Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Support Users in Understanding Intelligent Systems? Structuring the Discussion

The opaque nature of many intelligent systems violates established usability principles and thus presents a challenge for human-computer interaction. Research in the field therefore highlights the need for transparency, scrutability, intelligibility, interpretability and explainability, among others. While all of these terms carry a vision of supporting users in understanding intelligent systems, the underlying notions and assumptions about users and their interaction with the system often remain unclear. We review the literature in HCI through the lens of implied user questions to synthesise a conceptual framework integrating user mindsets, user involvement, and knowledge outcomes to reveal, differentiate and classify current notions in prior work. This framework aims to resolve conceptual ambiguity in the field and enables researchers to clarify their assumptions and become aware of those made in prior work. We thus hope to advance and structure the dialogue in the HCI research community on supporting users in understanding intelligent systems.
λ = 2 mm spectroscopy observations toward the circumnuclear disk of NGC 1068

Aims. We investigate the physical and chemical conditions of molecular gas in the circumnuclear disk (CND) region of NGC 1068. Methods. We carried out a spectral line survey with the IRAM 30 m telescope toward the center of NGC 1068 and mainly focused on the 2 mm band with a frequency coverage of 160.7–168.6 GHz and 176.5–184.3 GHz. Results. Fifteen lines are detected in NGC 1068, eight of which are new detections for this galaxy. We derive the rotation temperatures and column densities of fourteen molecular species. Conclusions. Based on the [HCO+ (2 − 1)]/[HOC+ (2 − 1)] ratio, we obtain a high ionization degree in the CND of NGC 1068. It is found that HC3N is concentrated in the east knot, while 13CCH, CH3CN, SO, HOC+, CS, CH3CCH, and H2CO are concentrated in the west knot. Compared to the star-forming galaxies M 82 and NGC 253, the chemistry of NGC 1068 might be less strongly affected by the UV radiation field, and its kinetic temperature might be lower.
Nine Localized Deviations from Keplerian Rotation in the DSHARP Circumstellar Disks: Kinematic Evidence for Protoplanets Carving the Gaps

We present evidence for localized deviations from Keplerian rotation, i.e., velocity “kinks,” in 8 of the 18 circumstellar disks observed by the DSHARP program: DoAr 25, Elias 2–27, GW Lup, HD 143006, HD 163296, IM Lup, Sz 129, and WaOph 6. Most of the kinks are detected over a small range in both radial extent and velocity, suggesting a planetary origin, but for some of them foreground contamination prevents us from measuring their spatial and velocity extent. Because of the DSHARP limited spectral resolution and signal to noise in the 12CO J = 2−1 line, as well as cloud contamination, the kinks are usually detected in only one spectral channel, and will require confirmation. The strongest circumstantial evidence for protoplanets in the absence of higher spectral resolution data and additional tracers is that, upon deprojection, we find that all of the candidate planets lie within a gap and/or at the end of a spiral detected in dust continuum emission. This suggests that a significant fraction of the dust gaps and spirals observed by Atacama Large Millimeter/submillimeter Array in disks are caused by embedded protoplanets.
Deep Technology Tracing for High-Tech Companies

Technological change and innovation are vitally important, especially for high-tech companies. However, factors influencing their future research and development (R&D) trends are both complicated and various, leading it a quite difficult task to make technology tracing for high-tech companies. To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company. Specially, DTF consists of three components: Potential Competitor Recognition (PCR), Collaborative Technology Recognition (CTR), and Deep Technology Tracing (DTT) neural network. For one thing, PCR and CTR aim to capture competitive relations among enterprises and collaborative relations among technologies, respectively. For another, DTT is designed for modeling dynamic interactions between companies and technologies with the above relations involved. Finally, we evaluate our DTF framework on real-world patent data, and the experimental results clearly prove that DTF can precisely help to prospect future technology emphasis of companies by exploiting hybrid factors.
Improving Label Ranking Ensembles using Boosting Techniques

Label ranking is a prediction task which deals with learning a mapping between an instance and a ranking (i.e., order) of labels from a finite set, representing their relevance to the instance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were developed for a multitude of machine learning tasks, label ranking tasks were overlooked. In this paper, we propose a boosting algorithm which was specifically designed for label ranking tasks. Extensive evaluation of the proposed algorithm on 24 semi-synthetic and real-world label ranking datasets shows that it significantly outperforms existing state-of-the-art label ranking algorithms.
Benchmarking Symbolic Execution Using Constraint Problems - Initial Results

