Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB

In the kernelized bandit problem, a learner aims to sequentially compute the optimum of a function lying in a reproducing kernel Hilbert space given only noisy evaluations at sequentially chosen points. In particular, the learner aims to minimize regret, which is a measure of the suboptimality of the choices made. Ar-guably the most popular algorithm is the Gaussian Process Upper Conﬁdence Bound (GP-UCB) algorithm, which involves acting based on a simple linear estimator of the unknown function. Despite its popularity, existing analyses of GP-UCB give a suboptimal regret rate, which fails to be sublinear for many commonly used kernels such as the Mat´ern kernel. This has led to a longstanding open question: are existing regret analyses for GP-UCB tight, or can bounds be improved by using more sophisticated analytical techniques? In this work, we resolve this open question and show that GP-UCB enjoys nearly optimal regret. In particular, our results directly imply sublinear regret rates for the Mat´ern kernel, improving over the state-of-the-art analyses and partially resolving a COLT open problem posed by Vakili et al. Our improvements rely on two key technical results. First, we use modern supermartingale techniques to construct a novel, self-normalized concentration inequality that greatly simpliﬁes existing approaches. Second, we address the importance of regularizing in proportion to the smoothness of the underlying kernel k . Together, these new technical tools enable a simpliﬁed, tighter analysis of the GP-UCB algorithm.
Lidar observations of Arctic polar stratospheric clouds, 1988: Signature of small, solid particles above the frost point

Polar stratospheric clouds (PSC's) in the 21-24 km altitude range were observed north of Greenland (82-84°N, 0-25°W) on January 29, 1988, using an airborne dual-polarization, 0.532-µm wavelength lidar. Synoptic analyses at the 30-mb level (≈22 km) show local temperatures of 191-193K, which are well above the estimated frost point temperature of 185K and indicate the PSC's were likely of the binary HNO3-H2O (Type I) class. Peak scattering ratios of 1.1-1.2 were measured in the PSC layer, compared to values near 1.05 for the ambient (non-PSC) aerosol over the same altitude range. Profiles of the depolarization ratio distinctly show the PSC layer, but maximum values are 0.06-0.07, only slightly greater than the value (0.04) measured in cloud-free regions and well below the values typically found in cirrus clouds (0.2-0.5). This signature suggests that the Type I PSC's observed during the January flight were composed of small, solid particles having radii on the order of the laser wavelength (≈0.5 µm).
Kalmanbot: Kalmannet-Aided Bollinger Bands for Pairs Trading

Pairs trading is a family of trading policies based on monitoring the relationships between pairs of assets. A common pairs trading approach relies on state space (SS) modeling, from which financial indicators can be obtained with low complexity and latency using a Kalman filter (KF), and processed using classic policies such as Bollinger bands (BB). However, such SS models are inherently approximated and mismatched, often degrading the revenue. In this work we propose KalmanNet Bollinger Trading (KalmanBOT), a dataaided policy that preserves the advantages of KF-aided BB policies while leveraging data to overcome the approximated nature of the SS model. We adopt the recent KalmanNet architecture, and approximate BB with a differentiable mapping, converting the policy into a trainable model. We empirically demonstrate that KalmanBOT yields improved rewards compared with model-based and data-driven benchmarks.
Analytical Eye-diagram Model for On-chip Distortionless Transmission Lines and Its Application to Design Space Exploration

This paper proposes a closed-form eye-diagram model for on-chip distortionless transmission lines with intentionally inserted shunt conductance. We derive expressions of eye-opening both in voltage and time, by assuming a piece-wise linear waveform model. The model is experimentally verified. We also apply the proposed model to design trade-off analysis.
JUNLP at SemEval-2020 Task 9: Sentiment Analysis of Hindi-English Code Mixed Data Using Grid Search Cross Validation

Code-mixing is a phenomenon which arises mainly in multilingual societies. Multilingual people, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic typing and the insertion of anglicisms in their main language. This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few. In this work, we focus on working out a plausible solution to the domain of Code-Mixed Sentiment Analysis. This work was done as participation in the SemEval-2020 Sentimix Task, where we focused on the sentiment analysis of English-Hindi code-mixed sentences. our username for the submission was “sainik.mahata” and team name was “JUNLP”. We used feature extraction algorithms in conjunction with traditional machine learning algorithms such as SVR and Grid Search in an attempt to solve the task. Our approach garnered an f1-score of 66.2% when tested using metrics prepared by the organizers of the task.
A composite L1 parameter estimator for model fitting in groundwater flow and solute transport simulation

