bluuebunny/crossref_metadata_2025 huggingface dataset:
Vision impairment and differential access to eye health services in Aotearoa New Zealand: a scoping review
Authors: Rogers Jaymie Tingkham , Black Joanna , Harwood Matire , Wilkinson Ben , Kandel Himal , Ramke Jacqueline
Published: March 2024

Introduction

In Aotearoa New Zealand, Māori and Pacific People experience worse health outcomes compared with other New Zealanders. No population-based eye health survey has been conducted, and eye health services do not generate routine monitoring reports, so the extent of eye health inequality is unknown. This information is required to plan equitable eye health services. In this scoping review, we aimed to summarise the nature and extent of the evidence reporting vision impairment, its main causes and access to eye health services by ethnicity in New Zealand.

Methods

This scoping review was reported according to Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. An information specialist conducted a search on MEDLINE and Embase databases in October 2022. Included studies reported outcomes among any population group resident in New Zealand or attendees at New Zealand health facilities. Data screening, full-text review and data extraction were performed independently by two authors. We summarised the characteristics of studies and outcomes, and the results were synthesised narratively.

Results

Our search identified 2711 reports, of which 53 (from 47 studies) were included. We mapped 72 outcomes, many of which were access-related (n=32), published since 2000 (n=28) and largely focused on diabetic retinopathy (n=21) or cataract (n=13) in facility-based settings (n=18). Over two-thirds of reported outcomes were disaggregated by at least two ethnicities. When outcomes were disaggregated by ethnicity, Māori and Pacific People were consistently included, and experienced worse access and outcomes compared with other New Zealanders.

Conclusion

The findings of this review highlight the presence of ethnic disparity in access to diabetic retinopathy and cataract services. Closing the evidence gap identified for refractive error, glaucoma and macular degeneration service coverage could be a priority for future research. Furthermore, future research can be strengthened to enable consistent monitoring of eye health service coverage over time.
Prevalence and factors associated with malnutrition on patients with cancer in Bangladesh: a cross-sectional study
Authors: Mohsin Faroque Md , Rahman Md Sorforajur , Shahjalal Md
Published: March 2024

Introduction

Malnutrition is related to the development of chronic diseases, including cancer and is a major cause of mortality in patients with cancer. The study aimed to understand the prevalence and factors associated with malnutrition among patients with cancer.

Methods

This cross-sectional study investigated the prevalence of malnutrition among patients with cancer and its relationship with sociodemographic status and treatments. Patients’ nutritional diagnosis was performed using the Patient-Generated Subjective Global Assessment (PG-SGA) tool. The performance status (PS) was obtained from the scale developed by the Eastern Cooperative Oncology Group (ECOG). Data were collected from two hospitals from January to March 2023. A χ2test and multinominal regression analysis were performed by SPSS V.25.

Results

Out of 275 patients, 164 (60%) were male, with the mean age of 49.54 years (SD 15.61). Of these, 184 patients (67%) were malnourished of whom 149 patients (54%) had moderate and 35 patients (13%) had severe malnutrition. Patients’ age, sex, PS, cancer site, treatment modalities, duration and hospitalisation were significantly associated with nutritional status (p<0.05). Patients between the ages of 40 and 60 were 2.96 times more likely to be malnourished compared with those younger than 40 (adjusted odd ratio (AOR) 2.96; 95% CI 1.40 to 6.24). Female patients had 7.74 times higher risk of malnutrition compared with male patients (AOR 7.74; 95% CI 2.03 to 19.80). Malnutrition was 2.6 times higher for surgical patients compared with nonsurgical ones (AOR 2.60; 95% CI 1.29 to 5.26). The risk of malnutrition was 4.06 times greater in patients treated longer than 12 months compared with those treated less than 6 months (AOR 4.06; 95% CI 1.82 to 9.08).

Conclusion

There is a high prevalence of malnutrition among patients with cancer, and it is essential to include regular nutritional assessment in the treatment process for better outcomes.
ABCC2p.R393W variant contributes to Dubin-Johnson syndrome by targeting MRP2 to proteasome degradation
Authors: Sun Rong-Yue , Chen Yi-Ming , Zhu Mian-Mian , Sun Ji-an , Wang Hong-Wei , Wu Chen-Yu , Zhu Ting , Gong Yu-Jing , Lu Chao-Sheng , Ronzoni Luisa , Valenti Luca , Zheng Ming-Hua , Wang Dan
Published: March 2024

Background

Dubin-Johnson syndrome (DJS), a rare autosomal recessive liver condition, is caused by biallelic loss-of-function mutations of theABCC2gene. This study aimed to investigate genetic variations in the drug efflux transporterABCC2(MRP2) gene in patients with DJS and to characterise the expression and mechanism of theABCC2gene variant.

Methods

Trio whole exome sequencing was performed in the family to identify the genetic causes. Bioinformatics analysis was performed to assess pathogenicity. Inin vitroexperiments, site-directed mutagenesis was used to introduceABCC2variants in constructs then expressed in HEK293T, HuH-7 and HepG2 cell lines. The expression of total and cell membrane MRP2 was quantified in cells expressing the wild-type or variant forms. Chloroquine and MG132 were used to evaluate the effects of p.R393W on lysosomal and/or proteasomal degradation.

