations and the bias of CO emission as a tracer of the underlying dark matter distribution. The COMAP Season 2 results place the most stringent limits on the CO tracer bias to date, at $\langle{Tb}\rangle<4.8$ $\mu$K. These limits narrow the model space significantly compared to previous CO line-intensity mapping results while maintaining consistency with small-volume interferometric surveys of resolved line candidates. The results also express a weak preference for CO emission models used to guide fiducial forecasts from COMAP Early Science, including our data-driven priors. We also consider directly constraining a model of the halo-CO connection, and show qualitative hints of capturing the total contribution of faint CO emitters through the improved sensitivity of COMAP data. With continued observations and matching improvements in analysis, the COMAP Pathfinder remains on track for a detection of cosmological clustering of CO emission., Comment: 9 pages + bibliography and appendices (13 pages total); 9 figures, 1 table; v2 reflects minor changes made for version submitted to A&A, with no changes to top-line results

We present updated constraints on the cosmological 3D power spectrum of carbon monoxide CO(1-0) emission in the redshift range $2.4$-$3.4$. The constraints are derived from the two first seasons of Carbon monOxide Mapping Array Project (COMAP) Pathfinder line-intensity mapping observations aiming to trace star-formation during the Epoch of Galaxy Assembly. These results improve on the previous Early Science (ES) results through both increased data volume and improved data processing methodology. On the methodological side, we now perform cross-correlations between groups of detectors (''feed-groups''), as opposed to cross-correlations between single feeds, and this new feed-group pseudo power spectrum (FGPXS) is constructed to be more robust against systematic effects. In terms of data volume, the effective mapping speed is significantly increased due to an improved observational strategy as well as better data selection methodology. The updated spherically- and field-averaged FGPXS, $\tilde{C}(k)$, is consistent with zero, at a probability-to-exceed of around $34\,\%$, with an excess of $2.7\,\sigma$ in the most sensitive bin. Our power spectrum estimate is about an order of magnitude more sensitive in our six deepest bins across ${0.09\,\mathrm{Mpc}^{-1} < k < 0.73\,\mathrm{Mpc}^{-1}}$, as compared to the feed-feed pseudo power spectrum (FPXS) of COMAP ES. Each of these bins individually constrains the CO power spectrum to ${kP_\mathrm{CO}(k)< 2400-4900\,\mathrm{\mu K^2 Mpc^{2}}}$ at $95\,\%$ confidence. To monitor potential contamination from residual systematic effects, we analyze a set of 312 difference-map null tests and find that these are consistent with the instrumental noise prediction. In sum, these results provide the strongest direct constraints on the cosmological 3D CO(1-0) power spectrum published to date., Comment: 17 pages, 10 figures, v2 reflects changes made for version submitted to Astronomy and Astrophysics, addition of additional figure clarifying methodology, no change to final results

The CO Mapping Array Project (COMAP) Pathfinder is performing line intensity mapping of CO emission to trace the distribution of unresolved galaxies at redshift $z \sim 3$. We present an improved version of the COMAP data processing pipeline and apply this to the first two seasons of observations. This analysis improves on the COMAP Early Science (ES) results in several key aspects. On the observational side, all second season scans were made in constant-elevation mode, after noting that the previous Lissajous scans were associated with increased systematic errors; those scans accounted for 50% of the total Season 1 data volume. Secondly, all new observations were restricted to an elevation range of 35-65 degrees, to minimize sidelobe ground pickup. On the data processing side, more effective data cleaning in both the time- and map-domain has allowed us to eliminate all data-driven power spectrum-based cuts. This increases the overall data retention and reduces the risk of signal subtraction bias. On the other hand, due to the increased sensitivity, two new pointing-correlated systematic errors have emerged, and we introduce a new map-domain PCA filter to suppress these. Subtracting only 5 out of 256 PCA modes, we find that the standard deviation of the cleaned maps decreases by 67% on large angular scales, and after applying this filter, the maps appear consistent with instrumental noise. Combining all these improvements, we find that each hour of raw Season 2 observations yields on average 3.2 times more cleaned data compared to ES analysis. Combining this with the increase in raw observational hours, the effective amount of data available for high-level analysis is a factor of 8 higher than in ES. The resulting maps have reached an uncertainty of $25$-$50\,\mu K$ per voxel, providing by far the strongest constraints on cosmological CO line emission published to date., Comment: 23 pages, 22 figures, for submission to Astronomy and Astrophysics

