Enhancing Spectrum Sensing via Reconfigurable Intelligent Surfaces: Passive or Active Sensing and How Many Reflecting Elements are Needed?

Cognitive radio has been proposed to alleviate the scarcity of available spectrum caused by the significant demand for wideband services and the fragmentation of spectrum resources. However, sensing performance is quite poor due to the low sensing signal-to-noise ratio, especially in complex environments with severe channel fading. Fortunately, reconfigurable intelligent surface (RIS)-aided spectrum sensing can effectively tackle the above challenge due to its high array gain. Nevertheless, the traditional passive RIS may suffer from the ``double fading'' effect, which severely limits the performance of passive RIS-aided spectrum sensing. Thus, a crucial challenge is how to fully exploit the potential advantages of the RIS and further improve the sensing performance. To this end, we introduce the active RIS into spectrum sensing and respectively formulate two optimization problems for the passive RIS and the active RIS to maximize the detection probability. In light of the intractability of the formulated problems, we develop a one-stage optimization algorithm with inner approximation and a two-stage optimization algorithm with a bisection method to obtain sub-optimal solutions, and apply the Rayleigh quotient to obtain the upper and lower bounds of the detection probability. Furthermore, in order to gain more insight into the impact of the RIS on spectrum sensing, we respectively investigate the number configuration for passive RIS and active RIS and analyze how many reflecting elements are needed to achieve the detection probability close to 1. Simulation results verify that the proposed algorithms outperform existing algorithms under the same parameter configuration, and achieve a detection probability close to 1 with even fewer reflecting elements or antennas than existing schemes

LPT: Long-tailed Prompt Tuning for Image Classification

For long-tailed classification, most works often pretrain a big model on a large-scale dataset, and then fine-tune the whole model for adapting to long-tailed data. Though promising, fine-tuning the whole pretrained model tends to suffer from high cost in computation and deployment of different models for different tasks, as well as weakened generalization ability for overfitting to certain features of long-tailed data. To alleviate these issues, we propose an effective Long-tailed Prompt Tuning method for long-tailed classification. LPT introduces several trainable prompts into a frozen pretrained model to adapt it to long-tailed data. For better effectiveness, we divide prompts into two groups: 1) a shared prompt for the whole long-tailed dataset to learn general features and to adapt a pretrained model into target domain; and 2) group-specific prompts to gather group-specific features for the samples which have similar features and also to empower the pretrained model with discrimination ability. Then we design a two-phase training paradigm to learn these prompts. In phase 1, we train the shared prompt via supervised prompt tuning to adapt a pretrained model to the desired long-tailed domain. In phase 2, we use the learnt shared prompt as query to select a small best matched set for a group of similar samples from the group-specific prompt set to dig the common features of these similar samples, then optimize these prompts with dual sampling strategy and asymmetric GCL loss. By only fine-tuning a few prompts while fixing the pretrained model, LPT can reduce training and deployment cost by storing a few prompts, and enjoys a strong generalization ability of the pretrained model. Experiments show that on various long-tailed benchmarks, with only ~1.1% extra parameters, LPT achieves comparable performance than previous whole model fine-tuning methods, and is more robust to domain-shift.Comment: ICLR 2023 (poster

Patched Line Segment Learning for Vector Road Mapping

This paper presents a novel approach to computing vector road maps from satellite remotely sensed images, building upon a well-defined Patched Line Segment (PaLiS) representation for road graphs that holds geometric significance. Unlike prevailing methods that derive road vector representations from satellite images using binary masks or keypoints, our method employs line segments. These segments not only convey road locations but also capture their orientations, making them a robust choice for representation. More precisely, given an input image, we divide it into non-overlapping patches and predict a suitable line segment within each patch. This strategy enables us to capture spatial and structural cues from these patch-based line segments, simplifying the process of constructing the road network graph without the necessity of additional neural networks for connectivity. In our experiments, we demonstrate how an effective representation of a road graph significantly enhances the performance of vector road mapping on established benchmarks, without requiring extensive modifications to the neural network architecture. Furthermore, our method achieves state-of-the-art performance with just 6 GPU hours of training, leading to a substantial 32-fold reduction in training costs in terms of GPU hours

Rapid flocculation-sedimentation of microalgae with organosilane-functionalized halloysite