Symbolic execution is a powerful technique for bug finding and program testing. It is successful in finding bugs in real-world code. The core reasoning techniques use constraint solving, path exploration, and search, which are also the same techniques used in solving combinatorial problems, e.g., finite-domain constraint satisfaction problems (CSPs). We propose CSP instances as more challenging benchmarks to evaluate the effectiveness of the core techniques in symbolic execution. We transform CSP benchmarks into C programs suitable for testing the reasoning capabilities of symbolic execution tools. From a single CSP P, we transform P depending on transformation choice into different C programs. Preliminary testing with the KLEE, Tracer-X, and LLBMC tools show substantial runtime differences from transformation and solver choice. Our C benchmarks are effective in showing the limitations of existing symbolic execution tools. The motivation for this work is we believe that benchmarks of this form can spur the development and engineering of improved core reasoning in symbolic execution engines.
A Signal-Space Distance Measure for Nondispersive Optical Fiber

The nondispersive per-sample channel model for the optical fiber channel is considered. Under certain smoothness assumptions, the problem of finding the minimum amount of noise energy that can render two different input points indistinguishable is formulated. This minimum noise energy is then taken as a measure of distance between the points in the input alphabet. Using the machinery of optimal control theory, necessary conditions that describe the minimum-energy noise trajectories are stated as a system of nonlinear differential equations. It is shown how to find the distance between two input points by solving this system of differential equations. The problem of designing signal constellations with the largest minimum distance subject to a peak power constraint is formulated as a clique-finding problem. As an example, a 16-point constellation is designed and compared with conventional quadrature amplitude modulation. A computationally efficient approximation for the proposed distance measure is provided. It is shown how to use this approximation to design large constellations with large minimum distances. Based on the control-theoretic viewpoint of this paper, a new decoding scheme for such nonlinear channels is proposed.
Optimal estimation of sparse topic models

Topic models have become popular tools for dimension reduction and exploratory analysis of text data which consists in observed frequencies of a vocabulary of $p$ words in $n$ documents, stored in a $p\times n$ matrix. The main premise is that the mean of this data matrix can be factorized into a product of two non-negative matrices: a $p\times K$ word-topic matrix $A$ and a $K\times n$ topic-document matrix $W$. This paper studies the estimation of $A$ that is possibly element-wise sparse, and the number of topics $K$ is unknown. In this under-explored context, we derive a new minimax lower bound for the estimation of such $A$ and propose a new computationally efficient algorithm for its recovery. We derive a finite sample upper bound for our estimator, and show that it matches the minimax lower bound in many scenarios. Our estimate adapts to the unknown sparsity of $A$ and our analysis is valid for any finite $n$, $p$, $K$ and document lengths. Empirical results on both synthetic data and semi-synthetic data show that our proposed estimator is a strong competitor of the existing state-of-the-art algorithms for both non-sparse $A$ and sparse $A$, and has superior performance is many scenarios of interest.
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations

Robots coexisting with humans in their environment and performing services for them need the ability to interact with them. One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user. In this work, we present a convolutional neural network for estimating pixelwise object placement probabilities for a set of spatial relations from a single input image. During training, our network receives the learning signal by classifying hallucinated high-level scene representations as an auxiliary task. Unlike previous approaches, our method does not require ground truth data for the pixelwise relational probabilities or 3D models of the objects, which significantly expands the applicability in practical applications. Our results obtained using real-world data and human-robot experiments demonstrate the effectiveness of our method in reasoning about the best way to place objects to reproduce a spatial relation. Videos of our experiments can be found at https://youtu.be/zaZkHTWFMKM
Nowhere to Hide: Cross-modal Identity Leakage between Biometrics and Devices

Along with the benefits of Internet of Things (IoT) come potential privacy risks, since billions of the connected devices are granted permission to track information about their users and communicate it to other parties over the Internet. Of particular interest to the adversary is the user identity which constantly plays an important role in launching attacks. While the exposure of a certain type of physical biometrics or device identity is extensively studied, the compound effect of leakage from both sides remains unknown in multi-modal sensing environments. In this work, we explore the feasibility of the compound identity leakage across cyber-physical spaces and unveil that co-located smart device IDs (e.g., smartphone MAC addresses) and physical biometrics (e.g., facial/vocal samples) are side channels to each other. It is demonstrated that our method is robust to various observation noise in the wild and an attacker can comprehensively profile victims in multi-dimension with nearly zero analysis effort. Two real-world experiments on different biometrics and device IDs show that the presented approach can compromise more than 70% of device IDs and harvests multiple biometric clusters with purity at the same time.
Fuzzy Logic Estimator for Variant SNR Environments