Results

The twin probands carry DJS-associated variants c.1177C>T (rs777902199) in theABCC2gene inherited from the father and the c.3632T>C mutation in the other allele inherited from the mother. TheABCC2variant, c.1177C>T, results in a p.R393W substitution in MRP2 that is highly conserved among vertebrates, drastically decreasing the expression of mutant protein by promoting proteasomal degradation. Another variant c.3632T>C results in a p.L1211P substitution in MRP2, decreasing the expression of membrane MRP2 but not changing the expression of total protein.

Conclusion

These results strongly suggest that the p.R393W variant affects the stability of the MRP2 protein and decreases its expression by ubiquitin-mediated proteasomal degradation, and the p.L1211P decreases the expression of membrane MRP2, indicating that these two variants, respectively, cause a loss-of-function of the MRP2 protein and membrane MRP2 ultimately leading to DJS development.
Incidence and clinical manifestations of giant cell arteritis in Spain: results of the ARTESER register
Authors: Fernández-Lozano Delia , Hernández-Rodríguez Iñigo , Narvaez Javier , Domínguez-Álvaro Marta , De Miguel Eugenio , Silva-Díaz Maite , Belzunegui Joaquín María , Moriano Morales Clara , Sánchez Julio , Galíndez-Agirregoikoa Eva , Aldaroso Vicente , Abasolo Lydia , Loricera Javier , Garrido-Puñal Noemi , Moya Alvarado Patricia , Larena Carmen , Navarro Vanessa Andrea , Calvet Joan , Casafont-Solé Ivette , Ortiz-Sanjuán Francisco , Salman Monte Tarek Carlos , Castañeda Santos , Blanco Ricardo
Published: March 2024

Objective

This study aimed to estimate the incidence of giant cell arteritis (GCA) in Spain and to analyse its clinical manifestations, and distribution by age group, sex, geographical area and season.

Methods

We included all patients diagnosed with GCA between 1 June 2013 and 29 March 2019 at 26 hospitals of the National Health System. They had to be aged ≥50 years and have at least one positive results in an objective diagnostic test (biopsy or imaging techniques), meet 3/5 of the 1990 American College of Rheumatology classification criteria or have a clinical diagnosis based on the expert opinion of the physician in charge. We calculated incidence rate using Poisson regression and assessed the influence of age, sex, geographical area and season.

Results

We identified 1675 cases of GCA with a mean age at diagnosis of 76.9±8.3 years. The annual incidence was estimated at 7.42 (95% CI 6.57 to 8.27) cases of GCA per 100 000 people ≥50 years with a peak for patients aged 80–84 years (23.06 (95% CI 20.89 to 25.4)). The incidence was greater in women (10.06 (95% CI 8.7 to 11.5)) than in men (4.83 (95% CI 3.8 to 5.9)). No significant differences were found between geographical distribution and incidence throughout the year (p=0.125). The phenotypes at diagnosis were cranial in 1091 patients, extracranial in 337 patients and mixed in 170 patients.

Conclusions

This is the first study to estimate the incidence of GCA in Spain at a national level. We found a predominance among women and during the ninth decade of life with no clear variability according to geographical area or seasons of the year.
The weak cosmic censorship conjecture in hairy Kerr black holes
Authors: Zhao Lai , Tang Meirong , Xu Zhaoyi
Published: March 2024

The weak cosmic censorship conjecture, since its proposal, has always been a controversial hypothesis, but its significance in astrophysics is undeniable. For a regular black hole, its center does not contain a singularity, and the destruction of the horizon of such black holes is not protected by the weak cosmic censorship conjecture. Therefore, we employ Gedanken experiments to study the hairy Kerr black holes, which are promising candidates to serve as ”simulators” of astrophysical black holes. By investigating these black holes through testing particles and scalar fields carrying large angular momentum, we explore whether these black holes can achieve overspinning. Our results suggest that the overspinning behavior of these hairy Kerr black holes in extremal or near-extremal conditions strongly depends on the hairy parameters ($${\alpha , l_0}$$

α
,

l
0

). This not only potentially offers us an opportunity to explore the interior structure of black holes, but may also provide clues for constraining the hairy parameters. This phenomenon might reveal the connection between the no-hair theorem of black holes and the weak cosmic censorship conjecture, bringing new perspectives to our understanding of these theories.
COMPARATIVE THERMAL KINETIC ANALYSIS OF NICKEL HYDROXIDE NANOPARTICLES THROUGH CHEMICAL PRECIPITATION AND SOL–GEL ROUTES
Authors: MOHAIDEEN H. MOHAMED , SIVABALAN G. , NATARAJAN B.
Published: March 2024

