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

Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing

Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable responses. However, the potential of LLMs are not fully harnessed by commonly-used prompting methods: many human-in-the-loop algorithms employ ad-hoc procedures for prompt selection; while auto prompt generation approaches are essentially searching all possible prompts randomly and inefficiently. We propose Evoke, an automatic prompt refinement framework. In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback. Such an author-reviewer feedback loop ensures that the prompt is refined in each iteration. We further aggregate a data selection approach to Evoke, where only the hard samples are exposed to the LLM. The hard samples are more important because the LLM can develop deeper understanding of the tasks out of them, while the model may already know how to solve the easier cases. Experimental results show that Evoke significantly outperforms existing methods. For instance, in the challenging task of logical fallacy detection, Evoke scores above 80, while all other baseline methods struggle to reach 20

CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training

Pre-training across 3D vision and language remains under development because of limited training data. Recent works attempt to transfer vision-language pre-training models to 3D vision. PointCLIP converts point cloud data to multi-view depth maps, adopting CLIP for shape classification. However, its performance is restricted by the domain gap between rendered depth maps and images, as well as the diversity of depth distributions. To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification. We introduce a new depth rendering setting that forms a better visual effect, and then render 52,460 pairs of images and depth maps from ShapeNet for pre-training. The pre-training scheme of CLIP2Point combines cross-modality learning to enforce the depth features for capturing expressive visual and textual features and intra-modality learning to enhance the invariance of depth aggregation. Additionally, we propose a novel Dual-Path Adapter (DPA) module, i.e., a dual-path structure with simplified adapters for few-shot learning. The dual-path structure allows the joint use of CLIP and CLIP2Point, and the simplified adapter can well fit few-shot tasks without post-search. Experimental results show that CLIP2Point is effective in transferring CLIP knowledge to 3D vision. Our CLIP2Point outperforms PointCLIP and other self-supervised 3D networks, achieving state-of-the-art results on zero-shot and few-shot classification

Climate Extremes Dominating Seasonal and Interannual Variations in Carbon Export from the Mississippi River Basinariations in Carbon Export from the Mississippi River Basin

Knowledge about the annual and seasonal patterns of organic and inorganic carbon (C) exports from the major rivers of the world to the coastal ocean is essential for our understanding and potential management of the global C budget so as to limit anthropogenic modification of global climate. Unfortunately our predictive understanding of what controls the timing, magnitude, and quality of C export is still rudimentary. Here we use a process-based coupled hydrologic/ecosystem biogeochemistry model (the Dynamic Land Ecosystem Model) to examine how climate variability and extreme events, changing land use, and atmospheric chemistry have affected the annual and seasonal patterns of C exports from the Mississippi River basin to the Gulf of Mexico. Our process-based simulations estimate that the average annual exports of dissolved organic C (DOC), particulate organic C (POC), and dissolved inorganic C (DIC) in the 2000s were 2.6 ± 0.4 Tg C yr−1, 3.4 ± 0.3 Tg C yr−1, and 18.8 ± 3.4 Tg C yr−1, respectively. Although land use change was the most important agent of change in C export over the past century, climate variability and extreme events (such as flooding and drought) were primarily responsible for seasonal and interannual variations in C export from the basin. The maximum seasonal export of DIC occurred in summer while for DOC and POC the maximum occurred in winter. Relative to the 10 year average (2001–2010), our modeling analysis indicates that the years of maximal and minimal C export cooccurred with wet and dry years (2008: 32% above average and 2006: 32% below average). Given Intergovernmental Panel on Climate Change-predicted changes in climate variability and the severity of rain events and droughts of wet and dry years for the remainder of the 21st century, our modeling results suggest major changes in the riverine link between the terrestrial and oceanic realms, which are likely to have a major impact on C delivery to the coastal ocean

Single charge control of localized excitons in heterostructures with ferroelectric thin films and two-dimensional transition metal dichalcogenides

Single charge control of localized excitons (LXs) in two-dimensional transition metal dichalcogenides (TMDCs) is crucial for potential applications in quantum information processing and storage. However, traditional electrostatic doping method with applying metallic gates onto TMDCs may cause the inhomogeneous charge distribution, optical quench, and energy loss. Here, by locally controlling the ferroelectric polarization of the ferroelectric thin film BiFeO3 (BFO) with a scanning probe, we can deterministically manipulate the doping type of monolayer WSe2 to achieve the p-type and n-type doping. This nonvolatile approach can maintain the doping type and hold the localized excitonic charges for a long time without applied voltage. Our work demonstrated that ferroelectric polarization of BFO can control the charges of LXs effectively. Neutral and charged LXs have been observed in different ferroelectric polarization regions, confirmed by magnetic optical measurement. Highly circular polarization degree about 90 % of the photon emission from these quantum emitters have been achieved in high magnetic fields. Controlling single charge of LXs in a non-volatile way shows a great potential for deterministic photon emission with desired charge states for photonic long-term memory.Comment: 13 pages, 5 figure

