Miracle-dz/UniFAR-FTUs huggingface dataset:
Procedural Game Level Design to Trigger Spatial Exploration

Synthesizing game levels that evoke players’ curiosity, driving them to explore different level parts, is time-consuming and tedious. Typically, game level designers manually perform this synthesis using trial and error. In this paper, we propose a method with which to replace this manual, time-consuming process. We benefited from recent work that had proposed game level design patterns to evoke curiosity, and we propose an approach to automatically synthesizing game levels in order to encourage players to pursue designer-specified exploration goals. We started by creating a dataset of level assets, based on the four design patterns that evoke curiosity-driven exploration in games (reaching extreme points, resolving visual obstructions, out-of-place objects, and understanding spatial connections). We annotated the assets in our dataset with spatial exploration measurements (the time players took to explore an asset over their total time spent in the game level). We then formulated game level design as an optimization problem, encoding both spatial exploration (mean spatial exploration, spatial exploration variance, and spatial exploration distribution) and game level design (occupied area, adjacent penalty, and height distribution) decisions. Then, we solved this problem by implementing a reversible-jump Markov chain Monte Carlo method. We demonstrate our method’s ability to synthesize game level variations with different spatial exploration and level design decisions. Finally, a user study showed that our approach can automatically synthesize game levels, encouraging a certain amount of spatial exploration by players.
Luminosity-dependent Quasar Lifetimes: A New Interpretation of the Quasar Luminosity Function

We propose a new interpretation of the quasar luminosity function (LF) derived from physically motivated models of quasar lifetimes and light curves. In our picture, quasars evolve rapidly, and their lifetime depends on both their instantaneous and peak luminosities. We study this model using simulations of galaxy mergers that successfully reproduce a wide range of observed quasar phenomena. With lifetimes inferred from the simulations, we deconvolve the observed quasar LF from the distribution of peak luminosities and show that they differ qualitatively, unlike the simple models of quasar lifetimes used previously. We find that the bright end of the LF traces the intrinsic peak quasar activity but that the faint end consists of quasars that are either undergoing exponential growth to much larger masses and higher luminosities, or are in sub-Eddington quiescent states going into or coming out of a period of peak activity. The "break" in the LF directly corresponds to the maximum in the intrinsic distribution of peak luminosities, which falls off at both brighter and fainter luminosities. Our interpretation of the quasar LF provides a physical basis for the nature and slope of the faint-end distribution, as well as the location of the break luminosity.
Charting the Uncharted with GUR: How AI Playtesting Can Supplement Expert Evaluation

Despite the advantages of using expert evaluation as a method within games user research (GUR) (i.e. provides stakeholders low cost, rapid feedback), it does not always accurately reflect the general player’s experience. Testing the game out with real users (also called playtesting) helps bridge this gap by giving game developers an in-depth look into the player experience. However, playtesting is resource intensive and time consuming, making it difficult to implement within the tight time frames of industry game development. AI can help to mitigate some of these issues by providing an automated way to simulate player behaviour and experience. In this paper, we introduce a tool called PathOS+—a playtesting interface which uses AI playtesting data to help enhance expert evaluation. Results from a study conducted with expert participants shows how PathOS+ could contribute to game design and assist developers and researchers in conducting expert evaluations. This is an important contribution as it provides game user researchers and designers with a fast, low-cost and effective game evaluation approach which has the potential to make game evaluation more accessible to indie and smaller game studios.
Plasmon dispersion in dilute two-dimensional electron systems: Quantum-classical and Wigner crystal-electron liquid crossover

We theoretically calculate the finite wave vector plasmon dispersion in a low density 2D electron layer taking into account finite temperature, finite layer width, and local field corrections. We compare our theoretical results with recent Raman scattering spectroscopic experimental 2D plasmon dispersion data in GaAs quantum wells at very low carrier densities ($r_s > 10$) and large wave vectors ($q \ge k_F$). We find good agreement with the experimental data, providing an explanation for why the experimentally measured dispersion seems to obey the simple classical long wavelength 2D plasmon dispersion formula. We also provide a critical discussion on the observable manifestations of the quantum-classical and the Wigner crystal - electron liquid crossover behavior in the 2D plasmon properties as a function of electron density and temperature in GaAs quantum well systems.
Exploring the Detection of Spontaneous Recollections during Video-viewing In-the-Wild using Facial Behavior Analysis

