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Quantized Precoding and RIS-Assisted Modulation for Integrated Sensing and Communications Systems

An RIS is introduced in the ISAC system to mitigate the effects of coarse quantization and to enable the co-existence between sensing and communication functionalities. Specifically, we design transmit precoder to obtain 1-bit sensing waveforms having a desired radiation pattern. The RIS phase shifts are then designed to modulate the 1-bit sensing waveform to transmit M-ary phase shift keying symbols to users. In this paper, we present a novel reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system with 1-bit quantization at the ISAC base station. An RIS is introduced in the ISAC system to mitigate the effects of coarse quantization and to enable the co-existence between sensing and communication functionalities. Specifically, we design transmit precoder to obtain 1-bit sensing waveforms having a desired radiation pattern. The RIS phase shifts are then designed to modulate the 1-bit sensing waveform to transmit M-ary phase shift keying symbols to users. Through numerical simulations, we show that the proposed method offers significantly improved symbol error probabilities when compared to MIMO communication systems having quantized linear precoders, while still offering comparable sensing performance as that of unquantized sensing systems.

Authors: R. S. Prasobh Sankar, Sundeep Prabhakar Chepuri.

Quantum Random Walker in Presence of a Moving Detector

The ratio of occupation probabilities of our walk to that of an Infinite walk shows a scaling behavior of $\frac{1}{n^2}$. It shows a definite scaling behavior with number of shifts $s$ also.

In this work, we study the effect of a moving detector on a discrete time one dimensional Quantum Random Walk where the movement is realized in the form of hopping/shifts. The occupation probability $f(x,t;n,s)$ is estimated as the number of detection $n$ and number of shift $s$ vary. It is seen that the occupation probability at the initial position $x_D$ of the detector is enhanced when $n$ is small which a quantum mechanical effect but decreases when $n$ is large. The ratio of occupation probabilities of our walk to that of an Infinite walk shows a scaling behavior of $\frac{1}{n^2}$. It shows a definite scaling behavior with number of shifts $s$ also. The limiting behaviours of the walk are observed when $x_D$ is large, $n$ is large and $s$ is large and the walker for these cases approach the Infinite Walk, The Semi Infinite Walk and the Quenched Quantum Walk respectively.

Authors: Md Aquib Molla, Sanchari Goswami.

DyOb-SLAM : Dynamic Object Tracking SLAM System

DyOb-SLAM creates two separate maps for both static and dynamic contents. For the static features, a sparse map is obtained. For the dynamic contents, a trajectory global map is created as output. As a result, a frame to frame real-time based dynamic object tracking system is obtained. With the pose calculation of the dynamic objects and camera, DyOb-SLAM can estimate the speed of the dynamic objects with time. Simultaneous Localization & Mapping (SLAM) is the process of building a mutual relationship between localization and mapping of the subject in its surrounding environment. With the help of different sensors, various types of SLAM systems have developed to deal with the problem of building the relationship between localization and mapping. A limitation in the SLAM process is the lack of consideration of dynamic objects in the mapping of the environment. We propose the Dynamic Object Tracking SLAM (DyOb-SLAM), which is a Visual SLAM system that can localize and map the surrounding dynamic objects in the environment as well as track the dynamic objects in each frame. With the help of a neural network and a dense optical flow algorithm, dynamic objects and static objects in an environment can be differentiated. DyOb-SLAM creates two separate maps for both static and dynamic contents. For the static features, a sparse map is obtained. For the dynamic contents, a trajectory global map is created as output. As a result, a frame to frame real-time based dynamic object tracking system is obtained. With the pose calculation of the dynamic objects and camera, DyOb-SLAM can estimate the speed of the dynamic objects with time. The performance of DyOb-SLAM is observed by comparing it with a similar Visual SLAM system, VDO-SLAM and the performance is measured by calculating the camera and object pose errors as well as the object speed error.

Authors: Rushmian Annoy Wadud, Wei Sun.

Approximate Gibbsian structure in strongly correlated point fields and generalized Gaussian zero ensembles

Gibbsian structure in random point fields has been a classical tool for studying their spatial properties. Our framework entails conditions that may be verified via finite particle approximations to the process, a phenomenon that we call an approximate Gibbs property. We demonstrate the scope of our approach by showing that a generalized Gibbs property holds with a logarithmic pair potential for the $\alpha$-GAFs for any value of $\alpha$.

