Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Complex Patterns in Social, Geographic, and Economic Inequities in COVID-19 Mortality at the County Level in the US Using Generalized Additive Models (preprint)

I present three types of applications of generalized additive models (GAMs) to COVID-19 mortality rates in the US for the purpose of advancing methods to document inequities with respect to which communities suffered disproportionate COVID-19 mortality rates at specific times during the first three years of the pandemic. First, GAMs can be used to describe the changing relationship between COVID-19 mortality and county-level covariates (sociodemographic, economic, and political metrics) over time. Second, GAMs can be used to perform spatiotemporal smoothing that pools information over time and space to address statistical instability due to small population counts or stochasticity resulting in a smooth, dynamic latent risk surface summarizing the mortality risk associated with geographic locations over time. Third, estimation of COVID-19 mortality associations with county-level covariates conditional on a smooth spatiotemporal risk surface allows for more rigorous consideration of how socio-environmental contexts and policies may have impacted COVID-19 mortality. Each of these approaches provides a valuable perspective to documenting inequities in COVID-19 mortality by addressing the question of which populations have suffered the worst burden of COVID-19 mortality taking into account the nonlinear spatial, temporal, and social patterning of disease.
Full counting statistics of interacting lattice gases after an expansion: The role of condensate depletion in many-body coherence

We study the full counting statistics (FCS) of quantum gases in samples of thousands of interacting bosons, detected atom-by-atom after a long free-fall expansion. In this far-field configuration, the FCS reveals the many-body coherence from which we characterize iconic states of interacting lattice bosons, by deducing the normalized correlations $g^{(n)}(0)$ up to the order $n=6$. In Mott insulators, we find a thermal FCS characterized by perfectly-contrasted correlations $g^{(n)}(0)= n! $. In interacting Bose superfluids, we observe small deviations to the Poisson FCS and to the ideal values $g^{(n)}(0)=1$ expected for a pure condensate. To describe these deviations, we introduce a heuristic model that includes an incoherent contribution attributed to the depletion of the condensate. The predictions of the model agree quantitatively with our measurements over a large range of interaction strengths, that includes the regime where the condensate is strongly depleted by interactions. These results suggest that the condensate component exhibits a full coherence $g^{(n)}(0) =1$ at any order $n$ up to $n=6$ and at arbitrary interaction strengths. The approach demonstrated here is readily extendable to characterize a large variety of interacting quantum states and phase transitions.
Honeycomb Tessellations and Graded Permutohedral Blades

We initiate the study of certain cyclically skewed tropical hyperplanes, called permutohedral blades by A. Ocneanu, and we make connections matroid theory, tropical geometry, moduli spaces and scattering amplitudes. We study two families of piecewise constant functions on Rn−1 taking values in {0, 1} and [n] = {1, 2, . , n}, called respectively characteristic functions and graduated functions. We show that their codimension-one level sets exactly permutohedral blades. Using ring-theoretic arguments we show that a blade decomposes as a Minkowski sum of tripods and one-dimensional subspaces. For each triangulation of a cyclically oriented polygon there exists such a factorization. In the language of tropical geometry, this constructs a tropical hypersurface as a Minkowski sum of tropical lines. We use the principle of inclusion/exclusion to construct a collection of piecewise constant functions of blades which we enumerate by the dimension of the support of the function. On the induced Q-vector space one has an induced grading; this grading is compatible with various quotient spaces appearing in algebra, topology and scattering amplitudes. This vector space maps homomorphically onto the so-called ∆-algebra, which appears in the study of non-planar MHV leading singularities, leading to non-planar analogs of the square move for plabic graphs for G(2, n), called sphere moves. We give a closed formula for the graded dimension of the basis. It is shown in an Appendix by Donghyun Kim that the coefficients appearing in the numerator of the generating function for the graded dimension are symmetric, and that they sum to (2j)! j!
Zykov sums of digraphs with diachromatic number equal to their harmonious number

The dichromatic number and the diachromatic number are generalizations of the chromatic number and the achromatic number for digraphs considering acyclic colorings. In this paper, we determine the diachromatic number of digraphs arising from the Zykov sum of digraphs that admit a complete k -coloring with k = 1+ √ 1+4 m 2 for a suitable m . Consequently, the diachromatic number equals the harmonious number for every digraph in this family. In particular, we study the chromatic number, the diachromatic number, and the harmonious chromatic number of the Zykov sum of cycles.
Attachment-based CBT models for psychosis: a PPI-informed approach for acute care settings

