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Human cognitive performance is an underlying factor in most of our daily lives, and numerous factors influence cognitive performance. In this work, we investigate how changes in sleep quality influence cognitive performance, measured from a dataset collected during a 2-month field study. We collected cognitive performance data (alertness) with the Psychomotor Vigilance Task (PVT), mobile keyboard typing metrics from participants' smartphones, and sleep quality metrics through a wearable sleep tracking ring. Our findings highlight that specific sleep metrics like night-time heart rate, sleep latency, sleep timing, sleep restfulness, and overall sleep quantity significantly influence cognitive performance. To strengthen the current research on cognitive measurements, we introduce smartphone typing metrics as a proxy or a complementary method for continuous passive measurement of cognitive performance. Together, our findings contribute to ubiquitous computing via a longitudinal case study with a novel wearable device, the resulting findings on the association between sleep and cognitive function, and the introduction of smartphone keyboard typing as a proxy of cognitive function., Comment: 27 pages, 6 figures. Submitted to Pervasive and Ubiquitous Computing

To accelerate ultra-relativistic charged particles, such as electrons, using an electromagnetic pulse along a hollow-core waveguide, the pulse needs to have a longitudinal electric field component and a phase velocity of $c$, the speed of light in vacuum. We derive an approximate closed-form expression for the wavelength at which the phase velocity of the TM$_{01}$ mode in a metal-clad hollow-core fiber with a dielectric layer is $c$. The expression is then used to derive conditions for material dispersion required of the dielectric in order to simultaneously have $c$ phase and group velocity. It is shown that the dispersion would need to be so heavily anomalous that the losses in the anomalously dispersive regime would render such a particle accelerator useless. We then propose the utilization of gain in the form of two spectral peaks in the dielectric to circumvent the otherwise fundamental limits and allow for TM$_{01}$ pulses with $c$ phase and group velocity and thus arbitrary length-scaling of fiber-based electron accelerators. In theory, the group velocity dispersion could also be made zero with further gain-assisted dispersion engineering, allowing for the co-propagation of dispersionless electromagnetic pulses with relativistic particles.

Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challenge, we developed Vocalizer, a mobile application that enables users to provide reviews through voice input, with enhancements from a large language model (LLM). In a longitudinal study, we analysed user interactions with the app, focusing on AI-driven features that help refine and improve reviews. Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews. We also show how interactive AI features can improve users self-efficacy and willingness to share reviews online. Finally, we discuss the opportunities and challenges of integrating AI assistance into review-writing systems., Comment: \c{opyright} Kavindu Ravishan | ACM 2024. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in the Proceedings of the ACM Conference on Mobile and Ubiquitous Multimedia (MUM '24), http://dx.doi.org/10.1145/3701571.3701593

We derive an analytical expression for the effective indices of modes of circular step-index fibers valid near their cutoff wavelengths. The approximation, being a first-order Taylor series of a smooth function, is also valid for the real part of the effective index beyond cutoff where the modes become lossy. The approximation is used to derive certain previously unknown mode properties. For example, it is shown that for non-dispersive materials the EH-mode group index at cutoff, surprisingly, does not depend on wavelength, core radius, or even radial mode order.

This paper presents an enhanced adaptive random Fourier features (ARFF) training algorithm for shallow neural networks, building upon the work introduced in "Adaptive Random Fourier Features with Metropolis Sampling", Kammonen et al., \emph{Foundations of Data Science}, 2(3):309--332, 2020. This improved method uses a particle filter-type resampling technique to stabilize the training process and reduce the sensitivity to parameter choices. The Metropolis test can also be omitted when resampling is used, reducing the number of hyperparameters by one and reducing the computational cost per iteration compared to the ARFF method. We present comprehensive numerical experiments demonstrating the efficacy of the proposed algorithm in function regression tasks as a stand-alone method and as a pretraining step before gradient-based optimization, using the Adam optimizer. Furthermore, we apply the proposed algorithm to a simple image regression problem, illustrating its utility in sampling frequencies for the random Fourier features (RFF) layer of coordinate-based multilayer perceptrons. In this context, we use the proposed algorithm to sample the parameters of the RFF layer in an automated manner., Comment: 41 pages

