Miracle-dz/UniFAR-FTUs huggingface dataset:
Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System

Scoring systems are commonly seen for platforms in the era of big data. From credit scoring systems in financial services to membership scores in E-commerce shopping platforms, platform managers use such systems to guide users towards the encouraged activity pattern, and manage resources more effectively and more efficiently thereby. To establish such scoring systems, several"empirical criteria"are firstly determined, followed by dedicated top-down design for each factor of the score, which usually requires enormous effort to adjust and tune the scoring function in the new application scenario. What's worse, many fresh projects usually have no ground-truth or any experience to evaluate a reasonable scoring system, making the designing even harder. To reduce the effort of manual adjustment of the scoring function in every new scoring system, we innovatively study the scoring system from the preset empirical criteria without any ground truth, and propose a novel framework to improve the system from scratch. In this paper, we propose a"counter-empirical attacking"mechanism that can generate"attacking"behavior traces and try to break the empirical rules of the scoring system. Then an adversarial"enhancer"is applied to evaluate the scoring system and find the improvement strategy. By training the adversarial learning problem, a proper scoring function can be learned to be robust to the attacking activity traces that are trying to violate the empirical criteria. Extensive experiments have been conducted on two scoring systems including a shared computing resource platform and a financial credit system. The experimental results have validated the effectiveness of our proposed framework.
Gauge-invariant cosmological perturbations in general teleparallel gravity

We study linear cosmological perturbations in the most general teleparallel gravity setting, where gravity is mediated by the torsion and nonmetricity of a flat connection alongside the metric. For a general linear perturbation of this geometry around a homogeneous and isotropic background geometry, we derive the irreducible decomposition of the perturbation variables, as well as their behavior under gauge transformations, i.e., infinitesimal diffeomorphisms generated by a vector field. In addition, we also study these properties for the most general set of matter variables and gravitational field equations. We then make use of these result to construct gauge-invariant perturbation variables, using a general approach based on gauge conditions. We further calculate these quantities also in the metric and symmetric teleparallel geometries, where nonmetricity or torsion is imposed to vanish. To illustrate our results, we derive the energy-momentum-hypermomentum conservation equations for both the cosmological background and the linear perturbations. As another example, we study the propagation of tensor perturbations in the $f(G)$, $f(T)$ and $f(Q)$ class of theories.
Real time holography for higher spin theories

Real time holography is studied in the context of the embedding space formalism. In the first part of this paper, we present matching conditions for on-shell integer spin fields when going from Euclidean to Lorentzian signature on AdS background. Using the BTZ black hole as an example, we discuss various ways of lifting the physical solution from the AdS surface to the whole embedding space. The BTZ propagator for higher spin field is expressed elegantly in terms of the embedding coordinates. In the second part of the paper, we develop the proposed duality between higher spin theory and vector models. We obtain a specific map between the field configurations of these two theories in real time, so called Lorentzian AdS/CFT map. We conclude by exploring the matching conditions for higher spin fields satisfying the proposed bulk quadratic action. The physical and ghost modes can be treated independently during the Wick rotation; only physical modes are considered to be external modes.
Astrometric and photometric calibrators for the 4-m International Liquid Mirror Telescope

The International Liquid Mirror Telescope (ILMT) is a 4-meter class survey telescope. It achieved its first light on 29$^{\rm th}$ April 2022 and is now undergoing the commissioning phase. It scans the sky in a fixed \ang{;22;} wide strip centred at the declination of $+$\ang{29;21;41.4} and works in \emph{Time Delay Integration (TDI)} mode. We present a full catalog of sources in the ILMT strip derived by crossmatching \textit{Gaia} DR3 with SDSS DR17 and PanSTARRS-1 (PS1) to supplement the catalog with apparent magnitudes of these sources in $g, r$, and $i$ filters. These sources can serve as astrometric calibrators. The release of Gaia DR3 provides synthetic photometry in popular broadband photometric systems, including the SDSS $g, r$, and $i$ bands for $\sim$220 million sources across the sky. We have used this synthetic photometry to verify our crossmatching performance and, in turn, create a subset of the catalog with accurate photometric measurements from two reliable sources.
Geometry and analytic properties of the sliced Wasserstein space

