860 research outputs found
Semantic Counting from Self-Collages
While recent supervised methods for reference-based object counting continue
to improve the performance on benchmark datasets, they have to rely on small
datasets due to the cost associated with manually annotating dozens of objects
in images. We propose Unsupervised Counter (UnCo), a model that can learn this
task without requiring any manual annotations. To this end, we construct
"SelfCollages", images with various pasted objects as training samples, that
provide a rich learning signal covering arbitrary object types and counts. Our
method builds on existing unsupervised representations and segmentation
techniques to successfully demonstrate the ability to count objects without
manual supervision. Our experiments show that our method not only outperforms
simple baselines and generic models such as FasterRCNN, but also matches the
performance of supervised counting models in some domains.Comment: 24 pages. Code available at
https://github.com/lukasknobel/SelfCollage
Prompt Generation Networks for Input-based Adaptation of Frozen Vision Transformers
With the introduction of the transformer architecture in computer vision,
increasing model scale has been demonstrated as a clear path to achieving
performance and robustness gains. However, with model parameter counts reaching
the billions, classical finetuning approaches are becoming increasingly
limiting and even unfeasible when models become hosted as inference APIs, as in
NLP. To this end, visual prompt learning, whereby a model is adapted by
learning additional inputs, has emerged as a potential solution for adapting
frozen and cloud-hosted models: During inference, this neither requires access
to the internals of models' forward pass function, nor requires any
post-processing. In this work, we propose the Prompt Generation Network (PGN)
that generates high performing, input-dependent prompts by sampling from an
end-to-end learned library of tokens. We further introduce the "prompt
inversion" trick, with which PGNs can be efficiently trained in a latent space
but deployed as strictly input-only prompts for inference. We show the PGN is
effective in adapting pre-trained models to various new datasets: It surpasses
previous methods by a large margin on 12/12 datasets and even outperforms
full-finetuning on 5/12, while requiring 100x less parameters.Comment: Tech report, 12 pages. Code: https://github.com/jochemloedeman/PG
Embarrassingly Simple Text Watermarks
We propose Easymark, a family of embarrassingly simple yet effective
watermarks. Text watermarking is becoming increasingly important with the
advent of Large Language Models (LLM). LLMs can generate texts that cannot be
distinguished from human-written texts. This is a serious problem for the
credibility of the text. Easymark is a simple yet effective solution to this
problem. Easymark can inject a watermark without changing the meaning of the
text at all while a validator can detect if a text was generated from a system
that adopted Easymark or not with high credibility. Easymark is extremely easy
to implement so that it only requires a few lines of code. Easymark does not
require access to LLMs, so it can be implemented on the user-side when the LLM
providers do not offer watermarked LLMs. In spite of its simplicity, it
achieves higher detection accuracy and BLEU scores than the state-of-the-art
text watermarking methods. We also prove the impossibility theorem of perfect
watermarking, which is valuable in its own right. This theorem shows that no
matter how sophisticated a watermark is, a malicious user could remove it from
the text, which motivate us to use a simple watermark such as Easymark. We
carry out experiments with LLM-generated texts and confirm that Easymark can be
detected reliably without any degradation of BLEU and perplexity, and
outperform state-of-the-art watermarks in terms of both quality and
reliability
Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data
SGD with momentum acceleration is one of the key components for improving the
performance of neural networks. For decentralized learning, a straightforward
approach using momentum acceleration is Distributed SGD (DSGD) with momentum
acceleration (DSGDm). However, DSGDm performs worse than DSGD when the data
distributions are statistically heterogeneous. Recently, several studies have
addressed this issue and proposed methods with momentum acceleration that are
more robust to data heterogeneity than DSGDm, although their convergence rates
remain dependent on data heterogeneity and decrease when the data distributions
are heterogeneous. In this study, we propose Momentum Tracking, which is a
method with momentum acceleration whose convergence rate is proven to be
independent of data heterogeneity. More specifically, we analyze the
convergence rate of Momentum Tracking in the standard deep learning setting,
where the objective function is non-convex and the stochastic gradient is used.
Then, we identify that it is independent of data heterogeneity for any momentum
coefficient . Through image classification tasks, we
demonstrate that Momentum Tracking is more robust to data heterogeneity than
the existing decentralized learning methods with momentum acceleration and can
consistently outperform these existing methods when the data distributions are
heterogeneous
Replicating the Disease framing problem during the 2020 COVID-19 pandemic : A study of stress, worry, trust, and choice under risk
In the risky-choice framing effect, different wording of the same options leads to predictably different choices. In a large-scale survey conducted from March to May 2020 and including 88,181 participants from 47 countries, we investigated how stress, concerns, and trust moderated the effect in the Disease problem, a prominent framing problem highly evocative of the COVID-19 pandemic. As predicted by the appraisal-tendency framework, risk aversion and the framing effect in our study were larger than under typical circumstances. Furthermore, perceived stress and concerns over coronavirus were positively associated with the framing effect. Contrary to predictions, however, they were not related to risk aversion. Trust in the governmentâs efforts to handle the coronavirus was associated with neither risk aversion nor the framing effect. The proportion of risky choices and the framing effect varied substantially across nations. Additional exploratory analyses showed that the framing effect was unrelated to reported compliance with safety measures, suggesting, along with similar findings during the pandemic and beyond, that the effectiveness of framing manipulations in public messages might be limited. Theoretical and practical implications of these findings are discussed, along with directions for further investigations
Periodic super-radiance in Er:YSO crystal
We observed periodic optical pulses from an Er:YSO crystal during irradiating
with an continuous-wave excitation laser. We refer to this new phenomenon as
"periodic super-radiance". This periodicity can be understood qualitatively by
a simple model, in which a cyclic process of a continuous supply of population
inversion and a sudden burst of super-radiance is repeated. The excitation
power dependences of peak interval and the pulse area can be interpreted with
our simple model. In addition, the linewidth of super-radiance is much narrower
than an inhomogeneous broadening in a crystal. This result suggests that only
Er3+ ions in a specific environment are involved in super-radiance.Comment: 7 pages, 5 figure
MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction.
