Miracle-dz/UniFAR-FTUs huggingface dataset:
Don’t Trust, Verify: Towards a Framework for the Greening of Bitcoin

For more than a decade, Bitcoin has gained as much adoption as it has received criticism. Fundamentally, Bitcoin is under fire for the high carbon footprint that results from the energy-intensive proof-of-work (PoW) consensus algorithm. There is a trend however for Bitcoin mining to adopt a trajectory toward achieving carbon-negative status, notably due to the adoption of methane-based mining and mining-based flexible load response (FLR) to complement variable renewable energy (VRE) generation. Miners and electricity sellers may increase their profitability not only by taking advantage of excess energy, but also by selling green tokens to buyers interested in greening their portfolios. Nevertheless, a proper ''green Bitcoin'' accounting system requires a standard framework for the accreditation of sustainable bitcoin holdings. The proper way to build such a framework remains contested. In this paper, we survey the different sustainable Bitcoin accounting systems. Analyzing the various alternatives, we suggest a path forward.
Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows

Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric generative modeling but has not been widely adopted due to its suboptimal generative quality and lack of conditional modeling capabilities. In this work, we make two major contributions to bridging this gap. First, based on a pleasant observation that (under certain conditions) the SWF of joint distributions coincides with those of conditional distributions, we propose Conditional Sliced-Wasserstein Flow (CSWF), a simple yet effective extension of SWF that enables nonparametric conditional modeling. Second, we introduce appropriate inductive biases of images into SWF with two techniques inspired by local connectivity and multiscale representation in vision research, which greatly improve the efficiency and quality of modeling images. With all the improvements, we achieve generative performance comparable with many deep parametric generative models on both conditional and unconditional tasks in a purely nonparametric fashion, demonstrating its great potential.
Evolving Dictionary Representation for Few-shot Class-incremental Learning

New objects are continuously emerging in the dynamically changing world and a real-world artificial intelligence system should be capable of continual and effectual adaptation to new emerging classes without forgetting old ones. In view of this, in this paper we tackle a challenging and practical continual learning scenario named few-shot class-incremental learning (FSCIL), in which labeled data are given for classes in a base session but very limited labeled instances are available for new incremental classes. To address this problem, we propose a novel and succinct approach by introducing deep dictionary learning which is a hybrid learning architecture that combines dictionary learning and visual representation learning to provide a better space for characterizing different classes. We simultaneously optimize the dictionary and the feature extraction backbone in the base session, while only finetune the dictionary in the incremental session for adaptation to novel classes, which can alleviate the forgetting on base classes compared to finetuning the entire model. To further facilitate future adaptation, we also incorporate multiple pseudo classes into the base session training so that certain space projected by dictionary can be reserved for future new concepts. The extensive experimental results on CIFAR100, miniImageNet and CUB200 validate the effectiveness of our approach compared to other SOTA methods.
Evaluating the Efficacy of Length-Controllable Machine Translation

Length-controllable machine translation is a type of constrained translation. It aims to contain the original meaning as much as possible while controlling the length of the translation. We can use automatic summarization or machine translation evaluation metrics for length-controllable machine translation, but this is not necessarily suitable and accurate. This work is the first attempt to evaluate the automatic metrics for length-controllable machine translation tasks systematically. We conduct a rigorous human evaluation on two translation directions and evaluate 18 summarization or translation evaluation metrics. We find that BLEURT and COMET have the highest correlation with human evaluation and are most suitable as evaluation metrics for length-controllable machine translation.
Diverse and Vivid Sound Generation from Text Descriptions

Previous audio generation mainly focuses on specified sound classes such as speech or music, whose form and content are greatly restricted. In this paper, we go beyond specific audio generation by using natural language description as a clue to generate broad sounds. Unlike visual information, a text description is concise by its nature but has rich hidden meanings beneath, which poses a higher possibility and complexity on the audio to be generated. A Variation-Quantized GAN is used to train a codebook learning discrete representations of spectrograms. For a given text description, its pre-trained embedding is fed to a Transformer to sample codebook indices to decode a spectrogram to be further transformed into waveform by a melgan vocoder. The generated waveform has high quality and fidelity while excellently corresponding to the given text. Experiments show that our proposed method is capable of generating natural, vivid audios, achieving superb quantitative and qualitative results.
Multi-Dimensional Signal Recovery Using Low-Rank Deconvolution

