Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface Tension Driven Film Flow due to Condensation With a Vapor Borne Surfactant

In absorption chillers, it is well known that the presence of small amounts of alcohol can enhance the absorber heat and mass transfer significantly. Similar enhancement is also observed in the condenser. Among the theories to explain such phenomena, the recently described Vapor Surfactant theory provides the most comprehensive explanation of the available data. According to this theory, the surfactant arrives at the liquid surface primarily from the vapor. As the water vapor flows toward eventual absorption/condensation, it carries the vapor borne surfactant toward the surface. Once the surfactant arrives at the surface, it tends to concentrate there due to its low solubility and low diffusivity in the liquid as well as the natural affinity of a surfactant for the surface. Non-uniform surface concentration of the surfactant leads to surface tension forces in the liquid surface layer that drive the surface flows often observed. These Marangoni flows are what enhance the heat and mass transfer processes. Although it was often theorized that Marangoni flow plays a role in enhancement, the important role of the vapor has not been well understood. A number of previous attempts have been made to compute Marangoni flow but all such attempts have assumed that the surfactant transport in the liquid is the key. This paper presents calculations of liquid film flows driven by the surfactant as it arrives at the surface from the vapor. This completely new model involves several very interesting features. A two-dimension numerical simulation of such a flow is reported here. The geometry is a thin film (3mm × 10cm) of liquid water with a small patch of cooling centered on the bottom side. The upper surface is exposed to steam+surfactant vapor so that condensation will occur. The model computes the surface concentration of surfactant and relates that to the surface tension. Calculations show that the condensation rate is much higher when Marangoni convection is occurring, even if the surfactant concentration in the vapor is very small (120ppm). This is in accordance with observations in absorption chillers. The result helps to clarify understanding of surfactant-induced enhancement of heat and mass transfer in absorption chillers.
AssistSR: Task-oriented Question-driven Video Segment Retrieval

It is still a pipe dream that AI assistants on phone and AR glasses can assist our daily life in addressing our questions like “how to adjust the date for this watch? ” and “how to set its heating duration? (while pointing at an oven)”. The queries used in conventional tasks (i.e. Video Question Answering, Video Retrieval, Moment Localization) are often factoid and based on pure text. In contrast, we present a new task called Task-oriented Question-driven Video Segment Retrieval (TQVSR). Each of our questions is an image-box-text query that focuses on affordance of items in our daily life and expects relevant answer segments to be retrieved from a corpus of instructional video-transcript segments. To support the study of this TQVSR task, we construct a new dataset called AssistSR. We design novel guidelines to create high-quality samples. This dataset contains 1.4k multimodal questions on 1k video segments from instructional videos on diverse daily-used items. To address TQVSR, we develop a straightforward yet effective model called Dual Multimodal Encoders (DME) that signiﬁcantly outperforms several baseline methods while still having large room for improvement in the future. Moreover, we present detailed ablation analyses. Our codes and data are available at https://github.com/StanLei52/ AQVSR .
Mathematical modelling of noise generation in turbofan aeroengines using Green's functions