Microalgae is a promising feedstock of biofuel for alternating fossil fuels. The major challenge of microalgal biofuels for commercial applications is in designing an efficient harvesting method with high economic feasibility. In this study, a rapid flocculation-sedimentation harvesting method induced by organosilane-functionalized halloysite flocculant was achieved for Scenedesmus dimorphus harvest. The harvesting efficiency was significantly influenced by the pH of microalgal dispersion and the dosage of flocculant. The optimized harvesting condition was pH 3.0 with flocculant dosage of 1.0 g.g(-1) cell dry mass. Under the optimized harvesting condition, microalgae rapidly reached 93% harvesting efficiency within 0.5 min of settling time, and reached 98% harvesting efficiency within 2 min of settling time. The rapid flocculation was attributed to the charge neutralization of the negatively-charged microalgae cells by the positively-charged organosilane-functionalized halloysite flocculant and to the sweep flocculation by organosilane-functionalized halloysite flocculant. The organosilane-functionalized halloysite flocculant did not affect the lipid extraction of microalgae, and not contaminate the extracted residuals. The organosilane-functionalized halloysite flocculant is of high efficient, cost-effective, and eco-friendly, makes it be of promising application for commercial microalgae harvesting.</p

Active screen plasma nitriding enhances cell attachment to polymer surfaces

Active screen plasma nitriding (ASPN) is a well-established technique used for the surface modification of materials, the result of which is often a product with enhanced functional performance. Here we report the modification of the chemical and mechanical properties of ultra-high molecular weight poly(ethylene) (UHMWPE) using 80:20 (v/v) N2/H2 ASPN, followed by growth of 3T3 fibroblasts on the treated and untreated polymer surfaces. ASPN-treated UHMWPE showed extensive fibroblast attachment within 3 h of seeding, whereas fibroblasts did not successfully attach to untreated UHMWPE. Fibroblast coated surfaces were maintained for up to 28 days, monitoring their metabolic activity and morphology throughout. The chemical properties of the ASPN-treated UHMWPE surface were studied using X-ray photoelectron spectroscopy, revealing the presence of C N, C N, and C N chemical bonds. The elastic modulus, surface topography, and adhesion properties of the ASPN-treated UHMWPE surface were studied over 28 days during sample storage under ambient conditions and during immersion in two commonly used cell culture media

AgentTuning: Enabling Generalized Agent Abilities for LLMs

Open large language models (LLMs) with great performance in various tasks have significantly advanced the development of LLMs. However, they are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. These agent tasks employ LLMs as the central controller responsible for planning, memorization, and tool utilization, necessitating both fine-grained prompting methods and robust LLMs to achieve satisfactory performance. Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities. In this work, we present AgentTuning, a simple and general method to enhance the agent abilities of LLMs while maintaining their general LLM capabilities. We construct AgentInstruct, a lightweight instruction-tuning dataset containing high-quality interaction trajectories. We employ a hybrid instruction-tuning strategy by combining AgentInstruct with open-source instructions from general domains. AgentTuning is used to instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show that AgentTuning enables LLMs' agent capabilities without compromising general abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent tasks, demonstrating generalized agent capabilities. We open source the AgentInstruct and AgentLM-7B, 13B, and 70B models at https://github.com/THUDM/AgentTuning, serving open and powerful alternatives to commercial LLMs for agent tasks.Comment: 31 page

Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning

Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent years for its pivotal role in addressing continuously arriving classes. However, it encounters additional challenges. The scarcity of samples in new sessions intensifies overfitting, causing incompatibility between the output features of new and old classes, thereby escalating catastrophic forgetting. A prevalent strategy involves mitigating catastrophic forgetting through the Explicit Memory (EM), which comprise of class prototypes. However, current EM-based methods retrieves memory globally by performing Vector-to-Vector (V2V) interaction between features corresponding to the input and prototypes stored in EM, neglecting the geometric structure of local features. This hinders the accurate modeling of their positional relationships. To incorporate information of local geometric structure, we extend the V2V interaction to Graph-to-Graph (G2G) interaction. For enhancing local structures for better G2G alignment and the prevention of local feature collapse, we propose the Local Graph Preservation (LGP) mechanism. Additionally, to address sample scarcity in classes from new sessions, the Contrast-Augmented G2G (CAG2G) is introduced to promote the aggregation of same class features thus helps few-shot learning. Extensive comparisons on CIFAR100, CUB200, and the challenging ImageNet-R dataset demonstrate the superiority of our method over existing methods.Comment: 12 Pages, 5 figure