476 research outputs found
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud
registration framework that achieves comparable registration accuracy to prior
state-of-the-art geometric methods. Different from other keypoint based methods
where a RANSAC procedure is usually needed, we implement the use of various
deep neural network structures to establish an end-to-end trainable network.
Our keypoint detector is trained through this end-to-end structure and enables
the system to avoid the inference of dynamic objects, leverages the help of
sufficiently salient features on stationary objects, and as a result, achieves
high robustness. Rather than searching the corresponding points among existing
points, the key contribution is that we innovatively generate them based on
learned matching probabilities among a group of candidates, which can boost the
registration accuracy. Our loss function incorporates both the local similarity
and the global geometric constraints to ensure all above network designs can
converge towards the right direction. We comprehensively validate the
effectiveness of our approach using both the KITTI dataset and the
Apollo-SouthBay dataset. Results demonstrate that our method achieves
comparable or better performance than the state-of-the-art geometry-based
methods. Detailed ablation and visualization analysis are included to further
illustrate the behavior and insights of our network. The low registration error
and high robustness of our method makes it attractive for substantial
applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results
updated, accepted by ICCV 201
Empirical Analysis of the Speculative Efficiency Hypothesis
Exchange market is one of the most important financial markets. An accurate prediction for future spot exchange rate is curial for modern businesses, specially the multinational enterprises, to hedge the volatility of currency exchange rate. The exchange rate also influence countries’ import and export significantly. If the home currency appreciates, this fact will significantly increase the amount of import and reduce the amount of export. In the opposite, any depreciation will help boosting the export and inhibit the import behaviour. Consequently, a mechanism of efficient forward exchange rate estimation is necessary based on both macro and micro-economic perspectives.
This paper will examine the evidence in Speculative Efficiency Hypothesis (SEH) which is the major theory of this paper. The SEH presumes the forward rate is an unbiased forecast of the expected future spot rate. In other words, the forward exchange rate is a good predictor of the future value of the spot exchange rate. This paper will use the forward rate and spot rate data of US dollar and UK sterling and select a long period of 12th of March 2001 to 31st of May 2006 and a short period from 1th January 2010 to 31st December 2010 to do the hypothesis tests in different model and get the evidence for SEH cannot stand in modern exchange market. In addition, with more deep study, more complex econometrics model should be use in testing the SEH, such as a non-linear model when dealing with the structural break.
Kay words: Foreign exchange market; Spot rate; Forward rate; Speculative Efficiency Hypothesis; Structural brea
Solution for SMART-101 Challenge of ICCV Multi-modal Algorithmic Reasoning Task 2023
In this paper, we present our solution to a Multi-modal Algorithmic Reasoning
Task: SMART-101 Challenge. Different from the traditional visual
question-answering datasets, this challenge evaluates the abstraction,
deduction, and generalization abilities of neural networks in solving
visuolinguistic puzzles designed specifically for children in the 6-8 age
group. We employed a divide-and-conquer approach. At the data level, inspired
by the challenge paper, we categorized the whole questions into eight types and
utilized the llama-2-chat model to directly generate the type for each question
in a zero-shot manner. Additionally, we trained a yolov7 model on the icon45
dataset for object detection and combined it with the OCR method to recognize
and locate objects and text within the images. At the model level, we utilized
the BLIP-2 model and added eight adapters to the image encoder VIT-G to
adaptively extract visual features for different question types. We fed the
pre-constructed question templates as input and generated answers using the
flan-t5-xxl decoder. Under the puzzle splits configuration, we achieved an
accuracy score of 26.5 on the validation set and 24.30 on the private test set
DasFormer: Deep Alternating Spectrogram Transformer for Multi/Single-Channel Speech Separation
For the task of speech separation, previous study usually treats
multi-channel and single-channel scenarios as two research tracks with
specialized solutions developed respectively. Instead, we propose a simple and
unified architecture - DasFormer (Deep alternating spectrogram transFormer) to
handle both of them in the challenging reverberant environments. Unlike
frame-wise sequence modeling, each TF-bin in the spectrogram is assigned with
an embedding encoding spectral and spatial information. With such input,
DasFormer is then formed by multiple repetition of simple blocks each of which
integrates 1) two multi-head self-attention (MHSA) modules alternately
processing within each frequency bin & temporal frame of the spectrogram 2)
MBConv before each MHSA for modeling local features on the spectrogram.
Experiments show that DasFormer has a powerful ability to model the
time-frequency representation, whose performance far exceeds the current SOTA
models in multi-channel speech separation, and also achieves single-channel
SOTA in the more challenging yet realistic reverberation scenario.Comment: 5 pages, accepted by ICASSP202
Transforming Programs between APIs with Many-to-Many Mappings
Transforming programs between two APIs or different versions of
the same API is a common software engineering task. However,
existing languages supporting for such transformation cannot
satisfactorily handle the cases when the relations between
elements in the old API and the new API are many-to-many
mappings: multiple invocations to the old API are supposed to be
replaced by multiple invocations to the new API. Since the
multiple invocations of the original APIs may not appear
consecutively and the variables in these calls may have different
names, writing a tool correctly to cover all such invocation
cases is not an easy task. In this paper we propose a novel
guided-normalization approach to address this problem. Our core
insight is that programs in different forms can be
semantics-equivalently normalized into a basic form guided by
transformation goals, and developers only need to write rules for
the basic form to address the transformation. Based on this
approach, we design a declarative program transformation
language, PATL, for adapting Java programs between different
APIs. PATL has simple syntax and basic semantics to handle
transformations only considering consecutive statements inside
basic blocks, while with guided-normalization, it can be extended
to handle complex forms of invocations. Furthermore, PATL ensures
that the user-written rules would not accidentally break def-use
relations in the program. We formalize the semantics of PATL on
Middleweight Java and prove the semantics-preserving property of
guided-normalization. We also evaluated our language with three
non-trivial case studies: i.e. updating Google Calendar API,
switching from JDom to Dom4j, and switching from Swing to
SWT. The result is encouraging; it shows that our language allows
successful transformations of real world programs with a small
number of rules and little manual resolution
Efficient Temporal Butterfly Counting and Enumeration on Temporal Bipartite Graphs
Bipartite graphs model relationships between two different sets of entities,
like actor-movie, user-item, and author-paper. The butterfly, a 4-vertices
4-edges bi-clique, is the simplest cohesive motif in a bipartite
graph and is the fundamental component of higher-order substructures. Counting
and enumerating the butterflies offer significant benefits across various
applications, including fraud detection, graph embedding, and community search.
While the corresponding motif, the triangle, in the unipartite graphs has been
widely studied in both static and temporal settings, the extension of butterfly
to temporal bipartite graphs remains unexplored. In this paper, we investigate
the temporal butterfly counting and enumeration problem: count and enumerate
the butterflies whose edges establish following a certain order within a given
duration. Towards efficient computation, we devise a non-trivial baseline
rooted in the state-of-the-art butterfly counting algorithm on static graphs,
further, explore the intrinsic property of the temporal butterfly, and develop
a new optimization framework with a compact data structure and effective
priority strategy. The time complexity is proved to be significantly reduced
without compromising on space efficiency. In addition, we generalize our
algorithms to practical streaming settings and multi-core computing
architectures. Our extensive experiments on 11 large-scale real-world datasets
demonstrate the efficiency and scalability of our solutions
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