With demand for aircraft travel set to double in the next twenty years, targets are in place to reduce noise levels and emissions. For example, one target is that the effective perceived noise from aircraft in 2020 should be half of the 2000 level. One of the key noise components is the aeroengine. Building and designing an aeroengine costs millions of pounds and furthermore, to prove the aeroengine is safe, it has to be tested to destruction. Engineers and mathematicians are employed to design aeroengines that will not only be quieter but more fuel efficient and produce fewer harmful emissions while maintaining or improving performance. The main topic of this thesis is investigating rotor-stator interaction which occurs when the turbulent, swirling air produced by the rotor hits the stator and generates noise. We do this in two distinct ways, firstly we calculate the Green’s function for pressure in a turbofan duct with swirling mean flow and secondly we investigate the effect of turbulence hitting an isolated aerofoil. The Green’s function allows engineers to calculate the noise from rotor-stator interaction in simple cases and can be used in beamforming to analyse noise sources in the aeroengine. We consider an infinite duct, and use the Euler equations to derive a sixth order partial differential equation for pressure in the duct. We then find a Green’s function of this equation, which can be done numerically or analytically using high-frequency asymptotics. Our main interest is the analytic Green’s function, which we compare to numerical results. We begin by assuming the base flow has shear and swirling components in a constantly lined duct, and our analytic Green’s function is a new result. We then calculate the Green’s function for a base flow with variable entropy and a lining that varies with circumferential position. To consider flow-blade interaction we simulate the turbulent wake of the rotor hitting a single stator blade. Tests in wind tunnels have shown that, depending on the parameters, introducing a serration on the leading edge of the aerofoil can reduce the noise significantly. We build an analytical model to investigate the effect of the serrated edge, which again involves solving a differential equation by using a Green’s function. It also requires modelling the turbulence, which we do by using either deterministic eddies or stochastic eddies. We show it is possible to reduce the noise by using a serrated leading edge, but it is hard to predict the correct choice of serration to minimise the noise.
Hippocampal Activity in Real and Virtual Environments

Author(s): Mashhadi Aghajan, Zahra | Advisor(s): Mehta, Mayank R | Abstract: The hippocampal formation has long been implicated in the formation of episodic memories. Equally, the activity pattern of neurons in these brain regions is thought to serve as a neural substrate for spatial representation thus constructing a cognitive map of space. This spatial aspect of hippocampal activity has been extensively studied over the past decades, particularly in rodents. In addition, hippocampal neurons exhibit a robust temporal code, termed phase precession, which is hypothesized to facilitate sequential learning and complement the hippocampal spatial code.Despite decades-long research, the underlying mechanisms governing the hippocampal spatial and temporal codes remain poorly understood. It has been shown that multiple sensory and motor inputs reach the hippocampus and can modulate its activity. In real world (RW) environments however, the contributions of these inputs are confounded. Thus, to dissociate these contributions and thereby elucidate the mechanisms of spatial selectivity, we used a virtual reality (VR) setup.We found comparable levels of hippocampal spatiotemporal selectivity on linear tracks in RW and VR. In contrast, during random foraging in two-dimensions, spatial selectivity was severely diminished in VR. Nevertheless, most spikes occurred within ~2-s-long hippocampal motifs—with similar structure to that in RW—within which the hippocampal temporal code was intact, demonstrating a decoupling between the spatial and the temporal codes. These observations also impose constraints on the validity of phase precession models. Further, additional experiments and analyses revealed significant directional selectivity in the hippocampus in RW and VR, challenging commonly held beliefs about hippocampal directionality. Notably, contrary to the impairment of spatial selectivity in VR, the degree of directional selectivity was identical in both worlds and was determined by the angular information contained in the visual cues. Similar to place cells, grid cells in the medial entorhinal cortex (MEC) lost their spatial selectivity and periodic firing pattern in VR. Finally, most MEC head-direction cells did not maintain their selectivity, though there was a small but significant subset of head-direction cells that displayed selectivity with respect to the visual cues in VR.These results suggest that visual cues alone are insufficient to generate a stable localized representation in the spatial domain in place cells and grid cells. Conversely, they are sufficient to elicit—and play a causal role in—hippocampal directional selectivity and drive directional responses in a subset of head-direction cells. Additionally, these findings are reminiscent of the reports in human and non-human primates and can thus bridge the gap between these studies. Taken together, the investigation of the influence of multisensory inputs on hippocampal responses can provide insight into the underlying neural mechanisms of spatial representation in the brain.
Crux, space constraints and subdivisions

