Calibration of Time-Series Forecasting Transformers: Detecting and Adapting Context-Driven Distribution Shift

Recent years have witnessed the success of introducing Transformers to time series forecasting. From a data generation perspective, we illustrate that existing Transformers are susceptible to distribution shifts driven by temporal contexts, whether observed or unobserved. Such context-driven distribution shift (CDS) introduces biases in predictions within specific contexts and poses challenges for conventional training paradigm. In this paper, we introduce a universal calibration methodology for the detection and adaptation of CDS with a trained Transformer model. To this end, we propose a novel CDS detector, termed the "residual-based CDS detector" or "Reconditionor", which quantifies the model's vulnerability to CDS by evaluating the mutual information between prediction residuals and their corresponding contexts. A high Reconditionor score indicates a severe susceptibility, thereby necessitating model adaptation. In this circumstance, we put forth a straightforward yet potent adapter framework for model calibration, termed the "sample-level contextualized adapter" or "SOLID". This framework involves the curation of a contextually similar dataset to the provided test sample and the subsequent fine-tuning of the model's prediction layer with a limited number of steps. Our theoretical analysis demonstrates that this adaptation strategy is able to achieve an optimal equilibrium between bias and variance. Notably, our proposed Reconditionor and SOLID are model-agnostic and readily adaptable to a wide range of Transformers. Extensive experiments show that SOLID consistently enhances the performance of current SOTA Transformers on real-world datasets, especially on cases with substantial CDS detected by the proposed Reconditionor, thus validate the effectiveness of the calibration approach

Rashba spin-orbit coupling enhanced magnetoresistance in junctions with one ferromagnet

We explain how Rashba spin-orbit coupling (SOC) in a two-dimensional electron gas (2DEG), or in a conventional $s$-wave superconductor, can lead to a large magnetoresistance even with one ferromagnet. However, such enhanced magnetoresistance is not generic and can be nonmonotonic and change its sign with Rashba SOC. For an in-plane rotation of magnetization, it is typically negligibly small for a 2DEG and depends on the perfect transmission which emerges from a spin-parity-time symmetry of the scattering states, while this symmetry is generally absent from the Hamiltonian of the system. The key difference from considering the normal-state magnetoresistance is the presence of the spin-dependent Andreev reflection at superconducting interfaces. In the fabricated junctions of quasi-2D van der Waals ferromagnets with conventional $s$-wave superconductors (Fe$_{0.29}$TaS$_2$/NbN) we find another example of enhanced magnetoresistance where the presence of Rashba SOC reduces the effective interfacial strength and is responsible for an equal-spin Andreev reflection. The observed nonmonotonic trend in the out-of-plane magnetoresistance with the interfacial barrier is an evidence for the proximity-induced equal-spin-triplet superconductivity.Comment: This work is submitted to the special issue of Phys. Rev. B dedicated to Professor Emmanuel Rashb

Demystifying Compiler Unstable Feature Usage and Impacts in the Rust Ecosystem

Rust programming language is gaining popularity rapidly in building reliable and secure systems due to its security guarantees and outstanding performance. To provide extra functionalities, the Rust compiler introduces Rust unstable features (RUF) to extend compiler functionality, syntax, and standard library support. However, these features are unstable and may get removed, introducing compilation failures to dependent packages. Even worse, their impacts propagate through transitive dependencies, causing large-scale failures in the whole ecosystem. Although RUF is widely used in Rust, previous research has primarily concentrated on Rust code safety, with the usage and impacts of RUF from the Rust compiler remaining unexplored. Therefore, we aim to bridge this gap by systematically analyzing the RUF usage and impacts in the Rust ecosystem. We propose novel techniques for extracting RUF precisely, and to assess its impact on the entire ecosystem quantitatively, we accurately resolve package dependencies. We have analyzed the whole Rust ecosystem with 590K package versions and 140M transitive dependencies. Our study shows that the Rust ecosystem uses 1000 different RUF, and at most 44% of package versions are affected by RUF, causing compiling failures for at most 12%. To mitigate wide RUF impacts, we further design and implement a RUF-compilation-failure recovery tool that can recover up to 90% of the failure. We believe our techniques, findings, and tools can help to stabilize the Rust compiler, ultimately enhancing the security and reliability of the Rust ecosystem.Comment: Published in ICSE'2024 Conference: https://conf.researchr.org/details/icse-2024/icse-2024-research-track/6/Demystifying-Compiler-Unstable-Feature-Usage-and-Impacts-in-the-Rust-Ecosystem. Project webiste: https://sites.google.com/view/ruf-study/home. Released Source Code Zonodo: https://zenodo.org/records/828937

Molecular cloning and function of two tumor necrosis factor receptor-associated factors genes (TRAF2 and TRAF4) from Pinctada fucata martensii

