Miracle-dz/UniFAR-FTUs huggingface dataset:
An Allocation Model for Attributing Emissions in Multi-tenant Cloud Data Centers

Cloud computing has become the de facto paradigm for delivering software to system users, with organizations and enterprises of all sizes making use of cloud services in some way. On the surface, adopting the cloud appears to be a very efficient approach for offloading concerns such as infrastructure management, logistics, and most importantly for this work, energy consumption and consequent carbon emissions to the cloud service provider. However, this is in many ways not an appropriately accountable solution to managing the contribution of the ICT sector to global emissions. To this effect, in this paper we report on an exploratory case study done in collaboration with a Software as a Service provider operating globally in the telecommunications sector. The study reckons with the service provider using multi-tenant, that is, shared, off-premises data centers for hosting their private cloud infrastructure towards developing a fair model of allocating operational emissions among the service tenants -- customer companies with many distinct users. The developed emissions model has to account for allocating in an appropriate manner the generated emissions between the tenants of the software provider services, and among the different tenants of the same data center. A carbon footprint report generator is developed building on the proposed model which is, in turn, used to present sustainability reports to involved stakeholders for evaluation purposes. Our results show that the model is perceived as transparent, informative, and fair, with requested improvements focusing mainly on the generated reports and the information contained therein.
Analyzing Norm Violations in Live-Stream Chat

Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35\%.
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping

Design of mechanisms towards deploying over-the-air federated learning (FL) encounters many key challenges. One of them is to comply with the power and bandwidth constraints of the shared channel, while causing minimum deterioration to the learning performance compared to baseline noiseless implementations. For additive white Gaussian noise (AWGN) channels with instantaneous per-device power constraints, prior work has demonstrated the optimality of a power control mechanism based on norm clipping. This was done through the minimization of an upper bound on the optimality gap for smooth learning objectives satisfying the Polyak-Lojasiewicz (PL) condition. In this paper, we make two contributions to the development of AirFL based on norm clipping, which we refer to as AirFL-Clip. First, we provide a convergence bound for AirFL-Clip that applies to general smooth and non-convex learning objectives. Unlike existing results, the derived bound is free from run-specific parameters, thus supporting an offline evaluation. Second, we extend AirFL-Clip to include Top-k sparsification and linear compression. For this generalized protocol, referred to as AirFL-Clip-Comp, we derive a convergence bound for general smooth and non-convex learning objectives. We argue and demonstrate via experiments, that the only time-varying quantities present in the bound can be efficiently estimated offline by leveraging the well-studied properties of sparse recovery algorithms.
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark

Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to benchmark the performance of Self-Supervised Learning (SSL) models on various speech processing tasks. However, SUPERB largely considers English speech in its evaluation. This paper presents multilingual SUPERB (ML-SUPERB), covering 143 languages (ranging from high-resource to endangered), and considering both automatic speech recognition and language identification. Following the concept of SUPERB, ML-SUPERB utilizes frozen SSL features and employs a simple framework for multilingual tasks by learning a shallow downstream model. Similar to the SUPERB benchmark, we find speech SSL models can significantly improve performance compared to FBANK features. Furthermore, we find that multilingual models do not always perform better than their monolingual counterparts. We will release ML-SUPERB as a challenge with organized datasets and reproducible training scripts for future multilingual representation research.
Node Attribute Prediction on Multilayer Networks with Weighted and Directed Edges