In most of the chemical methods, the as-prepared NPs have a metal hydroxide form. The calcination temperature is very important to prepare the metal oxide from metal hydroxide. Thus, in this work, the chemical precipitation and sol–gel routes were employed to produce the Nickel hydroxide nanoparticles and analyze their functional, structural, thermal, and morphological behavior of prepared NPs. The formation of Ni(OH)2 is confirmed by FTIR analysis including both techniques. Rietveld refinement techniques were used to diffraction patterns by the program FullProf. The well-matched XRD patterns reveal that prepared NPs have [Formula: see text]-Ni(OH)2 phase and hexagonal structure (HCP) with a space group of P3m1 for both the methods. The kinetic factors were calculated using different models. The Phadnis–Deshpande model is employed to identify the mechanism of solid state kinetic reaction. Solid state kinetic model observes nucleation and nuclei growth (Avrami–Erofeev nuclei growth) and 2D diffusion mechanism for chemical precipitation and sol–gel, respectively. From the FESEM analysis, flower-shaped architectures and nanosheet morphology are presented in the prepared sample by chemical precipitation and sol–gel, respectively. From the kinetic parameters, we conclude that the decomposition of Ni(OH)2 to NiO is thermally stable, has slow reaction and spontaneous process at 300∘C. The results show that the chemical precipitation method is more suitable for energy storage application compare to the Sol–gel method.
Optimizing Distributed Protocols with Query Rewrites
Authors: Chu David C.Y. , Panchapakesan Rithvik , Laddad Shadaj , Katahanas Lucky E. , Liu Chris , Shivakumar Kaushik , Crooks Natacha , Hellerstein Joseph M. , Howard Heidi
Published: March 2024

Distributed protocols such as 2PC and Paxos lie at the core of many systems in the cloud, but standard implementations do not scale. New scalable distributed protocols are developed through careful analysis and rewrites, but this process is ad hoc and error-prone. This paper presents an approach for scaling any distributed protocol by applying rule-driven rewrites, borrowing from query optimization. Distributed protocol rewrites entail a new burden: reasoning about spatiotemporal correctness. We leverage order-insensitivity and data dependency analysis to systematically identify correct coordination-free scaling opportunities. We apply this analysis to create preconditions and mechanisms for coordination-free decoupling and partitioning, two fundamental vertical and horizontal scaling techniques. Manual rule-driven applications of decoupling and partitioning improve the throughput of 2PC by 5× and Paxos by 3×, and match state-of-the-art throughput in recent work. These results point the way toward automated optimizers for distributed protocols based on correct-by-construction rewrite rules.
FedKNN: Secure Federated k-Nearest Neighbor Search
Authors: Zhang Xinyi , Wang Qichen , Xu Cheng , Peng Yun , Xu Jianliang
Published: March 2024

Nearest neighbor search is a fundamental task in various domains, such as federated learning, data mining, information retrieval, and biomedicine. With the increasing need to utilize data from different organizations while respecting privacy regulations, private data federation has emerged as a promising solution. However, it is costly to directly apply existing approaches to federated k-nearest neighbor (kNN) search with difficult-to-compute distance functions, like graph or sequence similarity. To address this challenge, we propose FedKNN, a system that supports secure federated kNN search queries with a wide range of similarity measurements. Our system is equipped with a new Distribution-Aware kNN (DANN) algorithm to minimize unnecessary local computations while protecting data privacy. We further develop DANN*, a secure version of DANN that satisfies differential obliviousness. Extensive evaluations show that FedKNN outperforms state-of-the-art solutions, achieving up to 4.8× improvement on federated graph kNN search and up to 2.7× improvement on federated sequence kNN search. Additionally, our approach offers a trade-off between privacy and efficiency, providing strong privacy guarantees with minimal overhead.
Determining Exact Quantiles with Randomized Summaries
Authors: Chen Ziling , Guan Haoquan , Song Shaoxu , Huang Xiangdong , Wang Chen , Wang Jianmin
Published: March 2024

Quantiles are fundamental statistics in various data science tasks, but costly to compute, e.g., by loading the entire data in memory for ranking. With limited memory space, prevalent in end devices or databases with heavy loads, it needs to scan the data in multiple passes. The idea is to gradually shrink the range of the queried quantile till it is small enough to fit in memory for ranking the result. Existing methods use deterministic sketches to determine the exact range of quantile, known as deterministic filter, which could be inefficient in range shrinking. In this study, we propose to shrink the ranges more aggressively, using randomized summaries such as KLL sketch. That is, with a high probability the quantile lies in a smaller range, namely probabilistic filter, determined by the randomized sketch. Specifically, we estimate the expected passes for determining the exact quantiles with probabilistic filters, and select a proper probability that can minimize the expected passes. Analyses show that our exact quantile determination method can terminate in P passes with 1-δ confidence, storing O(N 1/P logP-1/2P (1/δ)) items, close to the lower bound Ømega(N1/P) for a fixed δ. The approach has been deployed as a function in an LSM-tree based time-series database Apache IoTDB. Remarkably, the randomized sketches can be pre-computed for the immutable SSTables in LSM-tree. Moreover, multiple quantile queries could share the data passes for probabilistic filters in range estimation. Extensive experiments on real and synthetic datasets demonstrate the superiority of our proposal compared to the existing methods with deterministic filters. On average, our method takes 0.48 fewer passes and 18% of the time compared with the state-of-the-art deterministic sketch (GK sketch).
An LDP Compatible Sketch for Securely Approximating Set Intersection Cardinalities
Authors: Wang Pinghui , Liu Yitong , Li Zhicheng , Li Rundong
Published: March 2024

