Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Permutations for Hierarchical Clustering of Time Series

Two distances based on permutations are considered to measure the similarity of two time series according to their strength of dependency. The distance measures are used together with different linkages to get hierarchical clustering methods of time series by dependency. We apply these distances to both simulated theoretical and real data series. For simulated time series the distances show good clustering results, both in the case of linear and non-linear dependencies. The effect of the embedding dimension and the linkage method are also analyzed. Finally, several real data series are properly clustered using the proposed method.
Ultraviolet signatures of the multiphase intracluster and circumgalactic media in the romulusc simulation

Quasar absorption-line studies in the ultraviolet (UV) can uniquely probe the nature of the multiphase cool–warm (104 < T < 106 K) gas in and around galaxy clusters, promising to provide unprecedented insights into (1) interactions between the circumgalactic medium (CGM) associated with infalling galaxies and the hot (T > 106 K) X-ray emitting intracluster medium (ICM), (2) the stripping of metal-rich gas from the CGM, and (3) a multiphase structure of the ICM with a wide range of temperatures and metallicities. In this work, we present results from a high-resolution simulation of an $\sim 10^{14} \, \mathrm{M}_{\odot }$ galaxy cluster to study the physical properties and observable signatures of this cool–warm gas in galaxy clusters. We show that the ICM becomes increasingly multiphased at large radii, with the cool–warm gas becoming dominant in cluster outskirts. The diffuse cool–warm gas also exhibits a wider range of metallicity than the hot X-ray emitting gas. We make predictions for the covering fractions of key absorption-line tracers, both in the ICM and in the CGM of cluster galaxies, typically observed with the Cosmic Origins Spectrograph aboard the Hubble Space Telescope (HST). We further extract synthetic spectra to demonstrate the feasibility of detecting and characterizing the thermal, kinematic, and chemical composition of the cool–warm gas using H i, O vi, and C iv lines, and we predict an enhanced population of broad Ly α absorbers tracing the warm gas. Lastly, we discuss future prospects of probing the multiphase structure of the ICM beyond HST.
Internal Energy Dissipation in Enceladus's Subsurface Ocean From Tides and Libration and the Role of Inertial Waves

Enceladus is characterized by a south polar hot spot associated with a large outflow of heat, the source of which remains unclear. We compute the heat generated via viscous dissipation resulting from tidal and (longitudinal) libration forcing in the moon's subsurface ocean using the linearized Navier‐Stokes equation in a three‐dimensional spherical model. We conclude that libration is the dominant cause of dissipation at the linear order, providing up to ∼0.001 GW of heat to the ocean, which remains insufficient to explain the ∼10 GW observed by Cassini. We also illustrate how resonances with inertial modes can significantly augment the dissipation. Our work is an extension to Rovira‐Navarro et al. (2019, https://doi.org/10.1016/j.icarus.2018.11.010) to include the effects of libration and the presence of the icy crust. The model developed here is readily applicable to the study of other moons with a subsurface ocean and planets with a liquid core.
Ion Accelerator Facility of the Wakasa Wan Energy Research Center for the Study of Irradiation Effects on Space Electronics

The core facility of the Wakasa Wan Energy Research Center (WERC) consists of three ion accelerators: a synchrotron, a tandem accelerator and an ion-implanter. Research on the irradiation effects using these accelerators has been performed on space electronics such as solar cells, radiation detectors, image sensors and LSI circuits. In this report, the accelerator facility and ion-irradiation apparatuses at WERC are introduced, focusing on the research on irradiation effects on space electronics. Then, some recent results are summarized.
Graph clustering in industrial networks

The present work investigates clustering of a graph-based representation of industrial connections derived from international trade data by Hidalgo et al (2007) and confirms existence of around ten industrial clusters that are reasonably consistent with expected historical patterns of diffusion of innovation and technology. This supports the notion that technological development occurs in sequential innovation waves. The clustering method developed in this work follows conceptual ideas of Lambiotte and Barahona (2009), who suggested to use random walk to assess a hierarchical structure of network communities where different levels of the hierarchy correspond to different diffusion times. We, however, implement these ideas differently to match physics of the problem under consideration and introduce a hierarchal clustering procedure that is combined with convenient resorting of the elements. An equivalent spectral interpretation of the clustering is also given and discussed in the paper.
HST Resolves Stars in a Tiny Body Falling on the Dwarf Galaxy DDO 68

