Miracle-dz/UniFAR-FTUs huggingface dataset:
A Discrete-Mapping-Based Cross-Component Prediction Paradigm for Screen Content Coding

Cross-component prediction is an important intra-prediction tool in the modern video coders. Existing prediction methods to exploit cross-component correlation include cross-component linear model and its extension of multi-model linear model. These models are designed for camera captured content. For screen content coding, where videos exhibit different signal characteristics, a cross-component prediction model tailored to their characteristics is desirable. As a pioneering work, we propose a discrete-mapping based cross-component prediction model for screen content coding. Our model relies on the core observation that, screen content videos typically comprise of regions with a few distinct colors and luma value (almost always) uniquely conveys chroma value. Based on this, the proposed method learns a discrete-mapping function from available reconstructed luma-chroma pairs and uses this function to derive chroma prediction from the co-located luma samples. To achieve higher accuracy, a multi-filter approach is employed to derive co-located luma values. The proposed method achieves 2.61%, 3.51% and 3.92% Y, U and V bit-rate savings respectively over Enhanced Compression Model (ECM) 4.0, with negligible complexity, for text and graphics media under all-intra configuration.
REVIEW OF MICROSCOPIC MODEL FOR TRAFFIC FLOW

Today, the problem of cities urban transportation is becoming something we have to face in our daily life. In Indonesia, traffic congestion is increasingly serious. Several economic and social motivations can be related to the need to minimize the time spent in vehicles for transportation and consequently their related pollution problems. Due to these motivations, the literature on traffic phenomena is already vast and characterized by contributions covering modeling aspects, statement of problems, qualitative analysis, and particularly developed simulation generated by applications. This paper will provides a several literature review of microscopic model based on their utilities, including the critical review about the modeling approaches. Furthermore, some practical issues such as potential for future model improvement using existing and emerging data collection technologies is identified based on Indonesian traffic characteristics and will be presented as a contribution from this paper.
Optimizing Outcomes in Lymphedema Reconstruction

Learning Objectives: After studying this article, the participant should be able to: 1. Describe current surgical techniques for treating primary and secondary lymphedema. 2. Optimize the surgical care of patients with lymphedema. Summary: Over the past decade, significant advances have been made in the surgical treatment of lymphedema. The most notable changes have been the reintroduction and evolution of physiologic techniques, including lymphovenous bypass—sometimes referred to as lymphovenous anastomosis in the literature—and vascularized lymph node transplant. These surgical modalities are now often used as first-line surgical options or may be combined with nonphysiologic approaches, including direct excision and suction-assisted lipectomy. Surgeons continue to debate the most appropriate sequence and combination of surgical treatment, particularly for patients at both extremes of the severity spectrum. Furthermore, debate remains around the need to apply different treatment approaches for patients with upper versus lower extremity involvement and primary versus secondary cause. In this article, we provide a summary of the surgical techniques currently used for both primary and secondary lymphedema and provide our recommendations for optimizing the surgical care of patients with lymphedema.
Association Between High Expression of Phosphorylated-STMN1 and Mesenchymal Marker Expression and Cancer Stemness in Breast Cancer