BACKGROUND: Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans. RESULTS: We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 [Formula: see text] loss-trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones-reconstructs unseen healthy/abnormal scans; (Diagnosis) Average [Formula: see text] loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921. CONCLUSIONS: Similar to physicians' way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans
Revealing electronic state-switching at conical intersections in alkyl iodides by ultrafast XUV transient absorption spectroscopy
Conical intersections between electronic states often dictate the chemistry
of photoexcited molecules. Recently developed sources of ultrashort extreme
ultraviolet (XUV) pulses tuned to element-specific transitions in molecules
allow for the unambiguous detection of electronic state-switching at a conical
intersection. Here, the fragmentation of photoexcited iso-propyl iodide and
tert-butyl iodide molecules (i-CHI and t-CHI) through a
conical intersection between Q/Q spin-orbit states is
revealed by ultrafast XUV transient absorption measuring iodine 4d
core-to-valence transitions. The electronic state-sensitivity of the technique
allows for a complete mapping of molecular dissociation from photoexcitation to
photoproducts. In both molecules, the sub-100 fs transfer of a photoexcited
wave packet from the Q state into the Q state at the
conical intersection is captured. The results show how differences in the
electronic state-switching of the wave packet in i-CHI and
t-CHI directly lead to differences in the photoproduct branching
ratio of the two systems
Sustainability Assessment of Reuse and Recycling Management Options for End-of-Life Computers-Korean and Japanese Case Study Analysis
The depletion of natural resources and global warming have increased in severity globally. In the industrial field, assembly products, such as electronic products, should be disassembled for recycling and reuse to deal with these problems. Reuse and recycling can contribute to reducing GreenHouse Gas (GHG) emissions and less depletion of natural resources since GHG emissions for virgin material production can be saved using reused components and recycled materials. However, each component of selling revenue and material-based GHG emissions depends on the country because of the different energy mixes of electrical power. Moreover, each collected component embedded in End-of-Life (EOL) products needs to be selected as a life cycle option based on its remaining life. The purpose of this study is to decide life cycle options such as reuse, recycling, and disposal of each component environmentally-friendly and economically in Korea and Japanese cases for computers. Firstly, selecting the life cycle option for each component was formulated by 0â1 integer programming with Îľ constraints. Next, GHG emissions, profits, and costs in Korea and Japan were estimated and analyzed for each component. Finally, Korean and Japanese cases were analyzed to obtain an economic value in the same material-based GHG saving rate with each componentâs life cycle option selection by comparing each EOL product data. In the experiments, GHG recovery efficiency was higher in Japan 43 [g/Yen] than one in Korea 28 [g/Yen]. Therefore, it was better to retrieve and reutilize the components in Korea. However, if the maximum GHG recovery efficiency is desired, Japan is a better option
Candidate Brown-dwarf Microlensing Events with Very Short Timescales and Small Angular Einstein Radii
Short-timescale microlensing events are likely to be produced by substellar brown dwarfs (BDs), but it is difficult to securely identify BD lenses based on only event timescales t_E because short-timescale events can also be produced by stellar lenses with high relative lens-source proper motions. In this paper, we report three strong candidate BD-lens events found from the search for lensing events not only with short timescales (t_E Ⲡ6 days) but also with very small angular Einstein radii (θ_E Ⲡ0.05 mas) among the events that have been found in the 2016â2019 observing seasons. These events include MOA-2017-BLG-147, MOA-2017-BLG-241, and MOA-2019-BLG-256, in which the first two events are produced by single lenses and the last event is produced by a binary lens. From the Monte Carlo simulations of Galactic events conducted with the combined t_E and θ_E constraint, it is estimated that the lens masses of the individual events are
0.051^(+0.100)_(â0.027) Mâ, 0.044^(+0.090)_(â0.023) Mâ, and 0.046^(+0.067)_(â0.023) Mâ/0.038^(+0.056)_(â0.019) Mâ and the probability of the lens mass smaller than the lower limit of stars is ~80% for all events. We point out that routine lens mass measurements of short-timescale lensing events require survey-mode space-based observations
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