The existence of $H$-subdivisions within a graph $G$ has deep connections with topological, structural and extremal properties of $G$. One prominent example of such a connection, due to Bollob\‘{a}s and Thomason and independently Koml\‘os and Szemer\‘edi, asserts that the average degree of $G$ being $d$ ensures a $K_{\Omega(\sqrt{d})}$-subdivision in $G$. Although this square-root bound is the best possible, various results showed that much larger clique subdivisions can be found in a graph for many natural classes. We investigate the connection between crux, a notion capturing the essential order of a graph, and the existence of large clique subdivisions. Our main result gives an asymptotically optimal bound on the size of a largest clique subdivision in a generic graph $G$, which is determined by both its average degree and its crux size. As corollaries, we obtain \begin{itemize} \item a characterisation of extremal graphs for which the square-root bound above is tight: they are essentially a disjoint union of graphs each of which has the crux size linear in $d$; \item a unifying approach to find a clique subdivision of almost optimal size in graphs which do not contain a fixed bipartite graph as a subgraph; \item and that the clique subdivision size in random graphs $G(n,p)$ witnesses a dichotomy: when $p = \omega(n^{-1/2})$, the barrier is the space, while when $p=o( n^{-1/2})$, the bottleneck is the density. \end{itemize}
Probing Commonsense Knowledge in Pre-trained Language Models with Sense-level Precision and Expanded Vocabulary

Progress on commonsense reasoning is usually measured from performance improvements on Question Answering tasks designed to require commonsense knowledge. However, fine-tuning large Language Models (LMs) on these specific tasks does not directly evaluate commonsense learned during pre-training. The most direct assessments of commonsense knowledge in pre-trained LMs are arguably cloze-style tasks targeting commonsense assertions (e.g., A pen is used for [MASK].). However, this approach is restricted by the LM's vocabulary available for masked predictions, and its precision is subject to the context provided by the assertion. In this work, we present a method for enriching LMs with a grounded sense inventory (i.e., WordNet) available at the vocabulary level, without further training. This modification augments the prediction space of cloze-style prompts to the size of a large ontology while enabling finer-grained (sense-level) queries and predictions. In order to evaluate LMs with higher precision, we propose SenseLAMA, a cloze-style task featuring verbalized relations from disambiguated triples sourced from WordNet, WikiData, and ConceptNet. Applying our method to BERT, producing a WordNet-enriched version named SynBERT, we find that LMs can learn non-trivial commonsense knowledge from self-supervision, covering numerous relations, and more effectively than comparable similarity-based approaches.
Numerical Radiative Transfer

Preface Introduction Part I. Operator Perturbation: 1. Survey of operator perturbation methods W. Kalkofen 2. Line formation in expanding atmospheres: multilevel calculations using approximate lambda operators W. R. Hamann 3. Stellar atmospheres in non-LTE: model construction and line formation calculations using approximate lambda operators K. Werner 4. Acceleration of convergence L. H. Auer 5. Line formation in a time-dependent atmosphere W. Kalkofen 6. Iterative solution of multilevel transfer problems Eugene H. Avrett and Rudolf Loeser 7. An algorithm for the simultaneous solution of thousands of transfer equations under global constraints Lawrence S. Anderson 8. Operator perturbation for differential equations W. Kalkofen Part II. Polarised Radiation: 9. A gentle introduction to polarised radiative transfer David E. Rees 10. Non-LTE polarised radiative transfer in special lines David E. Rees and Graham A. Murphy 11. Transfer of polarised radiation using 4x4 matrices E. Landi Degli'Innocenti 12. Radiative transfer in the presence of strong magnetic fields A. A. van Ballegooijen 13. An integral operator technique of radiative transfer in spherical symmetry A. Peraiah 14. Discrete ordinate matrix method M. Schmidt and R. Wehrse.
On stochastic complexity and nonparametric density estimation

SUMMARY We use the concepts of stochastic complexity, description length, and model selection to develop data-based methods for choosing smoothing parameters in nonparametric density estimation. In the case of histogram estimators, we derive a simple, exact formula for stochastic complexity when the prior distribution of cell probabilities is uniform over the class of all possible choices. The formula depends only on the data and the smoothing parameter, which is readily chosen according to the criterion of minimum stochastic complexity. Approaches based on stochastic complexity and description length are shown to be asymptotically equivalent in certain circumstances. They produce a degree of smoothing which is almost optimal from the viewpoint of minimizing L? , or supremum, distance, but which smooths a little more than is optimal in the sense of minimizing L' distance for any finite value of r.
A note on degree theory for gradient mappings

