Tootfinder

(🎁) Will A.I. Ever Live Up to Its #Hype?
It’s looking less like an all-powerful being and more like an unreliable intern.
"Given these constraints, it seems just as likely to me that generative #AI could end up like the Roomba, the mediocre vacuum robot that does a passable job when you are…

"Will A.I. Ever Live Up to Its Hype?"
https://www.nytimes.com/2024/05/15/opinion/artificial-intelligence-ai-openai-chatgpt-overrated-hype.html?unlocked_article_code=1.sE0.2pJC.01Wt4_Q…

Opinion | A.I. and the Silicon Valley Hype Machine
It’s looking less like an all-powerful being and more like an unreliable intern.

Opinion | A.I. and the Silicon Valley Hype Machine - The New York Times
https://www.nytimes.com/2024/05/15/opinion

Opinion | A.I. and the Silicon Valley Hype Machine
A.I. is looking less like an all-powerful being and more like an unreliable intern.

How GPS warfare is affecting civilian populations; an analysis finds ~40M people lived in areas with unreliable GPS for at least half of the past six months (Financial Times)
https://t.co/mvay7kTXBc

Opinion | A.I. and the Silicon Valley Hype Machine - The New York Times
https://www.nytimes.com/2024/05/15/opinion

Opinion | A.I. and the Silicon Valley Hype Machine
A.I. is looking less like an all-powerful being and more like an unreliable intern.

This https://arxiv.org/abs/2203.13966 has been replaced.
link: https://scholar.google.com/scholar?q=a

Stability of Linear Set-Membership Filters With Respect to Initial Conditions: An Observation-Information Perspective
The issue of filter stability with respect to (w.r.t.) the initial condition refers to the unreliable filtering process caused by improper prior information of the initial state. This paper focuses on analyzing and resolving the stability issue w.r.t. the initial condition of the classical Set-Membership Filters (SMFs) for linear time-invariance systems, which has not yet been well understood in the literature. To this end, we propose a new concept -- the Observation-Information Tower (OIT), wh…

I thought I'd do another update of what I've been reading lately. This time, rather than post about each book, I'll just call out the ones I liked the most.
"Harrow the Ninth" by Tamsyn Muir is the sequel to "Gideon the Ninth", and it's just as good if not better. The story and world-building is unique, and the storytelling uses an unreliable narrator technique in a way I've never seen done before. Muir is such a beautiful and creative writer.…

This https://arxiv.org/abs/2403.16099 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

A Multi-Label Dataset of French Fake News: Human and Machine Insights
We present a corpus of 100 documents, OBSINFOX, selected from 17 sources of French press considered unreliable by expert agencies, annotated using 11 labels by 8 annotators. By collecting more labels than usual, by more annotators than is typically done, we can identify features that humans consider as characteristic of fake news, and compare them to the predictions of automated classifiers. We present a topic and genre analysis using Gate Cloud, indicative of the prevalence of satire-like text…

This https://arxiv.org/abs/1902.01353 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Session Type System for Asynchronous Unreliable Broadcast Communication
Session types are formal specifications of communication protocols, allowing protocol implementations to be verified by typechecking. Up to now, session type disciplines have assumed that the communication medium is reliable, with no loss of messages. However, unreliable broadcast communication is common in a wide class of distributed systems such as ad-hoc and wireless sensor networks. Often such systems have structured communication patterns that should be amenable to analysis by means of ses…

@… I’ve faced that problem too! I always end up writing a bridge that calls Swift from JavaScript, so I can use AVAudioPlayer, just to work around how unreliable audio is in WKWebView.

Wikipedia editors lowered CNET's source reliability rating after CNET used AI to create articles and are discussing reliability of other Red Ventures outlets (Maggie Harrison Dupré/Futurism)
https://futurism.com/wikipedia-cnet-unreliable-ai

Wikipedia No Longer Considers CNET a "Generally Reliable" Source After AI Scandal
Conversations between Wikipedia editors following CNET's AI scandal reflect the reputational hazards of AI content and dubious ownership.

I'm not saying gamers are unreliable flakes but this is at least the third time I have someone I've tried to work on doing hardware testing where I just get ghosted. Ugh...

