Probabilistic Rateless Multiple Access for Machine-to-Machine Communication

Future machine to machine (M2M) communications need to support a massive number of devices communicating with each other with little or no human intervention. Random access techniques were originally proposed to enable M2M multiple access, but suffer from severe congestion and access delay in an M2M system with a large number of devices. In this paper, we propose a novel multiple access scheme for M2M communications based on the capacity-approaching analog fountain code to efficiently minimize the access delay and satisfy the delay requirement for each device. This is achieved by allowing M2M devices to transmit at the same time on the same channel in an optimal probabilistic manner based on their individual delay requirements. Simulation results show that the proposed scheme achieves a near optimal rate performance and at the same time guarantees the delay requirements of the devices. We further propose a simple random access strategy and characterized the required overhead. Simulation results show the proposed approach significantly outperforms the existing random access schemes currently used in long term evolution advanced (LTE-A) standard in terms of the access delay.Comment: Accepted to Publish in IEEE Transactions on Wireless Communication

Comparative analysis of load responses and deformation for crust composite foundation and pile-supported embankment

Ground improvement using artificial crust composite foundation, consisting of stabilization of soft clay and composite foundation, is an effective technique for the treatment of deep soft soil layers under infrastructure embankments. In this study, the load responses and settlement performance of this improvement technique were investigated using two centrifuge model tests to compare the variations of the vertical deformation, pore water pressure, axial force of the piles and tensile stress at the bottom of the artificial crust in the crust composite foundation with those in pile-supported embankment. The results of centrifuge model tests showed that the load responses and settlement performance of artificial crust composite foundation was different from the pile-supported embankment, which displayed mainly that the final middle settlement of crust composite foundation can be reduced by about 15% compared with those of pile-supported embankment with the same length of pile and construction cost. The deformation of the crust with the characteristics of the plate was found based on the change of the tensile stress. Additionally, the excess pore water pressure in the crust composite foundation was lower owing to the stress diffusion effect of the crust during the loading period and the dissipation rate of excess pore water pressure was slower due to lower permeability of the crust at the same loading period. Eventually, the axial force of the middle piles was reduced. At the same time, the boundary stress was functioned with the crust, the axial force of the side piles was improved. The comparison of measured and calculated results was carried out using the stress reduction ratio, the result shows that the bearing capacity of the subsoil in the crust composite was improved

Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs

In the field of document understanding, significant advances have been made in the fine-tuning of Multimodal Large Language Models (MLLMs) with instruction-following data. Nevertheless, the potential of text-grounding capability within text-rich scenarios remains underexplored. In this paper, we present a text-grounding document understanding model, termed TGDoc, which addresses this deficiency by enhancing MLLMs with the ability to discern the spatial positioning of text within images. Empirical evidence suggests that text-grounding improves the model's interpretation of textual content, thereby elevating its proficiency in comprehending text-rich images. Specifically, we compile a dataset containing 99K PowerPoint presentations sourced from the internet. We formulate instruction tuning tasks including text detection, recognition, and spotting to facilitate the cohesive alignment between the visual encoder and large language model. Moreover, we curate a collection of text-rich images and prompt the text-only GPT-4 to generate 12K high-quality conversations, featuring textual locations within text-rich scenarios. By integrating text location data into the instructions, TGDoc is adept at discerning text locations during the visual question process. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple text-rich benchmarks, validating the effectiveness of our method

Continued Efforts in TI ARM M4 Microcontroller Curricula Developments and Assessments Between Three Different Institutions and Programs

This project is a continuation in efforts to upgrade the curricula in microcontroller related courses that are facing difficulties in the disappearing and lack technical supports in hardware and software of 68XXX and 80XXX microcontrollers. Through the study of a NSF supported project Transform the Innovated Design and Development of an Embedded Design Training System and Associated Support Curricula into a Commercial Available Product that interviewed 130 faculty/teachers/students across the U.S. has revealed on finding newly available microcontrollers is an urgent issue in the academic communities. Based on the supports on hardware and software and function libraries, the TI ARM M4 core is the choice for the join efforts in the new curriculum development and assessment between Old Dominion University, Farmingdale State College, Prairie View A&M University, and Ohio Northern University within the programs of CET, ECE, EET, and Tech Studies. The efforts were also a direct response to the industries suggestions and the needs of 32 bits ARM microcontroller’s skills from engineer and technology programs graduates to fill the job markets. This article presents a study and comparison that introduce a concept of collaborated efforts among different institutions and programs can work together to develop the comprehensive ARM curricula that fit the industry’s needs. These curricula development efforts are not only aim at on-campus face-to-face teaching and learning but also distance hands-on learning through delivering course modules using both synchronous and asynchronous. The assessment of this jointed efforts are part of the studies. Engineering and technology programs focus on both hands-on and mind-on design work and this article demonstrates the collaborated efforts in advanced curriculum development in the ARM microcontroller which is the key ingredient for success. Through the development efforts and online Learning Management System (LMS) designs that make the distance collaboration, delivery, and cyber-enabled learning possible. These efforts not only benefit the interested faculty/teachers in better teaching and learning, but also support the students who can learn more advanced technical concepts that are needed for emerging high-tech job skills. Highlights of the presentation will address the following: • Research and development of the virtual classrooms and open source service server. • Design and development of the supported material. • Implementation strategies and planning for the distance hands-on approach. • Assessment of the teaching and learning. • Recommendations of potential adoption of the development. • Continuous improvement of teaching and learning in academic community

VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic assessment (IAA) methods primarily rely on human-labeled rating scores, which oversimplify the visual aesthetic information that humans perceive. Conversely, user comments offer more comprehensive information and are a more natural way to express human opinions and preferences regarding image aesthetics. In light of this, we propose learning image aesthetics from user comments, and exploring vision-language pretraining methods to learn multimodal aesthetic representations. Specifically, we pretrain an image-text encoder-decoder model with image-comment pairs, using contrastive and generative objectives to learn rich and generic aesthetic semantics without human labels. To efficiently adapt the pretrained model for downstream IAA tasks, we further propose a lightweight rank-based adapter that employs text as an anchor to learn the aesthetic ranking concept. Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines. With only minimal finetuning parameters using the proposed adapter module, our model achieves state-of-the-art IAA performance over the AVA dataset.Comment: CVPR 2023, https://github.com/google-research/google-research/tree/master/vil

Gender Differences in PTSD: Susceptibility and Resilience

Posttraumatic stress disorder (PTSD) is anxiety disorder that has been estimated to affect individuals who are exposed to traumatic events. Women are diagnosed with PTSD approximately twice as often as men. In this review, we outline the evidence of gender differences related to PTSD, and the factors of resilience and susceptibility differ between men and women

EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association

Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms state-of-the-art techniques in accuracy and robustness. The source code is available on: https://github.com/yanmin-wu/EAO-SLAM.Comment: Accepted to IROS 2020. Project Page: https://yanmin-wu.github.io/project/eaoslam/; Code: https://github.com/yanmin-wu/EAO-SLA