141 research outputs found
Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems
Modern neural collaborative filtering techniques are critical to the success
of e-commerce, social media, and content-sharing platforms. However, despite
technical advances -- for every new application domain, we need to train an NCF
model from scratch. In contrast, pre-trained vision and language models are
routinely applied to diverse applications directly (zero-shot) or with limited
fine-tuning. Inspired by the impact of pre-trained models, we explore the
possibility of pre-trained recommender models that support building recommender
systems in new domains, with minimal or no retraining, without the use of any
auxiliary user or item information. Zero-shot recommendation without auxiliary
information is challenging because we cannot form associations between users
and items across datasets when there are no overlapping users or items. Our
fundamental insight is that the statistical characteristics of the user-item
interaction matrix are universally available across different domains and
datasets. Thus, we use the statistical characteristics of the user-item
interaction matrix to identify dataset-independent representations for users
and items. We show how to learn universal (i.e., supporting zero-shot
adaptation without user or item auxiliary information) representations for
nodes and edges from the bipartite user-item interaction graph. We learn
representations by exploiting the statistical properties of the interaction
data, including user and item marginals, and the size and density distributions
of their clusters
ICMRec: Item Cluster-Wise Multi-Objective Optimization for Unbiased Recommendation
The traditional observed data used to train the recommender model suffers
from severe bias issues (e.g., exposure bias, popularity bias). Interactions of
a small fraction of head items account for almost the whole training data. The
normal training paradigm from such biased data tends to repetitively generate
recommendations from the head items, which further exacerbates the biases and
affects the exploration of potentially interesting items from the niche set. In
this work, distinct from existing methods, we innovatively explore the central
theme of unbiased recommendation from an item cluster-wise multi-objective
optimization perspective. Aiming to balance the learning on various item
clusters that differ in popularity during the training process, we characterize
the recommendation task as an item cluster-wise multi-objective optimization
problem. To this end, we propose a model-agnostic framework namely Item
Cluster-Wise Multi-Objective Recommendation (ICMRec) for unbiased
recommendation. In detail, we define our item cluster-wise optimization target
that the recommender model should balance all item clusters that differ in
popularity. Thus we set the model learning on each item cluster as a unique
optimization objective. To achieve this goal, we first explore items'
popularity levels from a novel causal reasoning perspective. Then, we devise
popularity discrepancy-based bisecting clustering to separate the discriminated
item clusters. Next, we adaptively find the overall harmonious gradient
direction for multiple item cluster-wise optimization objectives from a
Pareto-efficient solver. Finally, in the prediction stage, we perform
counterfactual inference to further eliminate the impact of user conformity.
Extensive experimental results demonstrate the superiorities of ICMRec on
overall recommendation performance and biases elimination. Codes will be
open-source upon acceptance
CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation
Recent code translation techniques exploit neural machine translation models
to translate source code from one programming language to another to satisfy
production compatibility or to improve efficiency of codebase maintenance. Most
existing code translation datasets only focus on a single pair of popular
programming languages. To advance research on code translation and meet diverse
requirements of real-world applications, we construct CodeTransOcean, a
large-scale comprehensive benchmark that supports the largest variety of
programming languages for code translation. CodeTransOcean consists of three
novel multilingual datasets, namely, MultilingualTrans supporting translations
between multiple popular programming languages, NicheTrans for translating
between niche programming languages and popular ones, and LLMTrans for
evaluating executability of translated code by large language models (LLMs).
