Do personal conditions and circumstances surrounding partner loss explain loneliness in newly bereaved older adults?

This longitudinal study aims to explain loneliness in newly bereaved older adults, taking into account personal and circumstantial conditions surrounding the partner's death. A distinction is made between emotional and social loneliness. Data were gathered both before and after partner loss. Results were interpreted within the framework of the Theory of Mental Incongruity. The findings reveal that being unable to anticipate the partner's death is related to higher levels of emotional loneliness. Standards of instrumental support, measured indirectly by poor physical condition, lead to stronger emotional as well as social loneliness. Standards measured directly by importance attached to support or contacts result in higher emotional loneliness but, unexpectedly, in lower social loneliness. Furthermore, difficulties with establishing personal contacts, caused, for instance, by social anxiety, add to loneliness. It is concluded that circumstances related to the partner's illness may contribute to emotional loneliness after bereavement. Moreover, the results highlight the importance of taking coping attitudes into consideration for a better understanding of how newly bereaved older adults adapt to the loss of a partner

Imaginary Quadratic Class Groups and a Survey of Time-Lock Cryptographic Applications

Imaginary quadratic class groups have been proposed as one of the main hidden-order group candidates for time-lock cryptographic applications such as verifiable delay functions (VDFs). They have the advantage over RSA groups that they do \emph{not} need a trusted setup. However, they have historically been significantly less studied by the cryptographic research community. This survey provides an introduction to the theory of imaginary quadratic class groups and discusses several considerations that need to be taken into account for practical applications. In particular, we describe the relevant computational problems and the main classical and quantum algorithms that can be used to solve them. From this discussion, it follows that choosing a discriminant $\Delta=-p$ with $p\equiv 3\mod{4}$ prime is one of the most promising ways to pick a class group \CL(\Delta) without the need for a trusted setup, while simultaneously making sure that there are no easy to find elements of low order in \CL(\Delta). We provide experimental data on class groups belonging to discriminants of this form, and compare them to the Cohen-Lenstra heuristics which predict the average behaviour of \CL(\Delta) belonging to a random \emph{fundamental} discriminant. Afterwards, we describe the most prominent constructions of VDFs based on hidden-order groups, and discuss their soundness and sequentiality when implemented in imaginary quadratic class groups. Finally, we briefly touch upon the post-quantum security of VDFs in imaginary quadratic class groups, where the time on can use a fixed group is upper bounded by the runtime of quantum polynomial time order computation algorithms

Fuzzy Private Set Intersection with Large Hyperballs

Traditional private set intersection (PSI) involves a receiver and a sender holding sets $X$ and $Y$, respectively, with the receiver learning only the intersection $X\cap Y$. We turn our attention to its fuzzy variant, where the receiver holds $|X|$ hyperballs of radius $\delta$ in a metric space and the sender has $|Y|$ points. Representing the hyperballs by their center, the receiver learns the points $x\in X$ for which there exists $y\in Y$ such that $\mathsf{dist}(x,y)\leq \delta$ with respect to some distance metric. Previous approaches either require general-purpose multi-party computation (MPC) techniques like garbled circuits or fully homomorphic encryption (FHE), leak details about the sender’s precise inputs, support limited distance metrics, or scale poorly with the hyperballs\u27 volume. This work presents the first black-box construction for fuzzy PSI (including other variants such as PSI cardinality, labeled PSI, and circuit PSI), which can handle polynomially large radius and dimension (i.e., a potentially exponentially large volume) in two interaction messages, supporting general $L_{p\in[1,\infty]}$ distance, without relying on garbled circuits or FHE. The protocol excels in both asymptotic and concrete efficiency compared to existing works. For security, we solely rely on the assumption that the Decisional Diffie-Hellman (DDH) holds in the random oracle model

Amortizing Circuit-PSI in the Multiple Sender/Receiver Setting

Private set intersection (PSI) is a cryptographic functionality for two parties to learn the intersection of their input sets, without leaking any other information. Circuit-PSI is a stronger PSI functionality where the parties learn only a secret-shared form of the desired intersection, thus without revealing the intersection directly. These secret shares can subsequently serve as input to a secure multiparty computation of any function on this intersection. In this paper we consider several settings in which parties take part in multiple Circuit-PSI executions with the same input set, and aim to amortize communications and computations. To that end, we build up a new framework for Circuit-PSI around generalizations of oblivious (programmable) PRFs that are extended with offline setup phases. We present several efficient instantiations of this framework with new security proofs for this setting. As a side result, we obtain a slight improvement in communication and computation complexity over the state-of-the art Circuit-PSI protocol by Bienstock et al. (USENIX \u2723). Additionally, we present a novel Circuit-PSI protocol from a PRF with secret-shared outputs, which has linear communication and computation complexity in the parties\u27 input set sizes, and incidentally, it realizes ``almost malicious\u27\u27 security, making it the first major step in this direction since the protocol by Huang et al. (NDSS \u2712). Lastly, we derive the potential amortizations over multiple protocol executions, and observe that each of the presented instantiations is favorable in at least one of the multiple-execution settings

