Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Modulation for Short Packet Transmission in URLLC

One key feature of ultra-reliable low-latency communications (URLLC) in 5G is to support short packet transmission (SPT). However, the pilot overhead in SPT for channel estimation is relatively high, especially in high Doppler environments. In this paper, we advocate the adoption of differential modulation to support ultra-low latency services, which can ease the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Specifically, we consider a multi-connectivity (MC) scheme employing differential modulation to enable URLLC services. The popular selection combining and maximal ratio combining schemes are respectively applied to explore the diversity gain in the MC scheme. A first-order autoregressive model is further utilized to characterize the time-varying nature of the channel. Theoretically, the maximum achievable rate and minimum achievable block error rate under ergodic fading channels with PSK inputs and perfect CSI are first derived by using the non-asymptotic information-theoretic bounds. The performance of SPT with differential modulation and MC schemes is then analysed by characterizing the effect of differential modulation and time-varying channels as a reduction in the effective SNR. Simulation results show that differential modulation does offer a significant advantage over the pilot-assisted coherent scheme for SPT, especially in high Doppler environments.
Bridging Dense and Sparse Maximum Inner Product Search

Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top-$k$ retrieval in Information Retrieval. This duality exists because sparse and dense vectors serve different end goals. That is despite the fact that they are manifestations of the same mathematical problem. In this work, we ask if algorithms for dense vectors could be applied effectively to sparse vectors, particularly those that violate the assumptions underlying top-$k$ retrieval methods. We study IVF-based retrieval where vectors are partitioned into clusters and only a fraction of clusters are searched during retrieval. We conduct a comprehensive analysis of dimensionality reduction for sparse vectors, and examine standard and spherical KMeans for partitioning. Our experiments demonstrate that IVF serves as an efficient solution for sparse MIPS. As byproducts, we identify two research opportunities and demonstrate their potential. First, we cast the IVF paradigm as a dynamic pruning technique and turn that insight into a novel organization of the inverted index for approximate MIPS for general sparse vectors. Second, we offer a unified regime for MIPS over vectors that have dense and sparse subspaces, and show its robustness to query distributions.
CryoAlign: feature-based method for global and local 3D alignment of EM density maps

Advances on cryo-electron imaging technologies have led to a rapidly increasing number of density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we propose a fast and accurate global and local cryo-electron microscopy density map alignment method CryoAlign, which leverages local density feature descriptors to capture spatial structure similarities. CryoAlign is the first feature-based EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences and robust estimation of alignment parameters. Extensive experimental evaluations demonstrate the superiority of CryoAlign over the existing methods in both alignment accuracy and speed.
A Distributed Strategy to Maximize Coverage in a Heterogeneous Sensor Network in the Presence of Obstacles

In this paper, an efficient deployment strategy is proposed for a network of mobile and static sensors with non-identical sensing and communication radii. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is used to partition the field and assign the underlying coverage task to each mobile sensor. A gradient-based method is applied to find the best candidate point based on the detected coverage holes and the coverage priority considering the relative distance of the mobile sensor from the static ones and the obstacles in the field. The sensors move to a new position if such a relocation increases their local coverage. The efficiency of the proposed strategy in different scenarios is demonstrated by simulations.
Asymptotic Analysis of the Downlink in Cooperative Massive MIMO Systems

We consider the downlink of a cooperative cellular communications system, where several base-stations around each mobile cooperate and perform zero-forcing to reduce the received interference at the mobile. We derive closed-form expressions for the asymptotic performance of the network as the number of antennas per base station grows large. These expressions capture the trade off between various system parameters, and characterize the joint effect of noise and interference (where either noise or interference is asymptotically dominant and where both are asymptotically relevant). The asymptotic results are verified using Monte Carlo simulations, which indicate that they are useful even when the number of antennas per base station is only moderately large. Additionally, we show that when the number of antennas per base station grows large, power allocation can be optimized locally at each base station. We hence present a power allocation algorithm that achieves near optimal performance while significantly reducing the coordination overhead between base stations. The presented analysis is significantly more challenging than the uplink analysis, due to the dependence between beamforming vectors of nearby base stations. This statistical dependence is handled by introducing novel bounds on marked shot-noise point processes with dependent marks, which are also useful in other contexts.
Vertex Operators of the KP hierarchy and Singular Algebraic Curves