Anomalous Microwave Emission (AME) is a major component of Galactic emission in the frequency band 10 to 60 GHz and is commonly modelled as rapidly rotating spinning dust grains. The photodissociation region (PDR) at the boundary of the $\lambda$-Orionis Hii region has been identified by several recent analyses as one of the brightest spinning dust emitting sources in the sky. We investigate the Barnard 30 dark cloud, a dark cloud embedded within the $\lambda$-Orionis PDR. We use total-power observations of Barnard 30 from the CO Mapping Array Project (COMAP) pathfinder instrument at 26 to 34GHz with a resolution of 4.5 arcminutes alongside existing data from Planck, WISE, IRAS, ACT, and the 1.447GHz GALFACTS survey. We use aperture photometry and template fitting to measure the spectral energy distribution of Barnard 30. We find that the spinning dust is the dominant emission component in the 26 to 34GHz range at the $7 \sigma$ level ($S_{30GHz} = 2.85\pm0.43$Jy). We find no evidence that polycyclic aromatic hydrocarbons are the preferred carrier for the spinning dust emission, suggesting that the spinning dust carriers are due to a mixed population of very small grains. Finally, we find evidence for variations in spinning dust emissivity and peak frequency within Barnard 30, and that these variations are possibly driven by changes in dust grain population and the total radiation field. Confirming the origin of the variations in the spinning dust spectrum will require both future COMAP observations at 15GHz combined with spectroscopic mid-infrared data of Barnard 30., Comment: 21 pages, 13 figures. Submitted to MNRAS

A common issue for machine learning models applied to time-series forecasting is the temporal evolution of the data distributions (i.e., concept drift). Because most of the training data does not reflect such changes, the models present poor performance on the new out-of-distribution scenarios and, therefore, the impact of such events cannot be reliably anticipated ahead of time. We present and publicly release CounterfacTS, a tool to probe the robustness of deep learning models in time-series forecasting tasks via counterfactuals. CounterfacTS has a user-friendly interface that allows the user to visualize, compare and quantify time series data and their forecasts, for a number of datasets and deep learning models. Furthermore, the user can apply various transformations to the time series and explore the resulting changes in the forecasts in an interpretable manner. Through example cases, we illustrate how CounterfacTS can be used to i) identify the main features characterizing and differentiating sets of time series, ii) assess how the model performance depends on these characateristics, and iii) guide transformations of the original time series to create counterfactuals with desired properties for training and increasing the forecasting performance in new regions of the data distribution. We discuss the importance of visualizing and considering the location of the data in a projected feature space to transform time-series and create effective counterfactuals for training the models. Overall, CounterfacTS aids at creating counterfactuals to efficiently explore the impact of hypothetical scenarios not covered by the original data in time-series forecasting tasks., Comment: Submitted. Code publicly available

Privacy-preserving distributed average consensus has received significant attention recently due to its wide applicability. Based on the achieved performances, existing approaches can be broadly classified into perfect accuracy-prioritized approaches such as secure multiparty computation (SMPC), and worst-case privacy-prioritized approaches such as differential privacy (DP). Methods of the first class achieve perfect output accuracy but reveal some private information, while methods from the second class provide privacy against the strongest adversary at the cost of a loss of accuracy. In this paper, we propose a general approach named adaptive differentially quantized subspace perturbation (ADQSP) which combines quantization schemes with so-called subspace perturbation. Although not relying on cryptographic primitives, the proposed approach enjoys the benefits of both accuracy-prioritized and privacy-prioritized methods and is able to unify them. More specifically, we show that by varying a single quantization parameter the proposed method can vary between SMPC-type performances and DP-type performances. Our results show the potential of exploiting traditional distributed signal processing tools for providing cryptographic guarantees. In addition to a comprehensive theoretical analysis, numerical validations are conducted to substantiate our results.

