387 research outputs found
New Boolean satisfiability problem heuristic strategy: Minimal Positive Negative Product Strategy
This study presents a novel heuristic algorithm called the "Minimal Positive
Negative Product Strategy" to guide the CDCL algorithm in solving the Boolean
satisfiability problem. It provides a mathematical explanation for the
superiority of this algorithm over widely used heuristics such as the Dynamic
Largest Individual Sum (DLIS) and the Variable State Independent Decaying Sum
(VSIDS). Experimental results further confirm the effectiveness of this
heuristic strategy in problem-solving.Comment: 7 pages, 2 figure
MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base
The ability to understand and generate similes is an imperative step to
realize human-level AI. However, there is still a considerable gap between
machine intelligence and human cognition in similes, since deep models based on
statistical distribution tend to favour high-frequency similes. Hence, a
large-scale symbolic knowledge base of similes is required, as it contributes
to the modeling of diverse yet unpopular similes while facilitating additional
evaluation and reasoning. To bridge the gap, we propose a novel framework for
large-scale simile knowledge base construction, as well as two probabilistic
metrics which enable an improved understanding of simile phenomena in natural
language. Overall, we construct MAPS-KB, a million-scale probabilistic simile
knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB
corpora. We conduct sufficient experiments to justify the effectiveness and
necessity of the methods of our framework. We also apply MAPS-KB on three
downstream tasks to achieve state-of-the-art performance, further demonstrating
the value of MAPS-KB.Comment: Accepted to AAAI 202
Language Models as Knowledge Embeddings
Knowledge embeddings (KE) represent a knowledge graph (KG) by embedding
entities and relations into continuous vector spaces. Existing methods are
mainly structure-based or description-based. Structure-based methods learn
representations that preserve the inherent structure of KGs. They cannot well
represent abundant long-tail entities in real-world KGs with limited structural
information. Description-based methods leverage textual information and
language models. Prior approaches in this direction barely outperform
structure-based ones, and suffer from problems like expensive negative sampling
and restrictive description demand. In this paper, we propose LMKE, which
adopts Language Models to derive Knowledge Embeddings, aiming at both enriching
representations of long-tail entities and solving problems of prior
description-based methods. We formulate description-based KE learning with a
contrastive learning framework to improve efficiency in training and
evaluation. Experimental results show that LMKE achieves state-of-the-art
performance on KE benchmarks of link prediction and triple classification,
especially for long-tail entities.Comment: This revision corrects some texts after fixing a data leakage issu
Influence of Phosphorus Sources on the Compressive Strength and Microstructure of Ferronickel Slag-Based Magnesium Phosphate Cement
Electric furnace ferronickel slag (EFS) is a typical magnesium-rich industrial by-product discharged from the manufacture of nickel and iron-nickel alloys. The approach to use it as the raw material for the preparation of magnesium phosphate cement (MPC) has potential and proves effec-tive. In this study, three different phosphorus sources (PS) including phosphoric acid (H3PO4, PA), sodium dihydrogen phosphate (NaH2 PO4, SDP) and potassium dihydrogen phosphate (KH2 PO4, PDP) were used to react with EFS to prepare the EFS-based MPC (EMPC), and the effects of raw material mass ratio (EFS/PA, EFS/SDP, EFS/PDP) on the compressive strength, early hydration temperature and microstructure of EMPC pastes were investigated. Results showed that the compressive strength of EMPC paste is significantly impacted by the type of phosphorus source and the raw materials mass ratio. When the EFS/PDP ratio is 4.0, the compressive strength of the MPC paste reaches up to 18.8, 22.8 and 27.5 MPa at 3, 7 and 28 d, respectively. Cattiite (Mg3(PO4 )2·22H2 O), K-struvite (KMgPO4·6H2O) and/or Na-struvite (NaMgPO4·6H2O) were identified as the main hydration products of EMPC. The development of EMPC mainly involves the dissolution of a phosphorus source, MgO and Mg2SiO4, formation of hydration product as binder, and combination of the unreacted raw materials together by binders to build a compact form
Planting a SEED of Vision in Large Language Model
We present SEED, an elaborate image tokenizer that empowers Large Language
Models (LLMs) with the emergent ability to SEE and Draw at the same time.
Research on image tokenizers has previously reached an impasse, as frameworks
employing quantized visual tokens have lost prominence due to subpar
performance and convergence in multimodal comprehension (compared to BLIP-2,
etc.) or generation (compared to Stable Diffusion, etc.). Despite the
limitations, we remain confident in its natural capacity to unify visual and
textual representations, facilitating scalable multimodal training with LLM's
original recipe. In this study, we identify two crucial principles for the
architecture and training of SEED that effectively ease subsequent alignment
with LLMs. (1) Image tokens should be independent of 2D physical patch
positions and instead be produced with a 1D causal dependency, exhibiting
intrinsic interdependence that aligns with the left-to-right autoregressive
prediction mechanism in LLMs. (2) Image tokens should capture high-level
semantics consistent with the degree of semantic abstraction in words, and be
optimized for both discriminativeness and reconstruction during the tokenizer
training phase. As a result, the off-the-shelf LLM is able to perform both
image-to-text and text-to-image generation by incorporating our SEED through
efficient LoRA tuning. Comprehensive multimodal pretraining and instruction
tuning, which may yield improved results, are reserved for future
investigation. This version of SEED was trained in 5.7 days using only 64 V100
GPUs and 5M publicly available image-text pairs. Our preliminary study
emphasizes the great potential of discrete visual tokens in versatile
multimodal LLMs and the importance of proper image tokenizers in broader
research.Comment: Technical Report; Project released at:
https://github.com/AILab-CVC/SEE
StyleAdapter: A Single-Pass LoRA-Free Model for Stylized Image Generation
This paper presents a LoRA-free method for stylized image generation that
takes a text prompt and style reference images as inputs and produces an output
image in a single pass. Unlike existing methods that rely on training a
separate LoRA for each style, our method can adapt to various styles with a
unified model. However, this poses two challenges: 1) the prompt loses
controllability over the generated content, and 2) the output image inherits
both the semantic and style features of the style reference image, compromising
its content fidelity. To address these challenges, we introduce StyleAdapter, a
model that comprises two components: a two-path cross-attention module (TPCA)
and three decoupling strategies. These components enable our model to process
the prompt and style reference features separately and reduce the strong
coupling between the semantic and style information in the style references.
StyleAdapter can generate high-quality images that match the content of the
prompts and adopt the style of the references (even for unseen styles) in a
single pass, which is more flexible and efficient than previous methods.
Experiments have been conducted to demonstrate the superiority of our method
over previous works.Comment: AIG
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