Given two sets of elements held by two different parties separately, computing the cardinality (i.e., the number of distinct elements) of their intersection set is a fundamental task in applications such as network monitoring and database systems. To handle large sets with limited space, computation, and communication costs, lightweight probabilistic methods (i.e., sketch methods) such as the Flajolet-Martin (FM) sketch and the HyperLogLog (HLL) sketch are extensively used. However, when a set's probabilistic data summary and the hash functions used to construct the sketch are disclosed to an untrusted third party, the set's privacy is compromised. Directly applyingLocal Differential Privacy (LDP) techniques to safeguard the sketch collection results in extremely large estimation errors of set intersection cardinalities. To address this issue, we propose a novel sketch method that makes it easier to incorporate noise into the constructed sketch to achieve differential privacy. More importantly, our sketch method is compatible with the LDP noise. In other words, the probabilistic model underlying our LDP-based data summary is quite basic, allowing us to eliminate the estimation error generated by the noise. We perform extensive experiments on various synthetic and real-world datasets and the experimental results demonstrate that our method is orders of magnitude more accurate and several times faster than state-of-the-art methods.
Controllable Tabular Data Synthesis Using Diffusion Models
Authors: Liu Tongyu , Fan Ju , Tang Nan , Li Guoliang , Du Xiaoyong
Published: March 2024

Controllable tabular data synthesis plays a crucial role in numerous applications by allowing users to generate synthetic data with specific conditions. These conditions can include synthesizing tuples with predefined attribute values or creating tuples that exhibit a particular correlation with an external table. However, existing approaches lack the flexibility to support new conditions and can be time-consuming when dealing with multiple conditions. To overcome these limitations, we propose a novel approach that leverages diffusion models to first learn an unconditional generative model. Subsequently, we introduce lightweight controllers to guide the unconditional generative model in generating synthetic data that satisfies different conditions. The primary research challenge lies in effectively supporting controllability using lightweight solutions while ensuring the realism of the synthetic data. To address this challenge, we design an unconditional diffusion model tailored specifically for tabular data. Additionally, we propose a new sampling method that enables correlation-aware controls throughout the data generation process. We conducted extensive experiments across various applications for controllable tabular data synthesis, which show that our approach outperforms the state-of-the-art methods.
Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines
Authors: Chatterjee Subarna , Pekala Mark F. , Kruglyak Lev , Idreos Stratos
Published: March 2024

We present Limousine, a self-designing key-value storage engine, that can automatically morph to the near-optimal storage engine architecture shape given a workload, a cloud budget, and target performance. At its core, Limousine identifies the fundamental design principles of storage engines as combinations of learned and classical data structures that collaborate through algorithms for data storage and access. By unifying these principles over diverse hardware and three major cloud providers (AWS, GCP, and Azure), Limousine creates a massive design space of quindecillion (1048) storage engine designs the vast majority of which do not exist in literature or industry. Limousine contains a distribution-aware IO model to accurately evaluate any candidate design. Using these models, Limousine searches within the exhaustive design space to construct a navigable continuum of designs connected along a Pareto frontier of cloud cost and performance. If storage engines contain learned components, Limousine also introduces efficient lazy write algorithms to optimize the holistic read-write performance. Once the near-optimal design is decided for the given context, Limousine automatically materializes the corresponding design in Rust code. Using the YCSB benchmark, we demonstrate that storage engines automatically designed and generated by Limousine scale better by up to 3 orders of magnitude when compared with state-of-the-art industry-leading engines such as RocksDB, WiredTiger, FASTER, and Cosine, over diverse workloads, data sets, and cloud budgets.
The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format
Authors: Sirin Utku , Idreos Stratos
Published: March 2024

Numerous applications today rely on artificial intelligence over images. Image AI is, however, extremely expensive. In particular, the inference cost of image AI dominates the end-to-end cost. We observe that the image storage format lies at the root of the problem. Images today are predominantly stored in JPEG format. JPEG is a storage format designed for the human eye; it maximally compresses images without distorting the components of an image that are visible to the human eye. However, our observation is that during image AI, images are "seen'' by algorithms, not humans. In addition, every AI application is different regarding which data components of the images are the most relevant.

We present the Image Calculator, a self-designing image storage format that adapts to the given AI task, i.e., the specific neural network, the dataset, and the applications' specific accuracy, inference time, and storage requirements. Contrary to the state-of-the-art, the Image Calculator does not use a fixed storage format like JPEG. Instead, it designs and constructs a new storage format tailored to the context. It does so by constructing a massive design space of candidate storage formats from first principles, within which it searches efficiently using composite performance models (inference time, accuracy, storage). This way, it leverages the given AI task's unique characteristics to compress the data maximally. We evaluate the Image Calculator across a diverse set of data, image analysis tasks, AI models, and hardware. We show that the Image Calculator can generate image storage formats that reduce inference time by up to 14.2x and storage by up to 8.2x with a minimal loss in accuracy or gain, compared to JPEG and its state-of-the-art variants.
Sibyl: Forecasting Time-Evolving Query Workloads
Authors: Huang Hanxian , Siddiqui Tarique , Alotaibi Rana , Curino Carlo , Leeka Jyoti , Jindal Alekh , Zhao Jishen , Camacho-Rodríguez Jesús , Tian Yuanyuan
Published: March 2024

