Miracle-dz/UniFAR-FTUs huggingface dataset:
Looking for the Why in Event Logs for Robotic Process Automation

The concept of Robotic Process Automation (RPA) has gained relevant attention in both industry and academia. RPA raises a way of automating mundane and repetitive human tasks requiring less intrusiveness with the IT infrastructure. Besides traditional user interviews and process document analysis, a common practice starts by observing the behavior of humans with the information systems while they perform the process to be automated. This sequence of human interactions with the user interface (i.e., mouse clicks and keystrokes) is stored in logs for later analysis. Analyzing these interactions brings significant benefits when conducting RPA projects. Nonetheless, some decision-based behaviors of humans require additional information to be explained. For example, a human may reject an invoice because some field is missing on a form. However, there is no interaction with that field since such information is not stored in the log. Therefore, this Ph.D. elaborates on a method to obtain additional information based on screenshots collected during the process execution. Some features are extracted from the screenshots to enrich the log, which is later used for classifying human decisions in a machine-and-human-readable form. The proposed method can be applied to generate advanced support in the RPA projects, e.g., producing an enhanced process analysis, supporting the robot development, or generating predictions and simulations. The approach has been validated using synthetic data where promising results were obtained.
TA-CNN: A Unified Network for Human Behavior Analysis in Multi-Person Conversations

Human behavior analysis in multi-person conversations has been one of the most important research issues for natural human-robot interaction. However, previous datasets and studies mainly focus on single-person behavior analysis, therefore, can hardly be generalized in real-world application scenarios. Fortunately, the MultiMediate'22 Challenge provides various video clips of multi-party conversations. In this paper, we present a unified network named TA-CNN for both sub-challenges. Our TA-CNN can not only model the spatio-temporal dependencies for eye contact detection, but also capture the group-level discriminative features for multi-label next speaker prediction. We empirically evaluate the performance of our method on the officially provided datasets. Our method achieves the state-of-the-art result of 0.7261 for eye contact detection in terms of accuracy and the UAR of 0.5965 for next speaker prediction on the corresponding test sets.
The dark matter distribution function and halo thermalization from the Eddington equation in galaxies

We find the distribution function f(E) for dark matter (DM) halos in galaxies and the corresponding equation of state from the (empirical) DM density profiles derived from observations. We solve for DM in galaxies the analogous of the Eddington equation originally used for the gas of stars in globular clusters. The observed density profiles are a good realistic starting point and the distribution functions derived from them are realistic. We do not make any assumption about the DM nature, the methods developed here apply to any DM kind, though all results are consistent with warm dark matter (WDM). With these methods we find: (i) Cored density profiles behaving quadratically for small distances ρ(r)= r → 0ρ(0) − Kr2 produce distribution functions which are finite and positive at the halo center while cusped density profiles always produce divergent distribution functions at the center. (ii) Cored density profiles produce approximate thermal Boltzmann distribution functions for r ≲ 3rh where rh is the halo radius. (iii) Analytic expressions for the dispersion velocity and the pressure are derived yielding at each halo point an ideal DM gas equation of state with local temperature T(r) ≡ mv2(r)/3. T(r) turns out to be constant in the same region where the distribution function is thermal and exhibits the same temperature within the percent. The self-gravitating DM gas can thermalize despite being collisionless because it is an ergodic system. (iv) The DM halo can be consistently considered at local thermal equilibrium with: (a) a constant temperature T(r) = T0 for r ≲ 3rh, (b) a space dependent temperature T(r) for 3rh < r ≲ Rvirial, which slowly decreases with r. That is, the DM halo is realistically a collisionless self-gravitating thermal gas for r ≲ Rvirial. (v) T(r) outside the halo radius nicely follows the decrease of the circular velocity squared.
Image-Signal Correlation Network for Textile Fiber Identification