This paper proposes a composite L1 parameter estimator to solve the inverse problems in groundwater flow and solute transport modeling. The estimator is formulated using a weighted L1 norm as the error measure between the vector of the observed hydraulic heads and solute concentrations and the corresponding vector of the heads and concentrations computed from a finite element model for steady state groundwater flow and solute transport in a two-dimensional areal confined aquifer system. The parameters currently considered are the hydraulic conductivities, the dispersivities, the porosities of the aquifer system, and the solute source concentration(s). The gradients of the state variables with respect to the parameters are computed analytically using a formulation stemming from the finite element model for the state variables. The solutions to two hypothetical groundwater parameter estimation problems are presented to illustrate that the proposed estimator does have the property of being fairly accurate and, more significantly, being robust in handling observation data containing certain outliers.
Modes of Governance in Inter-Organizational Data Collaborations

Big data and data-driven innovation are drivers for economic growth. To capture this growth, data often need to be shared among organisations. However, many challenges to sharing data among organisations exist. This paper investigates how governance is organised in inter-organisational data collaborations. First, based on literature, four archetypical modes of governance are identified: Market, Hierarchy, Bazaar and Network. Subsequently, these theoretical modes are investigated empirically by exploring governance modes in four use cases. Based on a cross-case comparison, we find that major challenges to data sharing are the commercially sensitive nature of data and privacy risks. Due to legal implications, sharing of personal data always takes place hierarchically. Therefore, coordination and control over data need to be firmly in place before organisations engage in data sharing. Further research should look into how these aspects can be organised in inter-organisational data collaborations to foster innovation.
Workshop YOUR Study Design 2023!: Participatory Critique and Refinement of Participants' Studies

A well-designed and evaluated study plays an essential role in highlighting the impact and contribution of a research idea. However, novice Human-Robot Interaction (HRI) researchers often lack the experience and know-how to devise an effective study. This workshop aims to provide a platform for those doing research in HRI, and related fields to obtain expert feedback on their study design before running a user study. The workshop invites a 2-4 page long contribution from participants outlining an upcoming user study focusing on the methods section and planned analyses. Participants will take part in two separate mentoring sessions led by different mentors. The workshop is interactive in nature and will also include mentor-led discussion sessions on topics relevant to study design such as hypothesis design and analysis, and human-centered study design.
Randomized Root Finding over Finite FFT-fields using Tangent Graeffe Transforms

Consider a finite field Fq whose multiplicative group has smooth cardinality. We study the problem of computing all roots of a polynomial that splits over Fq, which was one of the bottlenecks for fast sparse interpolation in practice. We revisit and slightly improve existing algorithms and then present new randomized ones based on the Graeffe transform. We report on our implementation in the MATHEMAGIX computer algebra system, confirming that our ideas gain by a factor ten at least in practice, for sufficiently large inputs.
LISN: una red para la caracterización del estado actual y pronóstico de la ionosfera en América del Sur

Este articulo describe las caracteristicas y presenta recientes mediciones realizadas por el primer observatorio distribuido, que esta siendo instalado en America del Sur para estudiar la ionosfera y la alta atmosfera ecuatorial. El observatorio distribuido llamado LISN (Low-latitude Ionospheric Sensor Network) estara constituido por 70 receptores GPS con capacidad para medir parametros de la ionosfera como el Contenido Electronico Total (TEC), centelleos de amplitud y fase y perturbaciones ionosfericas mas conocidas como TIDs (Traveling Ionospheric Disturbances). La red tambien incluira 5 ionosondas y 5 magnetometros que seran colocados a lo largo del mismo meridiano magnetico. Esta red de receptores GPS y de sensores ionosfericos se extendera de norte a sur del continente Suramericano y al oeste del meridiano de longitud -55o. Los instrumentos componentes de la red se complementaran entre si para llevar a cabo, de manera permanente, continua, y espacialmente distribuida, observaciones de la densidad ionosferica, estudiar sus movimientos, y las estructuras e inestabilidades que se desarrollan en esta region dinamicamente activa. La red LISN se complementara con un modelo fisico asimilativo que permitira efectuar una prediccion actual de las mediciones ionosfericas tales como la densidad electronica, conductividad, y desplazamientos ExB del plasma incluyendo los vientos de las particulas neutras. Este nuevo observatorio distribuido permitira entender la variabilidad de dia-a-dia asi como la estabilidad de la ionosfera en bajas latitudes y hacer predicciones regionales de su estado. Este articulo describe la instrumentacion, presenta las primeras mediciones y discute los beneficios cientificos que ofrece la red LISN.
The Impact Of Virtual Communities On E-loyalty. A Conceptual Framework

Regarding the success of virtual communities, several factors and prerequisites are essential. At the same time, companies are looking for factors influencing e-loyalty. In this paper, a framework (VC2E-Loy) is presented, which compares the attitudes arising in virtual communities with prerequisites of e-loyalty. First, the fundamentals of both – e-loyalty and virtual communities – are discussed. Then, attitudes and factors derived from literature are presented. The framework is created based on the factors extracted from literature. Finally, the framework is described and information on validation, open questions and further research is given.
Texture analysis using probabilistic models of the unimodal and multimodal statistics of adaptive wavelet packet coefficients

