Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Rich and Efficient Spatial Temporal Interactions for Real-Time Video Salient Object Detection

We have witnessed a growing interest in video salient object detection (VSOD) techniques in today’s computer vision applications. In contrast with temporal information (which is still considered a rather unstable source thus far), the spatial information is more stable and ubiquitous, thus it could influence our vision system more. As a result, the current main-stream VSOD approaches have inferred and obtained their saliency primarily from the spatial perspective, still treating temporal information as subordinate. Although the aforementioned methodology of focusing on the spatial aspect is effective in achieving a numeric performance gain, it still has two critical limitations. First, to ensure the dominance by the spatial information, its temporal counterpart remains inadequately used, though in some complex video scenes, the temporal information may represent the only reliable data source, which is critical to derive the correct VSOD. Second, both spatial and temporal saliency cues are often computed independently in advance and then integrated later on, while the interactions between them are omitted completely, resulting in saliency cues with limited quality. To combat these challenges, this paper advocates a novel spatiotemporal network, where the key innovation is the design of its temporal unit. Compared with other existing competitors (e.g., convLSTM), the proposed temporal unit exhibits an extremely lightweight design that does not degrade its strong ability to sense temporal information. Furthermore, it fully enables the computation of temporal saliency cues that interact with their spatial counterparts, ultimately boosting the overall VSOD performance and realizing its full potential towards mutual performance improvement for each. The proposed method is easy to implement yet still effective, achieving high-quality VSOD at 50 FPS in real-time applications.
A mathematical model of the COVID-19 pandemic dynamics with dependent variable infection rate: Application to South Korea

This work constructs, analyzes, and simulates a new SEIR-type model for the dynamics and potential control of the current coronavirus (COVID-19) pandemic. It has a compartmental structure and a differential inclusion for a variable infection rate. The novelty in this work is three-fold. First, the population is separated, according to compliance with the disease control directives (shelter-in-place, masks/face coverings, physical distancing, etc.), into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile. This allows the assessment of the overall effectiveness of the control measures and the impact of their relaxing or tightening on the disease spread. Second, the model treats the infection rate as an unknown and keeps track of how it changes, due to virus mutations and saturation effects, via a differential inclusion. Third, by introducing randomness to some of the system coefficients, we study the model's sensitivity to these parameters and provide bounds and intervals of confidence for the model simulations. As a case study, the pandemic outbreak in South Korea is simulated. The model parameters were found by minimizing the deviation of the model prediction from the reported data. The simulations show that the model captures the pandemic dynamics in South Korea accurately, which provides confidence in the model predictions and its future use.
Which Kind Is Better in Open-domain Multi-turn Dialog, Hierarchical or Non-hierarchical Models? An Empirical Study

Currently, open-domain generative dialog systems have attracted considerable attention in academia and industry. Despite the success of single-turn dialog generation, multi-turn dialog generation is still a big challenge. So far, there are two kinds of models for open-domain multi-turn dialog generation: hierarchical and non-hierarchical models. Recently, some works have shown that the hierarchical models are better than non-hierarchical models under their experimental settings; meanwhile, some works also demonstrate the opposite conclusion. Due to the lack of adequate comparisons, it's not clear which kind of models are better in open-domain multi-turn dialog generation. Thus, in this paper, we will measure systematically nearly all representative hierarchical and non-hierarchical models over the same experimental settings to check which kind is better. Through extensive experiments, we have the following three important conclusions: (1) Nearly all hierarchical models are worse than non-hierarchical models in open-domain multi-turn dialog generation, except for the HRAN model. Through further analysis, the excellent performance of HRAN mainly depends on its word-level attention mechanism; (2) The performance of other hierarchical models will also obtain a great improvement if integrating the word-level attention mechanism into these models. The modified hierarchical models even significantly outperform the non-hierarchical models; (3) The reason why the word-level attention mechanism is so powerful for hierarchical models is because it can leverage context information more effectively, especially the fine-grained information. Besides, we have implemented all of the models and already released the codes.
The Entrepreneurial Engineer: A Quantitative Analysis of Personality Factors in the Social Cognitive Career Theory

Which personality traits foster Entrepreneurship in Engineering? What implications for Engineering/Entrepreneurship Education can be derived? Until today, most of the research about Personality and Entrepreneurship compared Entrepreneurs to Managers. This work focuses on Engineers and combines two validated constructs: the Big Five Personality Traits (BFPT: Openness to new experiences, Conscientiousness, Extraversion, Agreeableness and Emotional Stability [1]) and the Social Cognitive Career Theory (SCCT [2]). The research question is: How can we describe the relationship between the BFPT and SCCT constructs? We conducted a (mainly quantitative) onlinesurvey (n=465) among Alumni of the Stanford School of Engineering who graduated between 1 and 17 years ago and who had successfully the course ME203: Design and Manufacturing. Over 20 percent of the respondents had founded a venture. Results show that three out of the five personality traits have a significant positive relationship to certain SCCT constructs: a. Openness to new experiences and Extraversion are correlated to Entrepreneurial Self Efficacy, Entrepreneurial Intention, Entrepreneurial Outcome Expectations, Innovation Self Efficacy and Entrepreneurial Learning Experiences. and b. Emotional Stability correlated to Innovation Self Efficacy and Entrepreneurial Learning Experiences. Finally, we draw practical implications of these relationships for Entrepreneurship and Engineering Education, while at the same time keeping in mind that you cannot just “make” someone an Entrepreneur or change his or her personality. This paper is an explorative starting point that results in various findings that suggest future research. We recommend focusing on the three personality traits that showed significant results.
Noncommutative Networks on a Cylinder.

In this paper a double quasi Poisson bracket in the sense of Van den Bergh is constructed on the space of noncommutative weights of arcs of a directed graph embedded in a disk or cylinder $\Sigma$, which gives rise to the quasi Poisson bracket of G.Massuyeau and V.Turaev on the group algebra $\mathbf k\pi_1(\Sigma,p)$ of the fundamental group of a surface based at $p\in\partial\Sigma$. This bracket also induces a noncommutative Goldman Poisson bracket on the cyclic space $\mathcal C_\natural$, which is a $\mathbf k$-linear space of unbased loops. We show that the induced double quasi Poisson bracket between boundary measurements can be described via noncommutative $r$-matrix formalism. This gives a more conceptual proof of the result of N. Ovenhouse that traces of powers of Lax matrix form an infinite collection of noncommutative Hamiltonians in involution with respect to noncommutative Goldman bracket on $\mathcal C_\natural$.
The Global Radiative Impact of the Sea-Ice-Albedo Feedback in the Arctic

[1] A simple method for estimating the global radiative forcing caused by the sea ice–albedo feedback in the Arctic is presented. It is based on observations of cloud cover, sea ice concentration, and top-of-atmosphere broadband albedo. The method does not rely on any sort of climate model, making the assumptions and approximations clearly visible and understandable and allowing them to be easily changed. Results show that the globally and annually averaged radiative forcing caused by the observed loss of sea ice in the Arctic between 1979 and 2007 is approximately 0.1 W m−2; a complete removal of Arctic sea ice results in a forcing of about 0.7 W m−2, while a more realistic ice-free summer scenario (no ice for 1 month and decreased ice at all other times of the year) results in a forcing of about 0.3 W m−2, similar to present-day anthropogenic forcing caused by halocarbons. The potential for changes in cloud cover as a result of the changes in sea ice makes the evaluation of the actual forcing that may be realized quite uncertain since such changes could overwhelm the forcing caused by the sea ice loss itself, if the cloudiness increases in the summertime.
Structural controls on anomalous transport in fractured porous rock

Anomalous transport is ubiquitous in a wide range of disordered systems, notably in fractured porous formations. We quantitatively identify the structural controls on anomalous tracer transport in a model of a real fractured geological formation that was mapped in an outcrop. The transport, determined by a continuum scale mathematical model, is characterized by breakthrough curves (BTCs) that document anomalous (or “non‐Fickian”) transport, which is accounted for by a power law distribution of local transition times ψ(t) within the framework of a continuous time random walk (CTRW). We show that the determination of ψ(t) is related to fractures aligned approximately with the macroscopic direction of flow. We establish the dominant role of fracture alignment and assess the statistics of these fractures by determining a concentration‐visitation weighted residence time histogram. We then convert the histogram to a probability density function (pdf) that coincides with the CTRW ψ(t) and hence anomalous transport. We show that the permeability of the geological formation hosting the fracture network has a limited effect on the anomalous nature of the transport; rather, it is the fractures transverse to the flow direction that play the major role in forming the long BTC tail associated with anomalous transport. This is a remarkable result, given the complexity of the flow field statistics as captured by concentration transitions.
21st century projections of surface mass balance changes for major drainage systems of the Greenland ice sheet

Outputs from the regional climate model Modèle Atmosphérique Régionale at a spatial resolution of 25 km are used to study 21st century projected surface mass balance (SMB) over six major drainage basins of the Greenland ice sheet (GrIS). The regional model is forced with the outputs of three different Earth System Models (CanESM2, NorESM1 and MIROC5) obtained when considering two greenhouse gas future scenarios with levels of CO2 equivalent of, respectively, 850 and >1370 ppm by 2100. Results indicate that the increase in runoff due to warming will exceed the increased precipitation deriving from the increase in evaporation for all basins, with the amount of net loss of mass at the surface varying spatially. Basins along the southwest and north coast are projected to have the highest sensitivity of SMB to increasing temperatures. For these basins, the global temperature anomaly corresponding to a decrease of the SMB below the 1980–99 average (when the ice sheet was near the equilibrium) ranges between +0.60 and +2.16 °C. For the basins along the northwest and northeast, these values range between +1.50 and +3.40 °C. Our results are conservative as they do not account for ice dynamics and changes in the ice sheet topography.
Oligocene‐Miocene back‐thrusting in southern Mexico linked to the rapid subduction erosion of a large forearc block

Both the timing and mechanism for the removal of a ∼150–250 km wide forearc block from southern Mexico during the Cenozoic are controversial. Principal competing hypotheses are (1) removal due to sinistral strike‐slip shear, in which slow, diachronous removal of the Chortis Block throughout the Cenozoic is inferred, and (2) removal due to subduction erosion, in which rapid removal of a large forearc block during the late Oligocene/early Miocene is inferred to be synchronous with the rapid landward migration of the southern Mexican arc. New data indicate northeast‐directed back‐thrusting in (1) the Chacalapa shear zone west of −96.5°E, with the timing of shear deformation bracketed by a 25.5 ± 0.5 Ma U/Pb zircon age and a 20.7 ± 0.6 Ma Ar/Ar biotite age, and (2) in an unnamed shear zone to the south, with the timing of deformation bracketed by a 27.5 ± 0.5 Ma U/Pb zircon age and a 25.1 ± 0.2 Ma Ar/Ar biotite age. Zircon and biotite ages date the emplacement and cooling of deformed plutons, respectively. The observed back‐thrusting is consistent with a model of forearc removal due to subduction‐erosion processes because it is evidence for subduction‐orthogonal shortening occurring within the upper plate just before the landward migration of the southern Mexican arc. Rapid subduction of the southern Mexican forearc could have recycled continental lithosphere into the upper mantle at a rate up to half the global average rate of subduction erosion during the late Oligocene/early Miocene.
Acceleration of high energy protons in AGN relativistic jets

In this paper, we investigate the acceleration in relativistic jets of high-energy proton preaccelerated in the magnetosphere of a supermassive black hole. The proton reaches maximum energy when passing the total potential difference of $U$ between the jet axis and its periphery. This voltage is created by a rotating black hole and transmitted along magnetic field lines into the jet. It is shown that the trajectories of proton in the jet are divided into three groups: untrapped, trapped and not accelerated. Untrapped particles are not kept by poloidal and toroidal magnetic fields inside the jet, so they escape out the jet and their energy is equal to the maximum value, $eU$. Trapped protons are moving along the jet with oscillations in the radial direction. Their energy varies around the value of $0.74 eU$. In a strong magnetic field protons preaccelerated in the magnetosphere are pressed to the jet axis and practically are not accelerated in the jet. The work defines acceleration regimes for a range of the most well-known AGN objects with relativistic jets and for the microquasar SS433.
On Warm-Starting Neural Network Training

In many real-world deployments of machine learning systems, data arrive piecemeal. These learning scenarios may be passive, where data arrive incrementally due to structural properties of the problem (e.g., daily financial data) or active, where samples are selected according to a measure of their quality (e.g., experimental design). In both of these cases, we are building a sequence of models that incorporate an increasing amount of data. We would like each of these models in the sequence to be performant and take advantage of all the data that are available to that point. Conventional intuition suggests that when solving a sequence of related optimization problems of this form, it should be possible to initialize using the solution of the previous iterate---to "warm start" the optimization rather than initialize from scratch---and see reductions in wall-clock time. However, in practice this warm-starting seems to yield poorer generalization performance than models that have fresh random initializations, even though the final training losses are similar. While it appears that some hyperparameter settings allow a practitioner to close this generalization gap, they seem to only do so in regimes that damage the wall-clock gains of the warm start. Nevertheless, it is highly desirable to be able to warm-start neural network training, as it would dramatically reduce the resource usage associated with the construction of performant deep learning systems. In this work, we take a closer look at this empirical phenomenon and try to understand when and how it occurs. We also provide a surprisingly simple trick that overcomes this pathology in several important situations, and present experiments that elucidate some of its properties.
Approximating Constraint Satisfaction Problems Symmetrically

This thesis investigates the extent to which the optimal value of a constraint satisfaction problem (CSP) can be approximated by some sentence of fixed point logic with counting (FPC). It is known that, assuming $\mathsf{P} \neq \mathsf{NP}$ and the Unique Games Conjecture, the best polynomial time approximation algorithm for any CSP is given by solving and rounding a specific semidefinite programming relaxation. We prove an analogue of this result for algorithms that are definable as FPC-interpretations, which holds without the assumption that $\mathsf{P} \neq \mathsf{NP}$. While we are not able to drop (an FPC-version of) the Unique Games Conjecture as an assumption, we do present some partial results toward proving it. Specifically, we give a novel construction which shows that, for all $\alpha > 0$, there exists a positive integer $q = \text{poly}(\frac{1}{\alpha})$ such that no there is no FPC-interpretation giving an $\alpha$-approximation of Unique Games on a label set of size $q$.
DurIAN-SC: Duration Informed Attention Network based Singing Voice Conversion System

Singing voice conversion is converting the timbre in the source singing to the target speaker's voice while keeping singing content the same. However, singing data for target speaker is much more difficult to collect compared with normal speech this http URL this paper, we introduce a singing voice conversion algorithm that is capable of generating high quality target speaker's singing using only his/her normal speech data. First, we manage to integrate the training and conversion process of speech and singing into one framework by unifying the features used in standard speech synthesis system and singing synthesis system. In this way, normal speech data can also contribute to singing voice conversion training, making the singing voice conversion system more robust especially when the singing database is small.Moreover, in order to achieve one-shot singing voice conversion, a speaker embedding module is developed using both speech and singing data, which provides target speaker identify information during conversion. Experiments indicate proposed sing conversion system can convert source singing to target speaker's high-quality singing with only 20 seconds of target speaker's enrollment speech data.
Classification of genus-$1$ holomorphic Lefschetz pencils.