CodeTransOcean also includes a novel cross-framework dataset, DLTrans, for
translating deep learning code across different frameworks. We develop
multilingual modeling approaches for code translation and demonstrate their
great potential in improving the translation quality of both low-resource and
high-resource language pairs and boosting the training efficiency. We also
propose a novel evaluation metric Debugging Success Rate@K for program-level
code translation. Last but not least, we evaluate LLM ChatGPT on our datasets
and investigate its potential for fuzzy execution predictions. We build
baselines for CodeTransOcean and analyze challenges of code translation for
guiding future research. The CodeTransOcean datasets and code are publicly
available at https://github.com/WeixiangYAN/CodeTransOcean.Comment: Accepted by Findings of EMNLP 202
Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging
Deep learning has transformed computational imaging, but traditional
pixel-based representations limit their ability to capture continuous,
multiscale details of objects. Here we introduce a novel Local Conditional
Neural Fields (LCNF) framework, leveraging a continuous implicit neural
representation to address this limitation. LCNF enables flexible object
representation and facilitates the reconstruction of multiscale information. We
demonstrate the capabilities of LCNF in solving the highly ill-posed inverse
problem in Fourier ptychographic microscopy (FPM) with multiplexed
measurements, achieving robust, scalable, and generalizable large-scale phase
retrieval. Unlike traditional neural fields frameworks, LCNF incorporates a
local conditional representation that promotes model generalization, learning
multiscale information, and efficient processing of large-scale imaging data.
By combining an encoder and a decoder conditioned on a learned latent vector,
LCNF achieves versatile continuous-domain super-resolution image
reconstruction. We demonstrate accurate reconstruction of wide field-of-view,
high-resolution phase images using only a few multiplexed measurements. LCNF
robustly captures the continuous object priors and eliminates various phase
artifacts, even when it is trained on imperfect datasets. The framework
exhibits strong generalization, reconstructing diverse objects even with
limited training data. Furthermore, LCNF can be trained on a physics simulator
using natural images and successfully applied to experimental measurements on
biological samples. Our results highlight the potential of LCNF for solving
large-scale inverse problems in computational imaging, with broad applicability
in various deep-learning-based techniques
Rapid Generation of Optimal Generalized Monkhorst-Pack Grids
Computational modeling of the properties of crystalline materials has become
an increasingly important aspect of materials research, consuming hundreds of
millions of CPU-hours at scientific computing centres around the world each
year, if not more. A routine operation in such calculations is the evaluation
of integrals over the Brillouin zone. We have previously demonstrated that
performing such integrals using generalized Monkhorst-Pack k-point grids can
roughly double the speed of these calculations relative to the widely-used
traditional Monkhorst-Pack grids, and such grids can be rapidly generated by
querying a free, internet-accessible database of pre-generated grids. To
facilitate the widespread use of generalized k-point grids, we present new
algorithms that allow rapid generation of optimized generalized Monkhorst-Pack
grids on the fly, an open-source library to facilitate their integration into
external software packages, and an open-source implementation of the database
tool that can be used offline. We also present benchmarks of the speed of our
algorithms on structures randomly selected from the Inorganic Crystal Structure
Database. For grids that correspond to a real-space supercell with at least 50
angstroms between lattice points, which is sufficient to converge density
functional theory calculations within 1 meV/atom for nearly all materials, our
algorithm finds optimized grids in an average of 0.19 seconds on a single
processing core. For 100 angstroms between real-space lattice points, our
algorithm finds optimal grids in less than 5 seconds on average
Research on Water Pollution Control Based on STM32 Intelligent Vehicle
In order to solve the high cost and low efficiency of different degrees of pollution control of natural water resources in China at this stage, photocatalytic water purification technology is adopted to reduce the cost of water pollution treatment and improve the treatment efficiency, and an intelligent vehicle equipped with photocatalytic materials is proposed, which is equipped with industrial cameras, communication positioning modules and sensors, and realizes dynamic planning of navigation routes by improving ant colony algorithms, computer vision recognition, ultrasonic obstacle avoidance, and realizes photocatalytic fixed-point purification. Predict advanced photoelectric catalytic performance based on density functional theory and machine learning, solve the problem of BiVO4 photo corrosion and instability, and achieve efficient water purification at low cost
Surround-view Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-insensitive Multi-task Framework
Surround-view fisheye perception under valet parking scenes is fundamental
and crucial in autonomous driving. Environmental conditions in parking lots