Although subband histograms of the wavelet coefficients of natural images possess a characteristic leptokurtotic form, this is no longer true for wavelet packet bases adapted to a given texture. Instead, three types of subband statistics are observed: Gaussian, leptokurtotic, and interestingly, in some subbands, multimodal histograms. These subbands are closely linked to the structure of the texture, and guarantee that the most probable image is not flat. Motivated by these observations, we propose a probabilistic model that takes them into account. Adaptive wavelet packet subbands are modelled as Gaussian, generalized Gaussian, or a constrained Gaussian mixture. We use a Bayesian methodology, finding MAP estimates for the adaptive basis, for subband model selection, and for subband model parameters. Results confirm the effectiveness of the proposed approach, and highlight the importance of multimodal subbands for texture discrimination and modelling.
Toward the Use of Re-configurable Intelligent Surfaces in VLC Systems: Beam Steering

The emergence of visible light communication (VLC) technology as a solution to solve radio frequency impediments, such as spectrum shortage, is continuously appealing. In addition to its large and unlicensed bandwidth, VLC provides a high level of security in a closed room with zero radio frequency interference. However, loss of the VLC signal is experienced when the receiver rotates or moves. This challenge requires a special solution for integration into portable devices. On the other hand, re-configurable intelligent surface (RIS) is a technology exploited in radio frequency to solve dead zones and loss of signal. RIS elements are characterized by tunable physico-chemical characteristics including physical depth and refractive index. In this article, we exploit these RIS attributes to steer the incident light beam, offer the VLC receiver a large range of rotation angle, and improve its field-of-view. We show that instead of using convex, parabolic, or spherical lenses, adopting a meta-lens with artificial muscles or a thin-film Liquid-Crystal with embedded Titanium dioxide nano-disk, a VLC receiver can detect light rays at a high incidence angle with high precision and considerable improvement in the detected light intensity, even with a miniaturized single photodetector.
Predicted recovery of Mercury's internal structure by MESSENGER

Recent Earth‐based and forthcoming MESSENGER spacecraft observations of the spin state and gravity field of Mercury will provide the opportunity to determine that planet's internal structure with unprecedented precision. These observations will provide estimates of the dimensionless polar moment of inertia and the ratio of the moment of inertia of the mantle plus crust to that of the planet. In order to understand how well these two parameters will constrain Mercury's internal structure we calculate ∼800,000 Monte Carlo models of the planet's interior. Within the uncertainties currently predicted to be achieved by MESSENGER for these quantities we estimate that the radius and bulk density of an Fe‐FeS core and the bulk density of the silicate shell can be determined to within ±23 km, ±147 kg m−3, and ±72 kg m−3, respectively.
Analysis of evolution of exon-intron structure of eukaryotic genes

The availability of multiple, complete eukaryotic genome sequences allows one to address many fundamental evolutionary questions on genome scale. One such important, long-standing problem is evolution of exon-intron structure of eukaryotic genes. Analysis of orthologous genes from completely sequenced genomes revealed numerous shared intron positions in orthologous genes from animals and plants and even between animals, plants and protists. The data on shared and lineage-specific intron positions were used as the starting point for evolutionary reconstruction with parsimony and maximum-likelihood approaches. Parsimony methods produce reconstructions with intron-rich ancestors but also infer lineage-specific, in many cases, high levels of intron loss and gain. Different probabilistic models gave opposite results, apparently depending on model parameters and assumptions, from domination of intron loss, with extremely intron-rich ancestors, to dramatic excess of gains, to the point of denying any true conservation of intron positions among deep eukaryotic lineages. Development of models with adequate, realistic parameters and assumptions seems to be crucial for obtaining more definitive estimates of intron gain and loss in different eukaryotic lineages. Many shared intron positions were detected in ancestral eukaryotic paralogues which evolved by duplication prior to the divergence of extant eukaryotic lineages. These findings indicate that numerous introns were present in eukaryotic genes already at the earliest stages of evolution of eukaryotes and are compatible with the hypothesis that the original, catastrophic intron invasion accompanied the emergence of the eukaryotic cells. Comparison of various features of old and younger introns starts shedding light on probable mechanisms of intron insertion, indicating that propagation of old introns is unlikely to be a major mechanism for origin of new ones. The existence and structure of ancestral protosplice sites were addressed by examining the context of introns inserted within codons that encode amino acids conserved in all eukaryotes and, accordingly, are not subject to selection for splicing efficiency. It was shown that introns indeed predominantly insert into or are fixed in specific protosplice sites which have the consensus sequence (A/C)AG|Gt.
General Part Assembly Planning