Don't let any denier or fossil fuel apologist ever tell you that renewables are "unreliable" or that batteries are "unsustainable”.
You can make batteries from water, sand, and other material to store electricity as heat. This is centuries-old technology that can power the 21st century and beyond.
No more excuses.
#EndFossilFuels

Really dodgy unreliable studies of by far the least well-understood body part,
are absolutely no reason to reassess what we've known about young adults for millennia.

Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning
Xudong Yu, Chenjia Bai, Hongyi Guo, Changhong Wang, Zhen Wang
https://arxiv.org/abs/2404.06188

Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning
Offline Reinforcement Learning (RL) faces distributional shift and unreliable value estimation, especially for out-of-distribution (OOD) actions. To address this, existing uncertainty-based methods penalize the value function with uncertainty quantification and demand numerous ensemble networks, posing computational challenges and suboptimal outcomes. In this paper, we introduce a novel strategy employing diverse randomized value functions to estimate the posterior distribution of $Q$-values. I…

How the mighty have fallen.
I remember c|net as a standard-bearer of tech journalism back in the 1990s. Theirs was one of the few bookmarks I opened regularly to read about tech news.
Red Ventures has destroyed its reputation, along with all their other properties’ like ZDNet (the other fantastic tech news site). RV’s decisive pivot to AI is likely fatal; they will accelerate the descent of all their outlets into a content swill mill fit only for your blocklist.

Wikipedia No Longer Considers CNET a "Generally Reliable" Source After AI Scandal
Conversations between Wikipedia editors following CNET's AI scandal reflect the reputational hazards of AI content and dubious ownership.

In a world of unreliable narrators (including me, and you), I think consensus reality, ideally based on science, is the only approach that works.

Smart home equipment is still too fragmented and low quality to get right. Setup is complicated, and even when you get everything working, protocols are unreliable and individual pieces of equipment are of such varying quality that something inevitably doesn’t work.
My smart home setup feels like it’s held together with scotch tape and string most of the time. I’d even one thing gets disrupted I have to spent time fixing it.

The woes of a blundering first-time smart home owner
I thought a smart door lock, smart thermostat, and smart lighting would turn my house into a home. Turns out, it’s not always about the gadgets.

Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
Jun Li, Weiwei Zhang, Kang Wei, Guangji Chen, Long Shi, Wen Chen
https://arxiv.org/abs/2405.05802 …

Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
As an emerging artificial intelligence technology, graph neural networks (GNNs) have exhibited promising performance across a wide range of graph-related applications. However, information exchanges among neighbor nodes in GNN pose new challenges in the resource-constrained scenario, especially in wireless systems. In practical wireless systems, the communication links among nodes are usually unreliable due to wireless fading and receiver noise, consequently resulting in performance degradation…

Can My Microservice Tolerate an Unreliable Database? Resilience Testing with Fault Injection and Visualization
Michael Assad, Christopher Meiklejohn, Heather Miller, Stephan Krusche
https://arxiv.org/abs/2404.01886

Can My Microservice Tolerate an Unreliable Database? Resilience Testing with Fault Injection and Visualization
In microservice applications, ensuring resilience during database or service disruptions constitutes a significant challenge. While several tools address resilience testing for service failures, there is a notable gap in tools specifically designed for resilience testing of database failures. To bridge this gap, we have developed an extension for fault injection in database clients, which we integrated into Filibuster, an existing tool for fault injection in services within microservice applica…

Towards Trust and Reputation as a Service in a Blockchain-based Decentralized Marketplace
Stephen Olariu, Ravi Mukkamala, Meshari Aljohani
https://arxiv.org/abs/2403.04779

Towards Trust and Reputation as a Service in a Blockchain-based Decentralized Marketplace
Motivated by the challenges inherent in implementing trusted services in the Society 5.0 initiative, we propose a novel trust and reputation service for a decentralized marketplace. We assume that a Smart Contract is associated with each transaction and that the Smart Contract is responsible for providing automatic feedback, replacing notoriously unreliable buyer feedback by a more objective assessment of how well the parties have fulfilled their obligations. Our trust and reputation service wa…