Database systems often rely on historical query traces to perform workload-based performance tuning. However, real production workloads are time-evolving, making historical queries ineffective for optimizing future workloads. To address this challenge, we propose SIBYL, an end-to-end machine learning-based framework that accurately forecasts a sequence of future queries, with the entire query statements, in various prediction windows. Drawing insights from real-workloads, we propose template-based featurization techniques and develop a stacked-LSTM with an encoder-decoder architecture for accurate forecasting of query workloads. We also develop techniques to improve forecasting accuracy over large prediction windows and achieve high scalability over large workloads with high variability in arrival rates of queries. Finally, we propose techniques to handle workload drifts. Our evaluation on four real workloads demonstrates that SIBYL can forecast workloads with an 87.3% median F1 score, and can result in 1.7× and 1.3× performance improvement when applied to materialized view selection and index selection applications, respectively.
SkyPIE: A Fast & Accurate Oracle for Object Placement
Authors: Bang Tiemo , Douglas Chris , Crooks Natacha , Hellerstein Joseph M.
Published: March 2024

Cloud object stores offer vastly different price points for object storage as a function of workload and geography. Poor object placement can thus lead to significant cost overheads. Prior cost-saving techniques attempt to optimize placement policies on the fly, deciding object placements for each object individually. In practice, these techniques do not scale to the size of the modern cloud. In this work, we leverage the static nature and pay-per-use pricing model of cloud environments to explore a different approach. Rather than computing object placements on the fly, we precompute a SkyPIE oracle---a lookup structure representing all possible placement policies and the workloads for which they are optimal. Internally, SkyPIE represents placement policies as a matrix of cost-hyperplanes, which we effectively precompute through pruning and convex optimization. By leveraging a fast geometric algorithm, online queries then are 1 to 8 orders of magnitude faster but as accurate as Integer-Linear-Programming. This makes exact optimization tractable for real workloads and we show >10x cost savings compared to state-of-the-art heuristic approaches.
LeCo: Lightweight Compression via Learning Serial Correlations
Authors: Liu Yihao , Zeng Xinyu , Zhang Huanchen
Published: March 2024

Lightweight data compression is a key technique that allows column stores to exhibit superior performance for analytical queries. Despite a comprehensive study on dictionary-based encodings to approach Shannon's entropy, few prior works have systematically exploited the serial correlation in a column for compression. In this paper, we propose LeCo (i.e., Learned Compression), a framework that uses machine learning to remove the serial redundancy in a value sequence automatically to achieve an outstanding compression ratio and decompression performance. LeCo presents a general approach to this end, making existing algorithms such as Frame-of-Reference (FOR), Delta Encoding, and Run-Length Encoding (RLE) special cases under our framework. Our microbenchmark with three synthetic and eight real-world data sets shows that a prototype of LeCo achieves a Pareto improvement on both compression ratio and random access speed over the existing solutions. When integrating LeCo into widely-used applications, we observe up to 5.2× speed up in a data analytical query in the Arrow columnar execution engine, and a 16% increase in RocksDB's throughput.
TabEE: Tabular Embeddings Explanations
Authors: Copul Roni , Frost Nave , Milo Tova , Razmadze Kathy
Published: March 2024

Tabular embedding methods have become increasingly popular due to their effectiveness in improving the results of various tasks, including classic databases tasks and machine learning predictions. However, most current methods treat these embedding models as "black boxes" making it difficult to understand the insights captured by the models. Our research proposes a novel approach to interpret these models, aiming to provide local and global explanations for the original data and detect potential flaws in the embedding models. The proposed solution is appropriate for every tabular embedding algorithm, as it fits the black box view of the embedding model. Furthermore, we propose methods for comparing different embedding models, which can help identify data biases that might impact the models' credibility without the user's knowledge. Our approach is evaluated on multiple datasets and multiple embeddings, demonstrating that our proposed explanations provide valuable insights into the behavior of tabular embedding methods. By making these models more transparent, we believe our research will contribute to the development of more effective and reliable embedding methods for a wide range of applications.
Adopting and Sustaining Microservice-Based Software Development
Authors: Vitharana Padmal , Daya Shahir A.
Published: March 2024

Organizational challenges can be more difficult than technical ones.
Mitigating the Impact of Inaccurate Feedback in Dynamic Learning-to-Rank: A Study of Overlooked Interesting Items
Authors: Zhang Chenhao , Chen Weitong , Zhang Wei , Xu Miao
Published: March 2024