With the rapid development of digital platforms, users can now interact in endless ways from writing business reviews and comments to sharing information with their friends and followers. As a result, organizations have numerous digital social networks available for graph learning problems with little guidance on how to select the right graph or how to combine multiple edge types. In this paper, we first describe the types of user-to-user networks available across the Facebook (FB) and Instagram (IG) platforms. We observe minimal edge overlap between these networks, indicating users are exhibiting different behaviors and interaction patterns between platforms. We then compare predictive performance metrics across various node attribute prediction tasks for an ads click prediction task on Facebook and for a publicly available dataset from the Open Graph Benchmark. We adapt an existing node attribute prediction method for binary prediction, LINK-Naive Bayes, to account for both edge direction and weights on single-layer networks. We observe meaningful predictive performance gains when incorporating edge direction and weight. We then introduce an approach called MultiLayerLINK-NaiveBayes that can combine multiple network layers during training and observe superior performance over the single-layer results. Ultimately, whether edge direction, edge weights, and multi-layers are practically useful will depend on the particular setting. Our approach enables practitioners to quickly combine multiple layers and additional edge information such as direction or weight.
Mode Connectivity in Auction Design

Optimal auction design is a fundamental problem in algorithmic game theory. This problem is notoriously difficult already in very simple settings. Recent work in differentiable economics showed that neural networks can efficiently learn known optimal auction mechanisms and discover interesting new ones. In an attempt to theoretically justify their empirical success, we focus on one of the first such networks, RochetNet, and a generalized version for affine maximizer auctions. We prove that they satisfy mode connectivity, i.e., locally optimal solutions are connected by a simple, piecewise linear path such that every solution on the path is almost as good as one of the two local optima. Mode connectivity has been recently investigated as an intriguing empirical and theoretically justifiable property of neural networks used for prediction problems. Our results give the first such analysis in the context of differentiable economics, where neural networks are used directly for solving non-convex optimization problems.
Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructions

Human intelligence's adaptability is remarkable, allowing us to adjust to new tasks and multi-modal environments swiftly. This skill is evident from a young age as we acquire new abilities and solve problems by imitating others or following natural language instructions. The research community is actively pursuing the development of interactive"embodied agents"that can engage in natural conversations with humans and assist them with real-world tasks. These agents must possess the ability to promptly request feedback in case communication breaks down or instructions are unclear. Additionally, they must demonstrate proficiency in learning new vocabulary specific to a given domain. In this paper, we made the following contributions: (1) a crowd-sourcing tool for collecting grounded language instructions; (2) the largest dataset of grounded language instructions; and (3) several state-of-the-art baselines. These contributions are suitable as a foundation for further research.
Understanding Interactions Between Chip Architecture and Uncertainties in Semiconductor Supply and Demand

Mitigating losses from supply and demand volatility in the semiconductor supply chain and market has traditionally been cast as a logistics and forecasting problem. We investigate how the architecture of a family of chips influences how it is affected by supply and demand uncertainties. We observe that semiconductor supply chains become fragile, in part, due to single demand paths, where one chip can satisfy only one demand. Chip architects can enable multiple paths to satisfy a chip demand, which improves supply chain resilience. Based on this observation, we study composition and adaptation as architectural strategies to improve resilience to volatility and also introduce a third strategy of dispersion. These strategies allow multiple paths to satisfy a given chip demand. We develop a model to analyze the impact of these architectural techniques on supply chain costs under different regimes of uncertainties and evaluate what happens when they are combined. We present several interesting and even counterintuitive observations about the product configurations and market conditions where these interventions are impactful and where they are not. In all, we show that product redesign supported by architectural changes can mitigate nearly half of the losses caused by supply and demand volatility. As far as we know, this is the first such investigation concerning chip architecture.
Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpus

Several recent papers claim human parity at sentence-level Machine Translation (MT), especially in high-resource languages. Thus, in response, the MT community has, in part, shifted its focus to document-level translation. Translating documents requires a deeper understanding of the structure and meaning of text, which is often captured by various kinds of discourse phenomena such as consistency, coherence, and cohesion. However, this renders conventional sentence-level MT evaluation benchmarks inadequate for evaluating the performance of context-aware MT systems. This paper presents a new dataset with rich discourse annotations, built upon the large-scale parallel corpus BWB introduced in Jiang et al. (2022). The new BWB annotation introduces four extra evaluation aspects, i.e., entity, terminology, coreference, and quotation, covering 15,095 entity mentions in both languages. Using these annotations, we systematically investigate the similarities and differences between the discourse structures of source and target languages, and the challenges they pose to MT. We discover that MT outputs differ fundamentally from human translations in terms of their latent discourse structures. This gives us a new perspective on the challenges and opportunities in document-level MT. We make our resource publicly available to spur future research in document-level MT and the generalization to other language translation tasks.
Stability of the surface diffusion flow and volume-preserving mean curvature flow in the flat torus