The sliced Wasserstein metric compares probability measures on $\mathbb{R}^d$ by taking averages of the Wasserstein distances between projections of the measures to lines. The distance has found a range of applications in statistics and machine learning, as it is easier to approximate and compute than the Wasserstein distance in high dimensions. While the geometry of the Wasserstein metric is quite well understood, and has led to important advances, very little is known about the geometry and metric properties of the sliced Wasserstein (SW) metric. Here we show that when the measures considered are ``nice'' (e.g. within a positive multiples of the Lebesgue measure) then the SW metric is comparable to the (homogeneous) negative Sobolev norm $\dot H^{-(d+1)/2}$. On the other hand when the measures considered are close in the infinity transportation metric to a discrete measure, then the SW metric between them is close to a multiple of the Wasserstein metric. We characterize the tangent space of the SW space, show that the speed of curves in the space can be described by a quadratic form, but that the SW space is not a length space. We establish a number of properties of the metric given by the minimal length of curves between measures -- the SW length. Finally we highlight the consequences of these properties to the gradient flows in SW metric.
Artificial Neural Networks (ANNs) application to predict occurrence of phenological stages in wheat using climatic data.

1Agricultural Meteorology Department of Agronomy and Horticulture University of Nebraska-Lincoln, USA. 2Department of Irrigation and Reclamation Engineering College of Agriculture, University of Tehran, Karaj, Iran 3Department of ElectricalEngineering K.N. Toosi Technology University, Tehran, Iran. 4Department of Irrigation and Reclamation Engineering College of Agriculture, University of Tehran, Karaj, Iran
Usability and Adoption of Graphical Data-Driven Development Tools

Software development of modern, data-driven applications still relies on tools that use interaction paradigms that have remained mostly unchanged for decades. While rich forms of interactions exist as an alternative to textual command input, they find little adoption in professional software creation. In this work, we compare graphical programming using direct manipulation to the traditional, textual way of creating data-driven applications to determine the benefits and drawbacks of each. In a between-subjects user study (N=18), we compared developing a machine learning architecture with a graphical editor to traditional code-based development. While qualitative and quantitative measures show general benefits of graphical direct manipulation, the user's subjective perception does not always match this. Participants were aware of the possible benefits of such tools but were still biased in their perception. Our findings highlight that alternative software creation tools cannot just rely on good usability but must emphasize the demands of their specific target group, e.g. user control and flexibility, if they want long-term benefits and adoption.
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels

In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks. Typically, when annotating such tasks annotators are only asked to provide a single label for each sample and annotator disagreement is discarded when a final hard label is decided through majority voting. We challenge this traditional approach, acknowledging that determining the appropriate label can be difficult due to the ambiguity and lack of context in the data samples. Rather than discarding the information from such ambiguous annotations, our soft label method makes use of them for training. Our findings indicate that additional annotator information, such as confidence, secondary label and disagreement, can be used to effectively generate soft labels. Training classifiers with these soft labels then leads to improved performance and calibration on the hard label test set.
General Policies, Subgoal Structure, and Planning Width

It has been observed that many classical planning domains with atomic goals can be solved by means of a simple polynomial exploration procedure, called IW, that runs in time exponential in the problem width, which in these cases is bounded and small. Yet, while the notion of width has become part of state-of-the-art planning algorithms such as BFWS, there is no good explanation for why so many benchmark domains have bounded width when atomic goals are considered. In this work, we address this question by relating bounded width with the existence of general optimal policies that in each planning instance are represented by tuples of atoms of bounded size. We also define the notions of (explicit) serializations and serialized width that have a broader scope as many domains have a bounded serialized width but no bounded width. Such problems are solved non-optimally in polynomial time by a suitable variant of the Serialized IW algorithm. Finally, the language of general policies and the semantics of serializations are combined to yield a simple, meaningful, and expressive language for specifying serializations in compact form in the form of sketches, which can be used for encoding domain control knowledge by hand or for learning it from small examples. Sketches express general problem decompositions in terms of subgoals, and sketches of bounded width express problem decompositions that can be solved in polynomial time.
Fair Coresets via Optimal Transport

Data distillation and coresets have emerged as popular approaches to generate a smaller representative set of samples for downstream learning tasks to handle large-scale datasets. At the same time, machine learning is being increasingly applied to decision-making processes at a societal level, making it imperative for modelers to address inherent biases towards subgroups present in the data. Current approaches create fair synthetic representative samples by optimizing local properties relative to the original samples, but their effect on downstream learning processes has yet to be explored. In this work, we present fair Wasserstein coresets (FWC), a novel coreset approach which generates fair synthetic representative samples along with sample-level weights to be used in downstream learning tasks. FWC minimizes the Wasserstein distance between the original dataset and the weighted synthetic samples while enforcing demographic parity. We show that an unconstrained version of FWC is equivalent to Lloyd's algorithm for k-medians and k-means clustering. Experiments conducted on both synthetic and real datasets show that FWC: (i) achieves a competitive fairness-performance tradeoff in downstream models compared to existing approaches, (ii) improves downstream fairness when added to the existing training data and (iii) can be used to reduce biases in predictions from large language models (GPT-3.5 and GPT-4).
Finite Element Analysis of the Oseen eigenvalue problem