Quasi-periodic solutions of the KP hierarchy acted by vertex operators are studied. We show, with the aid of the Sato Grassmannian, that solutions thus constructed correspond to torsion free rank one sheaves on some singular algebraic curves whose normalizations are the non-singular curves corresponding to the seed quasi-periodic solutions. It means that the action of the vertex operator has an effect of creating singular points on an algebraic curve. We further check, by examples, that solutions obtained here can be considered as solitons on quasi-periodic backgrounds, where the soliton matrices are deterimed by parameters in the vertex operators.
Characterization of Solubilizers of Elements in Minimal Simple Groups

Given a finite group $G$, the solubilizer of an element $x$, denoted by $\Sol_G(x)$, is the set of all elements $y$ such that $\langle x, y\rangle$ is a soluble subgroup of $G$. In this paper, we provide a classification for all solubilizers of elements in minimal simple groups. We also examine these sets to explore their properties by discussing some computational methods and making some conjectures for further work.
Mobile Metaverse: A Road Map from Metaverse to Metavehicles

With the rapid development of communication technologies and extended reality (XR), the services and applications of the Metaverse are gradually entering our lives. However, the current development of the Metaverse provides users with services that are homogeneous with the user experience that the Internet has brought in the past, making them more like an extension of the Internet. In addition, as a mobile application carrier for the Metaverse, it is also worth considering how vehicles with diverse onboard components can develop in synergy with the Metaverse. In this article, we focus on the core of the Metaverse, namely user experience, and provide a road map from Metaverse to Metaverse vehicles (Metavehicles). Specifically, we first elaborate on six features of the Metaverse from the perspective of user experience and propose a hierarchical framework for the Metaverse based on the evolutionary logic of the features. Under the guidance of this framework, we discuss the empowerment of onboard components of Metavehicles on the development of the Metaverse, and analyze the service experience that Metavehicles can bring to two types of users, namely drivers and passengers. Finally, considering the differentiated development levels of Metaverse and autonomous driving, we further establish a hierarchical framework for Metavehicles from three aspects (i.e., enhance Metaverse, enhance driving experience, and enhance entertainment experience), providing an evolutionary path for the development of Metavehicles.
RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification

In this study, we present a novel and challenging multilabel Vietnamese dataset (RMDM) designed to assess the performance of large language models (LLMs), in verifying electronic information related to legal contexts, focusing on fake news as potential input for electronic evidence. The RMDM dataset comprises four labels: real, mis, dis, and mal, representing real information, misinformation, disinformation, and mal-information, respectively. By including these diverse labels, RMDM captures the complexities of differing fake news categories and offers insights into the abilities of different language models to handle various types of information that could be part of electronic evidence. The dataset consists of a total of 1,556 samples, with 389 samples for each label. Preliminary tests on the dataset using GPT-based and BERT-based models reveal variations in the models' performance across different labels, indicating that the dataset effectively challenges the ability of various language models to verify the authenticity of such information. Our findings suggest that verifying electronic information related to legal contexts, including fake news, remains a difficult problem for language models, warranting further attention from the research community to advance toward more reliable AI models for potential legal applications.
PromptVC: Flexible Stylistic Voice Conversion in Latent Space Driven by Natural Language Prompts

Style voice conversion aims to transform the style of source speech to a desired style according to real-world application demands. However, the current style voice conversion approach relies on pre-defined labels or reference speech to control the conversion process, which leads to limitations in style diversity or falls short in terms of the intuitive and interpretability of style representation. In this study, we propose PromptVC, a novel style voice conversion approach that employs a latent diffusion model to generate a style vector driven by natural language prompts. Specifically, the style vector is extracted by a style encoder during training, and then the latent diffusion model is trained independently to sample the style vector from noise, with this process being conditioned on natural language prompts. To improve style expressiveness, we leverage HuBERT to extract discrete tokens and replace them with the K-Means center embedding to serve as the linguistic content, which minimizes residual style information. Additionally, we deduplicate the same discrete token and employ a differentiable duration predictor to re-predict the duration of each token, which can adapt the duration of the same linguistic content to different styles. The subjective and objective evaluation results demonstrate the effectiveness of our proposed system.
Using LLMs to Facilitate Formal Verification of RTL

