3,439 research outputs found
Exponential representation and consistency checking for M-layer
M-layer,the tropospheric propagation effect prediction program is revised for greater accuracy, speed and stability. This is achieved through converting the extended complex number representation into the representation by complex exponent, improving the accuracy in Airy function computation, introducing a new mode locating algorithm and implementing a consistency checking procedure for determining the proper method to evaluate the height gain function. The revision has been documented and the new program source code has been delivered. It is recommended that the mode search protocol, not just the mode locating algorithm introduced in this revision, be completely revised. Unlike the current approach of blanketing the whole possible region until exhaustion, modes should be searched according to their range attenuation rates one by one along a well defined path. this should result in a faster and even more stable program. The program size can also be reducedNaval Command, Control and Ocean Surveillance Center, RDT&E Division, Code 543http://archive.org/details/exponentialrepre00leehNaval Command, Control and Ocean Surveillance
Center, KDT&E DivisionNAApproved for public release; distribution is unlimited
Understanding Pure CLIP Guidance for Voxel Grid NeRF Models
We explore the task of text to 3D object generation using CLIP. Specifically,
we use CLIP for guidance without access to any datasets, a setting we refer to
as pure CLIP guidance. While prior work has adopted this setting, there is no
systematic study of mechanics for preventing adversarial generations within
CLIP. We illustrate how different image-based augmentations prevent the
adversarial generation problem, and how the generated results are impacted. We
test different CLIP model architectures and show that ensembling different
models for guidance can prevent adversarial generations within bigger models
and generate sharper results. Furthermore, we implement an implicit voxel grid
model to show how neural networks provide an additional layer of
regularization, resulting in better geometrical structure and coherency of
generated objects. Compared to prior work, we achieve more coherent results
with higher memory efficiency and faster training speeds
Interfacial chemical bonding-mediated ionic resistive switching.
In this paper, we present a unique resistive switching (RS) mechanism study of Pt/TiO2/Pt cell, one of the most widely studied RS system, by focusing on the role of interfacial bonding at the active TiO2-Pt interface, as opposed to a physico-chemical change within the RS film. This study was enabled by the use of a non-conventional scanning probe-based setup. The nanoscale cell is formed by bringing a Pt/TiO2-coated atomic force microscope tip into contact with a flat substrate coated with Pt. The study reveals that electrical resistance and interfacial bonding status are highly coupled together. An oxygen-mediated chemical bonding at the active interface between TiO2 and Pt is a necessary condition for a non-polar low-resistance state, and a reset switching process disconnects the chemical bonding. Bipolar switching mode did not involve the chemical bonding. The nature of chemical bonding at the TiO2-metal interface is further studied by density functional theory calculations
Why We Should Report the Details in Subjective Evaluation of TTS More Rigorously
This paper emphasizes the importance of reporting experiment details in
subjective evaluations and demonstrates how such details can significantly
impact evaluation results in the field of speech synthesis. Through an analysis
of 80 papers presented at INTERSPEECH 2022, we find a lack of thorough
reporting on critical details such as evaluator recruitment and filtering,
instructions and payments, and the geographic and linguistic backgrounds of
evaluators. To illustrate the effect of these details on evaluation outcomes,
we conducted mean opinion score (MOS) tests on three well-known TTS systems
under different evaluation settings and we obtain at least three distinct
rankings of TTS models. We urge the community to report experiment details in
subjective evaluations to improve the reliability and interpretability of
experimental results.Comment: Interspeech 2023 camera-ready versio
When Social Influence Meets Item Inference
Research issues and data mining techniques for product recommendation and
viral marketing have been widely studied. Existing works on seed selection in
social networks do not take into account the effect of product recommendations
in e-commerce stores. In this paper, we investigate the seed selection problem
for viral marketing that considers both effects of social influence and item
inference (for product recommendation). We develop a new model, Social Item
Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we
formulate a seed selection problem, called Social Item Maximization Problem
(SIMP), and prove the hardness of SIMP. We design an efficient algorithm with
performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and
develop a new index structure, called SIG-index, to accelerate the computation
of diffusion process in HAG. Moreover, to construct realistic SIG models for
SIMP, we develop a statistical inference based framework to learn the weights
of hyperedges from data. Finally, we perform a comprehensive evaluation on our
proposals with various baselines. Experimental result validates our ideas and
demonstrates the effectiveness and efficiency of the proposed model and
algorithms over baselines.Comment: 12 page
Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS
Existing sentence textual similarity benchmark datasets only use a single
number to summarize how similar the sentence encoder's decision is to humans'.
However, it is unclear what kind of sentence pairs a sentence encoder (SE)
would consider similar. Moreover, existing SE benchmarks mainly consider
sentence pairs with low lexical overlap, so it is unclear how the SEs behave
when two sentences have high lexical overlap. We introduce a high-quality SE
diagnostic dataset, HEROS. HEROS is constructed by transforming an original
sentence into a new sentence based on certain rules to form a \textit{minimal
pair}, and the minimal pair has high lexical overlaps. The rules include
replacing a word with a synonym, an antonym, a typo, a random word, and
converting the original sentence into its negation. Different rules yield
different subsets of HEROS. By systematically comparing the performance of over
60 supervised and unsupervised SEs on HEROS, we reveal that most unsupervised
sentence encoders are insensitive to negation. We find the datasets used to
train the SE are the main determinants of what kind of sentence pairs an SE
considers similar. We also show that even if two SEs have similar performance
on STS benchmarks, they can have very different behavior on HEROS. Our result
reveals the blind spot of traditional STS benchmarks when evaluating SEs.Comment: ACL 2023 repl4nlp (representation learning for NLP) workshop poster
paper. Dataset at https://huggingface.co/datasets/dcml0714/Hero
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