We propose and analyze a finite element method for the Oseen eigenvalue problem. This problem is an extension of the Stokes eigenvalue problem, where the presence of the convective term leads to a non-symmetric problem and hence, to complex eigenvalues and eigenfunctions. With the aid of the compact operators theory, we prove that for inf-sup stable finite elements the convergence holds and hence, error estimates for the eigenvalues and eigenfunctions are derived. We also propose an a posteriori error estimator which results to be reliable and efficient. We report a series of numerical tests in two and three dimension in order to assess the performance of the method and the proposed estimator.
Are cascade dialogue state tracking models speaking out of turn in spoken dialogues?

In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs is key to a smooth interaction. Traditionally TOD systems are composed of several modules that interact with one another. While each of these components is the focus of active research communities, their behavior in interaction can be overlooked. This paper proposes a comprehensive analysis of the errors of state of the art systems in complex settings such as Dialogue State Tracking which highly depends on the dialogue context. Based on spoken MultiWoz, we identify that errors on non-categorical slots' values are essential to address in order to bridge the gap between spoken and chat-based dialogue systems. We explore potential solutions to improve transcriptions and help dialogue state tracking generative models correct such errors.
Matrix Completion via Memoryless Scalar Quantization

We delve into the impact of memoryless scalar quantization on matrix completion. We broaden our theoretical discussion to encompass the coarse quantization scenario with a dithering scheme, where the only available information for low-rank matrix recovery is few-bit low-resolution data. Our primary motivation for this research is to evaluate the recovery performance of nuclear norm minimization in handling quantized matrix problems without the use of any regularization terms such as those stemming from maximum likelihood estimation. We furnish theoretical guarantees for both scenarios: when access to dithers is available during the reconstruction process, and when we have access solely to the statistical properties of the dithers. Additionally, we conduct a comprehensive analysis of the effects of sign flips and prequantization noise on the recovery performance, particularly when the impact of sign flips is quantified using the well-known Hamming distance in the upper bound of recovery error.
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine

We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+ continuously monitored Emergency Department visits from 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at timescales from minutes to days, including predicting patient decompensation, disposition, and emergency department (ED) revisit, and includes a standardized evaluation framework with train-test splits and evaluation metrics. The multimodal dataset includes a wide range of detailed clinical data, including triage information, prior diagnoses and medications, continuously measured vital signs, electrocardiogram and photoplethysmograph waveforms, orders placed and medications administered throughout the visit, free-text reports of imaging studies, and information on ED diagnosis, disposition, and subsequent revisits. We provide performance baselines for each prediction task to enable the evaluation of multimodal, multitask models. We believe that MC-BEC will encourage researchers to develop more effective, generalizable, and accessible foundation models for multimodal clinical data.
Tests of two Water Repellent Substances on Brick: do they delay the Regrowth of Biofilms? The Case of the Cathedral of La Plata, Buenos Aires Province, Argentina

This is a study about the Cathedral of La Plata (Buenos Aires Province, Argentina), a neogothic temple made out of brick masonry and mortar. The construction was started in 1884, and was opened as a chapel in 1902, but only started functioning as a Cathedral in 1932. Then, the towers and many ornamental details were completed in 1999. As these works were in progress, the building was cleaned and a hydrophobe substance was used. Currently, the Cathedral is affected by the growth of lichens, green algae, cyanobacteria, mosses, and higher plants, especially in the humid places looking to the South and South East, so new cleaning and protection works are being planned. There are two big aims: first, to find an adequate cleaning process that is effective against biofilms without harming the material; and second, a water repellent treatment that delays new growth of microorganisms as much as possible. Although hydrophobe substances have no biocide effect, they block water absorption, so there is almost no water available for microorganisms. So, in theory, water repellents would have a double protective action: prevent the damage caused by water to materials create a barrier for biofilms, since microorganisms find it harder to grow on a surface with very low moisture.
ESPORT: Electronic Sports Professionals Observations and Reflections on Training