We present new Hubble Space Telescope imaging of a stream-like system associated with the dwarf galaxy DDO 68, located in the Lynx-Cancer void at a distance of D ∼ 12.65 Mpc from us. The stream, previously identified in deep Large Binocular Telescope images as a diffuse low surface brightness structure, is resolved into individual stars in the F606W (broad V) and F814W (∼I) images acquired with the Wide Field Camera 3. The resulting V, I color–magnitude diagram (CMD) of the resolved stars is dominated by old (age ≳ 1–2 Gyr) red giant branch (RGB) stars. From the observed RGB tip, we conclude that the stream is at the same distance as DDO 68, confirming the physical association with it. A synthetic CMD analysis indicates that the large majority of the star formation activity in the stream occurred at epochs earlier than ∼1 Gyr ago, and that the star formation at epochs more recent than ∼500 Myr ago is compatible with zero. The total stellar mass of the stream is ∼106M⊙, about 1/100 of that of DDO 68. This is a striking example of hierarchical merging in action at the dwarf galaxy scales.
Continuous Direct Sparse Visual Odometry from RGB-D Images

This paper reports on a novel formulation and evaluation of visual odometry from RGB-D images. Assuming a static scene, the developed theoretical framework generalizes the widely used direct energy formulation (photometric error minimization) technique for obtaining a rigid body transformation that aligns two overlapping RGB-D images to a continuous formulation. The continuity is achieved through functional treatment of the problem and representing the process models over RGB-D images in a reproducing kernel Hilbert space; consequently, the registration is not limited to the specific image resolution and the framework is fully analytical with a closed-form derivation of the gradient. We solve the problem by maximizing the inner product between two functions defined over RGB-D images, while the continuous action of the rigid body motion Lie group is captured through the integration of the flow in the corresponding Lie algebra. Energy-based approaches have been extremely successful and the developed framework in this paper shares many of their desired properties such as the parallel structure on both CPUs and GPUs, sparsity, semi-dense tracking, avoiding explicit data association which is computationally expensive, and possible extensions to the simultaneous localization and mapping frameworks. The evaluations on experimental data and comparison with the equivalent energy-based formulation of the problem confirm the effectiveness of the proposed technique, especially, when the lack of structure and texture in the environment is evident.
A Unified Algorithmic Framework of Symmetric Gauss-Seidel Decomposition Based Proximal Admms for Convex Composite Programming

This paper aims to present a fairly accessible generalization of several symmetric Gauss-Seidel decomposition based multi-block proximal alternating direction methods of multipliers (ADMMs) for convex composite optimization problems. The proposed method unifies and refines many constructive techniques that were separately developed for the computational efficiency of multi-block ADMM-type algorithms. Specifically, the majorized augmented Lagrangian functions, the indefinite proximal terms, the inexact symmetric Gauss-Seidel decomposition theorem, the tolerance criteria of approximately solving the subproblems, and the large dual step-lengths, are all incorporated in one algorithmic framework, which we named as sGS-imiPADMM. From the popularity of convergent variants of multi-block ADMMs in recent years, especially for high-dimensional multi-block convex composite conic programming problems, the unification presented in this paper, as well as the corresponding convergence results, may have the great potential of facilitating the implementation of many multi-block ADMMs in various problem settings.
Diversification of diversity: Migrations, cultural pluralism and urban transformations in Palermo (Italy): a case study

Italy experienced the transition from a country of emigration to a country of immigration only in the last decade of the 20th century. The extreme heterogeneity of the Italian scene – from the distribution and variety of productive sectors and local economic dimensions, to the geographical, cultural and linguistic varieties – results in an incredibly differentiated background on which the phenomenon of migrations fits as multiplier of diversity. But there are some particular fields in Italy where the challenges of superdiversity appear to be more prominent: the impact on the school system, in terms of linguistic-cultural pluralism; the change in religious belonging and identities; the dynamics of cohesion/marginality in everyday life; and the relationship between spaces and identities in a superdiverse context.
What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users’ emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users’ depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.
Feedback Schemes for the Action-Dependent Wiretap Channel with Noncausal State at the Transmitter

In this paper, we propose two feedback coding schemes for the action-dependent wiretap channel with noncausal state at the transmitter. The first scheme follows from the already existing secret key based feedback coding scheme for the wiretap channel. The second one follows from our recently proposed hybrid feedback scheme for the wiretap channel. We show that, for the action-dependent wiretap channel with noncausal state at the transmitter, the second feedback scheme performs better than the first one, and the capacity results of this paper are further explained via a Gaussian example, which we call the action-dependent dirty paper wiretap channel with noiseless feedback.
Spin-Orbit Angular Momentum Conversion in Metamaterials and Metasurfaces

