Miracle-dz/UniFAR-FTUs huggingface dataset:
Many-facet Rasch measurement

This chapter provides an introductory overview of many-facet Rasch measurement (MFRM). Broadly speaking, MFRM refers to a class of measurement models that extend the basic Rasch model by incorporating more variables (or facets) than the two that are typically included in a test (i.e., examinees and items), such as raters, scoring criteria, and tasks. Throughout the chapter, a sample of rating data taken from a writing performance assessment is used to illustrate the rationale of the MFRM approach and to describe the general methodological steps typically involved. These steps refer to identifying facets that are likely to be relevant in a particular assessment context, specifying a measurement model that is suited to incorporate each of these facets, and applying the model in order to account for each facet in the best possible way. The chapter focuses on the rater facet and on ways to deal with the perennial problem of rater variability. More specifically, the MFRM analysis of the sample data shows how to measure the severity (or leniency) of raters, to assess the degree of rater consistency, to correct examinee scores for rater severity differences, to examine the functioning of the rating scale, and to detect potential interactions between facets. Relevant statistical indicators are successively introduced as the sample data analysis proceeds. The final section deals with issues concerning the choice of an appropriate rating design to achieve the necessary connectedness in the data, the provision of feedback to raters, and applications of the MFRM approach to standard-setting procedures.
Entanglement signatures of a percolating quantum system

Entanglement measures have emerged as one of the versatile probes to diagnose quantum phases and their transitions. Universal features in them expand their applicability to a range of systems, including those with quenched disorder. In this work, we show that when the underlying lattice has percolation disorder, free fermions at a finite density show interesting entanglement properties. These emerge from massively degenerate eigenspaces where the notion of standard bipartite entanglement entropy needs to be revisited. We show that appropriately defined entanglement measures can capture both geometrical aspects and electronic correlations of the percolated quantum system. Using a variety of toy models, we uncover the rich phenomenology of this physics. We further show that there are decipherable signatures of the classical percolation transition in these entanglement properties. Our work presents an interesting interplay between the geometrical properties of a lattice and quantum entanglement in a many-body quantum system.
Comparison of electrodiagnostic criteria for primary demyelination in chronic polyneuropathy

Three sets of electrodiagnostic criteria for establishing primary demyelination in chronic poly neuropathy are evaluated. Sensitivity is assessed in 70 patients with clinically established chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). The criteria use different abnormal values, one adjusts for the effects of axonal loss, while another relies only on conduction velocity. However, even when consideration is given to sufficient number of nerves tested, there is no significant difference (P = 0.37) in diagnostic sensitivity among them, with 48% to 64% of CIDP patients fulfilling criteria for primary demyelination. Specificity is assessed by applying the criteria to 47 patients with motor neuron disease and 63 patients with diabetic polyneuropathy. No patients meet any of the criteria. Further analysis shows that as sensitivity increases specificity decreases, because of overlapping distributions of nerve conduction abnormalities in these neuropathic disorders. A sensitivity of approximately 66% is a practical limit for electrodiagnostic criteria in CIDP.
Constraining the history of reheating with the NANOGrav 15-year data

Over the last few years, primordial black holes (PBHs) have emerged as a strong candidate for cold dark matter. A significant number of PBHs are produced when the strength of the primordial scalar power spectrum is enhanced on small scales (compared to the COBE normalized values on large scales). Such primordial spectra also inevitably lead to strong amplification of the scalar-induced, secondary gravitational waves (GWs) at higher frequencies. The recent detection of the stochastic gravitational wave background (SGWB) by the pulsar timing arrays (PTAs) has opened up the possibility of directly probing the very early universe. Different studies have shown that, when PBHs are assumed to have been formed during the epoch of radiation domination, the mechanism for the amplification of the scalar-induced GWs that is required to explain the PTA data can overproduce the PBHs over some ranges of masses. In this work, we assume a specific functional form for the primordial scalar power spectrum and examine the production of PBHs and the scalar-induced secondary GWs during the phase of reheating, which precedes the standard epoch of radiation domination. Specifically, we account for the uncertainties in the conditions for the formation of PBHs and ensure that the extent of PBHs produced remains within the observational bounds. We find that the scalar-induced SGWB generated during a phase of reheating with a steeper equation of state (than that of radiation) fit the NANOGrav 15-year data with a stronger Bayesian evidence than the astrophysical scenario involving GWs produced by merging supermassive binary black holes.
Seasonal Dynamics of Litterfall in a Sub-Alpine Spruce-Fir Forest on the Eastern Tibetan Plateau: Allometric Scaling Relationships Based on One Year of Observations