Identifying fiber compositions is an important aspect of the textile industry. In recent decades, near-infrared spectroscopy has shown its potential in the automatic detection of fiber components. However, for plant fibers such as cotton and linen, the chemical compositions are the same and thus the absorption spectra are very similar, leading to the problem of "different materials with the same spectrum, whereas the same material with different spectrums" and it is difficult using a single mode of NIR signals to capture the effective features to distinguish these fibers. To solve this problem, textile experts under a microscope measure the cross-sectional or longitudinal characteristics of fibers to determine fiber contents with a destructive way. In this paper, we construct the first NIR signal-microscope image textile fiber composition dataset (NIRITFC). Based on the NIRITFC dataset, we propose an image-signal correlation network (ISiC-Net) and design image-signal correlation perception and image-signal correlation attention modules, respectively, to effectively integrate the visual features (esp. local texture details of fibers) with the finer absorption spectrum information of the NIR signal to capture the deep abstract features of bimodal data for nondestructive textile fiber identification. To better learn the spectral characteristics of the fiber components, the endmember vectors of the corresponding fibers are generated by embedding encoding, and the reconstruction loss is designed to guide the model to reconstruct the NIR signals of the corresponding fiber components by a nonlinear mapping. The quantitative and qualitative results are significantly improved compared to both single and bimodal approaches, indicating the great potential of combining microscopic images and NIR signals for textile fiber composition identification.
The First Impression: Understanding the Impact of Multimodal System Responses on User Behavior in Task-oriented Agents

In task-oriented dialog agents, the initial user experience sets the tone for future interactions and plays a major role in whether the user returns to the system. But with the diversity of human interaction, these systems often struggle with more complex requests that differ significantly from their training data creating an unpleasant and frustrating user experience. In this work, we formulate an alternative and less resource-intensive approach to improving user experience by implicitly conveying possible user action in system responses. We make use of real-world data to test and support our claims, showing that a significant share of the users made use of such information. Additionally, we extend this to product recommendation as a way to anchor the interaction to well-established flows, we then perform data analysis that shows that close to a third of the users tried benefited from such recommendations. We conclude with an overview of the plans for my Ph.D. thesis and how these findings line up with our goal of producing an ideal agent.
Simulating single photons with realistic photon sources

Quantum information processing provides remarkable advantages over its classical counterpart. Quantum optical systems have been proved to be sufficient for realizing general quantum tasks, which, however, often rely on single-photon sources. In practice, imperfect single-photon sources, such as a weak-coherent-state source, are used instead, which will inevitably limit the power in demonstrating quantum effects. For instance, with imperfect photon sources, the key rate of the Bennett-Brassard 1984 (BB84) quantum key distribution protocol will be very low, which fortunately can be resolved by utilizing the decoy-state method. As a generalization, we investigate an efficient way to simulate single photons with imperfect ones to an arbitrary desired accuracy when the number of photonic inputs is small. Based on this simulator, we can thus replace the tasks that involve only a few single-photon inputs with the ones that make use of only imperfect photon sources. In addition, our method also provides a quantum simulator to quantum computation based on quantum optics. In the main context, we take a phase-randomized coherent state as an example for analysis. A general photon source applies similarly and may provide some further advantages for certain tasks.
Breaking Isolation: Multimodal Graph Fusion for Multimedia Recommendation by Edge-wise Modulation

In a multimedia recommender system, rich multimodal dynamics of user-item interactions are worth availing ourselves of and have been facilitated by Graph Convolutional Networks (GCNs). Yet, the typical way of conducting multimodal fusion with GCN-based models is either through graph mergence fusion that delivers insufficient inter-modal dynamics, or through node alignment fusion that brings in noises which potentially harm multimodal modelling. Unlike existing works, we propose EgoGCN, a structure that seeks to enhance multimodal learning of user-item interactions. At its core is a simple yet effective fusion operation dubbed EdGe-wise mOdulation (EGO) fusion. EGO fusion adaptively distils edge-wise multimodal information and learns to modulate each unimodal node under the supervision of other modalities. It breaks isolated unimodal propagations, allows the most informative inter-modal messages to spread, whilst preserving intra-modal processing. We present a hard modulation and a soft modulation to fully investigate the multimodal dynamics behind. Experiments on two real-world datasets show that EgoGCN comfortably beats prior methods.
3.5 keV x-ray line from decaying gravitino dark matter