Amortizing Circuit-PSI in the Multiple Sender/Receiver Setting

Private set intersection (PSI) is a cryptographic functionality for two parties to learn the intersection of their input sets, without leaking any other information. Circuit-PSI is a stronger PSI functionality where the parties learn only a secret-shared form of the desired intersection, thus without revealing the intersection directly. These secret shares can subsequently serve as input to a secure multiparty computation of any function on this intersection.In this paper we consider several settings in which parties take part in multiple Circuit-PSI executions with the same input set, and aim to amortize communications and computations. To that end, we build up a new framework for Circuit-PSI around generalizations of oblivious (programmable) PRFs that are extended with offline setup phases. We present several efficient instantiations of this framework with new security proofs for this setting. As a side result, we obtain a slight improvement in communication and computation complexity over the state-of-the-art semi-honest Circuit-PSI protocol by Bienstock et al. (USENIX \u2723). Additionally, we present a novel Circuit-PSI protocol from a PRF with secret-shared outputs, which has linear communication and computation complexity in the parties\u27 input set sizes, and is able to realize a stronger security notion. Lastly, we derive the potential amortizations over multiple protocol executions, and observe that each of the presented instantiations is favorable in at least one of the multiple-execution settings. </p

Transcription profiling of rheumatic diseases

Rheumatic diseases are a diverse group of disorders. Most of these diseases are heterogeneous in nature and show varying responsiveness to treatment. Because our understanding of the molecular complexity of rheumatic diseases is incomplete and criteria for categorization are limited, we mainly refer to them in terms of group averages. The advent of DNA microarray technology has provided a powerful tool to gain insight into the molecular complexity of these diseases; this technology facilitates open-ended survey to identify comprehensively the genes and biological pathways that are associated with clinically defined conditions. During the past decade, encouraging results have been generated in the molecular description of complex rheumatic diseases, such as rheumatoid arthritis, systemic lupus erythematosus, Sjögren syndrome and systemic sclerosis. Here, we describe developments in genomics research during the past decade that have contributed to our knowledge of pathogenesis, and to the identification of biomarkers for diagnosis, patient stratification and prognostication

On time-lock cryptographic assumptions in abelian hidden-order groups

In this paper we study cryptographic finite abelian groups of unknown order and hardness assumptions in these groups. Abelian groups necessitate multiple group generators, which may be chosen at random. We formalize this setting and hardness assumptions therein. Furthermore, we generalize the algebraic group model and strong algebraic group model from cyclic groups to arbitrary finite abelian groups of unknown order. Building on these formalizations, we present techniques to deal with this new setting, and prove new reductions. These results are relevant for class groups of imaginary quadratic number fields and time-lock cryptography build upon them

Navigated intraoperative ultrasound in pediatric brain tumors

Purpose: The aim of this study was to evaluate the diagnostic value and accuracy of navigated intraoperative ultrasound (iUS) in pediatric oncological neurosurgery as compared to intraoperative magnetic resonance imaging (iMRI). Methods: A total of 24 pediatric patients undergoing tumor debulking surgery with iUS, iMRI, and neuronavigation were included in this study. Prospective acquisition of iUS images was done at two time points during the surgical procedure: (1) before resection for tumor visualization and (2) after resection for residual tumor assessment. Dice similarity coefficients (DSC), Hausdorff distances 95th percentiles (HD95) and volume differences, sensitivity, and specificity were calculated for iUS segmentations as compared to iMRI. Results: A high correlation (R = 0.99) was found for volume estimation as measured on iUS and iMRI before resection. A good spatial accuracy was demonstrated with a median DSC of 0.72 (IQR 0.14) and a median HD95 percentile of 4.98 mm (IQR 2.22 mm). The assessment after resection demonstrated a sensitivity of 100% and a specificity of 84.6% for residual tumor detection with navigated iUS. A moderate accuracy was observed with a median DSC of 0.58 (IQR 0.27) and a median HD95 of 5.84 mm (IQR 4.04 mm) for residual tumor volumes. Conclusion: We found that iUS measurements of tumor volume before resection correlate well with those obtained from preoperative MRI. The accuracy of residual tumor detection was reliable as compared to iMRI, indicating the suitability of iUS for directing the surgeon’s attention to areas suspect for residual tumor. Therefore, iUS is considered as a valuable addition to the neurosurgical armamentarium. Trial registration number and date: PMCLAB2023.476, February 12th 2024.</p