In the last decades, unprecedented progress in the manipulation of the spin angular momentum (SAM) and orbital angular momentum (OAM) of light has been achieved, enabling a number of applications, ranging from classical and quantum communication to optical microscopy and super-resolution imaging. Metasurfaces are artificially engineered 2D metamaterials with designed subwavelength-size building blocks, which allow the precise control of optical fields with unparalleled flexibility and performance. The reduced dimensionality of optical metasurfaces enables new physics and leads to functionalities and applications that are remarkably different from those achievable with bulk materials. In this review, we present an overview of the progress in optical metasurfaces for the manipultation of SAM and OAM of light, for applications in integrated spin-orbit conversion (SOC) devices.
Optimized Mixing by Cutting-and-Shuffling

Mixing by cutting-and-shuffling can be understood and predicted using dynamical systems based tools and techniques. In existing studies, mixing is generated by maps that repeat the same cut-and-shuffle process at every iteration, in a "fixed" manner. However, mixing can be greatly improved by varying the cut-and-shuffle parameters at each step, using a "variable" approach. To demonstrate this approach, we show how to optimize mixing by cutting-and-shuffling on the one-dimensional line interval, known as an interval exchange transformation (IET). Mixing can be significantly improved by optimizing variable protocols, especially for initial conditions more complex than just a simple two-color line interval. While we show that optimal variable IETs can be found analytically for arbitrary numbers of iterations, for more complex cutting-and-shuffling systems, computationally expensive numerical optimization methods would be required. Furthermore, the number of control parameters grows linearly with the number of iterations in variable systems. Therefore, optimizing over large numbers of iterations is generally computationally prohibitive. We demonstrate an ad hoc approach to cutting-and-shuffling that is computationally inexpensive and guarantees the mixing metric is within a constant factor of the optimum. This ad hoc approach yields significantly better mixing than fixed IETs which are known to produce weak-mixing, because cut pieces never reconnect. The heuristic principles of this method can be applied to more general cutting-and-shuffling systems.
Multi-reference Tacotron by Intercross Training for Style Disentangling, Transfer and Control in Speech Synthesis

Speech style control and transfer techniques aim to enrich the diversity and expressiveness of synthesized speech. Existing approaches model all speech styles into one representation, lacking the ability to control a specific speech feature independently. To address this issue, we introduce a novel multi-reference structure to Tacotron and propose intercross training approach, which together ensure that each sub-encoder of the multi-reference encoder independently disentangles and controls a specific style. Experimental results show that our model is able to control and transfer desired speech styles individually.
Efficient Algorithms for Coded Multicasting in Heterogeneous Caching Networks

Coded multicasting has been shown to be a promising approach to significantly improve the performance of content delivery networks with multiple caches downstream of a common multicast link. However, the schemes that have been shown to achieve order-optimal performance require content items to be partitioned into several packets that grows exponentially with the number of caches, leading to codes of exponential complexity that jeopardize their promising performance benefits. In this paper, we address this crucial performance-complexity tradeoff in a heterogeneous caching network setting, where edge caches with possibly different storage capacity collect multiple content requests that may follow distinct demand distributions. We extend the asymptotic (in the number of packets per file) analysis of shared link caching networks to heterogeneous network settings, and present novel coded multicast schemes, based on local graph coloring, that exhibit polynomial-time complexity in all the system parameters, while preserving the asymptotically proven multiplicative caching gain even for finite file packetization. We further demonstrate that the packetization order (the number of packets each file is split into) can be traded-off with the number of requests collected by each cache, while preserving the same multiplicative caching gain. Simulation results confirm the superiority of the proposed schemes and illustrate the interesting request aggregation vs. packetization order tradeoff within several practical settings. Our results provide a compelling step towards the practical achievability of the promising multiplicative caching gain in next generation access networks.
Towards Performance and Cost Simulation in Function as a Service (short paper)

