Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Sun-aligned Outdoor Scene Relighting based on TensoRF

In this work, we introduce our method of outdoor scene relighting for Neural Radiance Fields (NeRF) named Sun-aligned Relighting TensoRF (SR-TensoRF). SR-TensoRF offers a lightweight and rapid pipeline aligned with the sun, thereby achieving a simplified workflow that eliminates the need for environment maps. Our sun-alignment strategy is motivated by the insight that shadows, unlike viewpoint-dependent albedo, are determined by light direction. We directly use the sun direction as an input during shadow generation, simplifying the requirements of the inference process significantly. Moreover, SR-TensoRF leverages the training efficiency of TensoRF by incorporating our proposed cubemap concept, resulting in notable acceleration in both training and rendering processes compared to existing methods.
Orbit separation dimension as complexity measure for primitive inflation tilings

Orbit separation dimension (OSD), previously introduced as amorphic complexity, is a powerful complexity measure for topological dynamical systems with pure-point spectrum. Here, we develop methods and tools for it that allow a systematic application to translation dynamical systems of tiling spaces that are generated by primitive inflation rules. These systems share many nice properties that permit the explicit computation of the OSD, thus providing a rich class of examples with non-trivial OSD.
Main belt asteroids taxonomical information from dark energy survey data

While proper orbital elements are currently available for more than 1 million asteroids, taxonomical information is still lagging behind. Surveys like SDSS-MOC4 provided preliminary information for more than 100 000 objects, but many asteroids still lack even a basic taxonomy. In this study, we use Dark Energy Survey (DES) data to provide new information on asteroid physical properties. By cross-correlating the new DES data base with other data bases, we investigate how asteroid taxonomy is reflected in DES data. While the resolution of DES data is not sufficient to distinguish between different asteroid taxonomies within the complexes, except for V-type objects, it can provide information on whether an asteroid belongs to the C- or S-complex. Here, machine learning methods optimized through the use of genetic algorithms were used to predict the labels of more than 68 000 asteroids with no prior taxonomic information. Using a high-quality, limited set of asteroids with data on gri slopes and i − z colours, we detected 409 new possible V-type asteroids. Their orbital distribution is highly consistent with that of other known V-type objects.
Cup Curriculum: Curriculum Learning on Model Capacity

Curriculum learning (CL) aims to increase the performance of a learner on a given task by applying a specialized learning strategy. This strategy focuses on either the dataset, the task, or the model. There is little to no work analysing the possibilities to apply CL on the model capacity in natural language processing. To close this gap, we propose the cup curriculum. In a first phase of training we use a variation of iterative magnitude pruning to reduce model capacity. These weights are reintroduced in a second phase, resulting in the model capacity to show a cup-shaped curve over the training iterations. We empirically evaluate different strategies of the cup curriculum and show that it outperforms early stopping reliably while exhibiting a high resilience to overfitting.
Unsupervised Video Summarization

This paper introduces a new, unsupervised method for automatic video summarization using ideas from generative adversarial networks but eliminating the discriminator, having a simple loss function, and separating training of different parts of the model. An iterative training strategy is also applied by alternately training the reconstructor and the frame selector for multiple iterations. Furthermore, a trainable mask vector is added to the model in summary generation during training and evaluation. The method also includes an unsupervised model selection algorithm. Results from experiments on two public datasets (SumMe and TVSum) and four datasets we created (Soccer, LoL, MLB, and ShortMLB) demonstrate the effectiveness of each component on the model performance, particularly the iterative training strategy. Evaluations and comparisons with the state-of-the-art methods highlight the advantages of the proposed method in performance, stability, and training efficiency.
Culture and Consumer Behavior—A Study of Trinidad & Tobago and Jamaica

This study examines the impact of culture on consumer behavior in two Caribbean countries namely Trinidad & Tobago, and Jamaica. A set of hypotheses are developed to understand the impact of culture on consumer behavior. Data is collected by surveying consumers from both countries. The findings of the research suggest there is a relationship between family structure, language (local dialect), values and beliefs on consumer behavior in both Trinidad & Tobago and Jamaica.
Polyregular Functions on Unordered Trees of Bounded Height

