Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning.

While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework. In recent years, however, cybersecurity threats from malicious non-state actors and rogue entities have forced practitioners and researchers to rethink the robustness of distributed and decentralized algorithms against adversarial attacks. As a result, we now have a plethora of algorithmic approaches that guarantee robustness of distributed and/or decentralized inference and learning under different adversarial threat models. Driven in part by the world's growing appetite for data-driven decision making, however, securing of distributed/decentralized frameworks for inference and learning against adversarial threats remains a rapidly evolving research area. In this article, we provide an overview of some of the most recent developments in this area under the threat model of Byzantine attacks.
ADAPTATION OF IPCC DEFAULT VALUES ON NATIONAL LANDFILL CONDITIONS

Over the next 10 to 30 years methane emissions from landfills in Germany will continue to represent a major source of greenhouse-gas emissions in the waste sector. Methane emissions have been estimated for the National Inventory Report on the German Greenhouse Gas Inventory (NIR) using the First Order Decay (FOD) method and the IPCC Guidelines for National Greenhouse Gas Inventories. Both national data and default factors (standard values) provided by the IPCC were used in calculating the estimations. A review of the methodological basis and input data used to determine methane formation in landfills indicates that the previous approaches adopted in the NIR reports led to methane formation rates that clearly exceeded actual emissions from German landfills. Both the state of knowledge and the results of investigations focussed on landfill gas production and emissions from numerous German municipal solid waste landfills confirm the latter. To obtain a more accurate description of the methane generation potential and methane generation in landfills, DOC parameters, fraction of degradable organic carbon (DOCf), half-life and methane correction factor (MCF) can be adapted for the individual organic fractions of waste. When the adapted values for use in the estimation of methane emissions of German landfills are applied, the results yielded are in a range of approx. 50ϼompared to the estimated values reported in the German NIR to date.
Determinants of Consumers’ Intention to Use E-payment System in Shopping Malls in Dar es Salaam, Tanzania: Modified UTAUT Model Approach

The aim of the study was to determine factors that influence customers’ intention to use the e-payment system in Dar es Salaam’s shopping malls. Bearing the importance of financial inclusion, there is no doubt electronic payment development will strongly contribute to improving countries competitiveness in many ways. However, despite the growth rate of ICT utilization particularly internet and mobile phones and an exponential rate of online communication and interactions, Tanzania is yet to harness the opportunities for e -payment. The study applied the modified UTAUT model to investigate factors that influence customers’ intention to use the e-payment system in Dar es Salaam shopping malls. Data were collected from a sample of 460 customers selected from 5 shopping malls in Dar es Salaam and tested against the research model using SEM. The results showed that three factors (Social Influence and Perceived Security and Effort Expectancy) had a significant positive effect on customers’ intention to use the e-payment system in Tanzania; Perceived Security being the strongest determinant. Conversely, Perceived Cost was found to have a significant negative effect on customer intention on the use e-payment system in Tanzania.
Medical WordNet: A New Methodology for the Construction and Validation of Information Resources for Consumer Health

A consumer health information system must be able to comprehend both expert and non-expert medical vocabulary and to map between the two. We describe an ongoing project to create a new lexical database called Medical WordNet (MWN), consisting of medically relevant terms used by and intel-ligible to non-expert subjects and supplemented by a corpus of natural-language sentences that is designed to provide medically validated contexts for MWN terms. The corpus derives primarily from online health information sources targeted to consumers, and involves two sub-corpora, called Medical FactNet (MFN) and Medical BeliefNet (MBN), respectively. The former consists of statements accredited as true on the basis of a rigorous process of validation, the latter of statements which non-experts believe to be true . We summarize the MWN / MFN / MBN project, and describe some of its applications.
Different Mechanisms Involved in Adaptation to Stable and Unstable Dynamics

[PDF] [Full Text] [Abstract] , January 1, 2007; 97 (1): 331-347. J Neurophysiol E. Guigon, P. Baraduc and M. Desmurget Computational Motor Control: Redundancy and Invariance [PDF] [Full Text] [Abstract] , July 18, 2007; 27 (29): 7705-7716. J. Neurosci. D. W. Franklin, G. Liaw, T. E. Milner, R. Osu, E. Burdet and M. Kawato Endpoint Stiffness of the Arm Is Directionally Tuned to Instability in the Environment [PDF] [Full Text] [Abstract] , November 1, 2007; 98 (5): 3072-3080. J Neurophysiol M. R. Hinder and T. E. Milner Rapid Adaptation to Scaled Changes of the Mechanical Environment [PDF] [Full Text] [Abstract] , December 1, 2007; 98 (6): 3516-3524. J Neurophysiol D. R. Lametti, G. Houle and D. J. Ostry Control of Movement Variability and the Regulation of Limb Impedance [PDF] [Full Text] [Abstract] , March 1, 2008; 99 (3): 1253-1266. J Neurophysiol A. G. Richardson, G. Lassi-Tucci, C. Padoa-Schioppa and E. Bizzi Environment Neuronal Activity in the Cingulate Motor Areas During Adaptation to a New Dynamic
Rapid label-free identification of pathogenic bacteria species from a minute quantity 1 exploiting three-dimensional quantitative phase imaging and artificial neural network 2

Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic 6 of Korea. 7 KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea 8 Tomocube Inc., Daejeon 34109, Republic of Korea. 9 Smart Healthcare & Device Research Center, Samsung Medical Center, Sungkyunkwan University School of 10 Medicine, Seoul 06351, Republic of Korea. 11 Graduate School of Nanoscience and Technology, KAIST, Daejeon 34141, Republic of Korea. 12 Department of Biological Science, KAIST, Daejeon 34141, Republic of Korea. 13 Department of Emergency Medicine, Bundang CHA Hospital, Gyeonggi-Do 13496, Korea. 14 Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan 15 University School of Medicine, Seoul 06351, Republic of Korea. 16 Department of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of 17 Korea, Seoul 06591, Republic of Korea. 18 Present address: Department of Applied Physics, Stanford University, Stanford, CA 94305, United States 19 Present address: Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, United 20 States 21 Corresponding authors: micro.lee@samsung.com and yk.park@kaist.ac. kr 22
Are We Really Making Much Progress? Bag-of-Words vs. Sequence vs. Graph vs. Hierarchy for Single-label and Multi-label Text Classification

The popularity of graph neural networks has triggered a resurgence of graph-based methods for single-label and multi-label text classification. However, whether these graph-based methods are beneficial compared to standard machine learning methods and modern pre-trained language models is unclear. We compare a rich selection of bag-of-words, sequence-based, graph-based, and hierarchical methods for text classification. We aggregate results from the literature over five single-label and seven multi-label datasets and run own experiments. Our findings unambiguously demonstrate that for single-label and multi-label classification tasks, the graph-based methods fail to outperform fine-tuned language models and sometimes even perform worse than standard machine learning methods like multilayer perceptron (MLP) on a bag-of-words. This questions the enormous effort put into developing new graph-based methods in recent years and their promises for text classification. We argue that future work in text classification should thoroughly test against strong baselines like MLPs and state-of-the-art pre-trained language models to assess the true scientific progress. The source code is available: https://github.com/drndr/multilabel-text-clf CCS Concepts
Secure Secret Recovery by using Weighted Personal Entropy

Authentication plays a vital role in many secure systems. Most of these systems require user to log in with his or her secret password or pass phrase before entering it. This is to ensure all the valuables information is kept confidential guaranteeing also its integrity and availability. However, to achieve this goal, users are required to memorize high entropy passwords or pass phrases. Unfortunately, this sometimes causes difficulty for user to remember meaningless strings of data. This paper presents a new scheme which assigns a weight to each personal question given to the user in revealing the encrypted secrets or password. Concentration of this scheme is to offer fault tolerance to users by allowing them to forget the specific password to a subset of questions and still recover the secret and achieve successful authentication. Comparison on level of security for weight-based and weightless secret recovery scheme is also discussed. The paper concludes with the few areas that requires more investigation in this research. Keywords—Secret Recovery, Personal Entropy, Cryptography, Secret Sharing and Key Management.
Calcareous Nannofossils from the Shiranish Formation at Bekhaer anticline, Dhouk area, Northern Iraq

Calcareous nannofossils are examined up on the Shiranish Formation from the Bekhaer anticline in Northern Iraq. The study area includes about 55 meters to find calcareous nannofossils assemblages, from which (36) species have identified. Three biozones are postulated based on the above studied assemblages, in order of biozones: Tranolithus phacelosus biozone; Reinhardites levis biozone; Arkhangelskiella cymbiformis biozone. From a regional perspective, these biozones were connected with other calcareous nannofossils biozones, leading to the conclusion that the section is Late Campanian-Maastrichtian in age.
$C_{10}$ has positive Tur\'an density in the hypercube