Gibbsian structure in random point fields has been a classical tool for studying their spatial properties. However, exact Gibbs property is available only in a relatively limited class of models, and it does not adequately address many random fields with a strongly dependent spatial structure. In this work, we provide a general framework for approximate Gibbsian structure for strongly correlated random point fields. These include processes that exhibit strong spatial rigidity, in particular, a certain one-parameter family of analytic Gaussian zero point fields, namely the $\alpha$-GAFs. Our framework entails conditions that may be verified via finite particle approximations to the process, a phenomenon that we call an approximate Gibbs property. We show that these enable one to compare the spatial conditional measures in the infinite volume limit with Gibbs-type densities supported on appropriate singular manifolds, a phenomenon we refer to as a generalized Gibbs property. We demonstrate the scope of our approach by showing that a generalized Gibbs property holds with a logarithmic pair potential for the $\alpha$-GAFs for any value of $\alpha$. This establishes the level of rigidity of the $\alpha$-GAF zero process to be exactly $\lfloor \frac{1}{\alpha} \rfloor$, settling in the affirmative an open question regarding the existence of point processes with any specified level of rigidity. For processes such as the zeros of $\alpha$-GAFs, which involve complex, many-body interactions, our results imply that the local behaviour of the random points still exhibits 2D Coulomb-type repulsion in the short range. Our techniques can be leveraged to estimate the relative energies of configurations under local perturbations, with possible implications for dynamics and stochastic geometry on strongly correlated random point fields.

Authors: Ujan Gangopadhyay, Subhroshekhar Ghosh.

Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation

Hence, we need to design model selection techniques that do not explicitly rely on counterfactual data. However, the effectiveness of these metrics has only been studied on synthetic datasets as we can observe the counterfactual data for them. We conduct an extensive empirical analysis to judge the performance of these metrics, where we utilize the latest advances in generative modeling to incorporate multiple realistic datasets. We evaluate 9 metrics on 144 datasets for selecting between 415 estimators per dataset, including datasets that closely mimic real-world datasets. We study the problem of model selection in causal inference, specifically for the case of conditional average treatment effect (CATE) estimation under binary treatments. Unlike model selection in machine learning, we cannot use the technique of cross-validation here as we do not observe the counterfactual potential outcome for any data point. Hence, we need to design model selection techniques that do not explicitly rely on counterfactual data. As an alternative to cross-validation, there have been a variety of proxy metrics proposed in the literature, that depend on auxiliary nuisance models also estimated from the data (propensity score model, outcome regression model). However, the effectiveness of these metrics has only been studied on synthetic datasets as we can observe the counterfactual data for them. We conduct an extensive empirical analysis to judge the performance of these metrics, where we utilize the latest advances in generative modeling to incorporate multiple realistic datasets. We evaluate 9 metrics on 144 datasets for selecting between 415 estimators per dataset, including datasets that closely mimic real-world datasets. Further, we use the latest techniques from AutoML to ensure consistent hyperparameter selection for nuisance models for a fair comparison across metrics.

Authors: Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis.

betaclust: a family of mixture models for beta valued DNA methylation data

The DNA methylation process has been extensively studied for its role in cancer. There is a lack of suitable methods for modelling the beta values in their innate form. The DMCs are identified via multiple t-tests but this can be computationally expensive. The family of BMMs employs different parameter constraints that are applicable to different study settings. An R package betaclust is provided to facilitate the use of the developed BMMs.

The DNA methylation process has been extensively studied for its role in cancer. Promoter cytosine-guanine dinucleotide (CpG) island hypermethylation has been shown to silence tumour suppressor genes. Identifying the differentially methylated CpG (DMC) sites between benign and tumour samples can help understand the disease. The EPIC microarray quantifies the methylation level at a CpG site as a beta value which lies within [0,1). There is a lack of suitable methods for modelling the beta values in their innate form. The DMCs are identified via multiple t-tests but this can be computationally expensive. Also, arbitrary thresholds are often selected and used to identify the methylation state of a CpG site. We propose a family of novel beta mixture models (BMMs) which use a model-based clustering approach to cluster the CpG sites in their innate beta form to (i) objectively identify methylation state thresholds and (ii) identify the DMCs between different samples. The family of BMMs employs different parameter constraints that are applicable to different study settings. Parameter estimation proceeds via an EM algorithm, with a novel approximation during the M-step providing tractability and computational feasibility. Performance of the BMMs is assessed through a thorough simulation study, and the BMMs are used to analyse a prostate cancer dataset and an esophageal squamous cell carcinoma dataset. The BMM approach objectively identifies methylation state thresholds and identifies more DMCs between the benign and tumour samples in both cancer datasets than conventional methods, in a computationally efficient manner. The empirical cumulative distribution function of the DMCs related to genes implicated in carcinogenesis indicates hypermethylation of CpG sites in the tumour samples in both cancer settings. An R package betaclust is provided to facilitate the use of the developed BMMs.

Authors: Koyel Majumdar, Romina Silva, Antoinette Sabrina Perry, Ronald William Watson, Thomas Brendan Murphy, Isobel Claire Gormley.

