Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval

Cross-domain retrieval (CDR), as a crucial tool for numerous technologies, is finding increasingly broad applications. However, existing efforts face several major issues, with the most critical being the need for accurate supervision, which often demands costly resources and efforts. Cutting-edge studies focus on achieving unsupervised CDR but typically assume that the category spaces across domains are identical, an assumption that is often unrealistic in real-world scenarios. This is because only through dedicated and comprehensive analysis can the category spaces of different domains be confirmed as identical, which contradicts the premise of unsupervised scenarios. Therefore, in this work, we introduce the problem of Universal Unsupervised Cross-Domain Retrieval (U^2CDR) for the first time and design a two-stage semantic feature learning framework to address it. In the first stage, a cross-domain unified prototypical structure is established under the guidance of an instance-prototype-mixed contrastive loss and a semantic-enhanced loss, to counteract category space differences. In the second stage, through a modified adversarial training mechanism, we ensure minimal changes for the established prototypical structure during domain alignment, enabling more accurate nearest-neighbor searching. Extensive experiments across multiple datasets and scenarios, including closet, partial, and open-set CDR, demonstrate that our approach significantly outperforms existing state-of-the-art CDR works and some potentially effective studies from other topics in solving U^2CDR challenges.
Modeling the Multiwavelength Radiation Properties in Pulsar Dissipative Magnetospheres

We explore the multiwavelength radiation properties of the light curves and energy spectra in the dissipative magnetospheres of pulsars. The dissipative magnetospheres are simulated by the pseudo-spectral method with the combined force-free and Aristotelian electrodynamics, which can produce self-consistent accelerating electric fields mainly distributed in the equatorial current sheet outside the light cylinder. The multiwavelength light curves and spectra are computed by using the multiple emission mechanisms of both the primary particles accelerated by the accelerating electric fields in the equatorial current sheet and the secondary pairs with an assumed distribution spectrum. We then compare the predicted multiwavelength light curves and spectra with the observed data from the Crab, Vela, and Geminga pulsars. Our modeling results can systematically reproduce the observed trends of the multiwavelength light curves and the spectra for these three pulsars well.
A New Homogenization-Free Boundary Condition Towards Aperiodic Metasurface Design Using Full-Wave Surrogate Models of Printed Circuits

A new homogenization-free boundary condition is introduced for the design of metasurfaces. The boundary condition, linking the tangential electric field to the induced surface current density within a unit cell, is described as a matrix equation containing a surrogate model of a printed circuit. Full-wave simulations are performed to construct the data needed to capture the physics of a printed circuits response to exciting fields in terms of its induced current density. The matrix equation takes the form of a boundary condition which can be included in design and optimization algorithms incorporating mutual coupling in the form of an impedance matrix. Modelling the metasurface in this manner allows for direct design of the realizable model avoiding any homogenization or locally periodic approximations leading to a paradigm shift in metasurface design approaches. The surrogate models construction is described and its use in a 50 printed circuit example is provided.
A New Testing Method for Justification Bias Using High-Frequency Data of Health and Employment

Justification bias, wherein retirees may report poorer health to rationalize their retirement, poses a major concern to the widely-used measure of self-assessed health in retirement studies. This paper introduces a novel method for testing the presence of this bias in the spirit of regression discontinuity. The underlying idea is that any sudden shift in self-assessed health immediately following retirement is more likely attributable to the bias. Our strategy is facilitated by a unique high-frequency data that offers monthly, in contrast to the typical biennial, information on employment, self-assessed health, and objective health conditions. Across a wider post-retirement time frame, we observe a decline in self-assessed health, potentially stemming from both justification bias and changes in actual health. However, this adverse effect diminishes with shorter intervals, indicating no evidence of such bias. Our method also validates a widely-used indirect testing approach.
Quantized Constant-Envelope Waveform Design for Massive MIMO DFRC Systems

Both dual-functional radar-communication (DFRC) and massive multiple-input multiple-output (MIMO) have been recognized as enabling technologies for 6G wireless networks. This paper considers the advanced waveform design for hardware-efficient massive MIMO DFRC systems. Specifically, the transmit waveform is imposed with the quantized constant-envelope (QCE) constraint, which facilitates the employment of low-resolution digital-to-analog converters (DACs) and power-efficient amplifiers. The waveform design problem is formulated as the minimization of the mean square error (MSE) between the designed and desired beampatterns subject to the constructive interference (CI)-based communication quality of service (QoS) constraints and the QCE constraint. To solve the formulated problem, we first utilize the penalty technique to transform the discrete problem into an equivalent continuous penalty model. Then, we propose an inexact augmented Lagrangian method (ALM) algorithm for solving the penalty model. In particular, the ALM subproblem at each iteration is solved by a custom-built block successive upper-bound minimization (BSUM) algorithm, which admits closed-form updates, making the proposed inexact ALM algorithm computationally efficient. Simulation results demonstrate the superiority of the proposed approach over existing state-of-the-art ones. In addition, extensive simulations are conducted to examine the impact of various system parameters on the trade-off between communication and radar performances.
Reconstructing Visual Stimulus Images from EEG Signals Based on Deep Visual Representation Model