Reproducing published deep learning papers to validate their conclusions can be difficult due to sources of irreproducibility. We investigate the impact that implementation factors have on the results and how they affect reproducibility of deep learning studies. Three deep learning experiments were ran five times each on 13 different hardware environments and four different software environments. The analysis of the 780 combined results showed that there was a greater than 6% accuracy range on the same deterministic examples introduced from hardware or software environment variations alone. To account for these implementation factors, researchers should run their experiments multiple times in different hardware and software environments to verify their conclusions are not affected.

We report observations of the ground state transitions of $^{12}$CO, $^{13}$CO C$^{18}$O, HCN, and HCO$^+$ at 88-115 GHz in the inner region of the nearby galaxy IC 342. These data were obtained with the 16-pixel spectroscopic focal plane array Argus on the 100-m Robert C. Byrd Green Bank Telescope (GBT) at 6-9$^{\prime\prime}$ resolution. In the nuclear bar region, the intensity distributions of $^{12}$CO(1-0) and $^{13}$CO(1-0) emission trace moderate densities, and differ from the dense gas distributions sampled in C$^{18}$O(1-0), HCN(1-0), and HCO$^+$(1-0). We observe a constant HCN(1-0)-to-HCO$^+$(1-0) ratio of 1.2$\pm$0.1 across the whole $\sim$1 kpc bar. This indicates that HCN(1-0) and HCO$^+$(1-0) lines have intermediate optical depth, and that the corresponding $n_{\textrm{H}_2}$ of the gas producing the emission is of 10$^{4.5-6}$ cm$^{-3}$. We show that HCO$^+$(1-0) is thermalized and HCN(1-0) is close to thermalization. The very tight correlation between HCN(1-0) and HCO$^+$(1-0) intensities across the 1~kpc bar suggests that this ratio is more sensitive to the relative abundance of the two species than to the gas density. We confirm the angular offset ($\sim$10$^{\prime\prime}$) between the spatial distribution of molecular gas and the star formation sites. Finally, we find a breakdown of the $L_\textrm{IR}$-$L_\textrm{HCN}$ correlation at high spatial resolution due to the effect of incomplete sampling of star-forming regions by HCN emission in IC 342. The scatter of the $L_\textrm{IR}$-$L_\textrm{HCN}$ relation decreases as the spatial scale increases from 10$^{\prime\prime}$~to 30$^{\prime\prime}$ (170-510~pc), and is comparable to the scatter of the global relation at the scale of 340 pc., Comment: Accepted for publication in ApJ. 18 pages, 10 figures, and 4 tables. Comments are more than welcome!

Open-set recognition (OSR), the identification of novel categories, can be a critical component when deploying classification models in real-world applications. Recent work has shown that familiarity-based scoring rules such as the Maximum Softmax Probability (MSP) or the Maximum Logit Score (MLS) are strong baselines when the closed-set accuracy is high. However, one of the potential weaknesses of familiarity-based OSR are adversarial attacks. Here, we present gradient-based adversarial attacks on familiarity scores for both types of attacks, False Familiarity and False Novelty attacks, and evaluate their effectiveness in informed and uninformed settings on TinyImageNet., Comment: Published in: The 2nd Workshop and Challenges for Out-of-Distribution Generalization in Computer Vision, ICCV 2023

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear reporting standards for ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist ($\textbf{Re}$porting Standards $\textbf{For}$ $\textbf{M}$achine Learning Based $\textbf{S}$cience). It consists of 32 questions and a paired set of guidelines. REFORMS was developed based on a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility.