Dynamic Learning-to-Rank (DLTR) is a method of updating a ranking policy in real time based on user feedback, which may not always be accurate. Although previous DLTR work has achieved fair and unbiased DLTR under inaccurate feedback, they face the tradeoff between fairness and user utility and also have limitations in the setting of feeding items. Existing DLTR works improve ranking utility by eliminating bias from inaccurate feedback on observed items, but the impact of another pervasive form of inaccurate feedback, overlooked or ignored interesting items, remains unclear. For example, users may browse the rankings too quickly to catch interesting items or miss interesting items because the snippets are not optimized enough. This phenomenon raises two questions: (i)
Will overlooked interesting items affect the ranking results?
and (ii)
Is it possible to improve utility without sacrificing fairness if these effects are eliminated?
These questions are particularly relevant for small and medium-sized retailers who are just starting out and may have limited data, leading to the use of inaccurate feedback to update their models. In this article, we find that inaccurate feedback in the form of overlooked interesting items has a negative impact on DLTR performance in terms of utility. To address this, we treat the overlooked interesting items as noise and propose a novel DLTR method, the Co-teaching Rank (CoTeR), that has good utility and fairness performance when inaccurate feedback is present in the form of overlooked interesting items. Our solution incorporates a co-teaching-based component with a customized loss function and data sampling strategy, as well as a mean pooling strategy to further accommodate newly added products without historical data. Through experiments, we demonstrate that CoTeR not only enhances utilities but also preserves ranking fairness and can smoothly handle newly introduced items.
Deep Causal Reasoning for Recommendations
Authors: Zhu Yaochen , Yi Jing , Xie Jiayi , Chen Zhenzhong
Published: March 2024

Traditional recommender systems aim to estimate a user’s rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation. Consequently, causal inference has been introduced in recommendations to address the influence of unobserved confounders. Observing that confounders in recommendations are usually shared among items and are therefore multi-cause confounders, we model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. Specifically, to remedy the confounding bias, we estimate user-specific latent variables that render the item exposures independent Bernoulli trials. The generative distribution is parameterized by a DNN with factorized logistic likelihood and the intractable posteriors are estimated by variational inference. Controlling these factors as substitute confounders, under mild assumptions, can eliminate the bias incurred by multi-cause confounders. Furthermore, we show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional treatment space. Therefore, we theoretically demonstrate that controlling user features as pre-treatment variables can substantially improve sample efficiency and alleviate overfitting. Empirical studies on both simulated and real-world datasets demonstrate that the proposed deep causal recommender shows more robustness to unobserved confounders than state-of-the-art causal recommenders. Codes and datasets are released at https://github.com/yaochenzhu/Deep-Deconf.
Robust Structure-Aware Graph-based Semi-Supervised Learning: Batch and Recursive Processing
Authors: Chen Xu
Published: March 2024

Graph-based semi-supervised learning plays an important role in large scale image classification tasks. However, the problem becomes very challenging in the presence of noisy labels and outliers. Moreover, traditional robust semi-supervised learning solutions suffers from prohibitive computational burdens thus cannot be computed for streaming data. Motivated by that, we present a novel unified framework robust structure-aware semi-supervised learning called Unified RSSL (URSSL) for batch processing and recursive processing robust to both outliers and noisy labels. Particularly, URSSL applies joint semi-supervised dimensionality reduction with robust estimators and network sparse regularization simultaneously on the graph Laplacian matrix iteratively to preserve the intrinsic graph structure and ensure robustness to the compound noise. First, in order to relieve the influence from outliers, a novel semi-supervised robust dimensionality reduction is applied relying on robust estimators to suppress outliers. Meanwhile, to tackle noisy labels, the denoised graph similarity information is encoded into the network regularization. Moreover, by identifying strong relevance of dimensionality reduction and network regularization in the context of robust semi-supervised learning (RSSL), a two-step alternative optimization is derived to compute optimal solutions with guaranteed convergence. We further derive our framework to adapt to large scale semi-supervised learning particularly suitable for large scale image classification and demonstrate the model robustness under different adversarial attacks. For recursive processing, we rely on reparameterization to transform the formulation to unlock the challenging problem of robust streaming-based semi-supervised learning. Last but not least, we extend our solution into distributed solutions to resolve the challenging issue of distributed robust semi-supervised learning when images are captured by multiple cameras at different locations. Extensive experimental results demonstrate the promising performance of this framework when applied to multiple benchmark datasets with respect to state-of-the-art approaches for important applications in the areas of image classification and spam data analysis.
Towards the Development of Guidance to Support Self-Employed Workers with Visual Impairments
Authors: Vader Mei-Lian
Published: March 2024

Self-employment provides a unique opportunity for a customized work experience that can be specialized for an individual's skills and career goals. In particular, individuals with disabilities benefit from the flexible and accessible work environment of self-employment. The number of self-employed workers with disabilities is a small portion of the population and the number of initiatives and resources available is inadequate. The gap between the availability of resources and the benefits of the career can be attributed to a lack of support, inaccessible technology, and societal expectations. The research described in this paper, aims to examine the experiences of self-employed workers with visual impairments and better understand the technologies, training and resources currently used, with a goal of supporting operations and interactions. Two proposed studies are described. The research aims to culminate in the development of guidance for designers and researchers interested in developing solutions to support self-employed workers with visual impairments.
AI-Enabled Smart Glasses for People with Severe Vision Impairments
Authors: Gamage Bhanuka
Published: March 2024