A lot of fairly important notice/response traffic - such as e-bills, legal notices, etc - travels by e-mail.
Yet email has become notoriously unreliable as an ever increasing number of defenses are erected against an ever increasing number of spammers and phishers.
Email has achieved a legal status somewhat below that of US Postal Service first class mail (which is, under deJoy, pretty awful itself).
I am wondering whether that legal recognition of e-mail ought to collapse …

"But the attorney general claims that argument is based on the “false premise” that Trump needs to get a single, massive bond to cover what he owes. He should be more than able to get several smaller bonds to foot the bill, according to state lawyers."
NY AG blasts Trump for ‘unreliable’ claim he can’t secure bond for $454 million fraud judgment | Courthouse News Service

NY AG blasts Trump for ‘unreliable’ claim he can’t secure bond for $454 million fraud judgment
If Trump doesn't post a bond by Monday, the state can start seizing his assets to foot the bill.

There's a few different camps on generative "AI":
- "It's our lord and savior" (mostly AI companies pushing this)
- "It will kill us all" doomerism (it won't, it has no intelligence or sentience or agenda)
- "It has some use cases!" centrism (problematic because it enables the lord and savior types & it's also wrong as work products are highly unreliable, even for basic things like summaries and tabulation)
- &q…

Interesting. I hadn’t considered much of CNET “generally reliable” since about 2010… https://mastodon.online/@ia/112022838940762765

This https://arxiv.org/abs/2311.11167 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Benchmarking Machine Learning Models for Quantum Error Correction
Quantum Error Correction (QEC) is one of the fundamental problems in quantum computer systems, which aims to detect and correct errors in the data qubits within quantum computers. Due to the presence of unreliable data qubits in existing quantum computers, implementing quantum error correction is a critical step when establishing a stable quantum computer system. Recently, machine learning (ML)-based approaches have been proposed to address this challenge. However, they lack a thorough understa…

I can’t even browse recipes on my phone anymore, they’re so laden with JS bloatware and ads. My phone gets warm to the touch and scrolling becomes unreliable. What a mess

A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series
Ziquan Deng, Xiwei Xuan, Kwan-Liu Ma, Zhaodan Kong
https://arxiv.org/abs/2405.03234

A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series
Time series anomaly detection is a critical machine learning task for numerous applications, such as finance, healthcare, and industrial systems. However, even high-performed models may exhibit potential issues such as biases, leading to unreliable outcomes and misplaced confidence. While model explanation techniques, particularly visual explanations, offer valuable insights to detect such issues by elucidating model attributions of their decision, many limitations still exist -- They are prima…

Optimal Denial-of-Service Attacks Against Status Updating
Saad Kriouile, Mohamad Assaad, Deniz G\"und\"uz, Touraj Soleymani
https://arxiv.org/abs/2403.04489

Optimal Denial-of-Service Attacks Against Status Updating
In this paper, we investigate denial-of-service attacks against status updating. The target system is modeled by a Markov chain and an unreliable wireless channel, and the performance of status updating in the target system is measured based on two metrics: age of information and age of incorrect information. Our objective is to devise optimal attack policies that strike a balance between the deterioration of the system's performance and the adversary's energy. We model the optimal problem as a…

Towards Trust and Reputation as a Service in a Blockchain-based Decentralized Marketplace
Stephen Olariu, Ravi Mukkamala, Meshari Aljohani
https://arxiv.org/abs/2403.04779

Towards Trust and Reputation as a Service in a Blockchain-based Decentralized Marketplace
Motivated by the challenges inherent in implementing trusted services in the Society 5.0 initiative, we propose a novel trust and reputation service for a decentralized marketplace. We assume that a Smart Contract is associated with each transaction and that the Smart Contract is responsible for providing automatic feedback, replacing notoriously unreliable buyer feedback by a more objective assessment of how well the parties have fulfilled their obligations. Our trust and reputation service wa…

MaxCUCL: Max-Consensus with Deterministic Convergence in Networks with Unreliable Communication
Apostolos I. Rikos, Themistoklis Charalambous, Karl H. Johansson
https://arxiv.org/abs/2402.18719