Formal property verification (FPV) has existed for decades and has been shown to be effective at finding intricate RTL bugs. However, formal properties, such as those written as SystemVerilog Assertions (SVA), are time-consuming and error-prone to write, even for experienced users. Prior work has attempted to lighten this burden by raising the abstraction level so that SVA is generated from high-level specifications. However, this does not eliminate the manual effort of reasoning and writing about the detailed hardware behavior. Motivated by the increased need for FPV in the era of heterogeneous hardware and the advances in large language models (LLMs), we set out to explore whether LLMs can capture RTL behavior and generate correct SVA properties. First, we design an FPV-based evaluation framework that measures the correctness and completeness of SVA. Then, we evaluate GPT4 iteratively to craft the set of syntax and semantic rules needed to prompt it toward creating better SVA. We extend the open-source AutoSVA framework by integrating our improved GPT4-based flow to generate safety properties, in addition to facilitating their existing flow for liveness properties. Lastly, our use cases evaluate (1) the FPV coverage of GPT4-generated SVA on complex open-source RTL and (2) using generated SVA to prompt GPT4 to create RTL from scratch. Through these experiments, we find that GPT4 can generate correct SVA even for flawed RTL, without mirroring design errors. Particularly, it generated SVA that exposed a bug in the RISC-V CVA6 core that eluded the prior work's evaluation.
Single-Layer Wideband Circularly Polarized High-Efficiency Reflectarray for Satellite Communications

—This paper presents a single-layer circularly polarized (CP) reflectarray which achieves large bandwidth in terms of axial ratio (AR), gain, aperture efficiency and radiation pattern simultaneously. By using a novel wideband S-shaped phasing element, an offset-fed reflectarray with 20o offset beam is designed based on the element angular rotation method. Theoretical analysis is given to analyze the effect of angular rotated elements on the performance of the reflectarray, which indicates that the AR bandwidth of the reflectarray can exceed the AR bandwidth of the feed horn. Besides, the effect of the differential spatial phase delay on the bandwidth of the proposed reflectarray is analyzed quantitatively. To verify these concepts, a 180mm×180mm prototype with 15×15 elements is fabricated and measured. The measured results confirm that the proposed reflectarray can achieve a 68.5% 3dB AR bandwidth and a 47.8% 3dB gain bandwidth. Moreover, the aperture efficiency is larger than 50% in a 33% bandwidth and larger than 30% in a 64% bandwidth.
Non-negative Matrix Factorization using Partial Prior Knowledge for Radiation Dosimetry

Hyperspectral unmixing aims at decomposing a given signal into its spectral signatures and its associated fractional abundances. To improve the accuracy of this decomposition, algorithms have included different assumptions depending on the application. The goal of this study is to develop a new unmixing algorithm that can be applied for the calibration of multi-point scintillation dosimeters used in the field of radiation therapy. This new algorithm is based on a non-negative matrix factorization. It incorporates a partial prior knowledge on both the abundances and the endmembers of a given signal. It is shown herein that, following a precise calibration routine, it is possible to use partial prior information about the fractional abundances, as well as on the endmembers, in order to perform a simplified yet precise calibration of these dosimeters. Validation and characterization of this algorithm is made using both simulations and experiments. The experimental validation shows an improvement in accuracy compared to previous algorithms with a mean spectral angle distance (SAD) on the estimated endmembers of 0.0766, leading to an average error of $(0.25 \pm 0.73)$ % on dose measurements.
Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie

Large language models are quickly gaining momentum, yet are found to demonstrate gender bias in their responses. In this paper, we conducted a content analysis of social media discussions to gauge public perceptions of gender bias in LLMs which are trained in different cultural contexts, i.e., ChatGPT, a US-based LLM, or Ernie, a China-based LLM. People shared both observations of gender bias in their personal use and scientific findings about gender bias in LLMs. A difference between the two LLMs was seen -- ChatGPT was more often found to carry implicit gender bias, e.g., associating men and women with different profession titles, while explicit gender bias was found in Ernie's responses, e.g., overly promoting women's pursuit of marriage over career. Based on the findings, we reflect on the impact of culture on gender bias and propose governance recommendations to regulate gender bias in LLMs.
Most Hospitalized Patients with Significant Tricuspid Regurgitation Have Advanced Disease Preventing Transcatheter Tricuspid Valve Intervention

