Miracle-dz/UniFAR-FTUs huggingface dataset:
Kantorovich distance on a finite metric space

Kantorovich distance (or 1-Wasserstein distance) on the probability simplex of a finite metric space is the value of a Linear Programming problem for which a closed-form expression is known in some cases. When the ground distance is defined by a graph, a few examples have already been studied. In the present paper, after re-deriving, with different tools, the result for trees, we prove that, for an arbitrary weighted graph, the K-distance is the minimum of the K-distances over all the spanning trees associated with the graph. We work in the dual LP-problem by using Arens-Eells norm associated with the metric space. Finally, we introduce new norms that are naturally related to $\ell_1$-embeddable distances and allows for a partial extension of our results to this new setting.
The Use of Whole-Body MR Imaging in Children with HMO, an Extended Case Study in Two Patients

Background: Patients with hereditary multiple osteochondromas (HMO) undergo frequent radiographs to evaluate the growth of their osteochondromas. The conventional radiographic images clearly show the growth of the bony part of the osteochondromas and the growth direction of the long bones. The radiographs do not show the cartilage cap on top of the osteochondroma nor do they show the surrounding soft tissue or the cartilage of the nearby epiphysis. Alongside these disadvantages taking frequent radiographs carries the potential risk of inducing malignant degeneration through ionizing radiation. In this study we investigated the use of whole-body MR imaging as a screening tool to follow patients with HMO. Findings: Two HMO affected children underwent two whole-body MR imaging scans in one-year time to identify the osteochondromas and to evaluate their growth. The MR images were compared to regular follow-up radiographs of these patients. All radiographically detectable osteochondromas were visible on the whole-body MR images. At least one osteochondroma was clearly seen on the whole-body MR images before detection was possible on the radiographs. The proton density sequence with fat suppression proved to be the best sequence to visualize osteochondromas. Conclusion: Whole-body MR imaging is an effective follow-up tool for patients with hereditary multiple osteochondromas.
A perspective on multi-agent communication for information fusion

Collaborative decision making in multi-agent systems typically requires a predefined communication protocol among agents. Usually, agent-level observations are locally processed and information is exchanged using the predefined protocol, enabling the team to perform more efficiently than each agent operating in isolation. In this work, we consider the situation where agents, with complementary sensing modalities must co-operate to achieve a common goal/task by learning an efficient communication protocol. We frame the problem within an actor-critic scheme, where the agents learn optimal policies in a centralized fashion, while taking action in a distributed manner. We provide an interpretation of the emergent communication between the agents. We observe that the information exchanged is not just an encoding of the raw sensor data but is, rather, a specific set of directive actions that depend on the overall task. Simulation results demonstrate the interpretability of the learnt communication in a variety of tasks.
Learning Preferences and Demands in Visual Recommendation

Visual information is an important factor in recommender systems, in which users' selections consist of two components: \emph{preferences} and \emph{demands}. Some studies has been done for modeling users' preferences in visual recommendation. However, conventional methods models items in a common visual feature space, which may fail in capturing \emph{styles} of items. We propose a DeepStyle method for learning style features of items. DeepStyle eliminates the categorical information of items, which is dominant in the original visual feature space, based on a Convolutional Neural Networks (CNN) architecture. For modeling users' demands on different categories of items, the problem can be formulated as recommendation with contextual and sequential information. To solve this problem, we propose a Context-Aware Gated Recurrent Unit (CA-GRU) method, which can capture sequential and contextual information simultaneously. Furthermore, the aggregation of prediction on preferences and demands, i.e., prediction generated by DeepStyle and CA-GRU, can model users' selection behaviors more completely. Experiments conducted on real-world datasets illustrates the effectiveness of our proposed methods in visual recommendation.
AIM 2019 Challenge on Image Demoireing: Methods and Results

This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. This paper describes the challenge, and focuses on the proposed solutions and their results. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. A new dataset, called LCDMoire was created for this challenge, and consists of 10,200 synthetically generated image pairs (moire and clean ground truth). The challenge was divided into 2 tracks. Track 1 targeted fidelity, measuring the ability of demoire methods to obtain a moire-free image compared with the ground truth, while Track 2 examined the perceptual quality of demoire methods. The tracks had 60 and 39 registered participants, respectively. A total of eight teams competed in the final testing phase. The entries span the current the state-of-the-art in the image demoireing problem.
On the Equivalence of SDP Feasibility and a Convex Hull Relaxation for System of Quadratic Equations