MaxCUCL: Max-Consensus with Deterministic Convergence in Networks with Unreliable Communication
In this paper, we present a novel distributed algorithm (herein called MaxCUCL) designed to guarantee that max-consensus is reached in networks characterized by unreliable communication links (i.e., links suffering from packet drops). Our proposed algorithm is the first algorithm that achieves max-consensus in a deterministic manner (i.e., nodes always calculate the maximum of their states regardless of the nature of the probability distribution of the packet drops). Furthermore, it allows node…

InstantSplat: Unbounded Sparse-view Pose-free Gaussian Splatting in 40 Seconds
Zhiwen Fan, Wenyan Cong, Kairun Wen, Kevin Wang, Jian Zhang, Xinghao Ding, Danfei Xu, Boris Ivanovic, Marco Pavone, Georgios Pavlakos, Zhangyang Wang, Yue Wang
https://arxiv.org/abs/2403.20309

InstantSplat: Unbounded Sparse-view Pose-free Gaussian Splatting in 40 Seconds
While novel view synthesis (NVS) has made substantial progress in 3D computer vision, it typically requires an initial estimation of camera intrinsics and extrinsics from dense viewpoints. This pre-processing is usually conducted via a Structure-from-Motion (SfM) pipeline, a procedure that can be slow and unreliable, particularly in sparse-view scenarios with insufficient matched features for accurate reconstruction. In this work, we integrate the strengths of point-based representations (e.g.,…

Benchmarking Educational Program Repair
Charles Koutcheme, Nicola Dainese, Sami Sarsa, Juho Leinonen, Arto Hellas, Paul Denny
https://arxiv.org/abs/2405.05347

Benchmarking Educational Program Repair
The emergence of large language models (LLMs) has sparked enormous interest due to their potential application across a range of educational tasks. For example, recent work in programming education has used LLMs to generate learning resources, improve error messages, and provide feedback on code. However, one factor that limits progress within the field is that much of the research uses bespoke datasets and different evaluation metrics, making direct comparisons between results unreliable. Thus…

I came to the conclusion that impulsivity is not a symptom of ADD/ADHD but a consequence. Let me explain:
My main issue with ADD is that important thoughts (todos, dates, deadlines, promises, ...) never come up unless triggered somehow. Now, when any important thought comes up in my unreliable brain I have two choices:
1. act immediately
2. dismiss the thought, possibly forever
Note: ADD/ADHD is a real disorder in the brain's metabolism and not a popculture phenome…

This https://arxiv.org/abs/2311.06522 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Semantic-aware Sampling and Transmission in Energy Harvesting Systems: A POMDP Approach
We address the problem of real-time remote tracking of a partially observable Markov source in an energy harvesting system with an unreliable communication channel. We consider both sampling and transmission costs. Different from most prior studies that assume the source is fully observable, the sampling cost renders the source partially observable. The goal is to jointly optimize sampling and transmission policies for two semantic-aware metrics: i) a general distortion measure and ii) the age …

This https://arxiv.org/abs/2309.13198 has been replaced.
initial toot: https://mastoxiv.page/@a…

Associative memory by virtual oscillator network based on single spin-torque oscillator
A coupled oscillator network may be able to perform an energy-efficient associative memory operation. However, its realization has been difficult because inhomogeneities unavoidably arise among the oscillators during fabrication and lead to an unreliable operation. This issue could be resolved if the oscillator network were able to be formed from a single oscillator. Here, we performed numerical simulations and theoretical analyses on an associative memory operation that uses a virtual oscillat…

FaaF: Facts as a Function for the evaluation of RAG systems
Vasileios Katranidis, Gabor Barany
https://arxiv.org/abs/2403.03888 https://

FaaF: Facts as a Function for the evaluation of RAG systems
Factual recall from a reference source is crucial for evaluating the performance of Retrieval Augmented Generation (RAG) systems, as it directly probes into the quality of both retrieval and generation. However, it still remains a challenge to perform this evaluation reliably and efficiently. Recent work has focused on fact verification via prompting language model (LM) evaluators, however we demonstrate that these methods are unreliable in the presence of incomplete or inaccurate information. …