In this work we present Low-rank Deconvolution, a powerful framework for low-level feature-map learning for efficient signal representation with application to signal recovery. Its formulation in multi-linear algebra inherits properties from convolutional sparse coding and low-rank approximation methods as in this setting signals are decomposed in a set of filters convolved with a set of low-rank tensors. We show its advantages by learning compressed video representations and solving image in-painting problems.
On the work load process in a general preemptive resume priority queue

Consider the following queuing system: A sequence of customers arrive at a service unit in a recurrent stream. A customer is of priority k with probability πk , k = 1, …, n. A class i customer preempts service of class k, k > i. Interrupted service is resumed without loss or gain in service time. Service is FIFO within classes. Service times for class k are drawn from a general distribution function Bk (t). Using the method of phases and a resolution technique from the theory of Markov processes we obtain Laplace transforms of various distributions.
Datasheet for Subjective and Objective Quality Assessment Datasets

Over the years, many subjective and objective quality assessment datasets have been created and made available to the research community. However, there is no standard process for documenting the various aspects of the dataset, such as details about the source sequences, number of test subjects, test methodology, encoding settings, etc. Such information is often of great importance to the users of the dataset as it can help them get a quick understanding of the motivation and scope of the dataset. Without such a template, it is left to each reader to collate the information from the relevant publication or website, which is a tedious and time-consuming process. In some cases, the absence of a template to guide the documentation process can result in an unintentional omission of some important information. This paper addresses this simple but significant gap by proposing a datasheet template for documenting various aspects of sub-jective and objective quality assessment datasets for multimedia data. The contributions presented in this work aim to simplify the documentation process for existing and new datasets and improve their reproducibility. The proposed datasheet template is available on GitHub1, along with a few sample datasheets of a few open-source audiovisual subjective and objective datasets.
Wi-Fi Multi-Link Operation: An Experimental Study of Latency and Throughput

In this article, we investigate the real-world capability of the multi-link operation (MLO) framework—one of the key MAC-layer features included in the IEEE 802.11be amendment—by using a large dataset containing 5 GHz spectrum occupancy measurements on multiple channels. Our results show that when both available links are often busy, as is the case in ultra-dense and crowded scenarios, MLO attains the highest throughput gains over single-link operation (SLO) since it is able to leverage multiple intermittent transmission opportunities. As for latency, if the two links exhibit statistically the same level of occupancy, MLO can outperform SLO by one order of magnitude. In contrast, in asymmetrically occupied links, MLO can sometimes be detrimental and even increase latency. We study this somewhat unexpected phenomenon, and find its origins to be packets suboptimally mapped to either link before carrying out the backoff, with the latter likely to be interrupted on the busier link. We cross validate our study with real-time traffic generated by a cloud gaming application and quantify MLO’s benefits for latency-sensitive applications.
When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?

In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation. Nevertheless, multiple recent studies have revealed that the reported state-of-the-art results of many neural recommendation models cannot be reliably replicated. A primary reason is that existing evaluations are performed under various inconsistent protocols. Correspondingly, these replicability issues make it difficult to understand how much benefit we can actually gain from these neural models. It then becomes clear that a fair and comprehensive performance comparison between traditional and neural models is needed. Motivated by these issues, we perform a large-scale, systematic study to compare recent neural recommendation models against traditional ones in top-n recommendation from implicit data. We propose a set of evaluation strategies for measuring memorization performance, generalization performance, and subgroup-specific performance of recommendation models. We conduct extensive experiments with 13 popular recommendation models (including two neural models and 11 traditional ones as baselines) on nine commonly used datasets. Our experiments demonstrate that even with extensive hyper-parameter searches, neural models do not dominate traditional models in all aspects, e.g., they fare worse in terms of average HitRate. We further find that there are areas where neural models seem to outperform non-neural models, for example, in recommendation diversity and robustness between different subgroups of users and items. Our work illuminates the relative advantages and disadvantages of neural models in recommendation and is therefore an important step towards building better recommender systems.
Energy-dependent barren plateau in bosonic variational quantum circuits