We consider injective first-order interpretations that input and output trees of bounded height. The corresponding functions have polynomial output size, since a first-order interpretation can use a k-tuple of input nodes to represent a single output node. We prove that the equivalence problem for such functions is decidable, i.e. given two such interpretations, one can decide whether, for every input tree, the two output trees are isomorphic. We also give a calculus of typed functions and combinators which derives exactly injective first-order interpretations for unordered trees of bounded height. The calculus is based on a type system, where the type constructors are products, coproducts and a monad of multisets. Thanks to our results about tree-to-tree interpretations, the equivalence problem is decidable for this calculus. As an application, we show that the equivalence problem is decidable for first-order interpretations between classes of graphs that have bounded tree-depth. In all cases studied in this paper, first-order logic and MSO have the same expressive power, and hence all results apply also to MSO interpretations.
Real rank of extensions of C*-algebras

Given a closed ideal $I$ in a C*-algebra $A$, we develop techniques to bound the real rank of $A$ in terms of the real ranks of $I$ and $A/I$. Building on work of Brown, Lin and Zhang, we obtain complete solutions if $I$ belongs to any of the following classes: (1) C*-algebras with real rank zero, stable rank one and vanishing $K_1$-group; (2) simple, purely infinite C*-algebras; (3) simple, $\mathcal{Z}$-stable C*-algebras with real rank zero; (4) separable, stable C*-algebras with an approximate unit of projections and the Corona Factorization Property.
SoundCam: A Dataset for Finding Humans Using Room Acoustics

A room's acoustic properties are a product of the room's geometry, the objects within the room, and their specific positions. A room's acoustic properties can be characterized by its impulse response (RIR) between a source and listener location, or roughly inferred from recordings of natural signals present in the room. Variations in the positions of objects in a room can effect measurable changes in the room's acoustic properties, as characterized by the RIR. Existing datasets of RIRs either do not systematically vary positions of objects in an environment, or they consist of only simulated RIRs. We present SoundCam, the largest dataset of unique RIRs from in-the-wild rooms publicly released to date. It includes 5,000 10-channel real-world measurements of room impulse responses and 2,000 10-channel recordings of music in three different rooms, including a controlled acoustic lab, an in-the-wild living room, and a conference room, with different humans in positions throughout each room. We show that these measurements can be used for interesting tasks, such as detecting and identifying humans, and tracking their positions.
P-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models

The rapid development of language models (LMs) brings unprecedented accessibility and usage for both models and users. On the one hand, powerful LMs, trained with massive textual data, achieve state-of-the-art performance over numerous downstream NLP tasks. On the other hand, more and more attention is paid to unrestricted model accesses that may bring malicious privacy risks of data leakage. To address these issues, many recent works propose privacy-preserving language models (PPLMs) with differential privacy (DP). Unfortunately, different DP implementations make it challenging for a fair comparison among existing PPLMs. In this paper, we present P-Bench, a multi-perspective privacy evaluation benchmark to empirically and intuitively quantify the privacy leakage of LMs. Instead of only protecting and measuring the privacy of protected data with DP parameters, P-Bench sheds light on the neglected inference data privacy during actual usage. P-Bench first clearly defines multi-faceted privacy objectives during private fine-tuning. Then, P-Bench constructs a unified pipeline to perform private fine-tuning. Lastly, P-Bench performs existing privacy attacks on LMs with pre-defined privacy objectives as the empirical evaluation results. The empirical attack results are used to fairly and intuitively evaluate the privacy leakage of various PPLMs. We conduct extensive experiments on three datasets of GLUE for mainstream LMs.
A Narrow Boundary Current Along the Eastern Side of the Hawaiian Ridge; The North Hawaiian Ridge Current

Abstract Mesoscale baroclinic circulation features in the vicinity of the Hawaiian Ridge are delineated through the construction of the horizontal distribution of mean 300 m temperature on a 0.5° latitude/longitude grid. This high resolution is made possible through the gridding of all XBT observations taken in the area from 1968–80. Of particular interest is the discovery of a narrow band of northward shear flow, extending along the eastern side of the Hawaiian Ridge from 19.5–21.5°N. This band of shear flow has a width of 75–100 km, associated with an estimated near-surface speed (100/400 db) of ∼45 cm s−1. This narrow boundary current is associated with recirculation in a small, but intense, anticyclonic gyre that parallels the Hawaiian Ridge; it does not appear to be related to the North Equatorial Current that impinges upon the Hawaiian Ridge from the northeast. Because this current had not previously been observed, it is called the North Hawaiian Ridge Current.
Fluctuation Spectrum of 2+1D Critical Fermi Surface and its Application to Optical Conductivity and Hydrodynamics