Litterfall is the primary source of carbon and nutrients that determine soil fertility in forest ecosystems. Most current studies have focused on foliar litter, but the seasonal dynamics and allometric scaling relationships among different litter components (e.g., foliar litter, woody litter, reproductive litter, and epiphytic litter) are poorly understood. Here, we investigated the litter production of various litter components in a sub-alpine spruce-fir forest on the eastern Tibetan Plateau based on one year of observations (from August 2015 to July 2016). Our results showed that total litter production (LT) was 2380 kg·ha−1·year−1 (3% of the aboveground forest biomass), of which 73.6% was foliar litter (LF), 15.6% was woody litter (LW), 3.0% was reproductive litter (LR), 1.3% was epiphytic litter (LE), and 6.5% was miscellaneous material (LM). The total litterfall was bimodal (with peaks occurring in April and October) and was dominated by tree species (85.4% of LT, whereas shrubs accounted for 6.8% of LT). The litter production of evergreen species (68.4% of LT) was higher than that of deciduous species (23.8% of LT). Isometric relationships were observed between litter components and the total litter (i.e., LF∝LT0.99≈1 and LR∝LT0.98≈1), and allometric relationships were also found (i.e., LW∝LT1.40>1 and LM∝LT0.82<1). However, because some components did not exhibit obvious seasonal dynamics (i.e., LE), some relationships could not be expressed using allometric equations (i.e., LE versus LT, LF versus LE, LW versus LE, and LE versus LM). Thus, the different litter components showed different seasonal dynamics, and the total litter dynamics were primarily determined by the variation in foliar litter. In addition, the allometric relationships of the forest litterfall varied with the litter components, functional types (evergreen versus deciduous) and vertical structures (tree versus shrub). This study provides basic data and a new insight for future plant litter studies.
Modified FOLFOX6 With or Without Radiation Versus Fluorouracil and Leucovorin With Radiation in Neoadjuvant Treatment of Locally Advanced Rectal Cancer: Initial Results of the Chinese FOWARC Multicenter, Open-Label, Randomized Three-Arm Phase III Trial.

PURPOSE Total mesorectal excision with fluorouracil-based preoperative chemoradiotherapy and postoperative chemotherapy is a standard treatment of locally advanced rectal cancer. This study investigated the addition of oxaliplatin with and without preoperative radiotherapy. METHODS In this multicenter, open-label, phase III trial, we randomly assigned (1:1:1) Chinese adults (age 18 to 75 years) with locally advanced stage II/III rectal cancer to three treatments: five 2-week cycles of infusional fluorouracil (leucovorin 400 mg/m(2), fluorouracil 400 mg/m(2), and fluorouracil 2.4 g/m(2) over 48 h) plus radiotherapy (46.0 to 50.4 Gy delivered in 23 to 25 fractions during cycles 2 through 4) followed by surgery and seven cycles of infusional fluorouracil, the same treatment plus intravenous oxaliplatin 85 mg/m(2) on day 1 of each cycle (modified FOLFOX6 [mFOLFOX6]), or four to six cycles of mFOLFOX6 followed by surgery and six to eight cycles of mFOLFOX6. Random assignment was performed by using computer-generated block randomization codes. The primary end point was 3-year disease-free survival. Secondary end points of histopathologic response and toxicity are reported. RESULTS A total of 495 patients were enrolled from June 2010 to February 2015; 475 were evaluable (fluorouracil-radiotherapy, n = 155; mFOLFOX6-radiotherapy, n = 157; mFOLFOX6, n = 163). In the fluorouracil-radiotherapy, mFOLFOX6-radiotherapy, and mFOLFOX6 groups, the rate of pathologic complete response (pCR) was 14.0%, 27.5%, and 6.6%, and downstaging (ypStage 0 to 1) was achieved by 37.1%, 56.4%, and 35.5% of patients, respectively. Higher toxicity and more postoperative complications were observed in patients who received radiotherapy. CONCLUSION mFOLFOX6-based preoperative chemoradiotherapy results in a higher pCR rate than fluorouracil-based treatment. Perioperative mFOLFOX6 alone had inferior results and a lower pCR rate than chemoradiotherapy but led to a similar downstaging rate as fluorouracil-radiotherapy, with less toxicity and fewer postoperative complications.
Bisimulation through probabilistic testing (preliminary report)

