Polymeric semiconductor and transition-metal dichalcogenide nanocomposites for inkjet-printed thin-film transistor devices

Patterned using subtractive processes, conventional thin-film deposition techniques inevitably require high-vacuum deposition and photolithography to define functional layers to create a device structure. Inkjet printing technology has received considerable attention to realize low-cost and potential mass production of large-area electronics at low temperatures using an additive process approach. However, the materials used in the printing process are based on solution-based electronic inks formulated with organic electronic materials. Among them, conjugated polymers are widely used as a semiconductor for thin-film transistor (TFT) applications, but they possess poor charge transport properties compared to other single or polycrystalline inorganic semiconductors. Moreover, the inkjet printing method has a weakness for depositing polymeric solution that form thin films having a highly ordered molecular structure. To overcome this limitation when using printed polymers, a hybrid organic/inorganic semiconductor ink was explored. The hybrid semiconductor ink was prepared by mixing two different materials, molybdenum disulfide (MoS₂) nanosheets and solution-based poly(3-hexylthiopene-2,5-diyl) (P3HT), the former is a two-dimensional semiconductor and the latter a conjugated polymer. To enhance the level of exfoliation and stability of MoS₂ nanosheets in P3HT, the surfactant trichloro(dodecyl)silane (DDTS), was used to functionalize the MoS₂ surface. Printed TFTs using the nanosheet suspension were found to enhance the field-effect mobility by approximately 3× compared to TFTs without the suspension. The introduced single-crystalline MoS₂ nanosheets in the P3HT matrix improved the electrical and structural properties of the inkjet-printed thin-film polymer. Based on these findings and insights, the observed effects can be extended to second-generation polymeric semiconductors, specifically the donor-acceptor (D-A) co-polymers. These materials are renowned for exhibiting the highest mobilities among printable polymers while maintaining ambipolarity, a desirable trait for configuring complementary metal-oxide-semiconductor (CMOS) circuits. In light of this, novel nanocomposite semiconductor inks were developed to demonstrate the influence of 2D nanoparticles on the electronic properties of D-A copolymers, diketopyrrolopyrrole-thieno[3,2-b]thiophene (DPPT-TT). Printed TFTs using this new hybrid semiconductor showed that the field-effect mobility of the devices increased by 33 % and 140 % in both hole (p-type) and electron (n-type) transports, respectively. Atomic force microscopy (AFM) results of the printed hybrid thin film revealed that strongly aggregated polymer domains were observed in films containing the MoS₂ nanosheets. In ultraviolet–visible–near infrared spectroscopy (UV-vis-NIR) measurement, increased intensity of 0-0 and 0-1 peaks from hybrid film indicates improved charge transport was due to enhanced intermolecular charge transfer in the microstructure of the polymer film. Furthermore, the incorporation of hybrid nanocomposites proved particularly beneficial for inkjet-printed TFTs utilizing metal electrodes, as the latter had a tendency to augment contact resistance and thereby compromise device performance. However, the introduction of hybrid nanocomposites effectively counteracted the performance degradation arising from the printed metal electrodes by enhancing the crystallinity of the polymeric film. Moreover, these findings also highlight the feasibility of employing lower sintering temperatures for inkjet-printed metal electrodes. This is attributed to the fact that the result of increased contact resistance associated with lower sintering temperatures can be effectively mitigated by the nanocomposite semiconductor. Consequently, an overall enhancement in device performance was achieved by applying the hybrid nanocomposite ink. This study elucidated the advantageous influence of solution-processed MoS₂ nanosheets on the crystallinity and electrical properties of polymeric thin films, consequently leading to significant improvements in the performance parameters of inkjet-printed TFTs

Downward Longwave Radiation Retrieved from MODIS Imagery and Possible Application on Water Resource Management at Turkey Creek Watershed in South Carolina

2010 S.C. Water Resources Conferences - Science and Policy Challenges for a Sustainable Futur

A Curriculm Design for E-commerce Security

The low cost and wide availability of the Internet have revolutionized electronic commerce (e-commerce) and its applications. Security, then, has become one of the most important issues that must be resolved first to ensure its success. To protect an e-commerce system from existing threats, there must be e-commerce security experts who can help ensure its reliable deployment. This paper presents a curriculum design for e-commerce security in which the Delphi method and the Analytic Hierarchy Process (AHP) method were used. The AHP method determines the priorities of the e-commerce security courses, and the results of the study provide useful guidelines in the design of the e-commerce security curriculum

UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection

Recent advances in deep neural networks have achieved significant progress in detecting individual objects from an image. However, object detection is not sufficient to fully understand a visual scene. Towards a deeper visual understanding, the interactions between objects, especially humans and objects are essential. Most prior works have obtained this information with a bottom-up approach, where the objects are first detected and the interactions are predicted sequentially by pairing the objects. This is a major bottleneck in HOI detection inference time. To tackle this problem, we propose UnionDet, a one-stage meta-architecture for HOI detection powered by a novel union-level detector that eliminates this additional inference stage by directly capturing the region of interaction. Our one-stage detector for human-object interaction shows a significant reduction in interaction prediction time 4x~14x while outperforming state-of-the-art methods on two public datasets: V-COCO and HICO-DET.Comment: ECCV 202

NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA

Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions. Recent GNN-based approaches formulate this task as a KG path searching problem, where messages are sequentially propagated from the seed node towards the answer nodes. However, these messages are past-oriented, and they do not consider the full KG context. To make matters worse, KG nodes often represent proper noun entities and are sometimes encrypted, being uninformative in selecting between paths. To address these problems, we propose Neural Tree Search (NuTrea), a tree search-based GNN model that incorporates the broader KG context. Our model adopts a message-passing scheme that probes the unreached subtree regions to boost the past-oriented embeddings. In addition, we introduce the Relation Frequency-Inverse Entity Frequency (RF-IEF) node embedding that considers the global KG context to better characterize ambiguous KG nodes. The general effectiveness of our approach is demonstrated through experiments on three major multi-hop KGQA benchmark datasets, and our extensive analyses further validate its expressiveness and robustness. Overall, NuTrea provides a powerful means to query the KG with complex natural language questions. Code is available at https://github.com/mlvlab/NuTrea.Comment: Neural Information Processing Systems (NeurIPS) 202

Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment

This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization. We formulate online pseudo-labeling as an optimization problem to find pseudo-labels that maximize the mutual information between the label and data while being close to a given model probability. We derive a fixed-point iteration method and prove its convergence to the optimal solution. In contrast to baselines, MIRA combined with pseudo-label prediction enables a simple yet effective clustering-based representation learning without incorporating extra training techniques or artificial constraints such as sampling strategy, equipartition constraints, etc. With relatively small training epochs, representation learned by MIRA achieves state-of-the-art performance on various downstream tasks, including the linear/k-NN evaluation and transfer learning. Especially, with only 400 epochs, our method applied to ImageNet dataset with ResNet-50 architecture achieves 75.6% linear evaluation accuracy.Comment: NeurIPS 202

Satellite Based Downward Long Wave Radiation by Various Models in Northeast Asia

Satellite-based downward long wave radiation measurement under clear sky conditions in Northeast Asia was conducted using five well-known physical models (Brunt 1932, Idso and Jackson 1969, Brutsaert 1975, Satterlund 1979, Prata 1996) with a newly proposed global Rld model (Abramowitz et al. 2012). Data from two flux towers in South Korea were used to validate downward long wave radiation. Moderate resolution imaging spectroradiometer (MODIS) atmospheric profile products were used to develop the Rld models. The overall root mean square error (RMSE) of MODIS Rld with respect to two ecosystem-type flux towers was determined to be ≈ 20 W m-2. Based on the statistical analyses, MODIS Rld estimates with Brutsaert (1975) and Abramowitz et al. (2012) models were the most applicable for evaluating Rld for clear sky conditions in Northeast Asia. The Abramowitz Rld maps with MODIS Ta and ea showed reasonable seasonal patterns, which were well-aligned with other biophysical variables reported by previous studies. The MODIS Rld map developed in this study will be very useful for identifying spatial patterns that are not detectable from ground-based Rld measurement sites

Safe and Efficient Trajectory Optimization for Autonomous Vehicles using B-spline with Incremental Path Flattening

B-spline-based trajectory optimization is widely used for robot navigation due to its computational efficiency and convex-hull property (ensures dynamic feasibility), especially as quadrotors, which have circular body shapes (enable efficient movement) and freedom to move each axis (enables convex-hull property utilization). However, using the B-spline curve for trajectory optimization is challenging for autonomous vehicles (AVs) because of their vehicle kinodynamics (rectangular body shapes and constraints to move each axis). In this study, we propose a novel trajectory optimization approach for AVs to circumvent this difficulty using an incremental path flattening (IPF), a disc type swept volume (SV) estimation method, and kinodynamic feasibility constraints. IPF is a new method that can find a collision-free path for AVs by flattening path and reducing SV using iteratively increasing curvature penalty around vehicle collision points. Additionally, we develop a disc type SV estimation method to reduce SV over-approximation and enable AVs to pass through a narrow corridor efficiently. Furthermore, a clamped B-spline curvature constraint, which simplifies a B-spline curvature constraint, is added to dynamical feasibility constraints (e.g., velocity and acceleration) for obtaining the kinodynamic feasibility constraints. Our experimental results demonstrate that our method outperforms state-of-the-art baselines in various simulated environments. We also conducted a real-world experiment using an AV, and our results validate the simulated tracking performance of the proposed approach.Comment: 14 pages, 21 figures, 4 tables, 3 algorithm

Read-only Prompt Optimization for Vision-Language Few-shot Learning

In recent years, prompt tuning has proven effective in adapting pre-trained vision-language models to downstream tasks. These methods aim to adapt the pre-trained models by introducing learnable prompts while keeping pre-trained weights frozen. However, learnable prompts can affect the internal representation within the self-attention module, which may negatively impact performance variance and generalization, especially in data-deficient settings. To address these issues, we propose a novel approach, Read-only Prompt Optimization (RPO). RPO leverages masked attention to prevent the internal representation shift in the pre-trained model. Further, to facilitate the optimization of RPO, the read-only prompts are initialized based on special tokens of the pre-trained model. Our extensive experiments demonstrate that RPO outperforms CLIP and CoCoOp in base-to-new generalization and domain generalization while displaying better robustness. Also, the proposed method achieves better generalization on extremely data-deficient settings, while improving parameter efficiency and computational overhead. Code is available at https://github.com/mlvlab/RPO.Comment: Accepted at ICCV202

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory

The interactive use of large language models (LLMs) in AI assistants (at work, home, etc.) introduces a new set of inference-time privacy risks: LLMs are fed different types of information from multiple sources in their inputs and are expected to reason about what to share in their outputs, for what purpose and with whom, within a given context. In this work, we draw attention to the highly critical yet overlooked notion of contextual privacy by proposing ConfAIde, a benchmark designed to identify critical weaknesses in the privacy reasoning capabilities of instruction-tuned LLMs. Our experiments show that even the most capable models such as GPT-4 and ChatGPT reveal private information in contexts that humans would not, 39% and 57% of the time, respectively. This leakage persists even when we employ privacy-inducing prompts or chain-of-thought reasoning. Our work underscores the immediate need to explore novel inference-time privacy-preserving approaches, based on reasoning and theory of mind.Comment: confaide.github.i