Extremely weakly interacting particles like the gravitino may be stable enough on cosmological time scales to constitute a good dark matter candidate even in the presence of R-parity violation. We consider the possibility that the recently identified 3.5 keV x-ray line can be generated in light gravitino decays to neutrinos and photons. We find that this is indeed possible in loop processes induced by trilinear lepton-number-violating couplings. We show that in order to avoid overproduction of gravitinos, the reheating temperature has to be at most around 100 GeV to 1 TeV. Finally we briefly discuss associated LHC phenomenology due to a relatively light gluino and multijet/multilepton events from R-parity violating decays of neutralinos.
Guess-It-Generator: Generating in a Lewis Signaling Framework through Logical Reasoning

Human minds spontaneously integrate two inherited cognitive capabilities: perception and reasoning to accomplish cognitive tasks such as problem solving, imagination, and causation. It is observed in the primate brains that perception offers the assistance required for problem comprehension, whilst the reasoning elucidates upon the facts recovered during perception in order to make a decision. The field of artificial intelligence (AI) thus considers perception and reasoning as two complementary areas that are realized by machine learning and logic programming, respectively. In this work, we propose a generative model using a collaborative guessing game of the kind first introduced by David Lewis in his famous work called the Lewis signaling game that is synonymous with the "20 Questions'' game. Our proposed model, Guess-It-Generator (GIG) is a collaborative framework that engages two recurrent neural networks in a guessing game. GIG unifies perception and reasoning with a view to generating labeled images by capturing, (X, y), the underlying density of a data distribution, i.e. (X, y) - p(X, y). An encoder attends to a region of the input image and encodes that onto a latent variable that acts as a perception signal to a decoder. In contrast, the decoder leverages on the perception signals to guess the image and verifies the guess by reasoning with logical facts derived from the domain knowledge. Our experiments and comprehensive studies on seven datasets: PCAM, Chest-Xray-14, FIRE, HAM10000 from the medical domain, and CIFAR 10, LSUN, ImageNet, among standard benchmark datasets, show significant promise for the proposed method.
TURBULENT BOUNDARY LAYER AND VISCOUS RESISTANCE OF A SUBMARINE AT HIGH REYNOLDS NUMBER

Abstract : A limited amount of data about turbulent boundary layers, obtained on a full-scale submarine, is presented. Mean wall-shear stresses were measured at four hull locations with Preston tubes, and mean velocity profiles in the boundary layer were measured at one hull location with pitot tubes. These measurements were made over a range of Reynolds numbers from 11 to 300 million. The measured profile for boundary-layer velocity, both full-scale and model, are seen to agree well with the velocity-similarity laws, and the measurements of shear-stress distribution agree with a solution of the momentum equation for a body of revolution.
CLASSES OF FIRST-ORDER DIFFERENTIAL SUPERORDINATIONS

Let ip : C 2 —> C be an analytic function in a domain A C C 2 , let p an analytic function in the unit disc U such that ip(p(z), zp'(z)) is univalent in U and suppose that p satisfies the first-order differential superordination h(z) tp(p(z),zp'(z)). In the case when <p(p(z), zp (z)) = a(p(z)) + P(p(z)) j(zp'(z)) we determine conditions on h, a, 0 and 7 so that the above subordination implies q(z) -< p(z), where q is the largest function so that q(z) -< p(z) for all p functions satisfying the first-order differential superordination. The concept of differential superordination was introduced by S. S. Miller and P. T. Mocanu in [2] like a dual problem of differential subordination [1],
Examining Choice Overload across Single-list and Multi-list User Interfaces