We propose a language for testing concurrent processes and examine its strength in terms of the processes that are distinguished by a test. By using probabilistic transition systems as the underlying semantic model, we show how a testing algorithm with a probability arbitrary close to 1 can distinguish processes that are not bisimulation equivalent. We also show a similar result (in a slightly stronger form) for a new process relation called 2/3-bisimulation — lying strictly between that of simulation and bisimulation. Finally, the ultimately strength of the testing language is shown to identify an even stronger process relation, called probabilistic bisimulation.
Thirty-five years of synchrony in the organic matter concentrations of Swedish rivers explained by variation in flow and sulphate

Increasing concentrations of organic matter (OM) in surface waters have been noted over large parts of the boreal/nemoral zone in Europe and North America. This has raised questions about the causes and the likelihood of further increases. A number of drivers have been proposed, including temperature, hydrology, as well as SO 4 and Cl deposition. The data reported so far, however, have been insufficient to define the relative importance of different drivers in landscapes where they interact. Thirty-five years of monthly measurements of absorbance and chemical oxygen demand (COD), two common proxies for OM, from 28 large Scandinavian catchments provide an unprecedented opportunity to resolve the importance of hypothesized drivers. For 21 of the catchments, there are 18 years of total organic carbon (TOC) measurements as well. Despite the heterogeneity of the catchments with regards to climate, size and land use, there is a high degree of synchronicity in OM across the entire region. Rivers go from widespread trends of decreasing OM to increasing trends and back again three times in the 35-year record. This synchronicity in decadal scale oscillations and long-term trends suggest a common set of dominant OM drivers in these landscapes. Here, we use regression models to test the importance of different potential drivers. We show that flow and SO 4 together can predict most of the interannual variability in OM proxies, up to 88% for absorbance, up to 78% for COD. Two other candidate drivers, air temperature and Cl , add little explanatory value. Declines in anthropogenic SO 4 since the mid1970s are thus related to the observed OM increases in Scandinavia, but, in contrast to many recent studies, flow emerges as an even more important driver of OM variability. Stabilizing SO 4 levels also mean that hydrology is likely to be the major driver of future variability and trends in OM.
Cutting Edge: Regulatory T Cells Facilitate Cutaneous Wound Healing

Foxp3-expressing regulatory T cells (Tregs) reside in tissues where they control inflammation and mediate tissue-specific functions. The skin of mice and humans contain a large number of Tregs; however, the mechanisms of how these cells function in skin remain largely unknown. In this article, we show that Tregs facilitate cutaneous wound healing. Highly activated Tregs accumulated in skin early after wounding, and specific ablation of these cells resulted in delayed wound re-epithelialization and kinetics of wound closure. Tregs in wounded skin attenuated IFN-γ production and proinflammatory macrophage accumulation. Upon wounding, Tregs induce expression of the epidermal growth factor receptor (EGFR). Lineage-specific deletion of EGFR in Tregs resulted in reduced Treg accumulation and activation in wounded skin, delayed wound closure, and increased proinflammatory macrophage accumulation. Taken together, our results reveal a novel role for Tregs in facilitating skin wound repair and suggest that they use the EGFR pathway to mediate these effects.
Elemental chemical composition of some meadow plants in the Middle Ob basin