Background/Aim: In patients with breast cancer, the expression of stathmin1 (STMN1) has been significantly related to a poor prognosis, cancer aggressiveness, and expression of cancer stem cell markers. The STMN1 protein is closely regulated by phosphorylation in four sites. However, few studies have investigated the relationship between the expression of phosphorylated STMN1 (pSTMN1) and clinicopathological findings, including tumor-aggressive biomarkers, in patients with breast cancer. Materials and Methods: The expression levels of four pSTMN1 (Ser16, Ser25, Ser38, and Ser63) were immunohistochemically analyzed in 213 breast cancer cases. The clinicopathological factors evaluated included epithelial-mesenchymal transition (EMT) markers and cancer stem cell markers. Results: The cytoplasmic expression of pSTMN1 (Ser16, Ser25, Ser38, and Ser63) in normal breast tissues was low. The positive expression ratios of Ser25 (54.5%) and Ser38 (39.0%) were high compared to those of Ser16 (25.8%) and Ser63 (23.9%). The overexpression of pSTMN1 (Ser38) was associated with tumor-aggressive characteristics, such as triple-negative breast cancer (TNBC) phenotypes, high mesenchymal marker, and expression of cancer stem cell markers. Conclusion: STMN1 phosphorylation might be associated with clinicopathological factors, breast cancer subtypes, and expression of mesenchymal markers and breast cancer stem cell markers through the regulation of STMN1 function. Ser38 phosphorylation of STMN1 may be a novel biomarker for high-grade TNBC associated with mesenchymal marker expression and cancer stemness.
A youth-centred approach to improving engagement in HIV services: human-centred design methods and outcomes in a research trial in Kisumu County, Kenya

Introduction Innovative interventions are needed to improve HIV outcomes among adolescents and young adults (AYAs) living with HIV. Engaging AYAs in intervention development could increase effectiveness and youth acceptance, yet research is limited. We applied human-centred design (HCD) to refine adherence-support interventions pretrial and assessed HCD workshop acceptability. Methods We applied an iterative, four-phased HCD process in Kenya that included: (1) systematic review of extant knowledge, (2) prioritisation of design challenges, (3) a co-creation workshop and (4) translation tables to pair insights with trial intervention adaptations. The co-creation workshop was co-led by youth facilitators employing participatory activities to inform intervention adaptations. Iterative data analysis included rapid thematic analysis of visualised workshop outputs and notes using affinity mapping and dialogue to identify key themes. We conducted a survey to assess workshop acceptability among participants. Results Twenty-two participants engaged in the 4-day workshop. Co-creation activities yielded recommendations for improving planned interventions (eg, message frequency and content; strategies to engage hard-to-reach participants), critical principles to employ across interventions (eg, personalisation, AYA empowerment) and identification of unanticipated AYA HIV treatment priorities (eg, drug holidays, transition from adolescent to adult services). We revised intervention content, peer navigator training materials and study inclusion criteria in response to findings. The youth-led HCD workshop was highly acceptable to participants. Conclusions Research employing HCD among youth can improve interventions preimplementation through empathy, youth-led inquiry and real-time problem solving. Peer navigation may be most influential in improving retention when engagement with young people is based on mutual trust, respect, privacy and extends beyond HIV-specific support. Identifying opportunities for personalisation and adaptation within intervention delivery is important for AYAs. Patient engagement interventions that target young people should prioritise improved transition between youth and adult services, youth HIV status disclosure, AYA empowerment and healthcare worker responsiveness in interactions and episodic adherence interruptions.
A Markov analysis of azacitidine and venetoclax vs induction chemotherapy for medically fit patients with AML

Key Points • Medically fit patients with newly diagnosed adverse-risk AML may benefit from treatment with aza-ven (IC, 1.4 QALYs vs aza-ven, 2.0 QALYs). • IC remains the preferred induction regimen for patients with intermediate-risk AML.
Plankton distributions in relation to physical oceanographic features on the southern Malin Shelf, August 1996

The distribution of plankton in relation to physical oceanographic features on the southern Malin Shelf and in the North Channel of the Irish Sea was investigated during August 1996. The near surface 13.5°C isotherm and 35.20 isohaline marked the location of the Islay and Irish coastal fronts, respectively. An additional salinity front was observed to the south-east of Islay. A mergence of the Irish coastal front and the salinity component of the Islay front suggested that, at certain times, a frontal boundary separating coastal and oceanic water may extend from south-west Ireland north along the western Scottish Shelf. The presence of highly saline (>35.35) water on the outer shelf indicated the intrusion of Atlantic water.The frontal boundaries influenced the distribution of plankton on the southern Malin Shelf and in the North Channel. The outer shelf region was characterized by the presence of Chaetoceros atlanticus, Gonyaulax polygramma, Oxytoxum scolopax, Doliolum gegenbauri, Sagitta tasmanica, and S. maxima. Halosphaera minor and Limacina retroversa were distributed between the shelf edge and the Irish coastal and Islay fronts. Gyrodinium aureolum dominated the phytoplankton of the Islay front but in contrast, Ceratium longipes was restricted to the east of the Islay Front. The distinct spatial separation of species suggested that there was no exchange of southern Malin Shelf and North Channel water at the time of the study.
Dietary intake and serum levels of copper and zinc and risk of hepatocellular carcinoma: A matched case-control study

