Probability of Default as a Function of Correlation: The Problem of Non-uniqueness

It is common practise in industry for traders to use copula models combined with observed market prices to calculate implied correlations for firm defaults. The actual feasibility of this calculation depends on the assumption that there is a one-to-one mapping between values of CDO tranches and the correlation implicit in the copula. This paper presents several proofs which demonstrate that for sufficiently large portfolios of underlying credits the probability of certain number of default are hump shaped as a function of the correlation. We follow our analytical results with some numerical examples of pricing CDOs demonstrating the non-uniqueness problem of implied correlations

Shrinkage Function And Its Applications In Matrix Approximation

The shrinkage function is widely used in matrix low-rank approximation, compressive sensing, and statistical estimation. In this article, an elementary derivation of the shrinkage function is given. In addition, applications of the shrinkage function are demonstrated in solving several well-known problems, together with a new result in matrix approximation

Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semisupervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by Li- DAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a 2.3× reduction in model parameters and 641× fewer multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More)

Arsenite-Induced Alterations of DNA Photodamage Repair and Apoptosis After Solar-Simulation UVR in Mouse Keratinocytes in Vitro

Our laboratory has shown that arsenite markedly increased the cancer rate caused by solar-simulation ultraviolet radiation (UVR) in the hairless mouse skin model. In the present study, we investigated how arsenite affected DNA photodamage repair and apoptosis after solar-simulation UVR in the mouse keratinocyte cell line 291.03C. The keratinocytes were treated with different concentrations of sodium arsenite (0.0, 2.5, 5.0 μM) for 24 hr and then were immediately irradiated with a single dose of 0.30 kJ/m(2) UVR. At 24 hr after UVR, DNA photoproducts [cyclobutane pyrimidine dimers (CPDs) and 6–4 photoproducts (6-4PPs)] and apoptosis were measured using the enzyme-linked immunosorbent assay and the two-color TUNEL (terminal deoxynucleotide transferase dUTP nick end labeling) assay, respectively. The results showed that arsenite reduced the repair rate of 6-4PPs by about a factor of 2 at 5.0 μM and had no effect at 2.5 μM. UVR-induced apoptosis at 24 hr was decreased by 22.64% at 2.5 μM arsenite and by 61.90% at 5.0 μM arsenite. Arsenite decreased the UVR-induced caspase-3/7 activity in parallel with the inhibition of apoptosis. Colony survival assays of the 291.03C cells demonstrate a median lethal concentration (LC(50)) of arsenite of 0.9 μM and a median lethal dose (LD(50)) of UVR of 0.05 kJ/m(2). If the present results are applicable in vivo, inhibition of UVR-induced apoptosis may contribute to arsenite’s enhancement of UVR-induced skin carcinogenesis

Robust visual tracking via speedup multiple kernel ridge regression

Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize the nonlinear structure well. We propose an appearance model based on kernel ridge regression for visual tracking. Dense sampling is fulfilled around the target image patches to collect the training samples. In order to obtain a kernel space in favor of describing the target appearance, multiple kernel learning is introduced into the selection of kernels. Under the framework, instead of a single kernel, a linear combination of kernels is learned from the training samples to create a kernel space. Resorting to the circulant property of a kernel matrix, a fast interpolate iterative algorithm is developed to seek coefficients that are assigned to these kernels so as to give an optimal combination. After the regression function is learned, all candidate image patches gathered are taken as the input of the function, and the candidate with the maximal response is regarded as the object image patch. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art tracking methods

The role of membrane thickness in charged protein–lipid interactions

AbstractCharged amino acids are known to be important in controlling the actions of integral and peripheral membrane proteins and cell disrupting peptides. Atomistic molecular dynamics studies have shed much light on the mechanisms of membrane binding and translocation of charged protein groups, yet the impact of the full diversity of membrane physico-chemical properties and topologies has yet to be explored. Here we have performed a systematic study of an arginine (Arg) side chain analog moving across saturated phosphatidylcholine (PC) bilayers of variable hydrocarbon tail length from 10 to 18 carbons. For all bilayers we observe similar ion-induced defects, where Arg draws water molecules and lipid head groups into the bilayers to avoid large dehydration energy costs. The free energy profiles all exhibit sharp climbs with increasing penetration into the hydrocarbon core, with predictable shifts between bilayers of different thickness, leading to barrier reduction from 26kcal/mol for 18 carbons to 6kcal/mol for 10 carbons. For lipids of 10 and 12 carbons we observe narrow transmembrane pores and corresponding plateaus in the free energy profiles. Allowing for movements of the protein and side chain snorkeling, we argue that the energetic cost for burying Arg inside a thin bilayer will be small, consistent with recent experiments, also leading to a dramatic reduction in pKa shifts for Arg. We provide evidence that Arg translocation occurs via an ion-induced defect mechanism, except in thick bilayers (of at least 18 carbons) where solubility-diffusion becomes energetically favored. Our findings shed light on the mechanisms of ion movement through membranes of varying composition, with implications for a range of charged protein–lipid interactions and the actions of cell-perturbing peptides. This article is part of a Special Issue entitled: Membrane protein structure and function

Shaping the Emerging Norms of Using Large Language Models in Social Computing Research

The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analyzing vast amounts of textual data and generating human-like responses, enabling researchers to delve into complex social phenomena. On the other hand, concerns are emerging regarding the validity, privacy, and ethics of the research when LLMs are involved. This SIG aims at offering an open space for social computing researchers who are interested in understanding the impacts of LLMs to discuss their current practices, perspectives, challenges when engaging with LLMs in their everyday work and collectively shaping the emerging norms of using LLMs in social computing research

An Empathy-Based Sandbox Approach to Bridge Attitudes, Goals, Knowledge, and Behaviors in the Privacy Paradox

The "privacy paradox" describes the discrepancy between users' privacy attitudes and their actual behaviors. Mitigating this discrepancy requires solutions that account for both system opaqueness and users' hesitations in testing different privacy settings due to fears of unintended data exposure. We introduce an empathy-based approach that allows users to experience how privacy behaviors may alter system outcomes in a risk-free sandbox environment from the perspective of artificially generated personas. To generate realistic personas, we introduce a novel pipeline that augments the outputs of large language models using few-shot learning, contextualization, and chain of thoughts. Our empirical studies demonstrated the adequate quality of generated personas and highlighted the changes in privacy-related applications (e.g., online advertising) caused by different personas. Furthermore, users demonstrated cognitive and emotional empathy towards the personas when interacting with our sandbox. We offered design implications for downstream applications in improving user privacy literacy and promoting behavior changes