Abstract People with psychosis often have prolonged in-patient1 admissions at high personal and economic costs. This is due in part to cognitive, affective and behavioural processes that delay recovery and discharge. For many, these processes are affected by enduring insecure attachment styles. People with insecure attachment struggle to manage strong feelings when unwell, and ward staff may struggle to know how best to offer support. Here, we outline the model of interpersonal process in cognitive therapy, and how this may be adapted to capture beliefs and behaviours associated with insecure attachment. Psychological interventions in acute care often fail due to implementation issues. For this reason, and in line with current guidance on developing complex interventions, we report on a series of Patient and Public Involvement (PPI) consultations with people with lived experience of psychosis, family members and ward staff on the potential utility of these attachment-based CBT models. The PPI meetings highlighted three themes: (1) the need to improve staff–patient interactions on wards; (2) continuity in staff–patient relationships is key to recovery; and (3) advantages and barriers to an attachment-based CBT approach. We conclude by describing how the models can be implemented in routine clinical practice, and generalised across services where interpersonal cognitive and behavioural processes may contribute to delays in people’s recovery. Key learning aims (1) We need to adapt CBT models and skills to meet the needs of people in acute care. (2) People with psychosis, family members and ward staff highlight the need to improve staff–patient interactions on wards. (3) Attachment-based CBT models may be effective in conceptualising and responding more effectively to difficult interactions in these settings.
Acoustically generated temperature gradients in short plates

Thermoacoustic heat transport and its applications, thermoacoustic engines, have been discussed in a number of articles over the past several years. Lacking from these articles is a thorough, quantitative experimental investigation of the basic theory underlying thermoacoustic heat transport. A logical starting point for such a study is to investigate the simplest class of thermoacoustic engine—a stack of short plates referred to as a ThermoAcoustic Couple (TAC). The utility of this choice is that the theory can be reduced to its simplest form for analysis of the results. The results of measurements of thermoacoustically generated temperature gradients in TACs subjected to acoustic standing waves are reported. The value of the temperature gradient, which results from an acoustically generated entropy flow in the gas in thermal contact with the TAC, is a function of the peak acoustic pressure amplitude, the mean gas pressure, the Prandtl number of the gas, the configuration of the TAC, and its position in .
Investigating the Performance of Cosine Value and Jensen-Shannon Divergence in the kNN Algorithm

K Nearest Neighbor (kNN) is a commonly-used text categorization algorithm. Previous studies mainly focused on improvements of the algorithm by modifying feature selection and k value selection. This research investigates the possibility to use Jensen-Shannon Divergence as similarity measure in the kNN classifier, and compares the performance, in terms of classification accuracy. The experiment denotes that the kNN algorithm based on Jensen-Shannon Divergence outperforms that based on Cosine value, while the performance is also largely dependent on number of categories and number of documents in a category.
Mapping onto Solutions of the Gravitational Initial Value Problem

Two approaches to the Einstein initial value problem for vacuum gravitational fields are considered. In the first, the metric of a spacelike slice is prescribed arbitrarily and it is shown that momenta satisfying the constraints can be constructed by exploiting the well‐known relation of three of the four constraints to the three‐dimensional coordinate transformation group. Specifically, it is shown that there exists a coordinate mapping of a certain specified set of functions onto momenta satisfying the constraints for a specified 3‐metric. A further interpretation of this procedure is discussed. In the second approach the 3‐geometry of a spacelike slice is specified up to a conformal factor. It is shown that, using a coordinate transformation method similar to the above, transverse traceless momenta can be constructed and that this construction depends essentially only on the conformal geometry of the spacelike hypersurface. The remaining constraint is satisfied by a choice of the conformal factor. As a result, it follows that the initial value equations can be satisfied by mapping certain specified sets of functions onto solutions by using coordinate transformations and a group of scale transformations which include conformal transformation of the metric. This is significant because the unconstrained initial data (gravitational degrees of freedom) are represented by a pair of scale‐invariant transverse, traceless tensors of weight 5 3 . These objects, in turn, give irreducible representations of the coordinate and scaling groups which are used to effect solutions of the initial value equations.
Object-based building instance segmentation from airborne LiDAR point clouds

ABSTRACT Building instance segmentation is of very importance to parallel reconstruction, management and analysis of building instance. Previous studies of building instance segmentation mainly focused on the building scenes where the building spacing is much larger than the point spacing, while the accuracy of building instance segmentation for complex buildings scenes and the building point clouds where the space between buildings is similar with point spacing is low. To improve the accuracy of building instance segmentation for complex building scenes, we propose a novel object-based building instance segmentation (OBBIS) method from airborne light detection and ranging (LiDAR) point clouds. Firstly, our proposed method divides building point clouds into objects, and then the objects are classified according to the characteristics of building roof plane objects, roof accessory objects and building facade objects. Secondly, we use node to represent object and then a fix-size feature vector is inferred for each node. Thirdly, vertical cylinder neighbour node graph is constructed. Finally, the energy function is constructed according to the relationship between the nodes, and then the objects are merged according to the energy minimum (that is, objects are merged with a minimum energy to obtain the building instances). Comprehensive experiments on benchmark datasets demonstrate that the proposed OBBIS method performs better than eight state-of-the-art building instance segmentation methods.
Isotropic ARAP Energy Using Cauchy-Green Invariants