Abstract Background: Copper and zinc are involved in the development of multiple malignancies; yet, epidemiological evidence on hepatocellular carcinoma (HCC) is limited. This study aimed to investigate the association between dietary intake and serum levels of copper and zinc with the risk of HCC. Methods: A total of 434 case-control pairs matched for sex and age (±1 year) were included in this study. Cases with newly diagnosed HCC were from the Guangdong Liver Cancer Cohort (GLCC) study, and healthy controls were from the Guangzhou Nutrition and Health Study (GNHS). A semi-quantitative 79-item food frequency questionnaire (FFQ) was used to assess habitual dietary intakes of copper and zinc. Serum levels of copper and zinc were measured by using inductively coupled plasma mass spectrometry. The copper (Cu)/ zinc (Zn) ratio was computed by dividing copper levels by zinc levels. Conditional logistic regression models were performed to calculate the odds ratio (OR) and 95% confidence intervals (CI) for per 1 standard deviation increase (per-SD increase) in copper and zinc levels. Results: Higher dietary intake (ORper-SD increase = 0.65, 95% CI: 0.44, 0.96, Ptrend = 0.029) and serum levels of zinc (ORper-SD increase = 0.11, 95% CI: 0.04, 0.30, Ptrend <0.001) were both associated with a lower risk of HCC. Subgroup analyses showed that the inverse association was only pronounced in men but not in women (Pinteraction = 0.041 for dietary zinc intake and 0.010 for serum zinc levels). Serum copper levels (ORper-SD increase = 2.05, 95% CI: 1.39, 3.03, Ptrend = 0.020) and serum Cu/Zn ratio (ORper-SD increase = 6.53, 95% CI: 2.52, 16.92, Ptrend<0.001) were positively associated with HCC risk, while dietary copper intake and dietary Cu/Zn ratio were not associated with HCC risk. Conclusion: Zinc may be a protective factor for HCC, especially among men, but the effects of copper on HCC risk are not clear.
Length-of-stay times in hospital for COVID-19 patients using the smoothed Beran's estimator with bootstrap bandwidth selection (preprint)

The survival function of length-of-stay in hospital ward and ICU for COVID-19 patients is studied in this paper. Flexible statistical methods are used to estimate this survival function given relevant covariates such as age, sex, obesity and chronic obstructive pulmonary disease (COPD). A doubly-smoothed Beran's estimator has been considered to this aim. The bootstrap method has been used to produce new smoothing parameter selectors and to construct confidence regions for the conditional survival function. Some simulation studies show the good performance of the proposed methods.
Handling Cost and Constraints with Off-Policy Deep Reinforcement Learning