Recommender systems are prone to triggering choice overload among users due to the typically large set sizes. Various applications have been developed that aim to overcome this through interface design, notably by so-called multi-list recommender systems. However, to what extent such user interface design actually reduces choice overload compared to single-list interfaces has yet to be examined. In a user study ( 𝑁 = 150 ), we compared three common user interfaces (UIs) in the context of recipe recommendation: a single-list UI, a grid UI and a multi-list UI. Whereas earlier studies found differences in choice difficulty and choice satisfaction across grid-based and multi-list recommender interfaces, we observed no such differences, as the explanations were possibly not sufficiently helpful. Instead, we found that grid-based UIs and multi-list UIs had a higher perceived ease of use than a single-list UI, which in turn reduced choice difficulty. The benefits of such interfaces, thus, may lie in the organization of the UI, at least in the recipe domain.
Conformal Random Geometry

In these Notes, a comprehensive description of the universal fractal geometry of conformally-invariant scaling curves or interfaces, in the plane or half-plane, is given. The present approach focuses on deriving critical exponents associated with interacting random paths, by exploiting their underlying quantum gravity structure. The latter relates exponents in the plane to those on a random lattice, i.e., in a fluctuating metric, using the so-called Knizhnik, Polyakov and Zamolodchikov (KPZ) map. This is accomplished within the framework of random matrix theory and conformal field theory, with applications to geometrical critical models, like Brownian paths, self-avoiding walks, percolation, and more generally, the O(N) or Q-state Potts models and, last but not least, Schramm's Stochastic Loewner Evolution (SLE_kappa). These Notes can be considered as complementary to those by Wendelin Werner (2006 Fields Medalist! ), ``Some Recent Aspects of Random Conformally Invariant Systems,'' arXiv:math.PR/0511268.
MegaMorph - multiwavelength measurement of galaxy structure: complete Sersic profile information from modern surveys

In this paper, we demonstrate a new method for fitting galaxy p rofiles which makes use of the full multi-wavelength data provided by modern large optical-near-infrared imaging sur- veys. We present a new version of GALAPAGOS, which utilises a recently-developed multi- wavelength version of GALFIT, and enables the automated measurement of wavelength- dependent Sersic profile parameters for very large samples of galaxies. Our new technique is extensively tested to assess the reliability of both pieces of software, GALFIT and GALAPAGOS on both real ugrizY JHK imaging data from the GAMA survey and simulated data made to the same specifications. We find that fitting galaxy light profi les with multi-wavelength data increases the stability and accuracy of the measured parameters, and hence produces more complete and meaningful multi-wavelength photometry than has been available previously. The improvement is particularly significant for magnitudes in low S/N bands and for struc- tural parameters like half-light radius re and Sersic index n for which a prior is used by constraining these parameters to a polynomial as a function of wavelength. This allows the fitting routines to push the magnitude of galaxies for which s ensible values can be derived to fainter limits. The technique utilises a smooth transiti on of galaxy parameters with wave- length, creating more physically meaningful transitions t han single-band fitting and allows accurate interpolation between passbands, perfect for der ivation of rest-frame values.
EliMRec: Eliminating Single-modal Bias in Multimedia Recommendation

The main idea of multimedia recommendation is to introduce the profile content of multimedia documents as an auxiliary, so as to endow recommenders with generalization ability and gain better performance. However, recent studies using non-uniform datasets roughly fuse single-modal features into multi-modal features and adopt the strategy of directly maximizing the likelihood of user preference scores, leading to the single-modal bias. Owing to the defect in architecture, there is still room for improvement for recent multimedia recommendation. In this paper, we propose EliMRec, a generic and modal-agnostic framework to eliminate the single-modal bias in multimedia recommendation. From our observation, biased predictive reasoning is influenced directly by the single modality rather than considering the all given multiple views of the item. Through the novel perspective of causal inference, we manage to explain the single-modal issue and exploit the inner working of multi-modal fusion. To eliminate single-modal bias, we enhance the bias-capture ability of a general multimedia recommendation framework and imagine several counterfactual worlds that control one modality variant with other modality fixed or blank. Truth to be told, counterfactual analysis enables us to identify and eliminate bias lying in the direct effect from single-modal features to the preference score. Extensive experiments on real-world datasets demonstrate that our method significantly improves over several state-of-the-art baselines like LightGCN and MMGCN. Codes are available at https://github.com/Xiaohao-Liu/EliMRec.
Cardiorenal syndrome: Pathophysiology as a key to the therapeutic approach in an under‐diagnosed disease