Bar-Natan theory and tunneling between incompressible surfaces in 3-manifolds

The surface components are colored by elements of the Frobenius algebra. Such presentations have essentially been stated previously in work by the author and Asaeda-Frohman, using ad-hoc arguments. But they appear naturally on the background of the Bar-Natan functor and associated categorical considerations. We discuss in general presentations of colimit modules for functors into module categories in terms of minimal terminal sets of objects of the category in the categorical setting. The author defined for each (commutative) Frobenius algebra a skein module of surfaces in a $3$-manifold $M$ bounding a closed $1$-manifold $\alpha \subset \partial M$. The surface components are colored by elements of the Frobenius algebra. The modules are called the Bar-Natan modules of $(M,\alpha )$. In this article we show that Bar-Natan modules are colimit modules of functors associated to Frobenius algebras, decoupling topology from algebra. The functors are defined on a category of $3$-dimensional compression bordisms embedded in cylinders over $M$ and take values in a linear category defined from the Frobenius algebra. The relation with the $1+1$-dimensional topological quantum field theory functor associated to the Frobenius algebra is studied. We show that the geometric content of the skein modules is contained in a tunneling graph of $(M,\alpha )$, providing a natural presentation of the Bar-Natan module by application of the functor defined from the algebra. Such presentations have essentially been stated previously in work by the author and Asaeda-Frohman, using ad-hoc arguments. But they appear naturally on the background of the Bar-Natan functor and associated categorical considerations. We discuss in general presentations of colimit modules for functors into module categories in terms of minimal terminal sets of objects of the category in the categorical setting. We also introduce a $2$-category version of the Bar-Natan functor, thereby in some way Bar-Natan modules of $(M,\alpha )$.

Authors: Uwe Kaiser.

Differences in collaboration structures and impact among prominent researchers in Europe and North America

Scientists collaborate through intricate networks, which impact the quality and scope of their research. At the same time, funding and institutional arrangements, as well as scientific and political cultures, affect the structure of collaboration networks. Since such arrangements and cultures differ across regions in the world in systematic ways, we surmise that collaboration networks and impact should also differ systematically across regions. We find that prominent researchers in Europe establish denser collaboration networks, whereas those in North-America establish more decentralized networks.

Scientists collaborate through intricate networks, which impact the quality and scope of their research. At the same time, funding and institutional arrangements, as well as scientific and political cultures, affect the structure of collaboration networks. Since such arrangements and cultures differ across regions in the world in systematic ways, we surmise that collaboration networks and impact should also differ systematically across regions. To test this, we compare the structure of collaboration networks among prominent researchers in North America and Europe. We find that prominent researchers in Europe establish denser collaboration networks, whereas those in North-America establish more decentralized networks. We also find that the impact of the publications of prominent researchers in North America is significantly higher than for those in Europe, both when they collaborate with other prominent researchers and when they do not. Although Europeans collaborate with other prominent researchers more often, which increases their impact, we also find that repeated collaboration among prominent researchers decreases the synergistic effect of collaborating.

Authors: Lluis Danus, Carles Muntaner, Alexander Krauss, Marta Sales-Pardo, Roger Guimera.

Discovering a new well: Decaying dark matter with profile likelihoods

All such studies find a strong preference for either very long-lived or very short-lived dark matter. A large number of studies, all using Bayesian parameter inference from Markov Chain Monte Carlo methods, have constrained the presence of a decaying dark matter component. All such studies find a strong preference for either very long-lived or very short-lived dark matter. However, in this letter, we demonstrate that this preference is due to parameter volume effects that drive the model towards the standard $\Lambda$CDM model, which is known to provide a good fit to most observational data. Using profile likelihoods, which are free from volume effects, we instead find that the best-fitting parameters are associated with an intermediate regime where around $3 \%$ of cold dark matter decays just prior to recombination. With two additional parameters, the model yields an overall preference over the $\Lambda$CDM model of $\Delta \chi^2 \approx -2.8$ with \textit{Planck} and BAO and $\Delta \chi^2 \approx -7.8$ with the SH0ES $H_0$ measurement, while only slightly alleviating the $H_0$ tension. Ultimately, our results reveal that decaying dark matter is more viable than previously assumed, and illustrate the dangers of relying exclusively on Bayesian parameter inference when analysing extensions to the $\Lambda$CDM model.

Authors: Emil Brinch Holm, Laura Herold, Steen Hannestad, Andreas Nygaard, Thomas Tram.

Discovery of Optimal Thermometers with Spin Networks aided by Machine-Learning

Achieving this fundamental bound with realistic probes, i.e. experimentally amenable, remains an open problem.

The heat capacity $\mathcal{C}$ of a given probe is a fundamental quantity that determines, among other properties, the maximum precision in temperature estimation. In turn, $\mathcal{C}$ is limited by a quadratic scaling with the number of constituents of the probe, which provides a fundamental limit in quantum thermometry. Achieving this fundamental bound with realistic probes, i.e. experimentally amenable, remains an open problem. In this work, we exploit machine-learning techniques to discover optimal spin-network thermal probes, restricting ourselves to two-body interactions. This leads to simple architectures, which we show analytically to approximate the theoretical maximal value of $\mathcal{C}$ and maintain the optimal scaling for short- and long-range interactions. Our models can be encoded in currently available quantum annealers, and find application in other tasks requiring Hamiltonian engineering, ranging from quantum heat engines to adiabatic Grover's search.

Authors: Paolo Abiuso, Paolo Andrea Erdman, Michael Ronen, Frank Noé, Géraldine Haack, Martí Perarnau-Llobet.