By reusing data throughout training, off-policy deep reinforcement learning algorithms offer improved sample efficiency relative to on-policy approaches. For continuous action spaces, the most popular methods for off-policy learning include policy improvement steps where a learned state-action ($Q$) value function is maximized over selected batches of data. These updates are often paired with regularization to combat associated overestimation of $Q$ values. With an eye toward safety, we revisit this strategy in environments with"mixed-sign"reward functions; that is, with reward functions that include independent positive (incentive) and negative (cost) terms. This setting is common in real-world applications, and may be addressed with or without constraints on the cost terms. We find the combination of function approximation and a term that maximizes $Q$ in the policy update to be problematic in such environments, because systematic errors in value estimation impact the contributions from the competing terms asymmetrically. This results in overemphasis of either incentives or costs and may severely limit learning. We explore two remedies to this issue. First, consistent with prior work, we find that periodic resetting of $Q$ and policy networks can be used to reduce value estimation error and improve learning in this setting. Second, we formulate novel off-policy actor-critic methods for both unconstrained and constrained learning that do not explicitly maximize $Q$ in the policy update. We find that this second approach, when applied to continuous action spaces with mixed-sign rewards, consistently and significantly outperforms state-of-the-art methods augmented by resetting. We further find that our approach produces agents that are both competitive with popular methods overall and more reliably competent on frequently-studied control problems that do not have mixed-sign rewards.
Anisotropic Neural Representation Learning for High-Quality Neural Rendering

Neural radiance fields (NeRFs) have achieved impressive view synthesis results by learning an implicit volumetric representation from multi-view images. To project the implicit representation into an image, NeRF employs volume rendering that approximates the continuous integrals of rays as an accumulation of the colors and densities of the sampled points. Although this approximation enables efficient rendering, it ignores the direction information in point intervals, resulting in ambiguous features and limited reconstruction quality. In this paper, we propose an anisotropic neural representation learning method that utilizes learnable view-dependent features to improve scene representation and reconstruction. We model the volumetric function as spherical harmonic (SH)-guided anisotropic features, parameterized by multilayer perceptrons, facilitating ambiguity elimination while preserving the rendering efficiency. To achieve robust scene reconstruction without anisotropy overfitting, we regularize the energy of the anisotropic features during training. Our method is flexiable and can be plugged into NeRF-based frameworks. Extensive experiments show that the proposed representation can boost the rendering quality of various NeRFs and achieve state-of-the-art rendering performance on both synthetic and real-world scenes.
The Factory and the Beehive. V. Chromospheric and Coronal Activity and Its Dependence on Rotation in Praesepe and the Hyades

Low-mass (≲1.2 M ⊙) main-sequence stars lose angular momentum over time, leading to a decrease in their magnetic activity. The details of this rotation–activity relation remain poorly understood, however. Using observations of members of the ≈700 Myr old Praesepe and Hyades open clusters, we aim to characterize the rotation–activity relation for different tracers of activity at this age. To complement published data, we obtained new optical spectra for 250 Praesepe stars, new X-ray detections for 10, and new rotation periods for 28. These numbers for Hyads are 131, 23, and 137, respectively. The latter increases the number of Hyads with periods by 50%. We used these data to measure the fractional Hα and X-ray luminosities, L Hα /L bol and L X/L bol, and to calculate Rossby numbers R o. We found that at ≈700 Myr almost all M dwarfs exhibit Hα emission, with binaries having the same overall color–Hα equivalent width distribution as single stars. In the R o–L Hα /L bol plane, unsaturated single stars follow a power law with index β = −5.9 ± 0.8 for R o > 0.3. In the R o–L X/L bol plane, we see evidence for supersaturation for single stars with R o ≲ 0.01, following a power law with index β sup = 0.5 − 0.1 + 0.2 , supporting the hypothesis that the coronae of these stars are being centrifugally stripped. We found that the critical R o value at which activity saturates is smaller for L X/L bol than for L Hα /L bol. Finally, we observed an almost 1:1 relation between L Hα /L bol and L X/L bol, suggesting that both the corona and the chromosphere experience similar magnetic heating.
Time-of-Flight X-ray Measurements for Computed Tomography

Time-of-flight (ToF) measurements is a possible alternative to anti-scatter grids in computed tomography (CT). Simulations have shown a possible 75% reduction in the detrimental scattering contribution with a 100 ps FWHM timing resolution. A test bench comprising a pulsed X-ray source and a time-correlated detector has been designed to demonstrate the feasibility of time-of-flight measurements of X-rays using readout electronics inherited from a ToF-positron emission tomography project. A 86 ps FWHM coincidence time resolution have been achieved with 511 keV annihilation gamma-rays and a 155 ps FWHM timing jitter with a 120 kVp pulsed X-rays source.
X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning

Vision-language pre-training and instruction tuning have demonstrated general-purpose capabilities in 2D visual reasoning tasks by aligning visual encoders with state-of-the-art large language models (LLMs). In this paper, we introduce a simple, yet effective, cross-modality framework built atop frozen LLMs that allows the integration of various modalities without extensive modality-specific customization. To facilitate instruction-modality fine-tuning, we collect high-quality instruction tuning data in an automatic and scalable manner, composed of 24K QA samples for audio and 250K QA samples for 3D. Leveraging instruction-aware representations, our model performs comparably with leading-edge counterparts without the need of extensive modality-specific pre-training or customization. Furthermore, our approach demonstrates cross-modal reasoning abilities across two or more input modalities, despite each modality projection being trained individually. To study the model's cross-modal abilities, we contribute a novel Discriminative Cross-modal Reasoning (DisCRn) evaluation task, comprising 9K audio-video QA samples and 28K image-3D QA samples that require the model to reason discriminatively across disparate input modalities.
Learning Part Segmentation from Synthetic Animals

Semantic part segmentation provides an intricate and interpretable understanding of an object, thereby benefiting numerous downstream tasks. However, the need for exhaustive annotations impedes its usage across diverse object types. This paper focuses on learning part segmentation from synthetic animals, leveraging the Skinned Multi-Animal Linear (SMAL) models to scale up existing synthetic data generated by computer-aided design (CAD) animal models. Compared to CAD models, SMAL models generate data with a wider range of poses observed in real-world scenarios. As a result, our first contribution is to construct a synthetic animal dataset of tigers and horses with more pose diversity, termed Synthetic Animal Parts (SAP). We then benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods and further improve them as our second contribution. Concretely, we examine three Syn-to-Real adaptation methods but observe relative performance drop due to the innate difference between the two tasks. To address this, we propose a simple yet effective method called Class-Balanced Fourier Data Mixing (CB-FDM). Fourier Data Mixing aligns the spectral amplitudes of synthetic images with real images, thereby making the mixed images have more similar frequency content to real images. We further use Class-Balanced Pseudo-Label Re-Weighting to alleviate the imbalanced class distribution. We demonstrate the efficacy of CB-FDM on SynRealPart over previous methods with significant performance improvements. Remarkably, our third contribution is to reveal that the learned parts from synthetic tiger and horse are transferable across all quadrupeds in PartImageNet, further underscoring the utility and potential applications of animal part segmentation.
Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework

Fairness for machine learning predictions is widely required in practice for legal, ethical, and societal reasons. Existing work typically focuses on settings without unobserved confounding, even though unobserved confounding can lead to severe violations of causal fairness and, thus, unfair predictions. In this work, we analyze the sensitivity of causal fairness to unobserved confounding. Our contributions are three-fold. First, we derive bounds for causal fairness metrics under different sources of unobserved confounding. This enables practitioners to examine the sensitivity of their machine learning models to unobserved confounding in fairness-critical applications. Second, we propose a novel neural framework for learning fair predictions, which allows us to offer worst-case guarantees of the extent to which causal fairness can be violated due to unobserved confounding. Third, we demonstrate the effectiveness of our framework in a series of experiments, including a real-world case study about predicting prison sentences. To the best of our knowledge, ours is the first work to study causal fairness under unobserved confounding. To this end, our work is of direct practical value as a refutation strategy to ensure the fairness of predictions in high-stakes applications.
Size of the largest sum-free subset of $[n]^3$, $[n]^4$, and $[n]^5$

We determine the density of the largest sum-free subset of the lattice cube $\{1, 2, \dots, n\}^d$ for $d \in \{3, 4, 5\}$. This solves a conjecture of Cameron and Aydinian in dimensions $3$, $4$, and $5$.