Bosonic continuous-variable Variational quantum circuits (VQCs) are crucial for information processing in cavity quantum electrodynamics and optical systems, widely applicable in quantum communication, sensing and error correction. The trainability of such VQCs is less understood, hindered by the lack of theoretical tools such as $t$-design due to the infinite dimension of the physical systems involved. We overcome this difficulty to reveal an energy-dependent barren plateau in such VQCs. The variance of the gradient decays as $1/E^{M\nu}$, exponential in the number of modes $M$ but polynomial in the (per-mode) circuit energy $E$. The exponent $\nu=1$ for shallow circuits and $\nu=2$ for deep circuits. We prove these results for state preparation of general Gaussian states and number states. We also provide numerical evidence that the results extend to general state preparation tasks. As circuit energy is a controllable parameter, we provide a strategy to mitigate the barren plateau in continuous-variable VQCs.
Distributed Leader Follower Formation Control of Mobile Robots Based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter

This article investigated the distributed leader follower formation control problem for multiple differentially driven mobile robots. A distributed estimator is first introduced and it only requires the state information from each follower itself and its neighbors. Then, we propose a bioinspired neural dynamic based backstepping and sliding mode control hybrid formation control method with proof of its stability. The proposed control strategy resolves the impractical speed jump issue that exists in the conventional backstepping design. Additionally, considering the system and measurement noises, the proposed control strategy not only removes the chattering issue existing in the conventional sliding mode control but also provides smooth control input with extra robustness. After that, an adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates that are robust to modeling uncertainties. Finally, we performed multiple simulations to demonstrate the efficiency and effectiveness of the proposed formation control strategy.
Uncertainty Quantification of Mass Models using Ensemble Bayesian Model Averaging

Developments in the description of the masses of atomic nuclei have led to various nuclear mass models that provide predictions for masses across the whole chart of nuclides. These mass models play an important role in understanding the synthesis of heavy elements in the rapid neutron capture ($r$-) process. However, it is still a challenging task to estimate the size of uncertainty associated with the predictions of each mass model. In this work, a method to quantify the mass uncertainty using \textit{ensemble Bayesian model averaging} (EBMA) is introduced. This Bayesian method provides a natural way to perform model averaging, selection, calibration, and uncertainty quantification, by combining the mass models as a mixture of normal distributions, whose parameters are optimized against the experimental data, employing the Markov chain Monte Carlo (MCMC) method using the No-U-Turn sampler (NUTS). The average size of our best uncertainty estimates of neutron separation energies based on the AME2003 data is 0.48 MeV and covers 95% of new data in the AME2020. The uncertainty estimates can also be used to detect outliers with respect to the trend of experimental data and theoretical predictions.
Spectrum of Massive and Massless Ambitwistor Strings

Inspired by recent work arXiv:2301.11227 on massive ambitwistor strings this paper examines the spectrum of such models using oscillator expansions. The spectrum depends heavily on the constant related to the normal ordering of the zero mode operator ${L_0}$ of the Virasoro algebra. The supergravity model is investigated in more detail, and two anomaly-free variations are presented, both with a rich spectrum and with tree scattering amplitudes that include a kinematic Parke-Taylor factor for particles other than gravitons without a need for an external current algebra. The spectrum of some of the models can also be interpreted as containing three generations of the Pati-Salam model.
Visual Transformation Telling

In this paper, we propose a new visual reasoning task, called Visual Transformation Telling (VTT). This task requires a machine to describe the transformation that occurred between every two adjacent states (i.e. images) in a series. Unlike most existing visual reasoning tasks that focus on state reasoning, VTT emphasizes transformation reasoning. We collected 13,547 samples from two instructional video datasets, CrossTask and COIN, and extracted desired states and transformation descriptions to create a suitable VTT benchmark dataset. Humans can naturally reason from superficial states differences (e.g. ground wetness) to transformations descriptions (e.g. raining) according to their life experience but how to model this process to bridge this semantic gap is challenging. We designed TTNet on top of existing visual storytelling models by enhancing the model's state-difference sensitivity and transformation-context awareness. TTNet significantly outperforms other baseline models adapted from similar tasks, such as visual storytelling and dense video captioning, demonstrating the effectiveness of our modeling on transformations. Through comprehensive diagnostic analyses, we found TTNet has strong context utilization abilities, but even with some state-of-the-art techniques such as CLIP, there remain challenges in generalization that need to be further explored.
Fears about AI-mediated communication are grounded in different expectations for one's own versus others' use

