Tootfinder

kwh, wealth, old age,
examining something reminds me diverse backgrounds can uncover weird discrepancies in averages of data, or in what you yourself consider "typical".
asked contact list about how much power they use.
one of them made an order of magnitude more kwh than the rest (they're also a c-level executive)
talked about how 3 fridges, a pool, and a hot-tub "stuff comes with age" (but also that they're "trying to be energy efficient")
I mean... not all of the old people on the list have big houses like that. I think that might just be an executive thing?
but it's also like...
I think comparison is a useful tool to realize some parts of our lives go unchallenged.
also, some mistakes in my methodology:
- heating/cooling in different regions vastly changes
- not including power usage from gas heaters vs all electric houses
- not controlling for heating/location
I actually don't think the point would be to get grumpy at them, but to understand that they had zero clue they were 10x energy users

APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching
Yikuan Xia, Jiazun Chen, Xinchi Li, Jun Gao
https://arxiv.org/abs/2405.04820 https://…

APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching
Generalized Entity Matching (GEM), which aims at judging whether two records represented in different formats refer to the same real-world entity, is an essential task in data management. The prompt tuning paradigm for pre-trained language models (PLMs), including the recent PromptEM model, effectively addresses the challenges of low-resource GEM in practical applications, offering a robust solution when labeled data is scarce. However, existing prompt tuning models for GEM face the challenges …

LHAASO-KM2A detector simulation using Geant4
Zhen Cao, F. Aharonian, Q. An, Axikegu, Y. X. Bai, Y. W. Bao, D. Bastieri, X. J. Bi, Y. J. Bi, J. T. Cai, Q. Cao, W. Y. Cao, Zhe Cao, J. Chang, J. F. Chang, A. M. Chen, E. S. Chen, Liang Chen, Lin Chen, Long Chen, M. J. Chen, M. L. Chen, Q. H. Chen, S. H. Chen, S. Z. Chen, T. L. Chen, Y. Chen, N. Cheng, Y. D. Cheng, M. Y. Cui, S. W. Cui, X. H. Cui, Y. D. Cui, B. Z. Dai, H. L. Dai, Z. G. Dai, Danzengluobu, X. Q. Dong, K. K. Duan, J. H. Fan,…

LHAASO-KM2A detector simulation using Geant4
KM2A is one of the main sub-arrays of LHAASO, working on gamma ray astronomy and cosmic ray physics at energies above 10 TeV. Detector simulation is the important foundation for estimating detector performance and data analysis. It is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units (>6000) with large altitude difference (30 m) and huge coverage (1.3 km^2). In this paper, the design of the KM…

MIPI 2024 Challenge on Demosaic for HybridEVS Camera: Methods and Results
Yaqi Wu, Zhihao Fan, Xiaofeng Chu, Jimmy S. Ren, Xiaoming Li, Zongsheng Yue, Chongyi Li, Shangcheng Zhou, Ruicheng Feng, Yuekun Dai, Peiqing Yang, Chen Change Loy, Senyan Xu, Zhijing Sun, Jiaying Zhu, Yurui Zhu, Xueyang Fu, Zheng-Jun Zha, Jun Cao, Cheng Li, Shu Chen, Liang Ma, Shiyang Zhou, Haijin Zeng, Kai Feng, Yongyong Chen, Jingyong Su, Xianyu Guan, Hongyuan Yu, Cheng Wan, Jiamin Lin, Binnan Han, Yajun Zou, Z…

MIPI 2024 Challenge on Demosaic for HybridEVS Camera: Methods and Results
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 20…

APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching
Yikuan Xia, Jiazun Chen, Xinchi Li, Jun Gao
https://arxiv.org/abs/2405.04820 https://…

APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching
Generalized Entity Matching (GEM), which aims at judging whether two records represented in different formats refer to the same real-world entity, is an essential task in data management. The prompt tuning paradigm for pre-trained language models (PLMs), including the recent PromptEM model, effectively addresses the challenges of low-resource GEM in practical applications, offering a robust solution when labeled data is scarce. However, existing prompt tuning models for GEM face the challenges …

The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio
Yuankun Xie, Yi Lu, Ruibo Fu, Zhengqi Wen, Zhiyong Wang, Jianhua Tao, Xin Qi, Xiaopeng Wang, Yukun Liu, Haonan Cheng, Long Ye, Yi Sun
https://arxiv.org/abs/2405.04880

The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio
With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD) models…

X's audio and video calls, which are enabled by default, are peer-to-peer, revealing users' IP addresses to each other, unless they enable a privacy setting (TechCrunch)
https://techcrunch.com/2024/03/04/elon-musk-x-twitter-calling-privacy…

X's new calling feature hurts your privacy — here's how to switch it off | TechCrunch
TechCrunch ran tests of the feature to analyze how the calling feature works and to understand the risks associated with it.

HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech
Zhongren Dong, Zixing Zhang, Weixiang Xu, Jing Han, Jianjun Ou, Bj\"orn W. Schuller
https://arxiv.org/abs/2405.03952

HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech
Automatically detecting Alzheimer's Disease (AD) from spontaneous speech plays an important role in its early diagnosis. Recent approaches highly rely on the Transformer architectures due to its efficiency in modelling long-range context dependencies. However, the quadratic increase in computational complexity associated with self-attention and the length of audio poses a challenge when deploying such models on edge devices. In this context, we construct a novel framework, namely Hierarchical A…

X's audio and video calls, which are enabled by default, are peer-to-peer, revealing users' IP addresses to each other, unless they enable a privacy setting (TechCrunch)
https://techcrunch.com/2024/03/04/elon-musk-x-twitter-calling-privacy…

X's new calling feature hurts your privacy — here's how to switch it off | TechCrunch
TechCrunch ran tests of the feature to analyze how the calling feature works and to understand the risks associated with it.

"It is okay to be uncommon": Quantizing Sound Event Detection Networks on Hardware Accelerators with Uncommon Sub-Byte Support
Yushu Wu, Xiao Quan, Mohammad Rasool Izadi, Chuan-Che Huang
https://arxiv.org/abs/2404.04386

"It is okay to be uncommon": Quantizing Sound Event Detection Networks on Hardware Accelerators with Uncommon Sub-Byte Support
If our noise-canceling headphones can understand our audio environments, they can then inform us of important sound events, tune equalization based on the types of content we listen to, and dynamically adjust noise cancellation parameters based on audio scenes to further reduce distraction. However, running multiple audio understanding models on headphones with a limited energy budget and on-chip memory remains a challenging task. In this work, we identify a new class of neural network accelera…