Reconstructing visual stimulus images is a significant task in neural decoding, and up to now, most studies consider the functional magnetic resonance imaging (fMRI) as the signal source. However, the fMRI-based image reconstruction methods are difficult to widely applied because of the complexity and high cost of the acquisition equipments. Considering the advantages of low cost and easy portability of the electroencephalogram (EEG) acquisition equipments, we propose a novel image reconstruction method based on EEG signals in this paper. Firstly, to satisfy the high recognizability of visual stimulus images in fast switching manner, we build a visual stimuli image dataset, and obtain the EEG dataset by a corresponding EEG signals collection experiment. Secondly, the deep visual representation model(DVRM) consisting of a primary encoder and a subordinate decoder is proposed to reconstruct visual stimuli. The encoder is designed based on the residual-in-residual dense blocks to learn the distribution characteristics between EEG signals and visual stimulus images, while the decoder is designed based on the deep neural network to reconstruct the visual stimulus image from the learned deep visual representation. The DVRM can fit the deep and multiview visual features of human natural state and make the reconstructed images more precise. Finally, we evaluate the DVRM in the quality of the generated images on our EEG dataset. The results show that the DVRM have good performance in the task of learning deep visual representation from EEG signals and generating reconstructed images that are realistic and highly resemble the original images.
Volume and topology of bounded and closed hyperbolic 3-manifolds, II

Let $N$ be a compact, orientable hyperbolic 3-manifold whose boundary is a connected totally geodesic surface of genus $2$. If $N$ has Heegaard genus at least $5$, then its volume is greater than $2V_{\rm oct}$, where $V_{\rm oct}=3.66\ldots$ denotes the volume of a regular ideal hyperbolic octahedron in $\mathbb{H}^3$. This improves the lower bound given in our earlier paper ``Volume and topology of bounded and closed hyperbolic $3$-manifolds. '' One ingredient in the improved bound is that in a crucial case, instead of using a single ``muffin'' in $N$ in the sense of Kojima and Miyamoto, we use two disjoint muffins. By combining the result about manifolds with geodesic boundary with the $\log(2k-1)$ theorem and results due to Agol-Culler-Shalen and Shalen-Wagreich, we show that if $M$ is a closed, orientable hyperbolic $3$-manifold with $\mathop{\rm vol} M\le V_{\rm oct}/2$, then $\dim H_1(M;\mathbb{F}_2)\le4$. We also provide new lower bounds for the volumes of closed hyperbolic $3$-manifolds whose cohomology ring over $\mathbb{F}_2$ satisfies certain restrictions; these improve results that were proved in ``Volume and topology$\ldots$. ''
Management of indigenous human health knowledge in Tanzania.

and the extent to which the knowledge has been disseminated or shared between agents. The I-Space model, therefore, rests on the following tenets: first, the more the knowledge is structured, the easier and faster is its sharing and diffusion; and second, the more contextual the knowledge, is the more difficult to transfer and share such knowledge. In addition to what has been mentioned above, another strength of the I-Space model is that of the attributes that link to the concept of KM in an organisation. The codification dimension
Primordial dust universe in the Ho\v{r}ava-Lifshitz theory

We apply quantum cosmology to investigate the early moments of a Friedmann-Lema\^itre-Robertson-Walker (FLRW) cosmological model, using Ho\v{r}ava-Lifshitz (HL) as the gravitational theory. The matter content of the model is a dust perfect fluid. We start studying the classical model. Then, we write the total Hamiltonian of the model, quantize it and find the appropriate Wheeler-DeWitt equation. In order to avoid factor ordering ambiguities, in the Wheeler-DeWitt equation, we introduce a canonical transformation. We solve that equation using the Wentzel-Kramers-Brillouin (WKB) approximation and compute the tunneling probabilities for the birth of that universe ($TP_{WKB}$). Since the WKB wavefunction depends on the dust energy and the free coupling constants coming from the HL theory, we compute the behavior of $TP_{WKB}$ as a function of all these quantities.
Environmental quality intervention for waste disposal in the community in Kalinaun Village, East Likupang District