The acquisition system is one of the most sensitive stages in a Direct Sequence Spread Spectrum (DS-SS) receiver (Peterson, Ziemer & Borth, 1995), due to its critical position in order to demodulate the received information. There are several schemes to deal with this problem, such as serial search and parallel algorithms (Proakis, 1995). Serial search algorithms have slow convergence time but their computational load is very low; on the other hand, parallel systems converge very quickly but their computational load is very high. In our system, the acquisition scheme used is the multiresolutive structure presented in (Moran, Socoró, Jové, Pijoan & Tarrés, 2001), which combines quick convergence and low computational load. The decisional system that evaluates the acquisition stage is a key process in the overall system performance, being a drawback of the structure. This becomes more important when dealing with time-varying channels, where signal to noise ratio (called SNR) is not a constant parameter. Several factors contribute to the performance of the acquistion system (Glisic & Vucetic, 1997): channel distorsion and variations, noise and interference, uncertainty about the code phase, and data randomness. The existence of all these variables led us to think about the possibility of using fuzzy logic to solve this complex acquisition estimation (Zadeh, 1973). A fuzzy logic acquisition estimator had already been tested and used in our research group to control a serial search algorithm (Alsina, Morán & Socoró, 2005) with encouraging results, and afterwards in the multiresolutive scheme (Alsina, Mateo & Socoró, 2007), and other applications to this field can be found in bibliography as (Bas, Pérez & Lagunas, 2001) or (Jang, Ha, Seo, Lee & Lee, 1998). Several previous works have been focused in the development of acquisition systems for non frequency selective channels with fast SNR variations (Moran, Socoró, Jové, Pijoan & Tarrés, 2001) (Mateo & Alsina, 2004).
GRAMSES: a new route to general relativistic N-body simulations in cosmology. Part II. Initial conditions

We address the generation of initial conditions (ICs) for {\sc gramses}, a code for nonlinear general relativistic (GR) N-body cosmological simulations recently introduced in ref. [1]. {\sc gramses} adopts a constant mean curvature slicing with a minimal distortion gauge, where the linear growth rate is scale-dependent, and the standard method for realising initial particle data is not straightforwardly applicable. A new method is introduced, in which the initial positions of particles are generated from the displacement field realised for a matter power spectrum as usual, but the velocity is calculated by finite-differencing the displacement fields around the initial redshift. In this way, all the information required for setting up the initial conditions is drawn from three consecutive input matter power spectra, and additional assumptions such as scale-independence of the linear growth factor and growth rate are not needed. We implement this method in a modified {\sc 2LPTic} code, and demonstrate that in a Newtonian setting it can reproduce the velocity field given by the default {\sc 2LPTic} code with subpercent accuracy. We also show that the matter and velocity power spectra of the initial particle data generated for {\sc gramses} simulations using this method agree very well with the linear-theory predictions in the particular gauge used by {\sc gramses}. Finally, we discuss corrections to the finite difference calculation of the velocity when radiation is present, as well as additional corrections implemented in {\sc gramses} to ensure consistency. This method can be applied in ICs generation for GR simulations in generic gauges, and simulations of cosmological models with scale-dependent linear growth rate.
A Price-per-attention Auction Scheme Using Mouse Cursor Information

Payments in online ad auctions are typically derived from click-through rates, so that advertisers do not pay for ineffective ads. But advertisers often care about more than just clicks. That is, for example, if they aim to raise brand awareness or visibility. There is thus an opportunity to devise a more effective ad pricing paradigm, in which ads are paid only if they are actually noticed. This article contributes a novel auction format based on a pay-per-attention (PPA) scheme. We show that the PPA auction inherits the desirable properties (strategy-proofness and efficiency) as its pay-per-impression and pay-per-click counterparts, and that it also compares favourably in terms of revenues. To make the PPA format feasible, we also contribute a scalable diagnostic technology to predict user attention to ads in sponsored search using raw mouse cursor coordinates only, regardless of the page content and structure. We use the user attention predictions in numerical simulations to evaluate the PPA auction scheme. Our results show that, in relevant economic settings, the PPA revenues would be strictly higher than the existing auction payment schemes.
A Search for Neutron Star–Black Hole Binary Mergers in the Short Gamma-Ray Burst Population

Short gamma-ray bursts (SGRBs) are now known to be the product of the merger of two compact objects. However, two possible formation channels exist: neutron star–neutron star (NS–NS) or NS–black hole (BH). The landmark SGRB 170817A provided evidence for the NS–NS channel, thanks to analysis of its gravitational wave signal. We investigate the complete population of SGRBs with an associated redshift (39 events) and search for any divisions that may indicate that an NS–BH formation channel also contributes. Though no conclusive dichotomy is found, we find several lines of evidence that tentatively support the hypothesis that SGRBs with extended emission (EE; seven events) constitute the missing merger population: they are unique in the large energy-band sensitivity of their durations and have statistically distinct energies and host galaxy offsets when compared to regular (non-EE) SGRBs. If this is borne out via future gravitational wave detections, it will conclusively disprove the magnetar model for SGRBs. Furthermore, we identify the first statistically significant anticorrelation between the offsets of SGRBs from their host galaxies and their prompt emission energies.
A Deep Learning Approach to Behavior-Based Learner Modeling