We show {\it semidefinite programming} (SDP) feasibility problem is equivalent to solving a {\it convex hull relaxation} (CHR) for a finite system of quadratic equations. On the one hand, this offers a simple description of SDP. On the other hand, this equivalence makes it possible to describe a version of the {\it Triangle Algorithm} for SDP feasibility based on solving CHR. Specifically, the Triangle Algorithm either computes an approximation to the least-norm feasible solution of SDP, or using its {\it distance duality}, provides a separation when no solution within a prescribed norm exists. The worst-case complexity of each iteration is computing the largest eigenvalue of a symmetric matrix arising in that iteration. Alternate complexity bounds on the total number of iterations can be derived. The Triangle Algorithm thus provides an alternative to the existing interior-point algorithms for SDP feasibility and SDP optimization. In particular, based on a preliminary computational result, we can efficiently solve SDP relaxation of {\it binary quadratic} feasibility via the Triangle Algorithm. This finds application in solving SDP relaxation of MAX-CUT. We also show in the case of testing the feasibility of a system of convex quadratic inequalities, the problem is reducible to a corresponding CHR, where the worst-case complexity of each iteration via the Triangle Algorithm is solving a {\it trust region subproblem}. Gaining from these results, we discuss potential extension of CHR and the Triangle Algorithm to solving general system of polynomial equations.
Does hospital cooperation increase the quality of healthcare

Motivated by reasons such as altruism, managers from different hospitals may engage in cooperative behaviours, which shape the networked healthcare economy. In this paper we study the determinants of hospital cooperation and its association with the quality delivered by hospitals, using Italian administrative data. We explore the impact on patient transfers between hospitals (cooperation/network) of a set of demand-supply factors, as well as distance-based centrality measures. We then use this framework to assess how such cooperation is related to the overall quality for the hospital of origin and of destination of the patient transfer. The over-dispersed Poisson mixed model that we propose, inspired by the literature on social relations models, is suitably defined to handle network data, which are rarely used in health economics. The results show that distance plays an important role in hospital cooperation, though there are other factors that matter such as geographical centrality. Another empirical finding is the existence of a positive relationship between hospital cooperation and the overall quality of the connected hospitals. The absence of a source of information on the quality of hospitals accessible to all providers, such as in the form of star ratings, may prevent some hospitals to engage and cooperate with other hospitals of potentially higher quality. This may result in a lower degree of cooperation among hospitals and a reduction in quality overall.
Interactions between earthquakes and volcano activity

Using the 1973–2005 worldwide catalogues for M ≥ 4.8 seismicity and VEI ≥ 0 volcano eruptions, we find a significant, when tested against catalogue randomizations, increase of eruption onsets on the earthquake day. This result emerges from stacking time series of daily eruption rates relatively to earthquake time, t0, over the whole seismicity catalogue. It is stronger for earthquake‐volcano pairs for which the volcano is within ten rupture size from the epicenter. These results show that M ≥ 4.8 − VEI ≥ 0 earthquake‐volcano pairs are as important for interaction processes as the M ≥ 7 and VEI ≥ 2–5 pairs previously reported to interact. The clustering in time for earthquake‐eruption pairs is not bounded to t0. It remains above the background noise 6–10 days before and after t0, and follows a power law distribution. These patterns, which are similar to the direct and inverse Omori's laws observed for tectonic earthquakes, are the first evidence for the volcano eruptions to be contemporary of a stochastic brittle damage in the earth crust. The clustering we observe in time and space (i) rejects the earthquake triggering as the single process that drive earthquake‐volcano interactions; (ii) supports a regional tectonic coupling in the earth crust damage.
Dynamical Heart Beat Correlations as a Measure of Exercise Intensity

Fluctuations of the human heart beat constitute a complex system that has been studied mostly under resting conditions using conventional time series analysis methods. During physical exercise, the variability of the fluctuations is reduced, and the time series of beat-to-beat RR intervals (RRI) becomes highly non-stationary. Here we develop a dynamical approach to analyze the time evolution of RRI correlations in running across various training and racing events under real-world conditions. In particular, we introduce dynamical detrended fluctuation analysis and dynamical partial autocorrelation functions, which are able to detect real-time changes in the scaling and correlations of the RRI's. We relate these changes to the exercise intensity quantified by the heart rate. Beyond a certain intensity threshold, RRI's show strong short-time anticorrelations on different scales, which we relate to physiological hemodynamics. The results demonstrate the feasibility of dynamical statistical analysis of RRI's to monitor excercise load in real time with wearable devices and without previous knowledge of external parameters such as the maximum heart rate of the individual.
Optimal Experimental Design for Staggered Rollouts