Misinformation Resilient Search Rankings with Webgraph-based Interventions
Peter Carragher, Evan M. Williams, Kathleen M. Carley
https://arxiv.org/abs/2404.08869

Misinformation Resilient Search Rankings with Webgraph-based Interventions
The proliferation of unreliable news domains on the internet has had wide-reaching negative impacts on society. We introduce and evaluate interventions aimed at reducing traffic to unreliable news domains from search engines while maintaining traffic to reliable domains. We build these interventions on the principles of fairness (penalize sites for what is in their control), generality (label/fact-check agnostic), targeted (increase the cost of adversarial behavior), and scalability (works at w…

Modeling the Trade-off between Throughput and Reliability in a Bluetooth Low Energy Connection
Bozheng Pang, Tim Claeys, Hans Hallez, Jeroen Boydens
https://arxiv.org/abs/2405.01231

Modeling the Trade-off between Throughput and Reliability in a Bluetooth Low Energy Connection
The use of Bluetooth Low Energy in low-range Internet of Things systems is growing exponentially. Similar to other wireless communication protocols, throughput and reliability are two key performance metrics in Bluetooth Low Energy communications. However, electromagnetic interference from various sources can heavily affect the performance of wireless devices, leading to dropped throughput and unreliable communication. Therefore, there is a need for both theoretical and practical studies capabl…

A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series
Ziquan Deng, Xiwei Xuan, Kwan-Liu Ma, Zhaodan Kong
https://arxiv.org/abs/2405.03234

A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series
Time series anomaly detection is a critical machine learning task for numerous applications, such as finance, healthcare, and industrial systems. However, even high-performed models may exhibit potential issues such as biases, leading to unreliable outcomes and misplaced confidence. While model explanation techniques, particularly visual explanations, offer valuable insights to detect such issues by elucidating model attributions of their decision, many limitations still exist -- They are prima…

This https://arxiv.org/abs/2404.13236 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models
Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable performance in natural language processing-based applications, LLMs are susceptible to undesirable and erratic behaviors, including hallucinations, unreliable reasoning, and the generation of harmful content. These flawed behaviors undermine trust in LLMs and pose significant hurdles to their adoption in real-world applic…

This https://arxiv.org/abs/1902.01353 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Session Type System for Asynchronous Unreliable Broadcast Communication
Session types are formal specifications of communication protocols, allowing protocol implementations to be verified by typechecking. Up to now, session type disciplines have assumed that the communication medium is reliable, with no loss of messages. However, unreliable broadcast communication is common in a wide class of distributed systems such as ad-hoc and wireless sensor networks. Often such systems have structured communication patterns that should be amenable to analysis by means of ses…

The failure state of macOS application signature (and “receipt”) checking is to tell the user that the app has been “damaged” and needs to be reinstalled.
Helpfully side-stepping the complexity of explaining cryptographic processes to users, while also conveying the idea that Apple's own disks and filesystems are incredibly unreliable.

Reliable operation in high-mobility indium oxide thin film transistors
Prashant R. Ghediya, Yusaku Magari, Hikaru Sadahira, Takashi Endo, Mamoru Furuta, Yuqiao Zhang, Yasutaka Matsuo, Hiromichi Ohta
https://arxiv.org/abs/2404.03856

Reliable operation in high-mobility indium oxide thin film transistors
Transparent oxide semiconductors (TOSs) based thin-film transistors (TFTs) that exhibit higher field effect mobility (uFE) are highly required toward the realization of next-generation displays. Among numerous types of TOS-TFTs, In2O3-based TFTs are the front-running candidate because they exhibit the highest uFE ~100 cm2/Vs. However, the device operation of In2O3 TFTs is unreliable; a large voltage shift occurs especially when negative gate bias is applied due to adsorption/desorption of gas m…

Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
Zeya Wang, Chenglong Ye
https://arxiv.org/abs/2403.14830 https://

Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
Deep clustering, a method for partitioning complex, high-dimensional data using deep neural networks, presents unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering, which involves projecting data into lower-dimensional embeddings before partitioning. Two key issues are identified: 1) the curse of dimensionality when applying these measures to raw data, and 2) the unreliable comparison of clustering res…