Waste processing in various villages has not been good and there is a accumulation of garbage so that it can spoil the scenery / aesthetics of the environment and cause the spread of disease. This service activity aims to provide knowledge and understanding to the community of Environment V and VI of Kalinaun Village, East Likupang District regarding Waste Management through counselling and making fertilizer from organic waste. The method of counselling and making organic fertilizer is a method carried out in solving the problem of waste in Kalinaun village in guard V and Vl of East Likupang district. Before counselling is carried out, the community fills out the Pre-test first and post-test after counselling. There were 22 respondents in counselling, before counselling 10 respondents with good knowledge categories became 14 respondents after counselling. Continuous monitoring efforts from community leaders need to be carried out to monitor success in community-based waste management.
Application of an Anisotropy‐Based Correction to Relative Paleointensity Estimates of Experimentally Deposited Sediments

The magnetization of a sediment is acquired when ferromagnetic detrital particles align with the Earth's magnetic field as they settle through the water column. The relative continuity and ubiquity of the global sedimentary record makes sediments and sedimentary rocks an ideal target for studies of relative paleointensity (RPI) and the evolution of the Earth's magnetic field. However, processes including flocculation, contact with the substrate, bioturbation, and compaction disrupt this alignment, leading to shallowed remanent inclinations and biased intensities. The inclination of the depositional field also exerts an influence on the remanent intensity, thus affecting RPI estimates and leading to complications in interpretations of the global record. We present a series of 12 deposition experiments conducted in varying field strengths and inclinations, from which we determine RPI estimates. We then correct these estimates using a modified anisotropy‐based inclination correction technique. We find experimentally that our correction reduces the inclination dependence of relative paleointensity while preserving the RPI's dependence on the depositional intensity. Our results suggest that the anisotropy‐based correction should improve RPI estimates obtained from natural sediments and sedimentary rocks.
Strict hierarchy of optimal strategies for global estimations: Mapping global estimations into local ones

A crucial yet challenging issue in quantum metrology is to ascertain the ultimate precision achievable in estimation strategies. While there are two paradigms of estimations, local and global, current research is largely confined to local estimations, which are useful once the parameter of interest is approximately known. In this Letter we target a paradigm shift towards global estimations, which can operate reliably even with a few measurement data and no substantial prior knowledge about the parameter. The key innovation here is to develop a technique, dubbed virtual imaginary time evolution, capable of mapping global estimations into local ones. This offers an intriguing pathway to surmount challenges in the realm of global estimations by leveraging powerful tools tailored for local estimations. We explore our technique to unveil a strict hierarchy of achievable precision for different global estimation strategies, including parallel, sequential, and indefinite-causal-order strategies.
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation

Semi-supervised semantic segmentation allows model to mine effective supervision from unlabeled data to complement label-guided training. Recent research has primarily focused on consistency regularization techniques, exploring perturbation-invariant training at both the image and feature levels. In this work, we proposed a novel feature-level consistency learning framework named Density-Descending Feature Perturbation (DDFP). Inspired by the low-density separation assumption in semi-supervised learning, our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore, which is the regions with lower density. We propose to shift features with confident predictions towards lower-density regions by perturbation injection. The perturbed features are then supervised by the predictions on the original features, thereby compelling the classifier to explore less dense regions to effectively regularize the decision boundary. Central to our method is the estimation of feature density. To this end, we introduce a lightweight density estimator based on normalizing flow, allowing for efficient capture of the feature density distribution in an online manner. By extracting gradients from the density estimator, we can determine the direction towards less dense regions for each feature. The proposed DDFP outperforms other designs on feature-level perturbations and shows state of the art performances on both Pascal VOC and Cityscapes dataset under various partition protocols. The project is available at https://github.com/Gavinwxy/DDFP.
Advancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and Attention Mechanism Approach for Heterogeneous Graph-Structured Data