We extend the kinetic operator formalism developed in the companion paper [H.Guo,arXiv:2311.03455] to study the general eigenvalues of the fluctuation normal modes. We apply the formalism to calculate the optical conductivity of a critical Fermi surface near the Ising-Nematic quantum critical point. We find that the conductivity is the sum of multiple conduction channels including both the soft and non-soft eigenvectors of the kinetic operator, and therefore it is not appropriate to interpret the optical conductivity using extended Drude formula for momentum conserved systems. We also show that the propagation of the FS soft modes is governed by a Boltzmann equation from which hydrodynamics emerges. We calculate the viscosity and it shows clear signature of the non-Fermi liquid physics.
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction

Product attribute value extraction is an important task in e-Commerce which can help several downstream applications such as product search and recommendation. Most previous models handle this task using sequence labeling or question answering method which rely on the sequential position information of values in the product text and are vulnerable to data discrepancy between training and testing. This limits their generalization ability to real-world scenario in which each product can have multiple descriptions across various shopping platforms with different composition of text and style. They also have limited zero-shot ability to new values. In this paper, we propose a multi-task learning model with value generation/classification and attribute prediction called JPAVE to predict values without the necessity of position information of values in the text. Furthermore, the copy mechanism in value generator and the value attention module in value classifier help our model address the data discrepancy issue by only focusing on the relevant part of input text and ignoring other information which causes the discrepancy issue such as sentence structure in the text. Besides, two variants of our model are designed for open-world and closed-world scenarios. In addition, copy mechanism introduced in the first variant based on value generation can improve its zero-shot ability for identifying unseen values. Experimental results on a public dataset demonstrate the superiority of our model compared with strong baselines and its generalization ability of predicting new values.
Multi-View Causal Representation Learning with Partial Observability

We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities. We allow a partially observed setting in which each view constitutes a nonlinear mixture of a subset of underlying latent variables, which can be causally related. We prove that the information shared across all subsets of any number of views can be learned up to a smooth bijection using contrastive learning and a single encoder per view. We also provide graphical criteria indicating which latent variables can be identified through a simple set of rules, which we refer to as identifiability algebra. Our general framework and theoretical results unify and extend several previous works on multi-view nonlinear ICA, disentanglement, and causal representation learning. We experimentally validate our claims on numerical, image, and multi-modal data sets. Further, we demonstrate that the performance of prior methods is recovered in different special cases of our setup. Overall, we find that access to multiple partial views enables us to identify a more fine-grained representation, under the generally milder assumption of partial observability.
Universal productivity patterns in research careers

A common expectation is that career productivity peaks rather early and then gradually declines with seniority. But whether this holds true is still an open question. Here we investigate the productivity trajectories of almost 8,500 scientists from over fifty disciplines using methods from time series analysis, dimensionality reduction, and network science, showing that there exist six universal productivity patterns in research. Based on clusters of productivity trajectories and network representations where researchers with similar productivity patterns are connected, we identify constant, u-shaped, decreasing, periodic-like, increasing, and canonical productivity patterns, with the latter two describing almost three-fourths of researchers. In fact, we find that canonical curves are the most prevalent, but contrary to expectations, productivity peaks occur much more frequently around mid-career rather than early. These results outline the boundaries of possible career paths in science and caution against the adoption of stereotypes in tenure and funding decisions.
The Mechum River Formation, Virginia Blue Ridge: A Record of Neoproterozoic and Paleozoic Tectonics in Southeastern Laurentia