Machine and deep learning methods for predicting 3D genome organization
Brydon P. G. Wall, My Nguyen, J. Chuck Harrell, Mikhail G. Dozmorov
https://arxiv.org/abs/2403.03231

Machine and deep learning methods for predicting 3D genome organization
Three-Dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B compartments play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences i…

Notes on the Practical Application of Nested Sampling: MultiNest, (Non)convergence, and Rectification
Alexander J. Dittmann
https://arxiv.org/abs/2404.16928

Notes on the Practical Application of Nested Sampling: MultiNest, (Non)convergence, and Rectification
Nested sampling is a promising tool for Bayesian statistical analysis because it simultaneously performs parameter estimation and facilitates model comparison. MultiNest is one of the most popular nested sampling implementations, and has been applied to a wide variety of problems in the physical sciences. However, MultiNest results are frequently unreliable, and accompanying convergence tests are a necessary component of any analysis. Using simple, analytically tractable test problems, I illust…

This https://arxiv.org/abs/2403.09583 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
In image-based robot manipulation tasks with large observation and action spaces, reinforcement learning struggles with low sample efficiency, slow training speed, and uncertain convergence. As an alternative, large pre-trained foundation models have shown promise in robotic manipulation, particularly in zero-shot and few-shot applications. However, using these models directly is unreliable due to limited reasoning capabilities and challenges in understanding physical and spatial contexts. This…

Robust inference for the unification of confidence intervals in meta-analysis
Wei Liang, Haicheng Huang, Hongsheng Dai, Yinghui Wei
https://arxiv.org/abs/2404.13707

Robust inference for the unification of confidence intervals in meta-analysis
Traditional meta-analysis assumes that the effect sizes estimated in individual studies follow a Gaussian distribution. However, this distributional assumption is not always satisfied in practice, leading to potentially biased results. In the situation when the number of studies, denoted as K, is large, the cumulative Gaussian approximation errors from each study could make the final estimation unreliable. In the situation when K is small, it is not realistic to assume the random-effect follows…

HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation
Changkun Liu, Shuai Chen, Yukun Zhao, Huajian Huang, Victor Prisacariu, Tristan Braud
https://arxiv.org/abs/2402.14371

HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation
Absolute Pose Regressors (APRs) directly estimate camera poses from monocular images, but their accuracy is unstable for different queries. Uncertainty-aware APRs provide uncertainty information on the estimated pose, alleviating the impact of these unreliable predictions. However, existing uncertainty modelling techniques are often coupled with a specific APR architecture, resulting in suboptimal performance compared to state-of-the-art (SOTA) APR methods. This work introduces a novel APR-agno…

This https://arxiv.org/abs/2304.08063 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Data-Driven Equation Discovery of a Cloud Cover Parameterization
A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning-based parameterizations using output from global storm-resolving models. While neural networks can achieve state-of-the-art performance within their training distribution, they can make unreliable predictions outside of it. Additionally, they often require post-hoc tools for interpretation. To avoid these limitations, we combine symbolic regression, sequ…

This https://arxiv.org/abs/2306.13941 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Grassroots Social Networking: Where People have Agency over their Personal Information and Social Graph
Offering an architecture for social networking in which people have agency over their personal information and social graph is an open challenge. Here we present a grassroots architecture for serverless, permissionless, peer-to-peer social networks termed Grassroots Social Networking that aims to address this challenge. The architecture is geared for people with networked smartphones -- roaming (address-changing) computing devices communicating over an unreliable network (e.g., using UDP). The …

This https://arxiv.org/abs/2401.08680 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Proximity Ascertainment Bias in Early Covid Case Locations
A comparison of the distances to the Huanan Seafood Market of early Covid cases with known links to the market versus cases without known links shows results apparently incompatible with a location model lacking proximity ascertainment bias. The sign of the difference instead agrees with a model in which such ascertainment bias is large. In the presence of such bias inferences based on the clustering of case locations become unreliable.