perform differently from the common public datasets, such as imperfect light
and opacity, which substantially impacts on perception performance. Most
existing networks based on public datasets may generalize suboptimal results on
these valet parking scenes, also affected by the fisheye distortion. In this
article, we introduce a new large-scale fisheye dataset called Fisheye Parking
Dataset(FPD) to promote the research in dealing with diverse real-world
surround-view parking cases. Notably, our compiled FPD exhibits excellent
characteristics for different surround-view perception tasks. In addition, we
also propose our real-time distortion-insensitive multi-task framework Fisheye
Perception Network (FPNet), which improves the surround-view fisheye BEV
perception by enhancing the fisheye distortion operation and multi-task
lightweight designs. Extensive experiments validate the effectiveness of our
approach and the dataset's exceptional generalizability.Comment: 12 pages, 11 figure
What Matters for 3D Scene Flow Network
3D scene flow estimation from point clouds is a low-level 3D motion
perception task in computer vision. Flow embedding is a commonly used technique
in scene flow estimation, and it encodes the point motion between two
consecutive frames. Thus, it is critical for the flow embeddings to capture the
correct overall direction of the motion. However, previous works only search
locally to determine a soft correspondence, ignoring the distant points that
turn out to be the actual matching ones. In addition, the estimated
correspondence is usually from the forward direction of the adjacent point
clouds, and may not be consistent with the estimated correspondence acquired
from the backward direction. To tackle these problems, we propose a novel
all-to-all flow embedding layer with backward reliability validation during the
initial scene flow estimation. Besides, we investigate and compare several
design choices in key components of the 3D scene flow network, including the
point similarity calculation, input elements of predictor, and predictor &
refinement level design. After carefully choosing the most effective designs,
we are able to present a model that achieves the state-of-the-art performance
on FlyingThings3D and KITTI Scene Flow datasets. Our proposed model surpasses
all existing methods by at least 38.2% on FlyingThings3D dataset and 24.7% on
KITTI Scene Flow dataset for EPE3D metric. We release our codes at
https://github.com/IRMVLab/3DFlow.Comment: Accepted by ECCV 202
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization
Reinforcement learning (RL) has achieved promising results on most robotic
control tasks. Safety of learning-based controllers is an essential notion of
ensuring the effectiveness of the controllers. Current methods adopt whole
consistency constraints during the training, thus resulting in inefficient
exploration in the early stage. In this paper, we propose an algorithm named
Constrained Policy Optimization with Extra Safety Budget (ESB-CPO) to strike a
balance between the exploration efficiency and the constraints satisfaction. In
the early stage, our method loosens the practical constraints of unsafe
transitions (adding extra safety budget) with the aid of a new metric we
propose. With the training process, the constraints in our optimization problem
become tighter. Meanwhile, theoretical analysis and practical experiments
demonstrate that our method gradually meets the cost limit's demand in the
final training stage. When evaluated on Safety-Gym and Bullet-Safety-Gym
benchmarks, our method has shown its advantages over baseline algorithms in
terms of safety and optimality. Remarkably, our method gains remarkable
performance improvement under the same cost limit compared with baselines.Comment: 7 pages, 8 figure
Universal Sleep Decoder: Aligning awake and sleep neural representation across subjects
Decoding memory content from brain activity during sleep has long been a goal
in neuroscience. While spontaneous reactivation of memories during sleep in
rodents is known to support memory consolidation and offline learning,
capturing memory replay in humans is challenging due to the absence of
well-annotated sleep datasets and the substantial differences in neural
patterns between wakefulness and sleep. To address these challenges, we
designed a novel cognitive neuroscience experiment and collected a
comprehensive, well-annotated electroencephalography (EEG) dataset from 52
subjects during both wakefulness and sleep. Leveraging this benchmark dataset,
we developed the Universal Sleep Decoder (USD) to align neural representations
between wakefulness and sleep across subjects. Our model achieves up to 16.6%
top-1 zero-shot accuracy on unseen subjects, comparable to decoding
performances using individual sleep data. Furthermore, fine-tuning USD on test
subjects enhances decoding accuracy to 25.9% top-1 accuracy, a substantial
improvement over the baseline chance of 6.7%. Model comparison and ablation
analyses reveal that our design choices, including the use of (i) an additional
contrastive objective to integrate awake and sleep neural signals and (ii) the
pretrain-finetune paradigm to incorporate different subjects, significantly
contribute to these performances. Collectively, our findings and methodologies
represent a significant advancement in the field of sleep decoding
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