SpeechBrain is an open-source Conversational AI toolkit based on PyTorch, focused particularly on speech processing tasks such as speech recognition, speech enhancement, speaker recognition, text-to-speech, and much more. It promotes transparency and replicability by releasing both the pre-trained models and the complete "recipes" of code and algorithms required for training them. This paper presents SpeechBrain 1.0, a significant milestone in the evolution of the toolkit, which now has over 200 recipes for speech, audio, and language processing tasks, and more than 100 models available on Hugging Face. SpeechBrain 1.0 introduces new technologies to support diverse learning modalities, Large Language Model (LLM) integration, and advanced decoding strategies, along with novel models, tasks, and modalities. It also includes a new benchmark repository, offering researchers a unified platform for evaluating models across diverse tasks., Comment: Accepted to the Journal of Machine Learning research (JMLR), Machine Learning Open Source Software

Dwarf galaxies in groups of galaxies provide excellent test cases for models of structure formation. This led to a so-called small-scale crisis, including the famous missing satellite and too-big-to-fail problems. It was suggested that these two problems are solved by the introduction of baryonic physics in cosmological simulations. We test for the nearby grand spiral M83 - a Milky Way sibling - whether its number of dwarf galaxy companions is compatible with today's $\Lambda$ + Cold Dark Matter model using two methods: with cosmological simulations that include baryons, as well as with theoretical predictions from the sub-halo mass function. By employing distance measurements we recover a list of confirmed dwarf galaxies within 330 kpc around M83 down to a magnitude of $M_V =-10$. We found that both the state-of-the-art hydrodynamical cosmological simulation Illustris-TNG50 and theoretical predictions agree with the number of confirmed satellites around M83 at the bright end of the luminosity function (>10$^8$ solar masses) but underestimate it at the faint end (down to 10$^6$ solar masses) at more than 3$\sigma$ and 5$\sigma$ levels, respectively. This indicates a too-many satellites problem in $\Lambda$CDM for M83. The actual degree of tension to cosmological models is underestimated, because the number of observed satellites is incomplete due to the high contamination of spurious stars and galactic cirrus., Comment: Accepted for publication in Astronomy & Astrophysics Letters, 8 pages, 6 figures

We present experimental results highlighting two key differences resulting from the choice of training algorithm for two-layer neural networks. The spectral bias of neural networks is well known, while the spectral bias dependence on the choice of training algorithm is less studied. Our experiments demonstrate that an adaptive random Fourier features algorithm (ARFF) can yield a spectral bias closer to zero compared to the stochastic gradient descent optimizer (SGD). Additionally, we train two identically structured classifiers, employing SGD and ARFF, to the same accuracy levels and empirically assess their robustness against adversarial noise attacks., Comment: 6 Pages, 4 Figures; Accepted in the International Conference on Scientific Computing and Machine Learning

Generative artificial intelligence (AI) is a widely popular technology that will have a profound impact on society and individuals. Less than a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on many creative domains. In this paper, we present the findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their fears and concerns, and thoughts about risks and dangers of text-to-image generation to the individual and society. We find that while participants were aware of the risks and dangers associated with the technology, only few participants considered the technology to be a personal risk. The risks for others were more easy to recognize for participants. Artists were particularly seen at risk. Interestingly, participants who had tried the technology rated its future importance lower than those who had not tried it. This result shows that many people are still oblivious of the potential personal risks of generative artificial intelligence and the impending societal changes associated with this technology., Comment: ACM Academic Mindtrek 2023

The recent progress of text-to-image generation has been recognized in architectural design. Our study is the first to investigate the potential of text-to-image generators in supporting creativity during the early stages of the architectural design process. We conducted a laboratory study with 17 architecture students, who developed a concept for a culture center using three popular text-to-image generators: Midjourney, Stable Diffusion, and DALL-E. Through standardized questionnaires and group interviews, we found that image generation could be a meaningful part of the design process when design constraints are carefully considered. Generative tools support serendipitous discovery of ideas and an imaginative mindset, enriching the design process. We identified several challenges of image generators and provided considerations for software development and educators to support creativity and emphasize designers' imaginative mindset. By understanding the limitations and potential of text-to-image generators, architects and designers can leverage this technology in their design process and education, facilitating innovation and effective communication of concepts.