The increasing popularity of e-learning has created demand for improving online education through techniques such as predictive analytics and content recommendations. In this paper, we study learner outcome predictions, i.e., predictions of how they will perform at the end of a course. We propose a novel Two Branch Decision Network for performance prediction that incorporates two important factors: how learners progress through the course and how the content progresses through the course. We combine clickstream features which log every action the learner takes while learning, and textual features which are generated through pre-trained GloVe word embeddings. To assess the performance of our proposed network, we collect data from a short online course designed for corporate training and evaluate both neural network and non-neural network based algorithms on it. Our proposed algorithm achieves 95.7% accuracy and 0.958 AUC score, which outperforms all other models. The results also indicate the combination of behavior features and text features are more predictive than behavior features only and neural network models are powerful in capturing the joint relationship between user behavior and course content.
Efficient Steady-State Simulation of High-Dimensional Stochastic Networks

We propose and study an asymptotically optimal Monte Carlo estimator for steady-state expectations of a d-dimensional reflected Brownian motion. Our estimator is asymptotically optimal in the sense that it requires $\tilde{O}(d)$ (up to logarithmic factors in $d$) i.i. d. Gaussian random variables in order to output an estimate with a controlled error. Our construction is based on the analysis of a suitable multi-level Monte Carlo strategy which, we believe, can be applied widely. This is the first algorithm with linear complexity (under suitable regularity conditions) for steady-state estimation of RBM as the dimension increases.
Application-Level Scheduling With Probabilistic Deadline Constraints

Opportunistic scheduling of delay-tolerant traffic has been shown to substantially improve spectrum efficiency. To encourage users to adopt delay-tolerant scheduling for capacity -improvement, it is critical to provide guarantees in terms of completion time. In this paper, we study application-level scheduling with deadline constraints, where the deadline is pre-specified by users/applications and is associated with a deadline violation probability. To address the exponentially-high complexity due to temporally-varying channel conditions and deadline constraints, we develop a novel asymptotic approach that exploits the largeness of the network to our advantage. Specifically, we identify a lower bound on the deadline violation probability, and propose simple policies that achieve the lower bound in the large-system regime. The results in this paper thus provide a rigorous analytical framework to develop and analyze policies for application-level scheduling under very general settings of channel models and deadline requirements. Further, based on the asymptotic approach , we propose the notion of Application-Level Effective Capacity region, i.e., the throughput region that can be supported subject to deadline constraints, which allows us to quantify the potential gain of application-level scheduling. Simulation results show that application-level scheduling can improve the system capacity significantly while guaranteeing the deadline constraints.
Division of Labor Among Transgender and Gender Non-binary Parents: Association With Individual, Couple, and Children’s Behavioral Outcomes

The division of unpaid labor is an important aspect in understanding co-parenting dynamics, along with individual well-being, couple functioning, and family dynamics. This study explores the division of household and childcare unpaid labor, well-being, relationship functioning, and child behavioral outcomes in 163 transgender and gender non-binary (TGNB) parents. Research exploring the division of labor among cisgender heterosexual couples has found that cisgender women in heterosexual couples disproportionately conduct more of the household and childcare labor (e.g., Lachance-Grzela and Bouchard, 2010). In addition, among heterosexual (e.g., Lachance-Grzela and Bouchard, 2010) and same-sex couples (Tornello et al., 2015b), discrepancies in the division of unpaid labor has been associated with individual well-being, along with couple functioning. We know very little about the factors that predict how labor is divided, along with the impact these arrangements among of families headed by TGNB parents. In this study, TGNB parents reported dividing their household and childcare labor in an egalitarian fashion and wanted to divide their labor in that way. The gender of participants, gender design of the couple, educational attainment, and legal status of the couple’s relationship were not associated with the division of unpaid labor. In contrast, participants who reported making a lower proportion of the household income, worked less hours in paid employment, and were genetically related to their eldest child, reported completing significantly more childcare-related tasks, but not household labor. Using multiple regressions, participants’ genetic relatedness to their eldest child was the only significant predictor of performing greater unpaid childcare labor. Lastly, discrepancies in the household, but not childcare labor, predicted parental well-being and couple functioning. The division of labor among TGNB couples was unrelated to their child behavior outcomes. This study not only sheds light on the dynamics of TGNB-headed families, but also additional factors that influence the division of unpaid labor and how this division affects individuals within the family system.