: Most successes in autonomous robotic assembly have been restricted to single target or category. We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes. To tackle the planning of general part assembly, we present General Part Assembly Transformer (GPAT), a transformer-based model architecture that accurately predicts part poses by inferring how each part shape corresponds to the target shape. Our experiments on both 3D CAD models and real-world scans demonstrate GPAT’s generalization abilities to novel and diverse target and part shapes.
TrueÆdapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space

We present TrueÆdapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained to predict joint accelerations at regular intervals. The adapted trajectory is generated by linear interpolation of the predicted accelerations, leading to continuously differentiable joint velocities and positions. Bounded jerks, accelerations and velocities are guaranteed by calculating the range of valid accelerations at each decision step and clipping the network’s output accordingly. A deviation penalty during the training process causes the adapted trajectory to follow the original one. Smooth movements are encouraged by penalizing high accelerations and jerks. We evaluate our approach by training a simulated KUKA iiwa robot to balance a ball on a plate while moving and demonstrate that the balancing policy can be directly transferred to a real robot.
Implications of future climate for marine fish

Climate change has already been linked to significant impacts on Earth's ocean ecosystems including shifts in species geographic ranges, changes in population abundance, shits in timing of seasonal events, and establishment of introduced species (Walter et al. 2002, Parmesan and Yohe 2003). Global climate modelling for the Australian region has identified south-eastern Australia as the area that will be subject to the greatest impacts from climatic change (Lough 2009). The major changes predicted include warming of air and water, changes to wind patterns, changes to the strength and southerly extent of dominant oceanic currents flowing down the east and west coasts of Australia, changes to rainfall and run-off (distribution, timing and intensity), increasing ocean acidification, increasing exposure to UV light and sea level rise (Lough and Hobday 2011). Victorian species may be at greater risk than species in other areas of Australia, because Victorian marine waters are in a zone of predicted high climate change (Johnson et al. 2011, Wernberg et al. 2011).
Graph Learning with 1D Convolutions on Random Walks

We propose CR A W L ( C NNs for Ra ndom W a l ks), a novel neural network architecture for graph learning. It is based on processing sequences of small subgraphs induced by random walks with standard 1D CNNs. Thus, CR A W L is fundamentally different from typical message passing graph neural network architectures. It is inspired by techniques counting small subgraphs, such as the graphlet kernel and motif counting, and combines them with random walk based techniques in a highly efﬁcient and scalable neural architecture. We demonstrate empirically that CR A W L matches or outperforms state-of-the-art GNN architectures across a multitude of benchmark datasets for classiﬁcation and regression on graphs.
Reconstructing vehicles from a single image: Shape priors for road scene understanding

We present an approach for reconstructing vehicles from a single (RGB) image, in the context of autonomous driving. Though the problem appears to be ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles project to an image can be used to reason about the reverse process, i.e., how shapes (back-)project from 2D to 3D. We encode this knowledge in shape priors, which are learnt over a small keypoint-annotated dataset. We then formulate a shape-aware adjustment problem that uses the learnt shape priors to recover the 3D pose and shape of a query object from an image. For shape representation and inference, we leverage recent successes of Convolutional Neural Networks (CNNs) for the task of object and keypoint localization, and train a novel cascaded fully-convolutional architecture to localize vehicle keypoints in images. The shape-aware adjustment then robustly recovers shape (3D locations of the detected keypoints) while simultaneously filling in occluded keypoints. To tackle estimation errors incurred due to erroneously detected keypoints, we use an Iteratively Re-weighted Least Squares (IRLS) scheme for robust optimization, and as a by-product characterize noise models for each predicted keypoint. We evaluate our approach on autonomous driving benchmarks, and present superior results to existing monocular, as well as stereo approaches.
Willingness to Pay for Species Conservation Programs: Implications for National Park Funding

National park management increasingly considers voluntary contributions from visitors as potentially important sources of private funding for species conservation. We investigate visitors’ willingness to pay (WTP) for the protection of two species differing in degree of endangerment and popularity (alpine ibex and rock partridge) in Austria’s Hohe Tauern National Park. Our analysis reveals that visitors’ attitudes towards and preferences for nature conservation in general determine the WTP and not so much the species’ characteristics. The results suggest that conservation funding should be essentially public and only complemented by private funding because (i) potential (voluntary) contributions by visitors might not be sustainable in the long run, (ii) visitors’ WTP mirrors preferences for nature conservation in general rather than for specific species conservation programs, (iii) the collection of private contributions by a new system might be complicated, and (iv) many protected areas lack the resources and capacities for marketing and branding which are essential prerequisites for attracting funds in the first place.