We prove that, in the flat torus and in any dimension, the volume-preserving mean curvature flow and the surface diffusion flow, starting $C^{1,1}-$close to a strictly stable critical set of the perimeter $E$, exist for all times and converge to a translate of $E$ exponentially fast as time goes to infinity.
The First X-Ray Polarization Observation of the Black Hole X-Ray Binary 4U 1630–47 in the Steep Power-law State

The Imaging X-ray Polarimetry Explorer (IXPE) observed the black hole X-ray binary 4U 1630–47 in the steep power-law (or very high) state. The observations reveal a linear polarization degree of the 2–8 keV X-rays of 6.8% ± 0.2% at a position angle of 21. °3 ± 0. °9 east of north (all errors at 1σ confidence level). Whereas the polarization degree increases with energy, the polarization angle stays constant within the accuracy of our measurements. We compare the polarization of the source in the steep power-law state with the previous IXPE measurement of the source in the high soft state. We find that, even though the source flux and spectral shape are significantly different between the high soft state and the steep power-law state, their polarization signatures are similar. Assuming that the polarization of both the thermal and power-law emission components are constant over time, we estimate the power-law component polarization to be 6.8%–7.0% and note that the polarization angle of the thermal and power-law components must be approximately aligned. We discuss the implications for the origin of the power-law component and the properties of the emitting plasma.
FRIDA´s mechanisms control system structure and tests

FRIDA will be a near infrared imager and integral field spectrograph covering the wavelength range from 0.9 to 2.5 microns. FRIDA will work in two observing modes: direct imaging and integral field spectroscopy. This paper presents the main structure of the FRIDA mechanisms control system. In order to comply with a high level of re-configurability FRIDA will comprise eight cryogenic mechanisms and one room temperature mechanism. Most of these mechanisms require high positioning repeatability to ensure FRIDA fulfills with high astronomical specifications. In order to set up the mechanisms positioning control parameters a set of programs have been developed to perform several tests of mechanisms in both room and cryogenic environments. The embedded control software for most of the FRIDA mechanisms has been developed. A description of some mechanisms tests and the software used for this purpose are presented.
Probing non-standard neutrino interactions with interference: insights from dark matter and neutrino experiments

Neutrino-electron scattering experiments play a crucial role in investigating the non-standard interactions of neutrinos. In certain models, these interactions can include interference terms that may affect measurements. Next-generation direct detection experiments, designed primarily for dark-matter searches, are also getting sensitive to probe the neutrino properties. We utilise the data from XENONnT, a direct detection experiment, and Borexino, a low-energy solar neutrino experiment, to investigate the impact of interference on non-standard interactions. Our study considers models with an additional U(1) B-L , including U(1), U(1) L e - L µ , and U(1) L e - L τ , to investigate the impact of interference on non-standard neutrino interactions. We demonstrate that this interference can lead to a transition between the considered non-standard interaction models in the energy range relevant to both the XENONnT and Borexino experiments. This transition can be used to distinguish among the considered models if any signals are observed at direct detection or neutrino experiments. Our findings underscore the importance of accounting for the interference and incorporating both direct detection and solar neutrino experiments to gain a better understanding of neutrino interactions and properties.
Production of $p$-nuclei from $r$-process seeds: the $\nu r$-process