Isotropic As-Rigid-As-Possible (ARAP) energy has been popular for shape editing, mesh parametrisation and soft-body simulation for almost two decades. However, a formulation using Cauchy-Green (CG) invariants has always been unclear, due to a rotation-polluted trace term that cannot be directly expressed using these invariants. We show how this incongruent trace term can be understood via an implicit relationship to the CG invariants. Our analysis reveals this relationship to be a polynomial where the roots equate to the trace term, and where the derivatives also give rise to closed-form expressions of the Hessian to guarantee positive semi-definiteness for a fast and concise Newton-type implicit time integration. A consequence of this analysis is a novel analytical formulation to compute rotations and singular values of deformation-gradient tensors without explicit/numerical factorization which is significant, resulting in up-to 3.5× speedup and benefits energy function evaluation for reducing solver time. We validate our energy formulation by experiments and comparison, demonstrating that our resulting eigendecomposition using the CG invariants is equivalent to existing ARAP formulations. We thus reveal isotropic ARAP energy to be a member of the "Cauchy-Green club", meaning that it can indeed be defined using CG invariants and therefore that the closed-form expressions of the resulting Hessian are shared with other energies written in their terms.
Necessary and sufficient conditions for the solvability of the inverse problem for non-self-adjoint pencils of Sturm-Liouville operators on the half-line

Non-self-adjoint Sturm-Liouville differential operators on the half-line with a boundary condition depending polynomially on the spectral parameter are studied. We investigate the inverse problem of recovering the operator from the Weyl function. For this inverse problem we provide necessary and suffcient conditions for its solvability along with a procedure for constructing its solution by the method of spectral mappings.
Machine-Learning-Based Self-Optimizing Compiler Heuristics✱

Compiler optimizations are often based on hand-crafted heuristics to guide the optimization process. These heuristics are designed to benefit the average program and are otherwise static or only customized by profiling information. We propose machine-learning-based self-optimizing compiler heuristics, a novel approach for fitting optimization decisions in a dynamic compiler to specific environments. This is done by updating a machine learning model with extracted performance data at run time. Related work—which primarily targets static compilers—has already shown that machine learning can outperform hand-crafted heuristics. Our approach is specifically designed for dynamic compilation and uses concepts such as deoptimization for transparently switching between generating data and performing machine learning decisions in single program runs. We implemented our approach in the GraalVM, a high-performance production VM for dynamic compilation. When evaluating our approach by replacing loop peeling heuristics with learned models we encountered speedups larger than 30% for several benchmarks and only few slowdowns of up to 7%.
Neural Cloth Simulation

We present a general framework for the garment animation problem through unsupervised deep learning inspired in physically based simulation. Existing trends in the literature already explore this possibility. Nonetheless, these approaches do not handle cloth dynamics. Here, we propose the first methodology able to learn realistic cloth dynamics unsupervisedly, and henceforth, a general formulation for neural cloth simulation. The key to achieve this is to adapt an existing optimization scheme for motion from simulation based methodologies to deep learning. Then, analyzing the nature of the problem, we devise an architecture able to automatically disentangle static and dynamic cloth subspaces by design. We will show how this improves model performance. Additionally, this opens the possibility of a novel motion augmentation technique that greatly improves generalization. Finally, we show it also allows to control the level of motion in the predictions. This is a useful, never seen before, tool for artists. We provide of detailed analysis of the problem to establish the bases of neural cloth simulation and guide future research into the specifics of this domain.
Analyzing and modeling the latency and jitter behavior of mixed industrial TSN and DetNet networks

Today, industrial real-time communication is commonly designed based on two key principles to satisfy the challenging Quality of Service (QoS) requirements of industrial applications: a) local communication and b) purpose-built networks. IEEE Time-Sensitive Networking (TSN) and IETF Deterministic Networking (DetNet) promise to lift these two limitations. This facilitates the transformation of previously loosely integrated automation network parts from isolated, purpose-built real-time networks to more tightly integrated, open, multi-purpose networks of networks. With TSN and DetNet, each of these interconnected networks, e.g., machine or backbone networks, can and will be fined-tuned for optimal performance regarding the different real-time applications located inside them. The resulting patchwork of DetNet-connected TSN networks, however, creates a challenge for cross-network real-time communication: predicting QoS properties, such as the end-to-end latency. To address this challenge, we propose a model that allows calculating best-case and worst-case latencies for time-critical communication across different DetNet-connected TSN networks. This enables validating end-to-end communication requirements in open, multi-purpose industrial networks. Our evaluation with real industrial hardware shows the applicability of our proposed model.
Perceptions of the Helpfulness of Unexpected Agent Assistance