Esports and high performance human-computer interaction are on the forefront of applying new hardware and software technologies in practice. Despite that, there is a paucity of research on how semi-professional and professional championship level players approach aspects of their preparation. To address that, we have performed, transcribed, and analyzed interviews with top-tournament players, coaches, and managers across multiple game titles. The interviews range from competitive events occuring between 2015-2020. Initial processing included transcription and manual verification. The pre-processed interview data were then organized and structured into relevant categories, touching on psychological, physical, and nutritional aspects of esports preparation. Further, where applicable, interview responses where rated and quantified via consensus judgement by a panel of experts. The results indicate that physical training was most often mentioned as a relevant or consistent activity, while nutrition was indicated as relatively unimportant. Qualitative analysis also indicated that consistency and resiliency were noted as the most key factors recommended for upcoming esports competitors. It is also clear that many players put emphasis on balancing their gameplay time and with activities. Lastly, we identified important areas of inquiry towards a deeper understanding of the mental and physical demands of professional esports players.
SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers

Vision-based human-to-robot handover is an important and challenging task in human-robot interaction. Recent work has attempted to train robot policies by interacting with dynamic virtual humans in simulated environments, where the policies can later be transferred to the real world. However, a major bottleneck is the reliance on human motion capture data, which is expensive to acquire and difficult to scale to arbitrary objects and human grasping motions. In this paper, we introduce a framework that can generate plausible human grasping motions suitable for training the robot. To achieve this, we propose a hand-object synthesis method that is designed to generate handover-friendly motions similar to humans. This allows us to generate synthetic training and testing data with 100x more objects than previous work. In our experiments, we show that our method trained purely with synthetic data is competitive with state-of-the-art methods that rely on real human motion data both in simulation and on a real system. In addition, we can perform evaluations on a larger scale compared to prior work. With our newly introduced test set, we show that our model can better scale to a large variety of unseen objects and human motions compared to the baselines. Project page: https://eth-ait.github. io/synthetic-handovers/
Active Transfer Learning for Efficient Video-Specific Human Pose Estimation

Human Pose (HP) estimation is actively researched because of its wide range of applications. However, even estimators pre-trained on large datasets may not perform satisfactorily due to a domain gap between the training and test data. To address this issue, we present our approach combining Active Learning (AL) and Transfer Learning (TL) to adapt HP estimators to individual video domains efficiently. For efficient learning, our approach quantifies (i) the estimation uncertainty based on the temporal changes in the estimated heatmaps and (ii) the unnaturalness in the estimated full-body HPs. These quantified criteria are then effectively combined with the state-of-the-art representativeness criterion to select uncertain and diverse samples for efficient HP estimator learning. Furthermore, we reconsider the existing Active Transfer Learning (ATL) method to introduce novel ideas related to the retraining methods and Stopping Criteria (SC). Experimental results demonstrate that our method enhances learning efficiency and outperforms comparative methods. Our code is publicly available at: https://github.com/ImIntheMiddle/VATL4Pose-WACV2024
Tracking and Following a Suspended Moving Object using Camera-Based Vision System

When robots are able to see and respond to their surroundings, a whole new world of possibilities opens up. To bring these possibilities to life, the robotics industry is increasingly adopting camera-based vision systems, especially when a robotic system needs to interact with a dynamic environment or moving target. However, this kind of vision system is known to have low data transmission rates, packet loss during communication and noisy measurements as major disadvantages. These problems can perturb the control performance and the quality of the robot-environment interaction. To improve the quality of visual information, in this paper, we propose to model the dynamics of the motion of a target object and use this model to implement an Extended Kalman Filter based on Intermittent Observations of the vision system. The effectiveness of the proposed approach was tested through experiments with a robotic arm, a camera device in an eye-to-hand configuration, and an oscillating suspended block as a target to follow.
Classification of dipolar symmetry-protected topological phases: Matrix product states, stabilizer Hamiltonians, and finite tensor gauge theories

We classify one-dimensional symmetry-protected topological (SPT) phases protected by dipole symmetries. A dipole symmetry comprises two sets of symmetry generators: charge and dipole operators, which together form a non-trivial algebra with translations. Using matrix product states (MPS), we show that for a $G$ dipole symmetry with $G$ a finite abelian group, the one-dimensional dipolar SPTs are classified by the group $H^2[G\times G,U(1)]/H^2[G,U(1)]^2$. Because of the symmetry algebra, the MPS tensors exhibit an unusual property, prohibiting the fractionalization of charge operators at the edges. For each phase in the classification, we explicitly construct a stabilizer Hamiltonian to realize the SPT phase and derive the response field theories by coupling the dipole symmetry to background tensor gauge fields. These field theories generalize the Dijkgraaf-Witten theories to twisted finite tensor gauge theories.