A local homology theory for linearly compact modules

We introduce a local homology theory for linearly compact modules which is in some sense dual to the local cohomology theory of A. Grothendieck. Some basic properties such as the noetherianness, the vanishing and non-vanishing of local homology modules of linearly compact modules are proved. A duality theory between local homology and local cohomology modules of linearly compact modules is developed by using Matlis duality and Macdonald duality. As consequences of the duality theorem we obtain some generalizations of well-known results in the theory of local cohomology for semi-discrete linearly compact modules.Comment: 24 page

Solving Assignment and Routing Problems in Mixed Traffic Systems

This doctoral thesis presents not only a new traffic assignment model for mixed traffic systems but also new heuristics for multi-paths routing problems, a case study in Hanoi Vietnam, and a new software, named TranOpt Plus, supporting three major features: map editing, dynamic routing, and traffic assignment modeling. We investigate three routing problems: k shortest loop-less paths (KSLP), dissimilar shortest loop-less paths (DSLP), and multi-objective shortest paths (MOSP). By developing loop filters and a similarity filter, we create two new heuristics based on Eppstein's algorithm: one using loop filters for the KSLP problem (HELF), the other using loop-and-similarity filters for the DSLP problem (HELSF). The computational results on real street maps indicate that the new heuristics dominate the other algorithms considered in terms of either running time or the average length of the found paths. In traffic assignment modeling, we propose a new User Equilibrium (UE) model, named GUEM, for mixed traffic systems where 2- and 4-wheel vehicles travel together without any separate lanes for each kind of vehicle. At the optimal solution to the model, a user equilibrium for each kind of vehicle is obtained. The model is applied to the traffic system in Hanoi, Vietnam, where the traffic system is mixed traffic dominated by motorcycles. The predicted assignment by the GUEM model using real collected data in Hanoi is in high agreement with the real traffic situation in Hanoi. Finally, we present the TranOpt Plus software, containing the implementation of all the routing algorithms mentioned in the thesis, as well as the GUEM model and a number of popular traffic assignment models for both standard traffic systems and mixed traffic systems. With its intuitive graphical user interface (GUI) and its strong visualization tools, TranOpt Plus also enables users without any mathematical or computer science background to use conveniently. Nevertheless, TranOpt Plus can be easily extended by further map-related problems, e.g., transportation network design, facility location, and the traveling salesman problem. Keywords: mixed traffic assignment modeling, routing algorithms, shortest paths, dissimilar paths, Hanoi, TranOpt Plus, map visualizatio

Thermoresistance of p-Type 4H–SiC Integrated MEMS Devices for High-Temperature Sensing

There is an increasing demand for the development and integration of multifunctional sensing modules into power electronic devices that can operate in high temperature environments. Here, the authors demonstrate the tunable thermoresistance of p‐type 4H–SiC for a wide temperature range from the room temperature to above 800 K with integrated flow sensing functionality into a single power electronic chip. The electrical resistance of p‐type 4H–SiC is found to exponentially decrease with increasing temperature to a threshold temperature of 536 K. The temperature coefficient of resistance (TCR) shows a large and negative value from −2100 to −7600 ppm K−1, corresponding to a thermal index of 625 K. From the threshold temperature of 536–846 K, the electrical resistance shows excellent linearity with a positive TCR value of 900 ppm K−1. The authors successfully demonstrate the integration of p–4H–SiC flow sensing functionality with a high sensitivity of 1.035 μA(m s−1)−0.5 mW−1. These insights in the electrical transport of p–4H–SiC aid to improve the performance of p–4H–SiC integrated temperature and flow sensing systems, as well as the design consideration and integration of thermal sensors into 4H–SiC power electronic systems operating at high temperatures of up to 846 K

TabIQA: Table Questions Answering on Business Document Images

Table answering questions from business documents has many challenges that require understanding tabular structures, cross-document referencing, and additional numeric computations beyond simple search queries. This paper introduces a novel pipeline, named TabIQA, to answer questions about business document images. TabIQA combines state-of-the-art deep learning techniques 1) to extract table content and structural information from images and 2) to answer various questions related to numerical data, text-based information, and complex queries from structured tables. The evaluation results on VQAonBD 2023 dataset demonstrate the effectiveness of TabIQA in achieving promising performance in answering table-related questions. The TabIQA repository is available at https://github.com/phucty/itabqa.Comment: First two authors contributed equall

Relative Positional Encoding for Speech Recognition and Direct Translation

Transformer models are powerful sequence-to-sequence architectures that are capable of directly mapping speech inputs to transcriptions or translations. However, the mechanism for modeling positions in this model was tailored for text modeling, and thus is less ideal for acoustic inputs. In this work, we adapt the relative position encoding scheme to the Speech Transformer, where the key addition is relative distance between input states in the self-attention network. As a result, the network can better adapt to the variable distributions present in speech data. Our experiments show that our resulting model achieves the best recognition result on the Switchboard benchmark in the non-augmentation condition, and the best published result in the MuST-C speech translation benchmark. We also show that this model is able to better utilize synthetic data than the Transformer, and adapts better to variable sentence segmentation quality for speech translation.Comment: Submitted to Interspeech 202