Using very deep, high spectral resolution data from the SAMI Integral Field Spectrograph we study the stellar population properties of a sample of dwarf galaxies in the Fornax Cluster, down to a stellar mass of $10^{7}$ M$_{\odot}$, which has never been done outside the Local Group. We use full spectral fitting to obtain stellar population parameters. Adding massive galaxies from the ATLAS$^{3D}$ project, which we re-analysed, and the satellite galaxies of the Milky Way, we obtained a galaxy sample that covers the stellar mass range $10^{4}$ to $10^{12} M_{\odot}$. Using this large range we find that the mass - metallicity relation is not linear. We also find that the [$\alpha$/Fe]-stellar mass relation of the full sample shows a U-shape, with a minimum in [$\alpha$/Fe] for masses between $10^{9}-10^{10} M_{\odot}$. The relation between [$\alpha$/Fe] and stellar mass can be understood in the following way: When the faintest galaxies enter the cluster environment, a rapid burst of star formation is induced, after which the gas content is blown away by various quenching mechanisms. This fast star formation causes high [$\alpha$/Fe] values, like in the Galactic halo. More massive galaxies will manage to keep their gas longer and form several bursts of star formation, with lower [$\alpha$/Fe] as a result. For massive galaxies, stellar populations are regulated by internal processes, leading to [$\alpha$/Fe] increasing with mass. We confirm this model by showing that [$\alpha$/Fe] correlates with clustercentric distance in three nearby clusters, and also in the halo of the Milky Way., Comment: Accepted for publication in MNRAS. (17 pages, 11 figures)

Generative AI is an emerging technology that will have a profound impact on society and individuals. Only a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on creative domains. In this paper, we present the key findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their ideas for potential application areas, as well as concerns, risks, and dangers of text-to-image generation to society and the individual. The study found that participants were aware of the risks and dangers associated with the technology, but only few participants considered the technology to be a risk to themselves. Additionally, those who had tried the technology rated its future importance lower than those who had not., Comment: Accepted at Generative AI in HCI workshop, CHI '23

Weak phase noise present on an optical field can be amplified by a self-focusing nonlinearity and form intense "rogue wave" features. Here, we study the effect of the coherence length (or grain size) of this phase noise on the likelihood of rogue wave formation in the presence of a self-focusing nonlinearity. We show that while the likelihood of rogue wave formation increases with laser power when the coherence length is only slightly smaller that the beam diameter, the likelihood is minimally affected by change in laser power when the coherence length is significantly smaller than the beam diameter. Our study provides insight into the interaction of nonlinearity with phase instabilities on a field, and could be useful in applications such as reducing the effect of turbulence-induced breakup of intense laser beams, and developing radiance limiters to reduce the focusable power in a beam.

We explore the kinematic scaling relations of 38 dwarf galaxies in the Fornax Cluster using observations from the SAMI integral field spectrograph. We focus on the Fundamental Plane (FP), defined by the physical properties of the objects (scale length, surface brightness and velocity dispersion) and the Stellar Mass (Fundamental) Plane, where surface brightness is replaced by stellar mass, and investigate their dynamical-to-stellar-mass ratio. We confirm earlier results that the Fornax dEs are significantly offset above the FP defined by massive, hot stellar systems. For the Stellar Mass (Fundamental) Plane, which shows much lower scatter, we find that young and old dwarf galaxies lie at about the same distance from the plane, all with comparable scatter. We introduce the perpendicular deviation of dwarf galaxies from the Stellar Mass Plane defined by giant early-types as a robust estimate of their DM fraction, and find that the faintest dwarfs are systematically offset above the plane, implying that they have a higher dark matter fraction. This result is confirmed when estimating the dynamical mass of our dEs using a virial mass estimator, tracing the onset of dark matter domination in low mass stellar systems. We find that the position of our galaxies on the Stellar Mass FP agrees with the galaxies in the Local Group. This seems to imply that the processes determining the position of dwarf galaxies on the FP depend on the environment in the same way, whether the galaxy is situated in the Local Group or in the Fornax Cluster., Comment: 23 pages, 14 figures, accepted for publication in MNRAS

In this work, we extend the catalog of low-surface brightness (LSB) galaxies, including Ultra-Diffuse Galaxy (UDG) candidates, within $\approx 0.4R_{vir}$ of the Hydra I cluster of galaxies, based on deep images from the VST Early-type GAlaxy Survey (VEGAS). The new galaxies are found by applying an automatic detection tool and carrying out additional visual inspections of $g$ and $r$ band images. This led to the detection of 11 UDGs and 8 more LSB galaxies. For all of them, the cluster membership has been assessed using the color-magnitude relation derived for early-type giant and dwarf galaxies in Hydra I. The UDGs and new LSB galaxies found in Hydra I span a wide range of central surface brightness ($22.7 \lesssim \mu_{0,g} \lesssim 26.5$ mag/arcsec$^2$), effective radius ($0.6 \lesssim R_e \lesssim 4.0$ kpc) and color ($0.4 \leq g-r \leq 0.9$ mag), and have stellar masses in the range $\sim 5\times 10^6 - 2\times 10^8$M$_{\odot}$. The 2D projected distribution of both galaxy types is similar to the spatial distribution of dwarf galaxies, with over-densities in the cluster core and north of the cluster centre. They have similar color distribution and comparable stellar masses to the red dwarf galaxies. Based on photometric selection, we identify a total of 9 globular cluster candidates associated to the UDGs and 4 to the LSB galaxies, with the highest number of candidates in an individual UDG being three. We find that there are no relevant differences between dwarfs, LSB galaxies and UDGs: the structural parameters (that is surface brightness, size, colors, n-index) and GCs content of the three classes have similar properties and trends. This finding is consistent with UDGs being the extreme LSB tail of the size-luminosity distribution of dwarfs in this environment., Comment: Accepted for publication in A&A. 30 pages, 22 figures