This https://arxiv.org/abs/2403.17740 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendations and heterogeneous information network, to alleviate the data insufficiency issue for cold-start users and items. However, the explicit relations constructed based on data between different roles may be unreliable and irrelevant, which limits the …

Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks
Shufan Wang, Guojun Xiong, Shichen Zhang, Huacheng Zeng, Jian Li, Shivendra Panwar
https://arxiv.org/abs/2404.16920

Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks
We study the data packet transmission problem (mmDPT) in dense cell-free millimeter wave (mmWave) networks, i.e., users sending data packet requests to access points (APs) via uplinks and APs transmitting requested data packets to users via downlinks. Our objective is to minimize the average delay in the system due to APs' limited service capacity and unreliable wireless channels between APs and users. This problem can be formulated as a restless multi-armed bandits problem with fairness constr…

Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
Zeya Wang, Chenglong Ye
https://arxiv.org/abs/2403.14830 https://

Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
Deep clustering, a method for partitioning complex, high-dimensional data using deep neural networks, presents unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering, which involves projecting data into lower-dimensional embeddings before partitioning. Two key issues are identified: 1) the curse of dimensionality when applying these measures to raw data, and 2) the unreliable comparison of clustering res…

Detection of Energy Consumption Cyber Attacks on Smart Devices
Zainab Alwaisi, Simone Soderi, Rocco De Nicola
https://arxiv.org/abs/2404.19434 https://arxiv.org/pdf/2404.19434
arXiv:2404.19434v1 Announce Type: new
Abstract: With the rapid development of Internet of Things (IoT) technology, intelligent systems are increasingly integrating into everyday life and people's homes. However, the proliferation of these technologies raises concerns about the security of smart home devices. These devices often face resource constraints and may connect to unreliable networks, posing risks to the data they handle. Securing IoT technology is crucial due to the sensitive data involved.
Preventing energy attacks and ensuring the security of IoT infrastructure are key challenges in modern smart homes. Monitoring energy consumption can be an effective approach to detecting abnormal behavior and IoT cyberattacks. Lightweight algorithms are necessary to accommodate the resource limitations of IoT devices.
This paper presents a lightweight technique for detecting energy consumption attacks on smart home devices by analyzing received packets. The proposed algorithm considers TCP, UDP, and MQTT protocols, as well as device statuses (Idle, active, under attack). It accounts for resource constraints and promptly alerts administrators upon detecting an attack. The proposed approach effectively identifies energy consumption attacks by measuring packet reception rates for different protocols.

This https://arxiv.org/abs/2309.03194 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Signatures of Bayesian inference emerge from energy efficient synapses
Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mechanisms cost energy. We examined four such mechanisms along with the associated scaling of the energetic costs. We then embedded these energetic costs for reliability in artificial neural networks (ANN) with trainable stochastic synapses, and traine…

Vulnerability Detection with Code Language Models: How Far Are We?
Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair, David Wagner, Baishakhi Ray, Yizheng Chen
https://arxiv.org/abs/2403.18624

Vulnerability Detection with Code Language Models: How Far Are We?
In the context of the rising interest in code language models (code LMs) and vulnerability detection, we study the effectiveness of code LMs for detecting vulnerabilities. Our analysis reveals significant shortcomings in existing vulnerability datasets, including poor data quality, low label accuracy, and high duplication rates, leading to unreliable model performance in realistic vulnerability detection scenarios. Additionally, the evaluation methods used with these datasets are not representa…

This https://arxiv.org/abs/2309.13198 has been replaced.
initial toot: https://mastoxiv.page/@a…

Associative memory by virtual oscillator network based on single spin-torque oscillator
A coupled oscillator network may be able to perform an energy-efficient associative memory operation. However, its realization has been difficult because inhomogeneities unavoidably arise among the oscillators during fabrication and lead to an unreliable operation. This issue could be resolved if the oscillator network were able to be formed from a single oscillator. Here, we performed numerical simulations and theoretical analyses on an associative memory operation that uses a virtual oscillat…

This https://arxiv.org/abs/2404.09831 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Digging into contrastive learning for robust depth estimation with diffusion models
Recently, diffusion-based depth estimation methods have drawn widespread attention due to their elegant denoising patterns and promising performance. However, they are typically unreliable under adverse conditions prevalent in real-world scenarios, such as rainy, snowy, etc. In this paper, we propose a novel robust depth estimation method called D4RD, featuring a custom contrastive learning mode tailored for diffusion models to mitigate performance degradation in complex environments. Concretel…