Tumor necrosis factor receptor-associated factors (TRAFs) have been studied in a few mollusks and participate in various biological processes, like apoptosis, immune response, stress, and inflammatory response. However, TRAFs’ function and mechanism of pearl oysters (Pinctada fucata martensii) are still unclear. In this study, the novel PmTRAF2 and PmTRAF4 from P. f. martensii were cloned by rapid amplification of complementary DNA ends and their mRNA expression were analyzed by quantitative real-time PCR (qPCR). The interacting protein of PmTRAF2 was verified by the yeast two-hybrid assay. The result shows that full-length of PmTRAF2 and PmTRAF4 cDNA were 2055 bp and 2365 bp, respectively. The deduced PmTRAF2 and PmTRAF4 proteins contain TRAF-type zinc finger domain and MATH domain, while PmTRAF4 lacks a RING finger domain. Multiple sequence alignment revealed that PmTRAF2 and PmTRAF4 had high homology with the ortholog of other species. Phylogenic analysis indicated that PmTRAF4 clustered with the homolog protein of Mytilus edulis and Mytilus galloprovincialis, and PmTRAF2 has the closest genetic relationship to Crassostrea gigas TRAF2. The qPCR analysis revealed that PmTRAF2 and PmTRAF4 were expressed in all six tissues, and both of them were significantly expressed in hepatopancreas and gill (p < 0.01). Under lipopolysaccharide (LPS) stimulation, polyinosinic acid (PolyI:C) stimulation, and nucleus insertion surgery, the transcripts of PmTRAF2, PmTRAF3, PmTRAF4 and PmTRAF6 in hepatopancreas were markedly changed at corresponding time points. These results have indicated that these genes may play a role in P. f. martensii innate immunity. Yeast two-hybrid assays show that PmTRAF2 interacts with PmTRAF6 but not PmTRAF3, potentially affecting downstream immune signaling pathways. Our findings provide new perspectives for further investigation of TRAFs’ immune mechanisms in bivalves

Content-aware Neural Hashing for Cold-start Recommendation

Content-aware recommendation approaches are essential for providing meaningful recommendations for \textit{new} (i.e., \textit{cold-start}) items in a recommender system. We present a content-aware neural hashing-based collaborative filtering approach (NeuHash-CF), which generates binary hash codes for users and items, such that the highly efficient Hamming distance can be used for estimating user-item relevance. NeuHash-CF is modelled as an autoencoder architecture, consisting of two joint hashing components for generating user and item hash codes. Inspired from semantic hashing, the item hashing component generates a hash code directly from an item's content information (i.e., it generates cold-start and seen item hash codes in the same manner). This contrasts existing state-of-the-art models, which treat the two item cases separately. The user hash codes are generated directly based on user id, through learning a user embedding matrix. We show experimentally that NeuHash-CF significantly outperforms state-of-the-art baselines by up to 12\% NDCG and 13\% MRR in cold-start recommendation settings, and up to 4\% in both NDCG and MRR in standard settings where all items are present while training. Our approach uses 2-4x shorter hash codes, while obtaining the same or better performance compared to the state of the art, thus consequently also enabling a notable storage reduction.Comment: Accepted to SIGIR 202

Epidemic characteristics and transmission risk prediction of brucellosis in Xi'an city, Northwest China

Human brucellosis (HB) has re-emerged in China since the mid-1990s, and exhibited an apparent geographic expansion shifted from the traditional livestock regions to the inland areas of China. It is often neglected in non-traditional epidemic areas, posing a serious threat to public health in big cities. We carried out a retrospective epidemiological study in Xi'an, the largest city in northwestern China. It utilizes long-term surveillance data on HB during 2008–2021 and investigation data during 2014–2021. A total of 1989 HB cases were reported in Xi'an, consisting of 505 local cases, i.e., those located in Xi'an and 1,484 non-local cases, i.e., those located in other cities. Significantly epidemiological heterogeneity was observed between them, mainly owing to differences in the gender, occupation, diagnostic delays, and reporting institutions. Serological investigations suggested that 59 people and 1,822 animals (sheep, cattle, and cows) tested positive for brucellosis from 2014 to 2021, with the annual average seroprevalence rates were 1.38 and 1.54%, respectively. The annual animal seroprevalence rate was positively correlated with the annual incidence of non-local HB cases. Multivariate boosted regression tree models revealed that gross domestic product, population density, length of township roads, number of farms, and nighttime lights substantially contributed to the spatial distribution of local HB. Approximately 7.84 million people inhabited the potential infection risk zones in Xi'an. Our study highlights the reemergence of HB in non-epidemic areas and provides a baseline for large and medium-sized cities to identify regions, where prevention and control efforts should be prioritized in the future

Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer

ObjectivesThe purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients.MethodsAfter rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively.ResultsSix radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS.ConclusionsOur radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients