Miracle-dz/UniFAR-FTUs huggingface dataset:
Model of Above-ground Biomass Distribution of Norway Spruce (Picea abies L. (Karst.))

Climate change with more frequent extreme weather events and prolonged winter periods with un-frozen, wet soil is causing frequent wind damage events in forests. Trees with higher mass point and heavier weight are more prone to wind damage; however, limited information exists on distribution of biomass under naturally moist conditions. Such information is essential to improve models of wind damage prediction. Therefore, the aim of the present study was to assess the biomass distribution and the parameters important for wind-load of Norway spruce (Picea abies (L.) Karst. ). Samples were collected in the year 2019 from 87 trees growing on two different sites, corresponding to freely drained mineral and peaty mineral soils at the age of 55 and 88 years, respectively. Tree diameters at breast height, height, and height of first living branch were measured. Tree stems were pruned and cut into 2-m-long fragments and weighed (fresh weight) afterwards. A biomass distribution model was developed to estimate fresh weight of the stem of Norway spruce using easy measurable tree variables. Relative height of the mass point and height of living branches were higher in peaty mineral soil than on freely drained mineral soil, which was an indicator for higher windthrow risks.
Incremental learning of Bayesian sensorimotor models: from low-level behaviours to large-scale structure of the environment

This paper concerns the incremental learning of hierarchies of representations of space in artificial or natural cognitive systems. We propose a mathematical formalism for defining space representations (Bayesian Maps) and modelling their interaction in hierarchies of representations (sensorimotor interaction operator). We illustrate our formalism with a robotic experiment. Starting from a model based on the proximity to obstacles, we learn a new one related to the direction of the light source. It provides new behaviours, like phototaxis and photophobia. We then combine these two maps so as to identify parts of the environment where the way the two modalities interact is recognisable. This classification is a basis for learning a higher level of abstraction map that describes the large-scale structure of the environment. In the final model, the perception–action cycle is modelled by a hierarchy of sensorimotor models of increasing time and space scales, which provide navigation strategies of increasing complexities.
RouteLens: easy route following for map applications

Millions of people go to the Web to search for geographical itineraries. Inspecting those map itineraries remains tedious because they seldom fit on screen, requiring much panning & zooming to see details. Focus+context techniques address this problem by displaying routes at a scale that allows them to fully fit on screen: users see the entire route at once, and perform magnified steering using a lens to navigate along the path, revealing additional detail. Navigation based on magnified steering has been shown to outperform pan & zoom for large steering tasks. Yet, this task remains challenging, in part because paths have a tendency to "slip off" the side of the lens. RouteLenses automatically adjust their position based on the geometry of the path that users steer through. RouteLenses make it easier for users to follow a route, yet do not constrain movements too strictly, leaving them free to move the lens away from the path to explore its surroundings.
Scarcity risk premium*

This paper revisits the cost-of-carry model and proposes a decomposition of the futures basis that disentangles the seasonality risk premium from the scarcity risk premium. The contribution of this paper to the asset pricing literature is threefold. First, it brings novel insights on the fundamental relationship between the futures basis and inventory dynamics. Empirical evidence shows that the seasonality risk premium captures expectations about the inventory seasonalities while the scarcity risk premium refects the excess supply and demand imbalances over the expected seasonal fuctuations. Second, this papers investigates the pricing of expectations within the futures basis. Results suggest that the seasonality risk premium is priced-in and that the scarcity risk premium carries all the predictive power embedded in the futures basis. The third contribution of this paper is to provide evidence that the scarcity risk premium, and ultimately the futures basis, is mostly a compensation for the unexpected increase in the risk of stock-out and that the associated return predictability fnds its origin in the slow di ̇usion of information and underreaction to abnormal changes in inventories, above and beyond seasonal dynamics.
Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies

Background:: Countries around the globe have implemented unprecedented mitigation measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict COVID-19 cases and compare effectiveness of mitigation measures across coun- tries to inform policy decision making. Methods: We propose a survival-convolution model for predicting key statistics of COVID-19 epidemics (daily new cases). We account for transmission during a pre- symptomatic incubation period and use a time-varying reproduction number (Rt) to reflect the temporal trend of transmission and change in response to an intervention. We estimate the intervention effect on reducing the infection rate and quantify uncer- tainty by permutation. Findings: Our model adequately estimated observed daily new cases and could predict the entire disease epidemic using data from the early phase. A fast rate of decline in Rt was observed in China and South Korea. In Italy, Rt decreased at a slower rate and did not change significantly before the nation-wide lockdown and two-weeks after. In the United States (US), there was a significant change in Rt before and after the declaration of national emergency. Interpretation: Adopting mitigation strategies early in the epidemic is effective in reducing the infection rate. The lockdown in Italy did not further accelerate the speed at which the infection rate decreases and the epidemic is not yet under control. If the current trend continues in the US, COVID-19 may be controlled by May 24 (CI: May 15 to Jun 9). However, relaxing mitigation measures could delay the end date of the epidemic as long as 42 days.
Determination of Size, Albedo, and Thermal Inertia of 10 Vesta Family Asteroids with WISE/NEOWISE Observations

In this work, we investigate the size, thermal inertia, surface roughness, and geometric albedo of 10 Vesta family asteroids using the Advanced Thermophysical Model, based on the thermal-infrared data acquired by mainly NASA’s Wide-field Infrared Survey Explorer. Here, we show that the average thermal inertia and geometric albedo of the investigated Vesta family members are 42 J m−2 s−1/2 K−1 and 0.314, respectively, where the derived effective diameters are less than 10 km. Moreover, the family members have a relatively low roughness fraction on their surfaces. The similarity in thermal inertia and geometric albedo among the V-type Vesta family members may reveal their close connection in origin and evolution. As the fragments of the cratering event of Vesta, the family members may have undergone a similar evolutionary process, thereby leading to very close thermal properties. Finally, we estimate their regolith grain sizes with different volume filling factors.
That Message Went Viral?! Exploratory Analytics and Sentiment Analysis into the Propagation of Tweets

Information exchange and message diffusion have moved from traditional media to social media platforms. Messages on platforms such as Twitter have become the default mode of company communications replacing lengthier public announcements and updates. Businesses and organizations have increased their use of Twitter to connect with stakeholders. As a result, it is important to understand the key drivers of successful information exchange and message diffusion via Twitter. We conducted an exploratory analysis on a dataset of over a million Tweets, comprising of over 40,000 lead Tweets, further filtered to over 18,000 Tweets. We identified the most popular messages, and analyzed the tweets on multiple endogenous dimensions including content, sentiment, motive and richness, and exogenous dimensions such as fundamental events, social learning, and activism. We found some interesting patterns and uncovered new insights to help researchers and practitioners better understand the behavior of popular viral tweets. We also performed sentiment analysis and present an early stage model to explain tweet performance.
Sub-galactic scaling relations between X-ray luminosity, star formation rate, and stellar mass

X-ray luminosity ($L_X$) originating from high-mass X-ray binaries (HMXBs) is tightly correlated with the host galaxy's star-formation rate (SFR). We explore this connection at sub-galactic scales spanning ${\sim}$7 dex in SFR and ${\sim}$8 dex in specific SFR (sSFR). There is good agreement with established relations down to ${\rm SFR {\simeq} 10^{-3}\,M_\odot \, yr^{-1}}$, below which an excess of X-ray luminosity emerges. This excess likely arises from low mass X-ray binaries. The intrinsic scatter of the $L_X$-SFR relation is constant, not correlated with SFR. Different star formation indicators scale with $L_X$ in different ways, and we attribute the differences to the effect of star formation history. The SFR derived from H$\alpha$ shows the tightest correlation with X-ray luminosity because H$\alpha$ emission probes stellar populations with ages similar to HMXB formation timescales, but the H$\alpha$-based SFR is reliable only for $\rm sSFR{>}10^{-12}\,M_\odot \, yr^{-1}/M_\odot$.
Channel Estimation and Power Scaling Law of Large Reflecting Surface with Non-Ideal Hardware

Large reflecting surface (LRS) has emerged as a new solution to improve the energy and spectrum efficiency of wireless communication system. Most existing studies were conducted with an assumption of ideal hardware, and the impact of hardware impairments receives little attention. However, the non-negligible hardware impairments should be taken into consideration when we evaluate the system performance. In this paper, we consider an LRS assisted communication system with hardware impairments, and focus on the channel estimation study and the power scaling law analysis. First, with linear minimum mean square error estimation, we theoretically characterize the relationship between channel estimation performance and impairment level, number of reflecting elements, and pilot power. After that, we analyze the power scaling law and reveal that if the base station (BS) has perfect channel state information, the transmit power of user can be made inversely proportional to the number of BS antennas and the square of the number of reflecting elements with no reduction in performance; If the BS has imperfectly estimated channel state information, to achieve the same performance, the transmit power of user can be made inversely proportional to the square-root of the number of BS antennas and the square of the number of reflecting elements.
A Critical Overview of Privacy-Preserving Learning in Vector Autoregressive Models for Energy Forecasting

Cooperation between diﬀerent data owners may lead to an improvement of forecast skill by, for example, taking advantage of spatio-temporal dependencies in geographically distributed renewable energy time series. Due to business competitive factors and personal data protection, these data owners might be unwilling to share their information, which increases the interest in collaborative privacy-preserving forecasting. This paper analyses the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing Vector Autoregressive (VAR) models. Mathematical proofs and numerical analysis are conducted to evaluate existing privacy-preserving methods divided into three categories: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as a trade-oﬀ between privacy and the correct estimation of model coeﬃcients, while iterative processes in which intermediate results are shared can be exploited so that the original data can be inferred after some iterations.
KIC 10736223: An Algol-type Eclipsing Binary That Has Just Undergone the Rapid Mass-transfer Stage

This paper reports the discovery of an Algol system KIC 10736223 that just passed the rapid mass transfer stage. From the light-curve and radial-velocity modeling we find KIC 10736223 to be a detached Algol system with the less-massive secondary nearly filling its Roche lobe. Based on the short-cadence Kepler data, we analyzed intrinsic oscillations of the pulsator and identified six secured independent δ Scuti-type pulsation modes (f1, f3, f9, f19, f42, and f48). We compute two grids of theoretical models to reproduce the δ Scuti frequencies, and find that fitting results of mass-accreting models agree well with those of single-star evolutionary models. The fundamental parameters of the primary star yielded with asteroseismology are M⊙, Z = 0.009 ± 0.001, R⊙, , K, and L = L⊙. The asteroseismic parameters match well with the dynamical parameters derived from the binary model. Moreover, our asteroseismic results show that the pulsator is an almost unevolved star with an age between 9.46 and 11.65 Myr for single-star evolutionary models and 2.67–3.14 Myr for mass-accreting models. Therefore, KIC 10736223 may be an Algol system that has just undergone the rapid mass-transfer process.
A New Community Definition For MultiLayer Networks And A Novel Approach For Its Efficient Computation

As the use of MultiLayer Networks (or MLNs) for modeling and analysis is gaining popularity, it is becoming increasingly important to propose a community definition that encompasses the multiple features represented by MLNs and develop algorithms for efficiently computing communities on MLNs. Currently, communities for MLNs, are based on aggregating the networks into single graphs using different techniques (type independent, projection-based, etc.) and applying single graph community detection algorithms, such as Louvain and Infomap on these graphs. This process results in different types of information loss (semantics and structure). To the best of our knowledge, in this paper we propose, for the first time, a definition of community for heterogeneous MLNs (or HeMLNs) which preserves semantics as well as the structure. Additionally, our basic definition can be extended to appropriately match the analysis objectives as needed. In this paper, we present a structure and semantics preserving community definition for HeMLNs that is compatible with and is an extension of the traditional definition for single graphs. We also present a framework for its efficient computation using a newly proposed decoupling approach. First, we define a k-community for connected k layers of a HeMLN. Then we propose a family of algorithms for its computation using the concept of bipartite graph pairings. Further, for a broader analysis, we introduce several pairing algorithms and weight metrics for composing binary HeMLN communities using participating community characteristics. Essentially, this results in an extensible family of community computations. We provide extensive experimental results for showcasing the efficiency and analysis flexibility of the proposed computation using popular IMDb and DBLP data sets.
Noncommuting conserved charges in quantum many-body thermalization.

In statistical mechanics, a small system exchanges conserved quantities-heat, particles, electric charge, etc.-with a bath. The small system thermalizes to the canonical ensemble or the grand canonical ensemble, etc., depending on the quantities. The conserved quantities are represented by operators usually assumed to commute with each other. This assumption was removed within quantum-information-theoretic (QI-theoretic) thermodynamics recently. The small system's long-time state was dubbed "the non-Abelian thermal state (NATS). " We propose an experimental protocol for observing a system thermalize to the NATS. We illustrate with a chain of spins, a subset of which forms the system of interest. The conserved quantities manifest as spin components. Heisenberg interactions push the conserved quantities between the system and the effective bath, the rest of the chain. We predict long-time expectation values, extending the NATS theory from abstract idealization to finite systems that thermalize with finite couplings for finite times. Numerical simulations support the analytics: The system thermalizes to near the NATS, rather than to the canonical prediction. Our proposal can be implemented with ultracold atoms, nitrogen-vacancy centers, trapped ions, quantum dots, and perhaps nuclear magnetic resonance. This work introduces noncommuting conserved quantities from QI-theoretic thermodynamics into quantum many-body physics: atomic, molecular, and optical physics and condensed matter.
Fog-Based Detection for Random-Access IoT Networks with Per-Measurement Preambles

Internet of Things (IoT) systems may be deployed to monitor spatially distributed quantities of interests (QoIs), such as noise or pollution levels. This paper considers a fog-based IoT network, in which active IoT devices transmit measurements of the monitored QoIs to the local edge node (EN), while the ENs are connected to a cloud processor via limited-capacity fronthaul links. While the conventional approach uses preambles as metadata for reserving communication resources, here we consider assigning preambles directly to measurement levels across all devices. The resulting Type-Based Multiple Access (TBMA) protocol enables the efficient remote detection of the QoIs, rather than of the individual payloads. The performance of both edge and cloud-based detection or hypothesis testing is evaluated in terms of error exponents. Cloud-based hypothesis testing is shown theoretically and via numerical results to be advantageous when the intercell interference power and the fronthaul capacity are sufficiently large.
Vector Quantized Contrastive Predictive Coding for Template-based Music Generation

In this work, we propose a flexible method for generating variations of discrete sequences in which tokens can be grouped into basic units, like sentences in a text or bars in music. More precisely, given a template sequence, we aim at producing novel sequences sharing perceptible similarities with the original template without relying on any annotation; so our problem of generating variations is intimately linked to the problem of learning relevant high-level representations without supervision. Our contribution is two-fold: First, we propose a self-supervised encoding technique, named Vector Quantized Contrastive Predictive Coding which allows to learn a meaningful assignment of the basic units over a discrete set of codes, together with mechanisms allowing to control the information content of these learnt discrete representations. Secondly, we show how these compressed representations can be used to generate variations of a template sequence by using an appropriate attention pattern in the Transformer architecture. We illustrate our approach on the corpus of J.S. Bach chorales where we discuss the musical meaning of the learnt discrete codes and show that our proposed method allows to generate coherent and high-quality variations of a given template.
Gemini Near-Infrared Field Spectrograph Observations of the Seyfert 2 Galaxy Mrk 3: Feeding and Feedback on Galactic and Nuclear Scales

We explore the kinematics of the stars, ionized gas, and warm molecular gas in the Seyfert 2 galaxy Mrk 3 (UGC 3426) on nuclear and galactic scales with Gemini Near-Infrared Field Spectrograph observations, previous Hubble Space Telescope data, and new long-slit spectra from the Apache Point Observatory (APO) 3.5 m telescope. The APO spectra are consistent with our previous suggestion that a galactic-scale gas/dust disk at P.A. = 129°, offset from the major axis of the host S0 galaxy at P.A. = 28°, is responsible for the orientation of the extended narrow-line region. The disk is fed by an H i tidal stream from a gas-rich spiral galaxy (UGC 3422) ∼100 kpc to the NW of Mrk 3 and is ionized by the active galactic nucleus (AGN) to a distance of at least ∼20″ (∼5.4 kpc) from the central supermassive black hole (SMBH). The kinematics within at least 320 pc of the SMBH are dominated by outflows with radial (line-of-sight) velocities up to 1500 km s−1 in the ionized gas and 500 km s−1 in the warm molecular gas, consistent with in situ heating, ionization, and acceleration of ambient gas to produce the narrow-line region outflows. There is a disk of ionized and warm molecular gas within ∼400 pc of the SMBH that has reoriented close to the stellar major axis but is counterrotating, consistent with claims of external fueling of AGNs in S0 galaxies.
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects

Causal inference methods are widely applied in the fields of medicine, policy, and economics. Central to these applications is the estimation of treatment effects to make decisions. Current methods make binary yes-or-no decisions based on the treatment effect of a single outcome dimension. These methods are unable to capture continuous space treatment policies with a measure of intensity. They also lack the capacity to consider the complexity of treatment such as matching candidate treatments with the subject. We propose to formulate the effectiveness of treatment as a parametrizable model, expanding to a multitude of treatment intensities and complexities through the continuous policy treatment function, and the likelihood of matching. Our proposal to decompose treatment effect functions into effectiveness factors presents a framework to model a rich space of actions using causal inference. We utilize deep learning to optimize the desired holistic metric space instead of predicting single-dimensional treatment counterfactual. This approach employs a population-wide effectiveness measure and significantly improves the overall effectiveness of the model. The performance of our algorithms is. demonstrated with experiments. When using generic continuous space treatments and matching architecture, we observe a 41% improvement upon prior art with cost-effectiveness and 68% improvement upon a similar method in the average treatment effect. The algorithms capture subtle variations in treatment space, structures the efficient optimizations techniques, and opens up the arena for many applications.
Supersolid behavior in one-dimensional self-trapped Bose–Einstein condensate

Supersolid is an exotic state of matter, showing crystalline order with a superfluid background, observed recently in dipolar Bose–Einstein condensate in a trap. Here, we present exact Bloch wave function of the self-trapped supersolid phase, in the presence of mean-field and beyond mean-field interaction. Our general solutions of the amended nonlinear Schrödinger equation are obtained through Möbius transform, connecting a wide class of supersolid solutions to the ubiquitous cnoidal waves. The solutions yield the supersolid phase in the self-trapped quantum matter, where an array of quantum droplets exist, accompanied by a constant condensate. For the supersolid phase, the chemical potential for one class of solutions is the same as that of self-trapped quantum droplets, and is lower for the general non-perturbative solution. Due to the destabilizing effects of fluctuations on long range order in one dimension, the realization of the supersolid phase may be possible in a finite system.