Intelligent systems might benefit from automatically detecting when a stimulus has triggered a user’s recollection of personal memories, e.g., to identify that a piece of media content holds personal significance for them. While computational research has demonstrated the potential to identify related states based on facial behavior (e.g., mind-wandering), the automatic detection of spontaneous recollections specifically has not been investigated this far. Motivated by this, we present machine learning experiments exploring the feasibility of detecting whether a video clip has triggered personal memories in a viewer based on the analysis of their Head Rotation, Head Position, Eye Gaze, and Facial Expressions. Concretely, we introduce an approach for automatic detection and evaluate its potential for predictions using in-the-wild webcam recordings. Overall, our findings demonstrate the capacity for above chance detections in both settings, with substantially better performance for the video-independent variant. Beyond this, we investigate the role of person-specific recollection biases for predictions of our video-independent models and the importance of specific modalities of facial behavior. Finally, we discuss the implications of our findings for detecting recollections and user-modeling in adaptive systems.
Source-lens clustering and intrinsic-alignment bias of weak-lensing estimators

We estimate the amplitude of the source-lens clustering bias and of the intrinsic-alignment bias of weak lensing estimators of the two-point and three-point convergence and cosmic-shear correlation functions. We use a linear galaxy bias model for the galaxy-density correlations, as well as a linear intrinsic-alignment model. For the three-point and four-point density correlations, we use analytical or semi-analytical models, based on a hierarchical ansatz or a combination of one-loop perturbation theory with a halo model. For two-point statistics, we find that the source-lens clustering bias is typically several orders of magnitude below the weak lensing signal, except when we correlate a very low-redshift galaxy ($z_2 \la 0.05$) with a higher redshift galaxy ($z_1 \ga 0.5$), where it can reach $10\%$ of the signal for the shear. For three-point statistics, the source-lens clustering bias is typically of order $10\%$ of the signal, as soon as the three galaxy source redshifts are not identical. The intrinsic-alignment bias is typically about $10\%$ of the signal for both two-point and three-point statistics. Thus, both source-lens clustering bias and intrinsic-alignment bias must be taken into account for three-point estimators aiming at a better than $10\%$ accuracy.
Enclosure Utilization and Enrichment Structure Preferences of Captive Coyotes

Environmental enrichment improves well-being of captive animals using a variety of tools, including adding complexity to the physical environment. Designing enrichment structures requires an understanding of behavioral and biological responses to enrichment efforts. Captive coyotes (Canis latrans) utilize shelter structures to hide, rest, and display vigilant behavior. Because these simple structures are regularly used, new and more complex enrichment structures could enhance enclosure enrichment. This study examined the time captive coyotes spent at discrete, complex enclosure features to determine: (1) how coyotes utilize enclosure space and shelter structures; and (2) if coyotes have a preferred enrichment structure design. Three enrichment structure designs (ramp, closed, and neutral) were installed simultaneously in 0.6-ha enclosures during two breeding seasons (January – March). Additional coyote pairs were monitored in control enclosures with simple structures. GPS-collars and scan sampling were used throughout a 28-day testing period to record space use and behavior. Coyotes spent most of their time at perimeter and open areas, but also exhibited a preference for shelter structures. Coyotes utilized the complex enrichment structures in treatment enclosures more than simple structures in control enclosures. Although there was no statistical preference for one specific type of complex structure, composite evidence from GPS-collars and behavioral data suggested that coyotes were most frequently located at ramp structures. Coyotes utilized ramp structures more during the daytime and demonstrated higher rates of vigilance there. This study advances the knowledge of captive coyote spatial patterns while helping improve environmental enrichment planning for captive facilities through the exploration of adding complexity to animal enclosures.
Investigating Transformer Encoders and Fusion Strategies for Speech Emotion Recognition in Emergency Call Center Conversations.

There has been growing interest in using deep learning techniques to recognize emotions from speech. However, real-life emotion datasets collected in call centers are relatively rare and small, making the use of deep learning techniques quite challenging. This research focuses on the study of Transformer-based models to improve the speech emotion recognition of patients’ speech in French emergency call center dialogues. The experiments were conducted on a corpus called CEMO, which was collected in a French emergency call center. It includes telephone conversations with more than 800 callers and 6 agents. Four emotion classes were selected for these experiments: Anger, Fear, Positive and Neutral state. We compare different Transformer encoders based on the wav2vec2 and BERT models, and explore their fine-tuning as well as fusion of the encoders for emotion recognition from speech. Our objective is to explore how to use these pre-trained models to improve model robustness in the context of a real-life application. We show that the use of specific pre-trained Transformer encoders improves the model performance for emotion recognition in the CEMO corpus. The Unweighted Accuracy (UA) of the french pre-trained wav2vec2 adapted to our task is 73.1%, whereas the UA of our baseline model (Temporal CNN-LSTM without pre-training) is 55.8%. We also tested BERT encoders models: in particular FlauBERT obtained good performance for both manual 67.1% and automatic 67.9% transcripts. The late and model-level fusion of the speech and text models also improve performance (77.1% (late) - 76.9% (model-level)) compared to our best speech pre-trained model, 73.1% UA. In order to place our work in the scientific community, we also report results on the widely used IEMOCAP corpus with our best fusion strategy, 70.8% UA. Our results are promising for constructing more robust speech emotion recognition system for real-world applications.
Ticket to the Mind: A Mobile Eye-Tracking Exploration of Game Media and Cognitive Functions

Physical and digital games are increasingly used and acknowledged for their cognitive effects, but there is a lack of understanding of how a game’s medium directly impacts cognitive functions. This exploratory case study presents a novel method of using mobile eye-tracking to compare working memory and visual attention between physical and digital versions of the board game Ticket to Ride in natural settings. For visual attention, no significant differences between the versions were measured. However, results indicate that fixation duration is significantly greater while playing the digital version versus the physical version, suggesting the counter-intuitive finding that the digital version increases working memory load. The study design, with evidence for high ecological validity, and its findings provide a foundation for future studies for assessing cognitive processing of game media. This work also provides inspiration for making game media considerations for obtaining specific cognitive experiences and increasing accessibility.
An Exploratory Study on the Purchase Intentions of Modern Board Games

Board gaming as a leisure activity is becoming increasingly popular. The research on modern commercial board gaming is also gaining momentum. In this study, we aim to investigate the factors that may play a role in board game purchase intentions. We conducted an online survey and collected data from habitual board gamers. Multiple regression analyses showed that enjoyment, positive word of mouth, age and gender were positively associated with purchase intentions whereas income, play frequency, prior board gaming experience and feelings of presence were not. We discuss the results and present potential future research.
Mirror Segmentation via Semantic-aware Contextual Contrasted Feature Learning

Mirrors are everywhere in our daily lives. Existing computer vision systems do not consider mirrors, and hence may get confused by the reflected content inside a mirror, resulting in a severe performance degradation. However, separating the real content outside a mirror from the reflected content inside it is non-trivial. The key challenge is that mirrors typically reflect contents similar to their surroundings, making it very difficult to differentiate the two. In this article, we present a novel method to segment mirrors from a single RGB image. To the best of our knowledge, this is the first work to address the mirror segmentation problem with a computational approach. We make the following contributions: First, we propose a novel network, called MirrorNet+, for mirror segmentation, by modeling both contextual contrasts and semantic associations. Second, we construct the first large-scale mirror segmentation dataset, which consists of 4,018 pairs of images containing mirrors and their corresponding manually annotated mirror masks, covering a variety of daily-life scenes. Third, we conduct extensive experiments to evaluate the proposed method and show that it outperforms the related state-of-the-art detection and segmentation methods. Fourth, we further validate the effectiveness and generalization capability of the proposed semantic awareness contextual contrasted feature learning by applying MirrorNet+ to other vision tasks, i.e., salient object detection and shadow detection. Finally, we provide some applications of mirror segmentation and analyze possible future research directions. Project homepage: https://mhaiyang.github. io/TOMM2022-MirrorNet+/index.html.
AffectPro: Towards Constructing Affective Profile Combining Smartphone Typing Interaction and Emotion Self-reporting Pattern

The ubiquity of smartphones and the widespread usage of text entry by soft keyboard in different instant messaging applications (e.g., WhatsApp, FB messenger) have opened the possibilities of inferring emotions from longitudinal typing data. To build this emotion inference engine, we apply machine learning models on features extracted from user’s typing patterns (not content). However, one major challenge encountered while developing the emotion inference model is the requirement of individual training data as typing patterns are often person-specific. In this paper, we investigate the possibility of combining typing pattern with emotion self-reporting to identify a group of similar users so that the training data among these users can be shared to fulfill the requirement of personalized dataset. We develop a framework AffectPro, which quantifies the typing interaction behavior (e.g., typing speed, error rate) and self-reporting pattern (e.g., emotion state transition probability) to construct the affective profiles of users. We evaluated AffectPro in a 6-week in-the-wild study involving 28 users, who used an Android application encompassing a custom keyboard to perform all their typing activities, and to report their instantaneous emotions. We extracted different typing signatures and self-report behavior details from the collected dataset (≈ 5000 typing sessions, ≈ 108 hours of typing data) to construct the affective profile of users. Our results demonstrate similarity across users in terms of typing signature, emotion self-reporting pattern, and a combination of both; which can be leveraged to share training data among similar users to overcome the challenges of personalized data collection.
Detecting Change Talk in Motivational Interviewing using Verbal and Facial Information