In this paper, we study the design and analysis of experiments conducted on a set of units over multiple time periods in which the starting time of the treatment may vary by unit. The design problem involves selecting an initial treatment time for each unit in order to most precisely estimate both the instantaneous and cumulative effects of the treatment. We first consider nonadaptive experiments, in which all treatment assignment decisions are made prior to the start of the experiment. For this case, we show that the optimization problem is generally NP-hard, and we propose a near-optimal solution. Under this solution, the fraction entering treatment each period is initially low, then high, and finally low again. Next, we study an adaptive experimental design problem, in which both the decision to continue the experiment and treatment assignment decisions are updated after each period’s data are collected. For the adaptive case, we propose a new algorithm, the precision-guided adaptive experiment algorithm, which addresses the challenges at both the design stage and the stage of estimating treatment effects, ensuring valid post-experiment inference, accounting for the adaptive nature of the design. Using realistic settings, we demonstrate that our proposed solutions can reduce the opportunity cost of the experiments by more than 50%, compared with static design benchmarks. This paper was accepted by George Shanthikumar, data science. Funding: S. Athey and G. Imbens were supported by the Office of Naval Research [Grant N00014-19-1-2468]. M. Bayati was supported by the National Science Foundation [Grant CMMI 1554140]. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023. 4928 .
Statistical Analysis of weather variables of Antofagasta

The statistical behavior of weather variables of Antofagasta is described, especially the daily data of air as temperature, pressure and relative humidity measured at 08:00, 14:00 and 20:00. In this article, we use a time series deseasonalization technique, Q-Q plot, skewness, kurtosis and the Pearson correlation coefficient. We found that the distributions of the records are symmetrical and have positive kurtosis, so they have heavy tails. In addition, the variables are highly autocorrelated, extending up to one year in the case of pressure and temperature.
THE CORE–CUSP PROBLEM IN COLD DARK MATTER HALOS AND SUPERNOVA FEEDBACK: EFFECTS OF OSCILLATION

This study investigates the dynamical response of dark matter (DM) halos to recurrent starbursts in forming less-massive galaxies to solve the core–cusp problem. The gas, which is heated by supernova feedback after a starburst, expands and the star formation then terminates. This expanding gas loses energy by radiative cooling and then falls back toward the galactic center. Subsequently, the starburst is enhanced again. This cycle of expansion and contraction of the interstellar gas leads to a repetitive change in the gravitational potential of the gas. The resonance between DM particles and the density wave excited by the oscillating potential plays a key role in understanding the physical mechanism of the cusp–core transition of DM halos. DM halos effectively gain kinetic energy from the baryon potential through the energy transfer driven by the resonance between the particles and density waves. We determine that the critical condition for the cusp–core transition is such that the oscillation period of the gas potential is approximately the same as the local dynamical time of DM halos. We present the resultant core radius of a DM halo after the cusp–core transition induced by the resonance by using the conventional mass density profile predicted by the cold dark matter models. Moreover, we verify the analytical model by using N-body simulations, and the results validate the resonance model.
Key Players and Activities Across the ERP Life Cycle: A Temporal Perspective

Enterprise Resource Planning (ERP) systems are enterprise wide systems that, because of their integration, automate all of a company's business processes. They have rapidly become the de facto industry standard for replacement of legacy systems. In this paper I analyze using an information theory approach the temporal aspects of key players and activities specific to Romanian SME's ERP implementations with focus on both generic and unique aspects
Improved Large-Margin Softmax Loss for Speaker Diarisation

Speaker diarisation systems nowadays use embeddings generated from speech segments in a bottleneck layer, which are needed to be discriminative for unseen speakers. It is well-known that large-margin training can improve the generalisation ability to unseen data, and its use in such open-set problems has been widespread. Therefore, this paper introduces a general approach to the large-margin softmax loss without any approximations to improve the quality of speaker embeddings for diarisation. Furthermore, a novel and simple way to stabilise training, when large-margin softmax is used, is proposed. Finally, to combat the effect of overlapping speech, different training margins are used to reduce the negative effect overlapping speech has on creating discriminative embeddings. Experiments on the AMI meeting corpus show that the use of large-margin softmax significantly improves the speaker error rate (SER). By using all hyper parameters of the loss in a unified way, further improvements were achieved which reached a relative SER reduction of 24.6% over the baseline. However, by training overlapping and single speaker speech samples with different margins, the best result was achieved, giving overall a 29.5% SER reduction relative to the baseline.
Dense 3D Reconstruction for Visual Tunnel Inspection using Unmanned Aerial Vehicle