Much prior work on creating social agents that assist users relies on preconceived assumptions of what it means to be helpful. For example, it is common to assume that a helpful agent just assists with achieving a user’s objective. However, as assistive agents become more widespread, human-agent interactions may be more ad-hoc, providing opportunities for unexpected agent assistance. How would this affect human notions of an agent’s helpfulness? To investigate this question, we conducted an exploratory study (N=186) where participants interacted with agents displaying unexpected, assistive behaviors in a Space Invaders game and we studied factors that may influence perceived helpfulness in these interactions. Our results challenge the idea that human perceptions of the helpfulness of unexpected agent assistance can be derived from a universal, objective definition of help. Also, humans will reciprocate unexpected assistance, but might not always consider that they are in fact helping an agent. Based on our findings, we recommend considering personalization and adaptation when designing future assistive behaviors for prosocial agents that may try to help users in unexpected situations.
Causal Relationships of the Stock Market with other Asset Classes: An Indian Perspective

The recent decade has seen a rapid rise in risk assets. Stocks, commodities, and cryptocurrencies have exploded to the upside. Global central banks have maintained interest rates at record low levels following the COVID-19 crisis. This has further acted as tailwinds for risky assets. With asset classes being increasingly interlinked with each other, useful information can be gained by studying these inter-relationships. This paper looks at the interrelationships between the Indian stock market Nifty index and some key asset classes such as Gold, Crude oil, short-term and long-term Indian government bond yields, the USD/INR exchange rate, and the cryptocurrency Bitcoin for the period January 2011 to December 2020. Co-integration analysis suggests the absence of long-run relationships between the Nifty and the asset classes studied. Granger causality analysis reveals bi-directional causality between Nifty and USD/INR and Crude oil returns. Gold returns, Bitcoin returns, and changes in short and long-term government bond yields uni-directionally granger-caused Nifty returns. Impulse response analysis reveals that shocks in each of the independent variables caused a shock in the Nifty that persisted for 1 to 3 weeks. Traders in the Nifty can monitor these shocks and accordingly fine-tune their strategies for possible moves in the Nifty.
Single-lens theta microscopy: Resolution, efficiency and working distance

In single-lens theta microscopy (SLTM), a single objective lens in combination with a mirror unit is used to achieve a theta configuration, which leads to axial and volume resolution improvements. In this type of microscope, the usable numerical aperture (NA) is not limited by the physical size of the objective lenses, as it is in two-lens theta microscopy. Although the outer parts of larger apertures are not entirely used, an increase in volume resolution by a factor of 2.9 can be achieved by increasing the NA from 0.75 to 1.1 in an orthogonal system. In addition, the angle between the faces of the mirror unit can be decreased in order to accommodate even larger NA and thereby to improve the spatial resolution further. The trade-off between resolution and collection efficiency improvements and the usable working distance in SLTM is also quantitatively discussed.
Work Stress and Mental Health: A Moderated-Mediation Model

The main purpose of the current research was to explore the effects of work stress on employees’ mental health, especially the mediation role of emotional exhaustion and the moderation role of cognitive reappraisal. A total of 446 fulltime employees completed the survey. The results of SPSS Process indicated that (a) emotional exhaustion mediated the negative effect of both challenge and hindrance stressors on mental health; (b) cognitive appraisal negatively moderated the indirect effect of challenge stressors on mental health through emotional exhaustion, that is, with higher level of cognitive reappraisal this indirect effect was lower.
Research on Data Sharing Model Based on Multi-party Secure Computing

In order to better solve the problems of industry cooperation, mutual trust and data sharing, more accurate detection and prevention of security problems hidden in the business data of the State Grid, to achieve cross-industry model sharing training and ecological construction. This research proposes a multi-party data sharing model training engine based on "blockchain + federated learning", which can be used in scenarios such as smart retail, risk assessment and satisfaction prediction to achieve multi-party privacy data sharing, build a data ecology, break data silos, and mine data. Joint value, enabling multi-party secure computing. After testing, it is found that compared with the traditional consensus mechanism, the information sharing encryption scheme proposed in the article is more complete and more efficient in encryption processing, and can be used as a shared information encryption processing tool.