The rapid development of AI-mediated communication technologies (AICTs), which are digital tools that use AI to augment interpersonal messages, has raised concerns about the future of interpersonal trust and prompted discussions about disclosure and uptake. This paper contributes to this discussion by assessing perceptions about the acceptability and use of open and secret AICTs for oneself and others. In two studies with representative samples (UK: N=477, US: N=765), we found that secret AICT use is deemed less acceptable than open AICT use, people tend to overestimate others' AICT use, and people expect others to use AICTs irresponsibly. Thus, we raise concerns about the potential for misperceptions and different expectations for others to drive self-fulfilling pessimistic outlooks about AI-mediated communication.
Response-conditioned Turn-taking Prediction

Previous approaches to turn-taking and response generation in conversational systems have treated it as a two-stage process: First, the end of a turn is detected (based on conversation history), then the system generates an appropriate response. Humans, however, do not take the turn just because it is likely, but also consider whether what they want to say fits the position. In this paper, we present a model (an extension of TurnGPT) that conditions the end-of-turn prediction on both conversation history and what the next speaker wants to say. We found that our model consistently outperforms the baseline model in a variety of metrics. The improvement is most prominent in two scenarios where turn predictions can be ambiguous solely from the conversation history: 1) when the current utterance contains a statement followed by a question; 2) when the end of the current utterance semantically matches the response. Treating the turn-prediction and response-ranking as a one-stage process, our findings suggest that our model can be used as an incremental response ranker, which can be applied in various settings.
An Algorithmic Approach to Manifolds

An algorithmic approach to manifolds is presented. The initial purpose was to blend geometric and symbolic aspects, so as to equip computer assisted design with symbolic capabilities. Nevertheless, this investigation aims more generally at providing a uniform treatment of analytical geometry and field analysis, in view of applications to physics, system modelling and morphology. This computational approach is based on a reification of parametric plotting commands.
Bio-Inspired Simple Neural Network for Low-Light Image Restoration: A Minimalist Approach

In this study, we explore the potential of using a straightforward neural network inspired by the retina model to efficiently restore low-light images. The retina model imitates the neurophysiological principles and dynamics of various optical neurons. Our proposed neural network model reduces the computational overhead compared to traditional signal-processing models while achieving results similar to complex deep learning models from a subjective perceptual perspective. By directly simulating retinal neuron functionalities with neural networks, we not only avoid manual parameter optimization but also lay the groundwork for constructing artificial versions of specific neurobiological organizations.
History-deterministic Vector Addition Systems

We consider history-determinism, a restricted form of non-determinism, for Vector Addition Systems with States (VASS) when used as acceptors to recognise languages of finite words. History-determinism requires that the non-deterministic choices can be resolved on-the-fly; based on the past and without jeopardising acceptance of any possible continuation of the input word. Our results show that the history-deterministic (HD) VASS sit strictly between deterministic and non-deterministic VASS regardless of the number of counters. We compare the relative expressiveness of HD systems, and closure-properties of the induced language classes, with coverability and reachability semantics, and with and without $\varepsilon$-labelled transitions. Whereas in dimension 1, inclusion and regularity remain decidable, from dimension two onwards, HD-VASS with suitable resolver strategies, are essentially able to simulate 2-counter Minsky machines, leading to several undecidability results: It is undecidable whether a VASS is history-deterministic, or if a language equivalent history-deterministic VASS exists. Checking language inclusion between history-deterministic 2-VASS is also undecidable.
Expressive Mortality Models through Gaussian Process Kernels

We develop a flexible Gaussian process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative structure of GP kernels, we design a genetic programming algorithm to search for the most expressive kernel for a given population. Our compositional search builds off the Age–Period–Cohort (APC) paradigm to construct a covariance prior best matching the spatio-temporal dynamics of a mortality dataset. We apply the resulting genetic algorithm (GA) on synthetic case studies to validate the ability of the GA to recover APC structure and on real-life national-level datasets from the Human Mortality Database. Our machine learning-based analysis provides novel insight into the presence/absence of Cohort effects in different populations and into the relative smoothness of mortality surfaces along the Age and Year dimensions. Our modeling work is done with the PyTorch libraries in Python and provides an in-depth investigation of employing GA to aid in compositional kernel search for GP surrogates.