The data on the quantitative content of macro- and microelements in dry plant samples of meadow plants (Vicia cracca, Lathyrus pratensis, Cirsium setosum, Tanacetum vulgare, Inula salicina, Elytrigia repens, Carex cespitosa) in the Middle Ob basin within Tomsk region were obtained. We stated that these plant species differ in their ability to accumulate various elements. We showed the quantitative difference in the chemical composition of leaves and stems of three species. We made the conclusion about the dependence of absorption of elements on the plant species and element specificity.
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning

Heterogeneous Federated Learning (HtFL) enables collaborative learning on multiple clients with different model architectures while preserving privacy. Despite recent research progress, knowledge sharing in HtFL is still difficult due to data and model heterogeneity. To tackle this issue, we leverage the knowledge stored in pre-trained generators and propose a new upload-efficient knowledge transfer scheme called Federated Knowledge-Transfer Loop (FedKTL). Our FedKTL can produce client-task-related prototypical image-vector pairs via the generator's inference on the server. With these pairs, each client can transfer pre-existing knowledge from the generator to its local model through an additional supervised local task. We conduct extensive experiments on four datasets under two types of data heterogeneity with 14 kinds of models including CNNs and ViTs. Results show that our upload-efficient FedKTL surpasses seven state-of-the-art methods by up to 7.31% in accuracy. Moreover, our knowledge transfer scheme is applicable in scenarios with only one edge client. Code: https://github.com/TsingZ0/FedKTL
VP3D: Unleashing 2D Visual Prompt for Text-to-3D Generation

Recent innovations on text-to-3D generation have featured Score Distillation Sampling (SDS), which enables the zero-shot learning of implicit 3D models (NeRF) by directly distilling prior knowledge from 2D diffusion models. However, current SDS-based models still struggle with intricate text prompts and commonly result in distorted 3D models with unrealistic textures or cross-view inconsistency issues. In this work, we introduce a novel Visual Prompt-guided text-to-3D diffusion model (VP3D) that explicitly unleashes the visual appearance knowledge in 2D visual prompt to boost text-to-3D generation. Instead of solely supervising SDS with text prompt, VP3D first capitalizes on 2D diffusion model to generate a high-quality image from input text, which subsequently acts as visual prompt to strengthen SDS optimization with explicit visual appearance. Meanwhile, we couple the SDS optimization with additional differentiable reward function that encourages rendering images of 3D models to better visually align with 2D visual prompt and semantically match with text prompt. Through extensive experiments, we show that the 2D Visual Prompt in our VP3D significantly eases the learning of visual appearance of 3D models and thus leads to higher visual fidelity with more detailed textures. It is also appealing in view that when replacing the self-generating visual prompt with a given reference image, VP3D is able to trigger a new task of stylized text-to-3D generation. Our project page is available at https://vp3d-cvpr24.github. io.
Use of cellular HIV DNA to predict virologic response to maraviroc: performance of population-based and deep sequencing.

BACKGROUND A tropism test is required before administration of the antiretroviral drug maraviroc. However, plasma RNA testing is not possible in patients with undetectable plasma viral loads. Here we assess genotypic testing of cellular human immunodeficiency virus (HIV) DNA from peripheral blood mononuclear cells (PBMCs) to predict virologic responses in treatment-experienced patients beginning maraviroc-containing regimens. METHODS PBMC samples from 181 maraviroc recipients at study entry in MOTIVATE or A4001029 (51% R5 by original Trofile). The V3 loop was amplified in triplicate from cellular HIV DNA, and matching plasma RNA (n = 156). Sequencing was performed using standard population-based methods and next-generation deep sequencing, with tropism assessment as previously defined. RESULTS Genotypic DNA-based tropism testing from the cellular compartment had 78%-81% sensitivity relative to RNA-based Trofile at the same time point. Cell-based genotypic tropism methods and plasma-based phenotypic and genotypic methods were predictive of virologic response. However, when classifications were discordant, the outcomes favored the plasma predictions over the DNA ones. CONCLUSIONS Genotypic determination of HIV tropism can be performed using cell-derived viral DNA, and is a predictor of virologic success on maraviroc in therapy-experienced patients. However, the PBMC compartment appears to be a suboptimal predictor compared to plasma.
UPNeRF: A Unified Framework for Monocular 3D Object Reconstruction and Pose Estimation