The $n$-dimensional hypercube $Q_n$ is a graph with vertex set $\{0,1\}^n$ and there is an edge between two vertices if they differ in exactly one coordinate. For any graph $H$, define $\text{ex}(Q_n,H)$ to be the maximum number of edges of a subgraph of $Q_n$ without a copy of $H$. In this short note, we prove that for any $n \in \mathbb{N}$ $$\text{ex}(Q_n, C_{10})>0.024 \cdot e(Q_n). $$ Our construction is strongly inspired by the recent breakthrough work of Ellis, Ivan, and Leader, who showed that"daisy"hypergraphs have positive Tur\'an density with an extremely clever and simple linear-algebraic argument.
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e.g., geographical, traffic, social media, and environmental data) and modalities (e.g., spatio-temporal, visual, and textual modalities). Recently, we are witnessing a rising trend that utilizes various deep-learning methods to facilitate cross-domain data fusion in smart cities. To this end, we propose the first survey that systematically reviews the latest advancements in deep learning-based data fusion methods tailored for urban computing. Specifically, we first delve into data perspective to comprehend the role of each modality and data source. Secondly, we classify the methodology into four primary categories: feature-based, alignment-based, contrast-based, and generation-based fusion methods. Thirdly, we further categorize multi-modal urban applications into seven types: urban planning, transportation, economy, public safety, society, environment, and energy. Compared with previous surveys, we focus more on the synergy of deep learning methods with urban computing applications. Furthermore, we shed light on the interplay between Large Language Models (LLMs) and urban computing, postulating future research directions that could revolutionize the field. We firmly believe that the taxonomy, progress, and prospects delineated in our survey stand poised to significantly enrich the research community. The summary of the comprehensive and up-to-date paper list can be found at https://github.com/yoshall/Awesome-Multimodal-Urban-Computing.
Compact Speech Translation Models via Discrete Speech Units Pretraining

Using Self-Supervised Learning (SSL) as model initialization is now common to obtain strong results in Speech Translation (ST). However, they also impose a large memory footprint, hindering on-device deployment. In this paper, we leverage the SSL models by pretraining smaller models on their Discrete Speech Units (DSU). We pretrain encoder-decoder models on 1) Filterbank-to-DSU and 2) DSU-to-Translation data, and take the encoder from 1) and the decoder from 2) to initialise a new model, finetuning this on limited speech-translation data. The final model becomes compact by using the DSU pretraining to distil the knowledge of the SSL model. Our method has several benefits over using DSU as model inputs, such as shorter inference pipeline and robustness over (DSU) tokenization. In contrast to ASR pretraining, it does not require transcripts, making it applicable to low-resource settings. Evaluation on CoVoST-2 X-En shows that our method is>$0.5$ BLEU better than a ST model that directly finetune the SSL model, given only half the model size, and on a par with ASR pretraining.
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks

Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks, including abstained prediction, out-of-distribution detection, and aleatoric uncertainty quantification. The latest goal is disentanglement: the construction of multiple estimators that are each tailored to one and only one task. Hence, there is a plethora of recent advances with different intentions - that often entirely deviate from practical behavior. This paper conducts a comprehensive evaluation of numerous uncertainty estimators across diverse tasks on ImageNet. We find that, despite promising theoretical endeavors, disentanglement is not yet achieved in practice. Additionally, we reveal which uncertainty estimators excel at which specific tasks, providing insights for practitioners and guiding future research toward task-centric and disentangled uncertainty estimation methods. Our code is available at https://github.com/bmucsanyi/bud.
The All-Seeing Project V2: Towards General Relation Comprehension of the Open World

We present the All-Seeing Project V2: a new model and dataset designed for understanding object relations in images. Specifically, we propose the All-Seeing Model V2 (ASMv2) that integrates the formulation of text generation, object localization, and relation comprehension into a relation conversation (ReC) task. Leveraging this unified task, our model excels not only in perceiving and recognizing all objects within the image but also in grasping the intricate relation graph between them, diminishing the relation hallucination often encountered by Multi-modal Large Language Models (MLLMs). To facilitate training and evaluation of MLLMs in relation understanding, we created the first high-quality ReC dataset ({AS-V2) which is aligned with the format of standard instruction tuning data. In addition, we design a new benchmark, termed Circular-based Relation Probing Evaluation (CRPE) for comprehensively evaluating the relation comprehension capabilities of MLLMs. Notably, our ASMv2 achieves an overall accuracy of 52.04 on this relation-aware benchmark, surpassing the 43.14 of LLaVA-1.5 by a large margin. We hope that our work can inspire more future research and contribute to the evolution towards artificial general intelligence. Our project is released at https://github.com/OpenGVLab/all-seeing.
Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categories