Graph neural networks (GNNs) have proven effective in capturing relationships among nodes in a graph. This study introduces a novel perspective by considering a graph as a simplicial complex, encompassing nodes, edges, triangles, and $k$-simplices, enabling the definition of graph-structured data on any $k$-simplices. Our contribution is the Hodge-Laplacian heterogeneous graph attention network (HL-HGAT), designed to learn heterogeneous signal representations across $k$-simplices. The HL-HGAT incorporates three key components: HL convolutional filters (HL-filters), simplicial projection (SP), and simplicial attention pooling (SAP) operators, applied to $k$-simplices. HL-filters leverage the unique topology of $k$-simplices encoded by the Hodge-Laplacian (HL) operator, operating within the spectral domain of the $k$-th HL operator. To address computation challenges, we introduce a polynomial approximation for HL-filters, exhibiting spatial localization properties. Additionally, we propose a pooling operator to coarsen $k$-simplices, combining features through simplicial attention mechanisms of self-attention and cross-attention via transformers and SP operators, capturing topological interconnections across multiple dimensions of simplices. The HL-HGAT is comprehensively evaluated across diverse graph applications, including NP-hard problems, graph multi-label and classification challenges, and graph regression tasks in logistics, computer vision, biology, chemistry, and neuroscience. The results demonstrate the model's efficacy and versatility in handling a wide range of graph-based scenarios.
A Review of the Literature

The objective is to summarize the existing nursing literature on the concept of functional status after childbirth. Studies were selected by incorporating all existing published literature, including inquiry into functional status after childbirth as measured by the Inventory of Functional Status After Childbirth. The data were extracted from online nursing databases such as CINAHL (1982-present) and dissertation abstracts. Results of the literature review indicate the need for further development of functional status after childbirth and the instrument used to measure it, the Inventory of Functional Status after Childbirth. The author concludes that functional status after childbirth is a highly important nursing concept and further inquiry is needed to guide nursing practice.
The framework for estimating coverage in the 2011 Census of England and Wales: Combining dual-system estimation with ratio estimation

Dual-system estimation is a well-established approach for estimating an unknown population size from two independent but imperfect counts of the population. In this paper we develop the estimation framework for using a coverage survey and population census as the two sources and combining with ratio estimation to produce a set of population estimates. Adjustments are developed to correct for a failure of the key assumptions of homogeneity and independence that under-pin dual-system estimation using an external count of the number of households. The issue of over-count within the census is also discussed and a bootstrap approach to variance estimation is proposed. A comprehensive set of simulation results are presented to support the decision to implement the framework to estimate the population following the 2011 Census of England and Wales; and the implementation to the estimation of census coverage in 2011 is discussed.
Chronic Myelomonocytic Leukemia-Associated Immune Thrombocytopenic Purpura: A Report of a Rare Case and a Review of Literature

Chronic myelomonocytic leukemia (CMML) presents as a complex hematologic malignancy with myelodysplastic and myeloproliferative features. Our case report explores the rare coexistence of CMML with immune thrombocytopenic purpura (ITP) in a 63-year-old female patient. CMML diagnosis followed World Health Organization criteria, and the patient was classified as having high-risk myelodysplastic syndrome (MDS)-CMML stage 2. Initial treatment with subcutaneous azacytidine for CMML proved partially effective, highlighting persistent severe thrombocytopenia. Subsequent investigations revealed secondary ITP associated with Crohn's disease. Conventional ITP therapies, including high-dose steroids and intravenous immunoglobulin, showed limited efficacy. Eltrombopag, a thrombopoietin receptor agonist, was initiated, resulting in the normalization of platelet counts within six weeks. Our case emphasizes the diagnostic challenges and intricate treatment landscape of CMML-associated ITP, suggesting eltrombopag as a potential therapeutic option in refractory cases. The study contributes to the evolving understanding of the complex interplay between myeloid disorders and immune-mediated hematological conditions, calling for personalized and multidisciplinary approaches to enhance patient outcomes.
Closed-loop control of gamma oscillations in the brain connections through the transcranial stimulations

The reconstruction of brain neural network connections occurs not only during the infancy and early childhood stages of brain development, but also in patients with cognitive impairment in middle and old age under the therapy with stimulated external interference, such as the non-invasive repetitive transcranial magnetic stimulation (rTMS) and the transcranial direct current stimulation(tDCS). However, until now, it is not clear how brain stimulation triggers and controls the reconstruction of neural network connections in the brain. This paper combines the EEG data analysis and the cortical neuronal network modeling methods. On one hand, an E-I balanced cortical neural network model was constructed under a long-lasting external stimulation of sinusoidal-exponential form TMS or square-wave tDCS was introduced into the network model for simulate the treatment process for the brain connections. On the other hand, by combining Butterworth filter and functional connectivity algorithm, the paper analyzes the relations between the attentional gamma oscillation responses and the brain connection based on the publicly available EEGs during the pre-tDCS and post-tDCS treatment phases. Firstly, the simulation results indicate that, during long-lasting external stimulations of tDCS/rTMS, The sustained gamma oscillation was found to trigger more release of BDNF from astrocytes to participate in the positively reshaping the excitatory neuronal network connection.