This paper examines asymmetric and time-varying dependency structures between financial returns, using a novel approach consisting of a combination of regime-switching models and the local Gaussian correlation (LGC). We propose an LGC-based bootstrap test for whether the dependence structure in financial returns across different regimes is equal. We examine this test in a Monte Carlo study, where it shows good level and power properties. We argue that this approach is more intuitive than competing approaches, typically combining regime-switching models with copula theory. Furthermore, the LGC is a semi-parametric approach, hence avoids any parametric specification of the dependence structure. We illustrate our approach using returns from the US-UK stock markets and the US stock and government bond markets. Using a two-regime model for the US-UK stock returns, the test rejects equality of the dependence structure in the two regimes. Furthermore, we find evidence of lower tail dependence in the regime associated with financial downturns in the LGC structure. For a three-regime model fitted to US stock and bond returns, the test rejects equality of the dependence structures between all regime pairs. Furthermore, we find that the LGC has a primarily positive relationship in the time period 1980-2000, mostly a negative relationship from 2000 and onwards. In addition, the regime associated with bear markets indicates less, but asymmetric dependence, clearly documenting the loss of diversification benefits in times of crisis.

The Gaussian process latent variable model (GPLVM) is a popular probabilistic method used for nonlinear dimension reduction, matrix factorization, and state-space modeling. Inference for GPLVMs is computationally tractable only when the data likelihood is Gaussian. Moreover, inference for GPLVMs has typically been restricted to obtaining maximum a posteriori point estimates, which can lead to overfitting, or variational approximations, which mischaracterize the posterior uncertainty. Here, we present a method to perform Markov chain Monte Carlo (MCMC) inference for generalized Bayesian nonlinear latent variable modeling. The crucial insight necessary to generalize GPLVMs to arbitrary observation models is that we approximate the kernel function in the Gaussian process mappings with random Fourier features; this allows us to compute the gradient of the posterior in closed form with respect to the latent variables. We show that we can generalize GPLVMs to non-Gaussian observations, such as Poisson, negative binomial, and multinomial distributions, using our random feature latent variable model (RFLVM). Our generalized RFLVMs perform on par with state-of-the-art latent variable models on a wide range of applications, including motion capture, images, and text data for the purpose of estimating the latent structure and imputing the missing data of these complex data sets.

We present a new upper limit on the cosmic molecular gas density at $z=2.4-3.4$ obtained using the first year of observations from the CO Mapping Array Project (COMAP). COMAP data cubes are stacked on the 3D positions of 243 quasars selected from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS) catalog, yielding a 95% upper limit for flux from CO(1-0) line emission of 0.129 Jy km/s. Depending on the balance of the emission between the quasar host and its environment, this value can be interpreted as an average CO line luminosity $L'_\mathrm{CO}$ of eBOSS quasars of $\leq 1.26\times10^{11}$ K km pc$^2$ s$^{-1}$, or an average molecular gas density $\rho_\mathrm{H_2}$ in regions of the universe containing a quasar of $\leq 1.52\times10^8$ M$_\odot$ cMpc$^{-3}$. The $L'_\mathrm{CO}$ upper limit falls among CO line luminosities obtained from individually-targeted quasars in the COMAP redshift range, and the $\rho_\mathrm{H_2}$ value is comparable to upper limits obtained from other Line Intensity Mapping (LIM) surveys and their joint analyses. Further, we forecast the values obtainable with the COMAP/eBOSS stack after the full 5-year COMAP Pathfinder survey. We predict that a detection is probable with this method, depending on the CO properties of the quasar sample. Based on the achieved sensitivity, we believe that this technique of stacking LIM data on the positions of traditional galaxy or quasar catalogs is extremely promising, both as a technique for investigating large galaxy catalogs efficiently at high redshift and as a technique for bolstering the sensitivity of LIM experiments, even with a fraction of their total expected survey data., Comment: 26 pages, 22 figures; Version 2 is as accepted by ApJ