Background: More than moderate tricuspid regurgitation (TR) is associated with high mortality. Surgical tricuspid valve repair and replacements are rarely performed due to high operative mortality risk, mainly attributed to late presentation. Novel transcatheter tricuspid valve intervention (TTVI) devices are being developed as an alternative to surgery. The population of patients presenting to tertiary care centers who can benefit from TTVI has not been well defined. Our objective was to study the characteristics of these patients and to define the sub-population who may be candidates for TTVI. Methods: We analyzed 12,677 consecutive 2D echocardiograms completed at our tertiary care center between March 2021 and March 2022 and identified 581 inpatients with more than moderate TR. Clinical and echocardiographic data were collected by individual chart review. The 2021 European Society of Cardiology (ESC) guidelines on the management of valvular disease are currently the only guidelines that include TTVI. We used these guidelines to retrospectively assign the 581 patients to medical therapy, surgery, or TTVI treatment. Results: 470 patients (80.9%) were assigned to medical therapy, 57 (9.8%) were assigned to TTVI, and 54 (9.3%) were assigned to tricuspid valve surgery. Of note, 76.2% (443/581) of patients were precluded from any intervention due to advanced disease or anatomic ineligibility, and only 4.6% (27/581) presented too early for intervention, being both asymptomatic and without RV dilatation. Conclusion: Only 9.8 percent of patients presenting to a tertiary care center with significant TR would be hypothetical candidates for TTVI when these technologies are approved in the United States. Earlier identification and treatment of TR could increase the number of patients who may benefit from interventions including TTVI.
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning

Model-based reinforcement learning (RL), which learns environment model from offline dataset and generates more out-of-distribution model data, has become an effective approach to the problem of distribution shift in offline RL. Due to the gap between the learned and actual environment, conservatism should be incorporated into the algorithm to balance accurate offline data and imprecise model data. The conservatism of current algorithms mostly relies on model uncertainty estimation. However, uncertainty estimation is unreliable and leads to poor performance in certain scenarios, and the previous methods ignore differences between the model data, which brings great conservatism. Therefore, this paper proposes a milDly cOnservative Model-bAsed offlINe RL algorithm (DOMAIN) without estimating model uncertainty to address the above issues. DOMAIN introduces adaptive sampling distribution of model samples, which can adaptively adjust the model data penalty. In this paper, we theoretically demonstrate that the Q value learned by the DOMAIN outside the region is a lower bound of the true Q value, the DOMAIN is less conservative than previous model-based offline RL algorithms and has the guarantee of security policy improvement. The results of extensive experiments show that DOMAIN outperforms prior RL algorithms on the D4RL dataset benchmark, and achieves better performance than other RL algorithms on tasks that require generalization.
Droplet‐based optical trapping for cell separation in mock forensic samples

Optical tweezers have a wide range of uses for mechanical manipulation of objects in the microscopic range. This includes both living and static cells in a variety of biomedical and research applications. Single‐focus optical tweezers, formed by focusing a laser beam through a high numerical aperture immersion objective, create a significant force, which enables controlled transport of a variety of different cell types and morphologies in three dimensions. Optical tweezers have been previously reported to capture and separate spermatozoa from a reconstituted simulated postcoital sample. We report herein the development of a simplified, more efficient cell transfer protocol that can separate and isolate both spermatozoa as well as leukocytes, with similar efficiencies as those previously reported. The new cell transfer method was used to separate sperm cells from a reconstituted mixture of spermatozoa and vaginal epithelial cells, with complete STR profiles developed from 50 cells with little evidence of contribution from the female contributor to the mixture. This modified protocol was then used to separate 21 samples of enriched leukocytes, with trapped cells ranging from 5 to 22 cells. Complete STR profiles were developed from as few as 10 leukocytes. Thus, with minimal sample preparation and a short trapping time, this method has the potential to provide an alternative to traditional differential extraction methods for separation of sperm:nonsperm mixtures while also providing versatility for separation of cells with differing morphologies.
A marginal branch of the left hepatic artery running along the umbilical vein and supplying the anterior surface of the liver left lobe: a report of 5 cases in 12 Japanese human fetuses

In human fetuses, the left hepatic artery (LHA) issues the marginal artery that runs along the umbilical vein and, sometimes, reaches the umbilicus. The further observation demonstrated that, in 5 of 12 Japanese midterm fetuses (crown-rump length mm: 46, 50, 54, 59, 102), the marginal artery issued not only a thin umbilical branch but also a liver parenchymal branch that took a posterosuperior recurrent course in a peritoneal fold and supplied the anterior surface of the liver left lobe (segment III). However, in 22 Spanish fetuses of which gestational ages corresponded to the Japanese ones, we did not find the parenchymal branch. Therefore, between human populations, there seemed to be a considerable difference in the incidence as to whether or not the marginal artery issues the liver parenchymal branch. The parenchymal branch might be degenerated at the later stages due to friction between the liver free surface and growing diaphragm.