Function as a Service (FaaS) promises a more cost-efficient deployment and operation of cloud functions compared to related cloud technologies, like Platform as a Service (PaaS) and Container as a Service (CaaS). Scaling, cold starts, function configurations, dependent services, network latency etc. influence the two conflicting goals cost and performance. Since so many factors have impact on these two dimensions, users need a tool to simulate the function in an early development stage to solve these conflicting goals. Therefore, a simulation framework is proposed in this paper.
A Method Based on Differential Entropy-Like Function for Detecting Differentially Expressed Genes Across Multiple Conditions in RNA-Seq Studies

The advancement of high-throughput RNA sequencing has uncovered the profound truth in biology, ranging from the study of differential expressed genes to the identification of different genomic phenotype across multiple conditions. However, lack of biological replicates and low expressed data are still obstacles to measuring differentially expressed genes effectively. We present an algorithm based on differential entropy-like function (DEF) to test for the differential expression across time-course data or multi-sample data with few biological replicates. Compared with limma, edgeR, DESeq2, and baySeq, DEF maintains equivalent or better performance on the real data of two conditions. Moreover, DEF is well suited for predicting the genes that show the greatest differences across multiple conditions such as time-course data and identifies various biologically relevant genes.
On the Thermodynamic Origin of Gravitational Force by Applying Spacetime Entanglement Entropy and the Unruh Effect

We consider the thermodynamic origin of the gravitational force of matter by applying the spacetime entanglement entropy and the Unruh effect originating from vacuum quantum fluctuations. By analyzing both the local thermal equilibrium and quasi-static processes of a system, we may get both the magnitude and direction of Newton’s gravitational force in our theoretical model. Our work shows the possibility that the elusive Unruh effect has already shown its manifestation through gravitational force.
Metabolomics in the Cloud: Scaling Computational Tools to Big Data

Background: Metabolomics datasets are becoming increasingly large and complex, with multiple types of algorithms and workflows needed to process and analyse the data. A cloud infrastructure with portable software tools can provide much needed resources enabling faster processing of much larger datasets than would be possible at any individual lab. The PhenoMeNal project has developed such an infrastructure, allowing users to run analyses on local or commercial cloud platforms. We have examined the computational scaling behaviour of the PhenoMeNal platform using four different implementations across 1-1000 virtual CPUs using two common metabolomics tools. Results: Our results show that data which takes up to 4 days to process on a standard desktop computer can be processed in just 10 min on the largest cluster. Improved runtimes come at the cost of decreased efficiency, with all platforms falling below 80% efficiency above approximately 1/3 of the maximum number of vCPUs. An economic analysis revealed that running on large scale cloud platforms is cost effective compared to traditional desktop systems. Conclusions: Overall, cloud implementations of PhenoMeNal show excellent scalability for standard metabolomics computing tasks on a range of platforms, making them a compelling choice for research computing in metabolomics.
Hybridization Outcomes Have Strong Genomic and Environmental Contingencies

Extreme F2 phenotypes known as transgressive segregants can cause increased or decreased fitness in hybrids beyond the ranges seen in parental populations. Despite the usefulness of transgression for plant and animal breeding and its potential role in hybrid speciation, the genetic mechanisms and predictors of transgressive segregation remain largely untested. We generated seven hybrid crosses between five widely divergent Saccharomyces yeast species and measured the fitness of the parents and their viable F1 and F2 hybrids in seven stressful environments. We found that on average 16.6% of all replicate F2 hybrids had higher fitness than both parents. Against our predictions, transgression frequency was not a function of parental genetic and phenotypic distances across test environments. Within environments, some relationships were significant, but not in the predicted direction; for example, genetic distance was negatively related to transgression in ethanol and hydrogen peroxide. Significant effects of hybrid cross, test environment, and cross × environment interactions suggest that the amount of transgression produced in a hybrid cross is highly context specific and that outcomes of hybridization differ even among crosses made from the same two parents. If the goal is to reliably predict hybrid fitness and forecast the evolutionary potential of admixed populations, we need more efforts to identify patterns beyond the idiosyncrasies caused by specific genomic or environmental contexts.
Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings

A major challenge in the study of dynamical systems is that of model discovery: turning data into models that are not just predictive, but provide insight into the nature of the underlying dynamical system that generated the data. This problem is made more difficult by the fact that many systems of interest exhibit diverse behaviors across multiple time scales. We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the governing variables and (ii) systems for which we have incomplete measurements. For systems with full state measurements, we show that the recent sparse identification of nonlinear dynamical systems (SINDy) method can discover governing equations with relatively little data, provided that accurate measurements of the derivatives can be computed from the data. We introduce a sampling method that allows SINDy to scale efficiently t.