In this paper, we classify relatively minimal genus-$1$ holomorphic Lefschetz pencils up to smooth isomorphism. We first show that such a pencil is isomorphic to either the pencil on $\mathbb{P}^1\times \mathbb{P}^1$ of bi-degree $(2,2)$ or a blow-up of the pencil on $\mathbb{P}^2$ of degree $3$, provided that no fiber of a pencil contains an embedded sphere. (Note that one can easily classify genus-$1$ Lefschetz pencils with an embedded sphere in a fiber.) We further determine the monodromy factorizations of these pencils and show that the isomorphism class of a blow-up of the pencil on $\mathbb{P}^2$ of degree $3$ does not depend on the choice of blown-up base points. We also show that the genus-$1$ Lefschetz pencils constructed by Korkmaz-Ozbagci (with nine base points) and Tanaka (with eight base points) are respectively isomorphic to the pencils on $\mathbb{P}^2$ and $\mathbb{P}^1\times \mathbb{P}^1$ above, in particular these are both holomorphic.
P/2019 LD2 (ATLAS): An Active Centaur in Imminent Transition to the Jupiter Family

The recently discovered object P/2019 LD2 (by the Asteroid Terrestrial-impact Last Alert System) was initially thought to be a Jupiter Trojan asteroid, until dynamical studies and the appearance of persistent cometary activity revealed that this object is actually an active Centaur. However, the dynamical history, thermal environment, and impact of such environments on the activity of 2019 LD2 are poorly understood. Here we conduct dynamical simulations to constrain its orbital history and resulting thermal environment over the past 3000 yr. We find that 2019 LD2 is currently in the vicinity of a dynamical “Gateway” that facilitates the majority of transitions from the Centaur population into the Jupiter Family of Comets (JFC population). Our calculations show that it is unlikely to have spent significant amounts of time in the inner solar system, suggesting that its nucleus is relatively pristine in terms of physical, chemical, and thermal processing through its history. This could explain its relatively high level of distant activity as a recently activated primordial body. Finally, we find that the median frequency of transition from the Gateway population into the JFC population varies from once every ∼3 yr to less than once every 70 yr, if 2019 LD2's nucleus is ∼1 km in radius or greater than 3 km in radius. Forward modeling of 2019 LD2 shows that it will transition into the JFC population in 2063, representing the first known opportunity to observe the evolution of an active Centaur nucleus as it experiences this population-defining transition.
Objective quantification of Particle Pair Diffusion in Homogeneous Isotropic Turbulence

Turbulence consists of interacting flow structures covering a wide range of length and time scales. A long-standing question looms over pair diffusion of particles in close proximity i.e. particle pair diffusion at small separations: what range of turbulence length scales governs pair diffusion? Here, we attempt to answer this question by addressing pair diffusion by both fine scales and larger scale coherent structures in which the fine scales are embedded - we unavoidably encounter a combination of both local and non-local interactions associated with the small and large length scales. The local structures possess length scales of the same order of magnitude as the pair separation $l$, and they induce strong relative motion between the particle pair. However, the non-local structures, possessing length scales much larger than $l$, also induce (via Biot-Savart) significant relative motion, an effect ignored in prior studies (based on Richardson-Obukhov R-O theory). This fundamentally changes the interpretation of the pair diffusion process, giving the pair diffusivity $K$ scaling as $K\sim l^{1.556}$ -- agreeing within $1\%$ of data. The `R-O constant' $g_l$ is shown to be not a constant, although widely assumed to be a constant. However, new constants (representing pair diffusivity $G_K$ and pair separation $G_l$) are identified, which we show to asymptote to $G_K\approx 0.73$ and $G_l\sim 0.01$ at high Reynolds numbers. As an application, we show that the radius of a cloud of droplets in a spray is smaller by an order of magnitude as compared to R-O theory.