We present a new nucleosynthesis process that may take place on neutron-rich ejecta experiencing an intensive neutrino flux. The nucleosynthesis proceeds similarly to the standard $r$-process, a sequence of neutron-captures and beta-decays, however with charged-current neutrino absorption reactions on nuclei operating much faster than beta-decays. Once neutron capture reactions freeze-out the produced $r$-process neutron-rich nuclei undergo a fast conversion of neutrons into protons and are pushed even beyond the $\beta$-stability line producing the neutron-deficient $p$-nuclei. This scenario, which we denote as the $\nu r$-process, provides an alternative channel for the production of $p$-nuclei and the short-lived nucleus $^{92}$Nb. We discuss the necessary conditions posed on the astrophysical site for the $\nu r$-process to be realized in nature. While these conditions are not fulfilled by current neutrino-hydrodynamic models of $r$-process sites, future models, including more complex physics and a larger variety of outflow conditions, may achieve the necessary conditions in some regions of the ejecta.
Expanding the Role of Affective Phenomena in Multimodal Interaction Research

In recent decades, the field of affective computing has made substantial progress in advancing the ability of AI systems to recognize and express affective phenomena, such as affect and emotions, during human-human and human-machine interactions. This paper describes our examination of research at the intersection of multimodal interaction and affective computing, with the objective of observing trends and identifying understudied areas. We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing: ACM International Conference on Multimodal Interaction, AAAC International Conference on Affective Computing and Intelligent Interaction, Annual Meeting of the Association for Computational Linguistics, and Conference on Empirical Methods in Natural Language Processing. We identified 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find that this body of research has primarily focused on enabling machines to recognize or express affect and emotion; there has been limited research on how affect and emotion predictions might, in turn, be used by AI systems to enhance machine understanding of human social behaviors and cognitive states. Based on our analysis, we discuss directions to expand the role of affective phenomena in multimodal interaction research.
Stabilization Of Regression Trees

In this paper, we present a hierarchical approach to simultaneous regression and classification. Regression obviously becomes more accurate by assessing a regression surface to each of a given class of a finite sample set compared to one regression surface for the sample as a whole. For class formation, a tree of regression surfaces is constructed that balances minimization of the regression error and "stabilization" towards unseen data. Common tree-structured regression algorithms split nodes according to an independent variable. Terminal nodes correspond to one specific class. Such constructions gave rise to less complicated approaches that have mainly been used for adaptive classification in machine learning. The considerable advantage of regression trees over a single regression is often set off by trees' poor behaviour on unseen data of supposedly the same nature. Also, a tree formation solely based on minimizing the regression error may not generate information by which to assign new data to terminal nodes. Generalization is addressed by a stabilization operation. Each sample vector is assigned to its nearest neighbour which in turn is assigned to its nearest neighbour etc. This results in so-called neighbour chains that partition the sample set. Class splitting during tree formation is here restricted to classes that contain neighbour chains completely. For classification, unseen sample data are assigned to its closest neighbour used in tree formation. The regression surface of the class containing the corresponding neighbour chain is then used for estimation. Data Mining II, C.A. Brebbia & N.F. F. Ebecken (Editors) © 2000 WIT Press, www.witpress. com, ISBN 1-85312-821-X
Diffusion Language Models Generation Can Be Halted Early

Diffusion Language models (DLMs) are a promising avenue for text generation due to their practical properties on tractable controllable generation. They also have the advantage of not having to predict text autoregressively. However, despite these notable features, DLMs have not yet reached the performance levels of their autoregressive counterparts. One of the ways to reduce the performance gap between these two types of language models is to speed up the generation of DLMs. Therefore, we propose a novel methodology to address this issue in this work. It enables the execution of more generation steps within a given time frame, leading to higher-quality outputs. Specifically, our methods estimate DLMs completeness of text generation and allow adaptive halting of the generation process. We evaluate our methods on Plaid, SSD, and CDCD DLMs and create a cohesive perspective on their generation workflows. Finally, we confirm that our methods allow halting these models and decrease the generation time by $10$-$40$\% without a drop in the quality of model samples.