The field of Computer Vision (CV) is increasingly shifting towards ``high-level'' visual sensemaking tasks, yet the exact nature of these tasks remains unclear and tacit. This survey paper addresses this ambiguity by systematically reviewing research on high-level visual understanding, focusing particularly on Abstract Concepts (ACs) in automatic image classification. Our survey contributes in three main ways: Firstly, it clarifies the tacit understanding of high-level semantics in CV through a multidisciplinary analysis, and categorization into distinct clusters, including commonsense, emotional, aesthetic, and inductive interpretative semantics. Secondly, it identifies and categorizes computer vision tasks associated with high-level visual sensemaking, offering insights into the diverse research areas within this domain. Lastly, it examines how abstract concepts such as values and ideologies are handled in CV, revealing challenges and opportunities in AC-based image classification. Notably, our survey of AC image classification tasks highlights persistent challenges, such as the limited efficacy of massive datasets and the importance of integrating supplementary information and mid-level features. We emphasize the growing relevance of hybrid AI systems in addressing the multifaceted nature of AC image classification tasks. Overall, this survey enhances our understanding of high-level visual reasoning in CV and lays the groundwork for future research endeavors.
The Giant Graviton Expansion from Bubbling Geometry

The superconformal index of half-BPS states in ${\cal N}=4$ supersymmetric Yang-Mills with gauge group $U(N)$ admits an expansion in terms of giant gravitons, ${\cal I}_N(q)={\cal I}_\infty(q) \sum\limits_{m=0}^\infty q^{mN}\hat{\mathcal I}_m(q)$, where $m$ is the number of giant gravitons. We derive this expansion directly in supergravity from the class of half-BPS solutions due to Lin, Lunin, and Maldacena in type IIB supergravity. The moduli space of these configurations can be quantized using covariant quantization methods. We review how this quantization leads to the graviton index, ${\cal I}_\infty(q)$, and present a modification that leads to the precise expression for the expansion in terms of giant gravitons. Our proposal provides a derivation of the giant graviton expansion directly in terms of supergravity degrees of freedom. We also comment on how to derive the expansion in terms of the effective Fermi droplet picture.
Fault-tolerant structures for measurement-based quantum computation on a network

In this work, we introduce a method to construct fault-tolerant measurement-based quantum computation (MBQC) architectures and numerically estimate their performance over various types of networks. A possible application of such a paradigm is distributed quantum computation, where separate computing nodes work together on a fault-tolerant computation through entanglement. We gauge error thresholds of the architectures with an efficient stabilizer simulator to investigate the resilience against both circuit-level and network noise. We show that, for both monolithic (i.e., non-distributed) and distributed implementations, an architecture based on the diamond lattice may outperform the conventional cubic lattice. Moreover, the high erasure thresholds of non-cubic lattices may be exploited further in a distributed context, as their performance may be boosted through entanglement distillation by trading in entanglement success rates against erasure errors during the error-decoding process. These results highlight the significance of lattice geometry in the design of fault-tolerant measurement-based quantum computing on a network, emphasizing the potential for constructing robust and scalable distributed quantum computers.
Simple, High Saturation Power, Quantum-limited, RF SQUID Array-based Josephson Parametric Amplifiers

High-fidelity quantum non-demolition qubit measurement is critical to error correction and rapid qubit feedback in large-scale quantum computing. High-fidelity readout requires passing a short and strong pulse through the qubit's readout resonator, which is then processed by a sufficiently high bandwidth, high saturation power, and quantum-limited amplifier. We have developed a design pipeline that combines time-domain simulation of the un-truncated device Hamiltonian, fabrication constraints, and maximization of saturation power. We have realized an amplifier based on a modified NIST tri-layer Nb fabrication suite which utilizes an array of 25 radio frequency Superconducting QUantum Interference Devices (rf SQUIDs) embedded within a low-Q resonator powered by a high-power voltage pump delivered via a diplexer on the signal port. We show that, despite the intensity of the pump, the device is quantum-efficient and capable of high-fidelity measurement limited by state transitions in the transmon. We present experimental data demonstrating up to -91.2 dBm input saturation power with 20 dB gain, up to 28 MHz instantaneous bandwidth, and phase-preserving qubit measurements with 62% quantum efficiency.
Unveiling Internet Censorship: Analysing the Impact of Nation States' Content Control Efforts on Internet Architecture and Routing Patterns

Heightened interest from nation states to perform content censorship make it evermore critical to identify the impact of censorship efforts on the Internet. We undertake a study of Internet architecture, capturing the state of Internet topology with greater completeness than existing state-of-the-art. We describe our methodology for this, including the tooling we create to collect and process data from a wide range of sources. We analyse this data to find key patterns in nation states with higher censorship, discovering a funnelling effect wherein higher Internet censorship effort is reflected in a constraining effect on a state's Internet routing architecture. However, there are a small number of nation states that do not follow this trend, for which we provide an analysis and explanation, demonstrating a relationship between geographical factors in addition to geopolitics. In summary, our work provides a deeper understanding of how these censorship measures impact the overall functioning and dynamics of the Internet.