Cardiorenal syndrome is a clinical condition that impacts both the heart and the kidneys. One organ's chronic or acute impairment can lead to the other's chronic or acute dysregulation. The cardiorenal syndrome has been grouped into five subcategories that describe the etiology, pathophysiology, duration, and pattern of cardiac and renal dysfunction. This classification reflects the large spectrum of interrelated dysfunctions and underlines the bidirectional nature of heart‐kidney interactions. However, more evidence is needed to apply these early findings in medical practice. Understanding the relationship between these two organs during each organ's impairment has significant clinical implications that are relevant for therapy in both chronic and acute conditions. The epidemiology, definition, classification, pathophysiology, therapy, and outcome of each form of cardiorenal syndrome are all examined in this review.
SWIFT AND RXTE OBSERVATIONS OF THE BLACK HOLE TRANSIENT GX 339-4 IN THE HARD STATE BETWEEN OUTBURSTS

We use simultaneous Swift and RXTE observations of the black hole binary GX 339-4 to measure the inner radius of its accretion disk in the hard state down to 0.4% LEdd via modeling of the thermal disk emission and the relativistically broadened iron line. For the luminosity range covered in this work, our results rule out a significantly truncated disk at 100–1000 Rg as predicted by the advection-dominated accretion flow paradigm. The measurements depend strongly on the assumed emission geometry, with most results providing no clear picture of radius evolution. If the inclination is constrained to roughly 20°, however, the measurements based on the thermal disk emission suggest a mildly receding disk at a luminosity of 0.4% LEdd. The iron abundance varies between ∼1 and 2 solar abundances, with the i = 20° results indicating a negative correlation with luminosity, though this is likely due to a change in disk illumination geometry.
Statistical properties of mass, star formation, chemical content and rotational patterns in early z ≳ 9 structures

We study the baryonic, chemical and dynamical properties of a significantly large sample of early proto-galaxies in the first 500 Myr of the Universe (redshift z>9), obtained from high-resolution numerical, N-body, hydrodynamical, chemistry simulations including atomic and molecular networks, gas cooling, star formation, stellar evolution and metal spreading for population III and population II-I regimes according to proper stellar yields and lifetimes. We find that first star formation events take place in halos with dark-matter mass M_{DM} > 2\times 10^6 M_{sun}. Early star forming objects have: molecular fractions from x_{mol} 0.1 in active regions; star formation rates SFR ~10^{-8}-10^{-3}M_{sun}/yr; and metallicities in the range ~10^{-8}-10^{-2}Z_{sun}. Roughly ~10% of high-z haloes host population II-I star formation and dominate the cosmic SFR density. They usually are bursty objects with mean specific SFR around ~10 Gyr^{-1} at z~9 and increasing with redshift up to ~10^2 Gyr^{-1}. Stellar feedback effects alter the baryonic content of the haloes and locally affect their chemical and thermo-dynamical properties, as reflected by the broadening of various physical relations. The establishment of gaseous rotationally-supported cores is quite uncommon, weakly related to the large-scale dark-matter behaviour and evolving in an intermittent fashion. The colder, molecular-rich phase tends to maintain any established rotational motion longer with respect to the hotter, metal-rich component, which is very sensitive to environmental processes. While the fraction of haloes featuring a significant amount of co-rotating, molecular-rich gas increases with cosmic time (from a few percent at z~20 up to ~5-15% at z~9), the chaotic nature of metal-enriched material does not lead to particular trends.