Robust inference for the unification of confidence intervals in meta-analysis
Wei Liang, Haicheng Huang, Hongsheng Dai, Yinghui Wei
https://arxiv.org/abs/2404.13707

Robust inference for the unification of confidence intervals in meta-analysis
Traditional meta-analysis assumes that the effect sizes estimated in individual studies follow a Gaussian distribution. However, this distributional assumption is not always satisfied in practice, leading to potentially biased results. In the situation when the number of studies, denoted as K, is large, the cumulative Gaussian approximation errors from each study could make the final estimation unreliable. In the situation when K is small, it is not realistic to assume the random-effect follows…

This https://arxiv.org/abs/2304.08063 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Data-Driven Equation Discovery of a Cloud Cover Parameterization
A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning-based parameterizations using output from global storm-resolving models. While neural networks can achieve state-of-the-art performance within their training distribution, they can make unreliable predictions outside of it. Additionally, they often require post-hoc tools for interpretation. To avoid these limitations, we combine symbolic regression, sequ…

All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Shuheng Fang, Kangfei Zhao, Yu Rong, Zhixun Li, Jeffrey Xu Yu
https://arxiv.org/abs/2403.17740

All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendations and heterogeneous information network, to alleviate the data insufficiency issue for cold-start users and items. However, the explicit relations constructed based on data between different roles may be unreliable and irrelevant, which limits the …

This https://arxiv.org/abs/2404.09831 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Digging into contrastive learning for robust depth estimation with diffusion models
Recently, diffusion-based depth estimation methods have drawn widespread attention due to their elegant denoising patterns and promising performance. However, they are typically unreliable under adverse conditions prevalent in real-world scenarios, such as rainy, snowy, etc. In this paper, we propose a novel robust depth estimation method called D4RD, featuring a custom contrastive learning mode tailored for diffusion models to mitigate performance degradation in complex environments. Concretel…

Versatile Capillary Cells for Handling Concentrated Samples in Analytical Ultracentrifugation
Quy Ong, Xufeng Xu, Francesco Stellacci
https://arxiv.org/abs/2403.10418

Versatile Capillary Cells for Handling Concentrated Samples in Analytical Ultracentrifugation
In concentrated macromolecular dispersions, far-from-ideal intermolecular interactions determine the dispersion behaviors including phase transition, crystallization, and liquid-liquid phase separation. Here, we present a novel versatile capillary-cell design for analytical ultracentrifugation-sedimentation equilibrium (AUC-SE), ideal for studying samples at high concentrations. Current setups for such studies are difficult and unreliable to handle, leading to a low experimental success rate. T…

LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models
Mouhamed Amine Bouchiha, Quentin Telnoff, Souhail Bakkali, Ronan Champagnat, Mourad Rabah, Micka\"el Coustaty, Yacine Ghamri-Doudane
https://arxiv.org/abs/2404.13236

LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models
Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable performance in natural language processing-based applications, LLMs are susceptible to undesirable and erratic behaviors, including hallucinations, unreliable reasoning, and the generation of harmful content. These flawed behaviors undermine trust in LLMs and pose significant hurdles to their adoption in real-world applic…

This https://arxiv.org/abs/2305.16794 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Secure Vertical Federated Learning Under Unreliable Connectivity
Most work in privacy-preserving federated learning (FL) has focused on horizontally partitioned datasets where clients hold the same features and train complete client-level models independently. However, individual data points are often scattered across different institutions, known as clients, in vertical FL (VFL) settings. Addressing this category of FL necessitates the exchange of intermediate outputs and gradients among participants, resulting in potential privacy leakage risks and slow co…

This https://arxiv.org/abs/2309.03194 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Signatures of Bayesian inference emerge from energy efficient synapses
Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mechanisms cost energy. We examined four such mechanisms along with the associated scaling of the energetic costs. We then embedded these energetic costs for reliability in artificial neural networks (ANN) with trainable stochastic synapses, and traine…