Monocular 3D reconstruction for categorical objects heavily relies on accurately perceiving each object's pose. While gradient-based optimization within a NeRF framework updates initially given poses, this paper highlights that such a scheme fails when the initial pose even moderately deviates from the true pose. Consequently, existing methods often depend on a third-party 3D object to provide an initial object pose, leading to increased complexity and generalization issues. To address these challenges, we present UPNeRF, a Unified framework integrating Pose estimation and NeRF-based reconstruction, bringing us closer to real-time monocular 3D object reconstruction. UPNeRF decouples the object's dimension estimation and pose refinement to resolve the scale-depth ambiguity, and introduces an effective projected-box representation that generalizes well cross different domains. While using a dedicated pose estimator that smoothly integrates into an object-centric NeRF, UPNeRF is free from external 3D detectors. UPNeRF achieves state-of-the-art results in both reconstruction and pose estimation tasks on the nuScenes dataset. Furthermore, UPNeRF exhibits exceptional Cross-dataset generalization on the KITTI and Waymo datasets, surpassing prior methods with up to 50% reduction in rotation and translation error.
Development of a Chinese Human-Automation Trust Scale

The development of a reliable and valid assessment tool of human-automation trust is an important topic. This study aimed to develop a Chinese version of human-automation trust scale (C-HATS) with reasonable reliability and validity based on Lee and See (2004)'s trust model. After three phases of assessments including exploratory factor analysis, item analysis, and confirmatory factor analysis, different dimensions and items were considered for initial and posttask human-automation trust. For post-task trust, the scale had three dimensions and 11 items and reflected Lee and See (2004)'s model, whereas different from Lee and See (2004)'s model, the final scale had 14 items but only two dimensions for initial trust. Nevertheless, for both initial and post-task trust, reasonable reliability and validity of the scale were verified with various consumer automation products. Although further verification is still necessary, the developed C-HATS could be used to effectively assess human-automation trust in the Chinese context.
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors

Explainable Artificial Intelligence (XAI) strategies play a crucial part in increasing the understanding and trustworthiness of neural networks. Nonetheless, these techniques could potentially generate misleading explanations. Blinding attacks can drastically alter a machine learning algorithm's prediction and explanation, providing misleading information by adding visually unnoticeable artifacts into the input, while maintaining the model's accuracy. It poses a serious challenge in ensuring the reliability of XAI methods. To ensure the reliability of XAI methods poses a real challenge, we leverage statistical analysis to highlight the changes in CNN weights within a CNN following blinding attacks. We introduce a method specifically designed to limit the effectiveness of such attacks during the evaluation phase, avoiding the need for extra training. The method we suggest defences against most modern explanation-aware adversarial attacks, achieving an approximate decrease of ~99\% in the Attack Success Rate (ASR) and a ~91\% reduction in the Mean Square Error (MSE) between the original explanation and the defended (post-attack) explanation across three unique types of attacks.
CFAT: Unleashing TriangularWindows for Image Super-resolution

Transformer-based models have revolutionized the field of image super-resolution (SR) by harnessing their inherent ability to capture complex contextual features. The overlapping rectangular shifted window technique used in transformer architecture nowadays is a common practice in super-resolution models to improve the quality and robustness of image upscaling. However, it suffers from distortion at the boundaries and has limited unique shifting modes. To overcome these weaknesses, we propose a non-overlapping triangular window technique that synchronously works with the rectangular one to mitigate boundary-level distortion and allows the model to access more unique sifting modes. In this paper, we propose a Composite Fusion Attention Transformer (CFAT) that incorporates triangular-rectangular window-based local attention with a channel-based global attention technique in image super-resolution. As a result, CFAT enables attention mechanisms to be activated on more image pixels and captures long-range, multi-scale features to improve SR performance. The extensive experimental results and ablation study demonstrate the effectiveness of CFAT in the SR domain. Our proposed model shows a significant 0.7 dB performance improvement over other state-of-the-art SR architectures.