Advances in Unmanned Aerial Vehicle (UAV) opens venues for application such as tunnel inspection. Owing to its versatility to fly inside the tunnels, it can quickly identify defects and potential problems related to safety. However, long tunnels, especially with repetitive or uniform structures pose a significant problem for UAV navigation. Furthermore, post-processing visual data from the camera mounted on the UAV is required to generate useful information for the inspection task. In this work, we design a UAV with a single rotating camera to accomplish the task. Compared to other platforms, our solution can fit the stringent requirement for tunnel inspection, in terms of battery life, size and weight. While the current state-of-the-art can estimate camera pose and 3D geometry from a sequence of images, they assume large overlap, small rotational motion, and many distinct matching points between images. These assumptions severely limit their effectiveness in tunnel-like scenarios where the camera has erratic or large rotational motion, such as the one mounted on the UAV. This paper presents a novel solution which exploits Structure-from-Motion, Bundle Adjustment, and available geometry priors to robustly estimate camera pose and automatically reconstruct a fully-dense 3D scene using the least possible number of images in various challenging tunnel-like environments. We validate our system with both Virtual Reality application and experimentation with a real dataset. The results demonstrate that the proposed reconstruction along with texture mapping allows for remote navigation and inspection of tunnel-like environments, even those which are inaccessible for humans.
Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning

In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to decision-making in the presence of diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over existing approximate approaches that unnecessarily try to search in the space of models while also failing to facilitate the full gamut of behaviors enabled by our framework.
Efficient Dialogue State Tracking by Selectively Overwriting Memory

Recent works in dialogue state tracking (DST) focus on an open vocabulary-based setting to resolve scalability and generalization issues of the predefined ontology-based approaches. However, they are inefficient in that they predict the dialogue state at every turn from scratch. Here, we consider dialogue state as an explicit fixed-sized memory and propose a selectively overwriting mechanism for more efficient DST. This mechanism consists of two steps: (1) predicting state operation on each of the memory slots, and (2) overwriting the memory with new values, of which only a few are generated according to the predicted state operations. Our method decomposes DST into two sub-tasks and guides the decoder to focus only on one of the tasks, thus reducing the burden of the decoder. This enhances the effectiveness of training and DST performance. Our SOM-DST (Selectively Overwriting Memory for Dialogue State Tracking) model achieves state-of-the-art joint goal accuracy with 51.72% in MultiWOZ 2.0 and 53.01% in MultiWOZ 2.1 in an open vocabulary-based DST setting. In addition, we analyze the accuracy gaps between the current and the ground truth-given situations and suggest that it is a promising direction to improve state operation prediction to boost the DST performance.
Game Effectiveness of Cognitive Behavior Therapy on Improving Social Skills in Children with AD - Hyperactive in Sanandaj

The purpose of this study, the efficacy of cognitive-behavioral play therapy to improve social skills in children with AD - more active Sanandaj. Is a quasi-experimental study pretest - post test control group not equivalent. The study population consisted of all elementary school students with "11- 9" years , referring to a centers of learning disorders in Sanandaj 94-95. Using convenience sampling of 20 children with learning disorders were selected and assigned to two experimental and control groups. For data collection, a checklist was used to assess social skills children Matson. After 10 sessions of cognitive-behavioral play therapy was applied to the experimental group, the results showed that the game - the treatment is to increase and improve social skills.
Provably Convergent Off-Policy Actor-Critic with Function Approximation

We present the first provably convergent off-policy actor-critic algorithm (COF-PAC) with function approximation in a two-timescale form. Key to COF-PAC is the introduction of a new critic, the emphasis critic, which is trained via Gradient Emphasis Learning (GEM), a novel combination of the key ideas of Gradient Temporal Difference Learning and Emphatic Temporal Difference Learning. With the help of the emphasis critic and the canonical value function critic, we show convergence for COF-PAC, where the critics are linear and the actor can be nonlinear.
Searching for Legal Clauses by Analogy. Few-shot Semantic Retrieval Shared Task

We introduce a novel shared task for semantic retrieval from legal texts, where one is expected to perform a so-called contract discovery -- extract specified legal clauses from documents given a few examples of similar clauses from other legal acts. The task differs substantially from conventional NLI and legal information extraction shared tasks. Its specification is followed with evaluation of multiple k-NN based solutions within the unified framework proposed for this branch of methods. It is shown that state-of-the-art pre-trained encoders fail to provide satisfactory results on the task proposed, whereas Language Model based solutions perform well, especially when unsupervised fine-tuning is applied. In addition to the ablation studies, the questions regarding relevant text fragments detection accuracy depending on number of examples available were addressed. In addition to dataset and reference results, legal-specialized LMs were made publicly available.