Over the last decade, there has been significant research on how smart assistive devices with artificial intelligence (AI) built into them can assist people with severe vision impairments to comprehend their surroundings. These devices come in various forms such as smartphone applications, smart-glasses, and smart canes. Smart glasses have gained popularity lately due to recent technological advancements, as well as their natural position in front of the user's eyes. However, there has been limited research to understand how people with severe vision impairments would prefer to interact with them. The objective of this project is to investigate the use of AI-enabled smart-glasses to aid individuals with severe vision impairments. The research aims to comprehend the differences between the types of research conducted by researchers and the needs and desires of the community. The study will utilise a Design Thinking approach and involve vision-impaired users throughout the project by utilising co-design methods. The research will involve the development of smart-glass application prototypes through iterative case studies with individuals who are blind, have low-vision, and have cerebral vision impairment (CVI). The goal is to understand the users' preferred interaction model from their lived experiences. The outcome of the research will also be a software architecture that enables people with severe vision impairments to seamlessly access information about their surroundings.
Designing Accessible Content Creation Support with Blind and Low Vision Creators
Authors: Zhang Lotus
Published: March 2024

Today, digital creative tools are still largely designed without disabled people in mind and thus require retrofitting to achieve basic accessibility. As more creative tools begin to incorporate generative artificial intelligence, I propose a research agenda that centers the design of human-AI co-creation experiences on disabled creators and leverages such technology for accessibility from the start. Specifically, I focus on researching ways that AI-assisted creative tools could be designed to lift the expression ceiling and reduce effort for blind and low vision creators. Starting with a formative mixed-method study that uncovers the blind and low vision community's needs for visual content creation and editing support, this dissertation explores and designs accessible content creation support for three highly desired visual creative tasks: (1) private visual content obfuscation, (2) social media video editing, and (3) aesthetic visual content authoring. Together, I believe this dissertation will contribute actionable insights to lower the barriers to expressive and efficient digital content creation for blind and low vision creators.
The Book Review Column
Authors: Tran Nicholas
Published: March 2024

I am a fan of Sheldon Ross' textbooks on probability; they are concise and full of interesting exercises. He and Erol Pek¨oz have just released a second edition of A Second Course in Probability (Cambridge University Press, 2023), which promises a rigorous but accessible and modern introduction to a selection of advanced topics in the field.

Javier Esparza's work includes using automata to study model checking, program analysis and verification. His book (coauthored with Michael Blondin) Automata Theory: An Algorithmic Approach (The MIT Press, 2023) emphasizes efficient constructions of (ω-)automata and other algorithmic concerns.

Filterworld: How Algorithms Flattened Culture by New Yorker staff writer Kyle Chayka (Doubleday, 2024) investigates the pervasive impact of algorithms on consumption and distribution of culture and explores ways to reclaim our freedom from the digital overlord.
Technical Report Column
Authors: Kelley Dean
Published: March 2024

Welcome to the Technical Reports Column. If your institution publishes technical reports that you'd like to have included here, please contact me at the email address above.
Brief Introduction to Quantum Computing for Undergraduate Students: Lecture One
Authors: Chen Li
Published: March 2024

This article provides an introductory overview of quantum computing for college students through intuitive examples. We explore key concepts, starting with the basic principles of quantum mechanics. The discussion covers essential elements such as Qubits, superposition, entanglement, and introduces quantum gates. The article concludes by examining the first quantum algorithm: the solution to Deutsch's Problem.
Synergism Between Hypothalamic Astrocytes and Neurons in Metabolic Control
Authors: Frago Laura M. , Gómez-Romero Alfonso , Collado-Pérez Roberto , Argente Jesús , Chowen Julie A.
Published: March 2024

Astrocytes are no longer considered as passive support cells. In the hypothalamus, these glial cells actively participate in the control of appetite, energy expenditure, and the processes leading to obesity and its secondary complications. Here we briefly review studies supporting this conclusion and the advances made in understanding the underlying mechanisms.
Design of a Low Sidelobe Feed Network Based on the Louver-Shaped Defected Ground Structure
Authors: Zhang Yuan , Xi Songtao
Published: March 2024

In this paper, a low sidelobe feeding network has been developed utilizing the louver-shaped defected ground structure (DGS). By adjusting the louver-shaped DGS, the output amplitude and phase of the corresponding ports can be altered, minimizing deviations from theoretical values. This enables antenna arrays equipped with this feeding network to more easily achieve low sidelobe performance. The impact of the louver-shaped DGS on the amplitude and phase of each port in the power divider within the feeding network is analyzed, and a 16-channel feeding network incorporating the louver-shaped DGS has been designed, fabricated, and then measured. The test results indicate that the performance of the line-feeding network is effectively improved by designing and adjusting the louver-shaped DGS. Through the debugging procedure, the amplitude deviation of the feeding network has been reduced from ±0.45 dB to ±0.2 dB, while the phase deviation of the feeding network has been reduced from ±8° to ±2.5°, and the maximum value of the first sidelobe has been reduced from −24.2 dB to −28.1 dB.
Microbiological Analysis and Content of Heavy Metals in Different Candies, Chocolates, and Their Wrappers in Bangladesh
Authors: Prattay Kazi Milenur Rahman , Chowdhury Md. Tanvir Ahmed , Sarkar Md. Raihan , Rahman Tanvir , Emon Sharmin Zaman , Hossain Abrar , Rajib Hredoy Ghosh , Abdurrahim Md.
Published: March 2024