Behavior change is one of the most important goals in psychotherapy. This study focuses on Motivational Interviewing (MI), which is collaborative communication aimed at eliciting the client’s own reasons for behavior change. To investigate the effectiveness of facial information in modeling MI, we collected an MI encounter corpus with speech and video data in the nutrition and fitness domains and annotated client utterances using the Manual for the Motivational Interviewing Skill Code (MISC). By analyzing client answers to the questions after the session, we found that clients who expressed more Change Talk were more motivated to change their behavior than those who expressed less Change Talk. We then proposed RNN-based multimodal models to detect Change Talk by setting a 2-class classification task: "Change Talk" and "not Change Talk. " Our experiment showed that the best performing model was a multimodal BiLSTM model that fused language and client facial information. We also found that fusing language and facial information as context achieved better performance than the unimodal and no-context models. Moreover, we discuss the label imbalance problem and conduct an additional analysis using turns as a unit of analysis. As a result, our best model reached F1-score of 0.65 for Change Talk detection.
The halos of satellite galaxies: the companion of the massive elliptical lens SL2S J08544−0121

Strong gravitational lensing by groups or clusters of galaxies provides a powerful technique to measure the dark matter properties of individual lens galaxies. We study in detail the mass distribution of the satellite lens galaxy in the group-scale lens SL2S J08544−0121 by modelling simultaneously the spatially extended surface brightness distribution of the source galaxy and the lens mass distribution using Markov chain Monte Carlo methods. In particular, we measure the dark matter halo size of the satellite lens galaxy to be 6.0 +2.9 −2.0 kpc with a fiducial velocity dispersion of 127 +21 −12 km s −1 . This is the first time the size of an individual galaxy halo in a galaxy group has been measured using strong gravitational lensing without assumptions of mass following light. We verify the robustness of our halo size measurement using mock data resembling our lens system. Our measurement of the halo size is compatible with the estimated tidal radius of the satellite galaxy, suggesting that halos of galaxies in groups experience significant tidal stripping, a process that has been previously observed on galaxies in clusters. Our mass model of the satellite galaxy is elliptical with its major axis misaligned with that of the light by ∼50 deg. The major axis of the total matter distribution is oriented more towards the centre of the host halo, exhibiting the radial alignment found in N-body simulations and observational studies of satellite galaxies. This misalignment between mass and light poses a significant challenge to modified Newtonian dynamics.
An observer's guide to the (Local Group) dwarf galaxies: predictions for their own dwarf satellite populations

A recent surge in the discovery of new ultrafaint dwarf satellites of the Milky Way has inspired the idea of searching for faint satellites, $10^3\, \mathrm{M_{\odot}} 99\%$ chance that at least one satellite with stellar mass $M_*> 10^5\, \mathrm{M_{\odot}}$ exists around the combined five Local Group field dwarf galaxies with the largest stellar mass. When considering satellites with $M_*> 10^4\, \mathrm{M_{\odot}}$, we predict a combined $5-25$ satellites for the five largest field dwarfs, and $10-50$ for the whole Local Group field dwarf population. Because of the relatively small number of predicted dwarfs, and their extended spatial distribution, a large fraction each Local Group dwarf's virial volume will need to be surveyed to guarantee discoveries. We compute the predicted number of satellites in a given field of view of specific Local Group galaxies, as a function of minimum satellite luminosity, and explicitly obtain such values for the Solitary Local dwarfs survey. Uncertainties in abundance matching and reionization models are large, implying that comprehensive searches could lead to refinements of both models.
Keck Spectra of Brown Dwarf Candidates and a Precise Determination of the Lithium Depletion Boundary in the α Persei Open Cluster

We have identified 27 candidate very low mass members of the relatively young α Persei open cluster from a 6 deg2 CCD imaging survey. Based on their I magnitudes and the nominal age and distance to the cluster, these objects should have masses less than 0.1 ℳ☉ if they are cluster members. We subsequently obtained intermediate-resolution spectra of 17 of these objects using the Keck II telescope and Low-Resolution Imaging Spectrograph (LRIS). We also obtained near-IR photometry for many of the stars. Our primary goal was to determine the location of the "lithium depletion boundary" and, hence, to derive a precise age for the cluster. Most of our program objects have radial velocities consistent with cluster membership, moderately strong Hα emission, and spectral types of M5.5 to M8, as expected from their (R-I)C colors. We detected lithium with equivalent widths greater than or equal to 0.4 Å in five of the program objects. We have constructed a color-magnitude diagram for the faint end of the α Persei main sequence, including stars for which high signal-to-noise spectra in the region of the lithium λ6708 absorption line have been obtained. These data allow us to accurately determine the α Persei single-star lithium depletion boundary at M(IC) = 11.47, Mbol = 11.42, (R-I)C0 = 2.12, spectral type M6.0. By reference to theoretical evolutionary models, this converts fairly directly into an age for the α Persei cluster of 90 ± 10 Myr. That age is considerably older than most previously quoted ages for the cluster but consistent with ages estimated from the upper main-sequence turnoff using recent models that include a moderate amount of convective core overshoot. At this age, the two faintest of our spectroscopically confirmed members should be substellar (i.e., brown dwarfs) according to theoretical models.