We investigate the relations between nuclear star clusters (NSCs) and their host galaxies, and between the structural properties of nucleated and non-nucleated galaxies. We also address the environmental influences on the nucleation of galaxies in the Fornax main cluster and the Fornax A group. We select 557 Fornax galaxies ($10^{5.5} M_{\odot} < M_{\rm *,galaxy} < 10^{11.5} M_{\odot} $) for which structural decomposition models and non-parametric indices are available. We determine galaxy nucleation based on a combination of visual inspection and a model selection statistic, the Bayesian information criterion (BIC). We also test the BIC as an unsupervised method to determine nucleation labels. We find a dichotomy in the properties of nuclei which reside in galaxies more or less massive than $M_{\rm *,galaxy} \approx 10^{8.5} M_{\odot}$. Specifically, the nuclei tend to be bluer than their host galaxies and follow a scaling relation of $M_{\rm *,nuc} \propto {M_{\rm *,galaxy}}^{0.5}$ for $M_{\rm *,galaxy} < 10^{8.5} M_{\odot}$. In galaxies with $M_{\rm *,galaxy} > 10^{8.5} M_{\odot}$ we find that nuclei are redder compared to the host and follow $M_{\rm *,nuc} \propto M_{\rm *,galaxy}$. Comparing early-type galaxies, we find that nucleated galaxies tend to be redder in global ($g'-r'$) colour, have redder outskirts relative to their own inner regions ($\Delta (g'-r')$), be less asymmetric ($A$) and exhibit less scatter in the brightest second order moment of light ($M_{20}$) than their non-nucleated counterparts at a given stellar mass. Additionally, we find the nucleation fractions to be typically higher in the Fornax main cluster than in the Fornax A group, and that the nucleation fraction is highest towards the centre of their respective environments. We also find that the BIC can recover our labels of nucleation up to an accuracy of 97\%. (abridged), Comment: accepted to be published in A&A

Public sources like parliament meeting recordings and transcripts provide ever-growing material for the training and evaluation of automatic speech recognition (ASR) systems. In this paper, we publish and analyse the Finnish parliament ASR corpus, the largest publicly available collection of manually transcribed speech data for Finnish with over 3000 hours of speech and 449 speakers for which it provides rich demographic metadata. This corpus builds on earlier initial work, and as a result the corpus has a natural split into two training subsets from two periods of time. Similarly, there are two official, corrected test sets covering different times, setting an ASR task with longitudinal distribution-shift characteristics. An official development set is also provided. We develop a complete Kaldi-based data preparation pipeline, and hidden Markov model (HMM), hybrid deep neural network (HMM-DNN) and attention-based encoder-decoder (AED) ASR recipes. We set benchmarks on the official test sets, as well as multiple other recently used test sets. Both temporal corpus subsets are already large, and we observe that beyond their scale, ASR performance on the official test sets plateaus, whereas other domains benefit from added data. The HMM-DNN and AED approaches are compared in a carefully matched equal data setting, with the HMM-DNN system consistently performing better. Finally, the variation of the ASR accuracy is compared between the speaker categories available in the parliament metadata to detect potential biases based on factors such as gender, age, and education., Comment: Submitted to Language Resources and Evaluation

The Donate Speech campaign has so far succeeded in gathering approximately 3600 hours of ordinary, colloquial Finnish speech into the Lahjoita puhetta (Donate Speech) corpus. The corpus includes over twenty thousand speakers from all the regions of Finland and from all age brackets. The primary goals of the collection were to create a representative, large-scale resource to study spontaneous spoken Finnish and to accelerate the development of language technology and speech-based services. In this paper, we present the collection process and the collected corpus, and showcase its versatility through multiple use cases. The evaluated use cases include: automatic speech recognition of spontaneous speech, detection of age, gender, dialect and topic and metadata analysis. We provide benchmarks for the use cases, as well down loadable, trained baseline systems with open-source code for reproducibility. One further use case is to verify the metadata and transcripts given in this corpus itself, and to suggest artificial metadata and transcripts for the part of the corpus where it is missing., Comment: Submitted to Language Resources and Evaluation