Present study investigates 39 brands of candies, chocolate, and litchis, purchased from Dhaka City, Bangladesh, for their moisture content, sulphated ash value, heavy metal, and bacterial contamination. All the brands showed moisture content (0.64%-4.775%) within the BSTI range, but sulphated ash values (18.80%-25.72%) were beyond the accepted value. Pb, Cd, Ni, and Cr ranged from 0.24-2.40 μg/g, 0.071-0.44 μg/g, 0.38-48.10 μg/g, and 0.50-12.79 μg/g, respectively, in the tested brands. Most of the brands contained Pb and Cd beyond the acceptable limits of WHO/FDA. Pb (2.24-2586.75 μg/g) was found in high concentration in the packaging of most brands, and Ni and Cd ranged from 2.10-108.05 μg/g and 1.68-45 μg/g, respectively. Bacterial presence was found in 15 brands, and 4 of them had a total aerobic bacterial count of >1 log CFU/g. Consumption of such contaminated confectionaries holds significant public health risks, specially in children, and demands necessary precautionary steps.
Prevalence of Erectile Dysfunction among Patients with Coronary Artery Disease
Authors: Mohamed Abd Elsamie Islam , Abou Khodair Hassan , Bashandy Mohamed Sayed , Aboelwafa Hany
Published: March 2024

Both coronary artery disease (CAD) and erectile dysfunction (ED) are widespread illnesses that frequently coexist. Numerous triggering risk factors and pathophysiological mechanisms contribute to both diseases. Penile color Doppler ultrasound can serve as a crucial tool to evaluate penile arterial blood flow. Therefore, this study aims to assess the ED prevalence in CAD patients, to determine the socioeconomic and clinical profiles of the population, and to define risk factors of their development. The cross‐sectional study included 100 male patients who were admitted to the coronary angiography (CAG) unit. Patients were asked if they had ED, and if present, it was graded using the International Index of Erectile Function 5. Right and left cavernosal arteries were examined using color Doppler and erection hardness to assess the peak systolic velocity (PSV) and end‐diastolic velocity. Among the 52 cases that suffered from ED, most of the cases had mild grades (38.5%). All cases without ED (100%) had penile artery Doppler PSV findings ≥30 (cm/s) with an average Doppler PSV of 48.02 ± 7.49 cm/s for the right cavernosal arteries and 47.4 ± 7.17 cm/s for the left cavernosal arteries. Yet, 71.2% of cases with ED had penile artery Doppler PSV findings <30 (cm/s) with an average Doppler PSV of 29.1 ± 7.57 cm/s for the right cavernosal arteries and 28.69 ± 7.11 cm/s for the left cavernosal arteries. Furthermore, by CAG, 50% of cases without ED had blockage not necessitating a coronary intervention (no significant disease) compared to 25% of cases with ED. There was positive correlation between advanced grade of ED and age (r = 0.327, P  < 0.001), hypertension (r = 0.213, P = 0.033), DM (r = 0.253, P = 0.011), smoking (r = 0.202, P = 0.044), and severity of CAD (r = 0.332, P  < 0.001). Patients with multivessel CAD have a significantly higher incidence and more severity of ED. Moreover, pathological penile vessels PSV (<25 or <30 cm/s) via Doppler ultrasound is mostly valuable as a noninvasive tool to define ED attributable to vascular impairment that may be antecedent to atherosclerosis in the coronary vessels.
Hypoxia-Inducible Factor-1α Regulates High Phosphate-Induced Vascular Calcification via Type III Sodium-Dependent Phosphate Cotransporter 1
Authors: Guo Chengkun , Quan Zhengli , Ke Jingjing , Zang Hualong , Teng Qiuping , Li Xin , Peng Dan , Wang Ping
Published: March 2024

Vascular calcification (VC) has a high incidence in patients with chronic kidney disease, which is a worldwide public health problem and presents a heavy burden to society. Hypoxia-inducible factor (HIF)-1α, the active subunit of HIF-1, has been reported to play a vital role in high phosphate-induced VC. However, the underlying mechanism is still undetermined, and effective treatment is unavailable. In the present study, human aortic smooth muscle cells (HASMCs) were cultured under normal or high phosphate media conditions. HIF-1α small interfering RNA and overexpression plasmids were employed to regulate HIF-1α expression. Phosphonoformic acid was employed to restrain the function of type III sodium-dependent phosphate cotransporter 1 (Pit-1). The expression levels of HIF-1α, Pit-1, runt-related transcription factor 2 (Runx2), and smooth muscle 22 alpha (SM22α) were evaluated, and the calcium contents were also examined. Cell growth was assessed using an MTT assay. High phosphate stimulation caused an upregulation in HIF-1α and Pit-1 expression levels and induced calcium depositions in HASMCs. Upregulation of Runx2 expression accompanied by downregulation of SM22α expression was observed in the high phosphate group. Following the suppression of HIF-1α expression, there was a concomitant attenuation in Pit-1 expression, calcium deposition, the alteration of phenotypic transition marker genes, and vice versa. The most serious calcium deposition was noted in HASMCs cultured under high phosphate conditions which were pretreated with a HIF-1α overexpression plasmid. However, when the biological functions of Pit-1 were restrained, the putative serious calcium deposition was not formed even in HASMCs transfected with a HIF-1α overexpression plasmid. The findings confirmed that HIF-1α regulated Pit-1 expression and exerted its pro-calcifying effect through Pit-1, which identified HIF-1α and Pit-1 as therapeutic targets for high phosphate-induced VC.