In this note we give a simple proof for the essentially known fact, that the Leray-Schauder degree of the gradient of a coercive functional on a large ball of a Hilbert space is one. As a simple application we show that the local index of an isolated local minimum of a Cl-functional on a Hilbert space equals one. 1. Let U be an open subset of some real Hilbert space H and suppose that the gradient Vf: U -H of a given function f E C1 (U, R) is a compact vector field, that is, Vf = id F, where F E C(U, H) maps bounded sets into compact sets. In this note we give a simple proof for the following useful theorem, where B(xo, r) denotes the open ball in H, with center x0 and radius r, and a bar denotes the closure in H. THEOREM. Suppose that, for some / E R, the set V: f 1(-oo, 3) is bounded and V C U. Moreover, suppose that there are numbers a 0, and a point xo E U such that f1(-oo,a] C B(xo,r) C V and Vf(x) $0 Vx E f-1 [a, 3]. Then deg(Vf, V, 0) = 1. As easy consequences of this theorem we obtain the following corollaries: COROLLARY 1. Suppose that U = H and f(x) -oo as |lx|| _ oo. Moreover, suppose that Vf(x) $4 0 for llxii > rO and some rO > 0. Then there is a number r, > rO such that deg(Vf, B(O, r), 0) = 1 Vr > rl. PROOF. Observe that f(x) = (iixi12/2) 4? (x), where Vl) = F. Since F is compact, 4) is weakly sequentially continuous (on convex sets) by Vainberg's theorem [10, Theorem 8.2]. Hence f maps bounded sets into bounded sets. Thus, let a:= supf(B(O,ro)) and ri := sup{llxll Ix E f 1(-oo,a]}. Moreover, given r > r1, fix /3 > sup f(B(O, r)). Then the assertion follows from the theorem (with xo = 0) and the excision property of the degree. El In the following corollary we denote by i(Vf, xo) the index of Vf at an isolated zero x0. Received by the editors November 16, 1981. 1980 Mathematics Subject Classification. Primary 58E05; Secondary 47H15, 47H10, 34G20.
Inequalities concerning polar derivative of polynomials

In this paper we obtain certain results for the polar derivative of a polynomial \(p(z) = c_nz^n +\sum_{j=\mu}^n c_{n-j}z^{n-j}\), \(1\leq\mu\leq n\), having all its zeros on \(|z| = k\), \(k\leq 1\), which generalizes the results due to Dewan and Mir, Dewan and Hans. We also obtain certain new inequalities concerning the maximum modulus of a polynomial with restricted zeros. [Editor's note: There are flaws in the paper, see M. A. Qazi, Remarks on some recent results about polynomials with restricted zeros, Ann. Univ. Mariae Curie-Sklodowska Sect. A 67 (2), (2013), 59-64 ]
Detection of susceptible Norway spruce to bark beetle attack using PlanetScope multispectral imagery

Climate change-related acute or long-term drought stress can weaken forest ecosystems and result in widespread bark beetle infestations. Eurasian spruce bark beetle (Ips typographus L.) infestations have been occurring in Norway spruce [Picea abies (L.) Karst. ]-dominated forests in central Europe including the Czechia. These infestations appear regularly, especially in homogeneous spruce stands, and the impact varies with the climate-induced water stress conditions. The removal of infected trees before the beetles leave the bark is an important step in forest pest management. Early identification of susceptible trees to infestations is also very important but quite challenging since stressed tree-tops show no sign of discolouration in the visible spectrum. We investigated if individual spectral bandwidths or developed spectral vegetation indices (SVIs), can be used to differentiate non-attacked trees, assumed to be healthy, from trees susceptible to attacks in the later stages of a growing season. And, how the temporal-scale patterns of individual bands and developed SVIs of susceptible trees to attacks, driven by changes in spectral characteristics of trees, behave differently than those patterns observed for healthy trees. The multispectral imagery from the PlanetScope satellite coupled with field data were used to statistically test the competency of the individual band and/or developed SVIs to differentiate two designated classes of healthy and susceptible trees. We found significant differences between SVIs of the susceptible and healthy spruce forests using the Enhanced Vegetation Index (EVI) and Visible Atmospherically Resistant Index (VARI). The accuracy for both indices ranged from 0.7 to 0.78; the highest among all examined indices. The results indicated that the spectral differences between the healthy and susceptible trees were present at the beginning of the growing season before the attacks. The existing spectral differences, likely caused by water-stress stimuli such as droughts, may be a key to detecting forests susceptible to early infestations. Our introduced methodology can also be applied in future research, using new generations of the PlanetScope imagery, to assess forests susceptibility to bark beetle infestations early in the growing season.
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges

There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we apply a common equation-discovery technique with expansive libraries to learn closures from filtered direct numerical simulations of 2D turbulence and Rayleigh-B\'enard convection (RBC). Across common filters (e.g., Gaussian, box), we robustly discover closures of the same form for momentum and heat fluxes. These closures depend on nonlinear combinations of gradients of filtered variables, with constants that are independent of the fluid/flow properties and only depend on filter type/size. We show that these closures are the nonlinear gradient model (NGM), which is derivable analytically using Taylor-series. Indeed, we suggest that with common (physics-free) equation-discovery algorithms, for many common systems/physics, discovered closures are consistent with the leading term of the Taylor-series (except when cutoff filters are used). Like previous studies, we find that large-eddy simulations with NGM closures are unstable, despite significant similarities between the true and NGM-predicted fluxes (correlations $>0.95$). We identify two shortcomings as reasons for these instabilities: in 2D, NGM produces zero kinetic energy transfer between resolved and subgrid scales, lacking both diffusion and backscattering. In RBC, potential energy backscattering is poorly predicted. Moreover, we show that SGS fluxes diagnosed from data, presumed the ''truth'' for discovery, depend on filtering procedures and are not unique. Accordingly, to learn accurate, stable closures in future work, we propose several ideas around using physics-informed libraries, loss functions, and metrics. These findings are relevant to closure modeling of any multi-scale system.
Signature Schemes in the Quantum Random-Oracle Model

A signature scheme is a fundamental component in modern digital communication. It allows for authenticated messages, without which it would be nearly impossible to ensure security when using most modern technologies. However, there is a growing threat to this fundamental piece of electronic infrastructure. Universal quantum computers, which were originally envisioned by Richard Feynman, have moved from being a theoretical future technology into one that could realistically be available in a matter of decades. In 1994, Peter Shor devised an algorithm that would run on a quantum computer that could be used to solve mathematical problems that formed the foundation of public-key cryptography. While Shor’s algorithm clearly establishes that new mathematical problems must be found and studied that can admit efficient cryptographic protocols, it is equally important that the models in which we consider security are also updated to consider the possibility of a malicious adversary having a quantum computer. In the random-oracle model, a hash function is replaced by a truly random function that any relevant party is able to query. This model can enable security reductions where otherwise none are known. However, it has been noted that this model does not properly consider the possibility of a quantum computer. For this, we must instead consider the quantum random-oracle model. In this thesis, we explain the basics of quantum physics and quantum computation in order to give a complete motivation for the quantum random-oracle model. We explain many of the difficulties that may be encountered in the quantum random-oracle model, and how some of these problems may be solved. We then show prove three signature schemes secure in the quantum random-oracle model: the LMS hash-based scheme, TESLA, a lattice-based scheme, and the TOO transformation using chameleon hashes. The first two schemes are strong candidates for post-quantum standardization.