On the HI content, dust-to-gas ratio and nature of MgII absorbers

We estimate the mean dust-to-gas ratio of MgII absorbers as a function of rest equivalent width W_0 and redshift over the range 0.5<z<1.4. Using the expanded SDSS/HST sample of low-redshift Lyman-alpha absorbers we first show the existence of a 8-sigma correlation between the mean hydrogen column density and W_0, an indicator of gas velocity dispersion. By combining these results with recent dust-reddening measurements we show that the mean dust-to-gas ratio of MgII absorbers does not appreciably depend on rest equivalent width. Assuming that, on average, dust-to-gas ratio is proportional to metallicity, we find its redshift evolution to be consistent with that of L^star galaxies from z=0.5 to 1.4 and we show that our constraints disfavor dwarf galaxies as the origin of such absorbers. We discuss other scenarii and favor galactic outflows from ~L^star galaxies as the origin of the majority of strong MgII absorbers. Finally, we show that, once evolutionary effects are taken into account, the Bohlin et al. relation between A_V and N_H is also satisfied by strong MgII systems down to lower column densities than those probed in our Galaxy.Comment: 9 pages, minor changes to match the version accepted for publication in MNRA

Asymmetric Chiral Coupling in a Topological Resonator

Chiral light-matter interactions supported by topological edge modes at the interface of valley photonic crystals provide a robust method to implement the unidirectional spin transfer. The valley topological photonic crystals possess a pair of counterpropagating edge modes. The edge modes are robust against the sharp bend of $60^{\circ}$ and $120^{\circ}$, which can form a resonator with whispering gallery modes. Here, we demonstrate the asymmetric emission of chiral coupling from single quantum dots in a topological resonator by tuning the coupling between a quantum emitter and a resonator mode. Under a magnetic field in Faraday configuration, the exciton state from a single quantum dot splits into two exciton spin states with opposite circularly polarized emissions due to Zeeman effect. Two branches of the quantum dot emissions couple to a resonator mode in different degrees, resulting in an asymmetric chiral emission. Without the demanding of site-control of quantum emitters for chiral quantum optics, an extra degree of freedom to tune the chiral contrast with a topological resonator could be useful for the development of on-chip integrated photonic circuits.Comment: 13 pages, 4 figure

Controllable Spin-Resolved Photon Emission Enhanced by Slow-Light Mode in Photonic Crystal Waveguides on Chip

We report the slow-light enhanced spin-resolved in-plane emission from a single quantum dot (QD) in a photonic crystal waveguide (PCW). The slow light dispersions in PCWs are designed to match the emission wavelengths of single QDs. The resonance between two spin states emitted from a single QD and a slow light mode of a waveguide is investigated under a magnetic field with Faraday configuration. Two spin states of a single QD experience different degrees of enhancement as their emission wavelengths are shifted by combining diamagnetic and Zeeman effects with an optical excitation power control. A circular polarization degree up to 0.81 is achieved by changing the off-resonant excitation power. Strongly polarized photon emission enhanced by a slow light mode shows great potential to attain controllable spin-resolved photon sources for integrated optical quantum networks on chip.Comment: 7 pages,5 figure

Metal Abundances at z < 1.5: Fresh Clues to the Chemical Enrichment History of Damped Lyman alpha Systems

We explore the redshift evolution of the metal content of damped Lyman alpha systems (DLAs) with new observations of four absorbers at z < 1.5 . The main conclusion is that the column density--weighted mean metallicity, [] = -1.03 +/- 0.23 (on a logarithmic scale), is not significantly higher at z < 1.5 than at earlier epochs, despite the fact that the comoving star formation rate density of the universe was near its maximum value at this redshift. For three of the four DLAs our observations include absorption lines of Si, Mn, Cr, Fe, and Ni, as well as Zn. We argue that the relative abundances of these elements are consistent with a moderate degree of dust depletion which, once accounted for, leaves no room for the enhancement of the alpha-elements over iron seen in metal poor stars in the Milky Way. This is contrary to previous assertions that DLAs have been enriched solely by Type II supernovae, but can be understood if the rate of star formation in the systems studied proceeded more slowly than in the early history of our Galaxy. These results add to a growing body of data all pointing to the conclusion that known DLAs do not trace the galaxy population responsible for the bulk of star formation. Possible reasons are that sight-lines through metal rich gas are systematically underrepresented because the background QSOs are reddened, and that the most actively star forming galaxies are also the most compact, presenting too small a cross-section to have been probed yet with the limited statistics of current samples.Comment: 40 pages, LaTex, 9 Postscript Figures. Accepted for publication in the Astrophysical Journa

A review into the use of ceramics in microbial fuel cells

© 2016 The Authors. Microbial fuel cells (MFCs) offer great promise as a technology that can produce electricity whilst at the same time treat wastewater. Although significant progress has been made in recent years, the requirement for cheaper materials has prevented the technology from wider, out-of-the-lab, implementation. Recently, researchers have started using ceramics with encouraging results, suggesting that this inexpensive material might be the solution for propelling MFC technology towards real world applications. Studies have demonstrated that ceramics can provide stability, improve power and treatment efficiencies, create a better environment for the electro-active bacteria and contribute towards resource recovery. This review discusses progress to date using ceramics as (i) the structural material, (ii) the medium for ion exchange and (iii) the electrode for MFCs