At ~50 Mpc, the Hydra I cluster of galaxies is among the closest cluster in the z=0 Universe, and an ideal environment to study dwarf galaxy properties in a cluster environment. We exploit deep imaging data of the Hydra I cluster to construct a new photometric catalog of dwarf galaxies in the cluster core, which is then used to derive properties of the Hydra I cluster dwarf galaxies population as well as to compare with other clusters. Moreover, we investigate the dependency of dwarf galaxy properties on their surrounding environment. The new Hydra I dwarf catalog contains 317 galaxies with luminosity between -18.5<$M_r$<-11.5 mag, a semi-major axis larger than ~200 pc (a=0.84 arcsec), of which 202 are new detections, previously unknown dwarf galaxies in the Hydra I central region. We estimate that our detection efficiency reaches 50% at the limiting magnitude $M_r$=-11.5 mag, and at the mean effective surface brightness $\overline{\mu}_{e,r}$=26.5 mag/$arcsec^2$. We present the standard scaling relations for dwarf galaxies and compare them with other nearby clusters. We find that there are no observational differences for dwarfs scaling relations in clusters of different sizes. We study the spatial distribution of galaxies, finding evidence for the presence of substructures within half the virial radius. We also find that mid- and high-luminosity dwarfs ($M_r$<-14.5 mag) become on average redder toward the cluster center, and that they have a mild increase in $R_e$ with increasing clustercentric distance, similar to what is observed for the Fornax cluster. No clear clustercentric trends are reported with surface brightness and S\'ersic index. Considering galaxies in the same magnitude-bins, we find that for high and mid-luminosity dwarfs ($M_r$<-13.5 mag) the g-r color is redder for the brighter surface brightness and higher S\'ersic n index objects., Comment: Accepted for publication in A&A. 25 pages, 21 figures

In this work we use Max-Tree Objects, (MTO) on the FDS data in order to detect previously undetected Low surface brightness (LSB) galaxies. After extending the existing Fornax dwarf galaxy catalogs with this sample, our goal is to understand the evolution of LSB dwarfs in the cluster. We also study the contribution of the newly detected galaxies to the faint end of the luminosity function. We test the detection completeness and parameter extraction accuracy of MTO. We then apply MTO to the FDS images to identify LSB candidates. The identified objects are fitted with 2D S\'ersic models using GALFIT and classified based on their morphological appearance, colors, and structure. With MTO, we are able to increase the completeness of our earlier FDS dwarf catalog (FDSDC) 0.5-1 mag deeper in terms of total magnitude and surface brightness. Due to the increased accuracy in measuring sizes of the detected objects, we also add many small galaxies to the catalog that were previously excluded as their outer parts had been missed in detection. We detect 265 new LSB dwarf galaxies in the Fornax cluster, which increases the total number of known dwarfs in Fornax to 821. Using the extended catalog, we show that the luminosity function has a faint-end slope of -1.38+/-0.02. We compare the obtained luminosity function with different environments studied earlier using deep data but do not find any significant differences. On the other hand, the Fornax-like simulated clusters in the IllustrisTNG cosmological simulation have shallower slopes than found in the observational data. We also find several trends in the galaxy colors, structure, and morphology that support the idea that the number of LSB galaxies is higher in the cluster center due to tidal forces and the age dimming of the stellar populations. The same result also holds for the subgroup of large LSB galaxies, so-called ultra-diffuse galaxies., Comment: Accepted for publication in Astronomy & Astrophysics. 29 pages, 27 figures

In electrical impedance tomography, we aim to solve the conductivity within a target body through electrical measurements made on the surface of the target. This inverse conductivity problem is severely ill-posed, especially in real applications with only partial boundary data available. Thus regularization has to be introduced. Conventionally regularization promoting smooth features is used, however, the Mumford--Shah regularizer familiar for image segmentation is more appropriate for targets consisting of several distinct objects or materials. It is, however, numerically challenging. We show theoretically through $\Gamma$-convergence that a modification of the Ambrosio--Tortorelli approximation of the Mumford--Shah regularizer is applicable to electrical impedance tomography, in particular the complete electrode model of boundary measurements. With numerical and experimental studies, we confirm that this functional works in practice and produces higher quality results than typical regularizations employed in electrical impedance tomography when the conductivity of the target consists of distinct smoothly-varying regions., Comment: 28 pages, 7 figures