Neoproterozoic metasedimentary rocks of the Mechum River Formation crop out in an elongate northeast-southwest trending belt in the central Virginia Blue Ridge province. The northwestern contact of the Mechum River Formation is nonconformable above Mesoproterozoic basement, whereas the southeastern contact is a set of steeply dipping, brittle reverse faults that juxtapose basement against Mechum River rocks. The internal structure of the belt consists of northwest-verging, open to tight, moderately to steeply plunging asymmetric folds. Although previous workers have interpreted the outcrop belt of the Mechum River Formation as a graben formed during Laurentian rifting, structural relations require that its present geometry is a structural inlier related to Paleozoic contractional deformation. Strain analysis reveals that grain-scale deformation processes produced up to 35 percent shortening during foliation development whereas map-scale folds account for an additional 30 to 50 percent shortening. Restoration of the Mechum River Formation to its pre-contractional geometry reveals little about the geometry of the original depositional basin. The bounding reverse faults on the east side of the Mechum River Formation are interpreted as out-of-sequence structures that developed after regional folding, metamorphism and foliation development that may be related to the emplacement of the Blue Ridge thrust sheet over a tectonic ramp. Sediment transport indicators are consistent with a source area to the east, suggesting that the Mechum River Formation was separated from similar units in the eastern Blue Ridge by an asymmetric basement high that may have been produced by block rotation above listric normal faults.
Spectral functions of the strongly interacting 3D Fermi gas

Computing dynamical properties of strongly interacting quantum many-body systems poses a major challenge to theoretical approaches. Usually, one has to resort to numerical analytic continuation of results on imaginary frequencies, which is a mathematically ill-defined procedure. Here, we present an efficient method to compute the spectral functions of the two-component Fermi gas near the strongly interacting unitary limit directly in real frequencies. To this end, we combine the Keldysh path integral that is defined in real time with the self-consistent T-matrix approximation. The latter is known to predict thermodynamic and transport properties in good agreement with experimental observations in ultracold atoms. We validate our method by comparison with thermodynamic quantities obtained from imaginary time calculations and by transforming our real-time propagators to imaginary time. By comparison with state-of-the-art numerical analytic continuation of the imaginary time results, we show that our real-time results give qualitative improvements for dynamical quantities. Moreover, we show that no significant pseudogap regime exists in the self-consistent T-matrix approximation above the critical temperature $T_c$, an issue that has been under significant debate. We close by pointing out the versatile nature of our method as it can be extended to other systems, like the spin- or mass-imbalanced Fermi gas, other Bose-Fermi models, 2D systems as well as systems out of equilibrium.
Revisiting Loop Corrections in Single Field USR Inflation

We revisit the one-loop corrections on CMB scale perturbations induced from small scale modes in single field models which undergo a phase of ultra slow-roll inflation. There were concerns that large loop corrections are against the notion of the decoupling of scales and they are cancelled out once the boundary terms are included in Hamiltonian. We highlight that the non-linear coupling between the long and short modes and the modulation of the short mode power spectrum by the long mode are the key physical reasons behind the large loop corrections. In particular, in order for the modulation by the long mode to be significant there should be a strong scale-dependent enhancement in power spectrum of the short mode which is the hallmark of the USR inflation. We highlight the important roles played by the would-be decaying mode which were not taken into account properly in recent works claiming the loop cancellation. We confirm the original conclusion that the loop corrections are genuine and they can be dangerous for PBHs formation unless the transition to the final attractor phase is mild.
Dynamics of Carrollian Scalar Fields

Adopting an intrinsic Carrollian viewpoint, we show that the generic Carrollian scalar field action is a combination of electric and magnetic actions, found in the literature by taking the Carrollian limit of the relativistic scalar field. This leads to non-trivial dynamics: even a single particle with non-vanishing energy can move in Carrollian physics.
Enhancing the Chemical Stability and Photovoltaic Properties of Highly Efficient Nonfullerene Acceptors by Chalcogen Substitution: Insights from Quantum Chemical Calculations

The chemical stability of nonfullerene acceptor (NFA) is the Achilles' heel of the research on state-of-the-art organic solar cells (OSC). The fragility of the NFA is essentially due to the weak bond that links the central donor core of the molecules with their acceptor moieties at the edges. Here we proposed the replacement of thiophene at the outer-core position of traditional NFAs for tellurophene, a hitherto unexplored modification. Since tellurium is a distinctive element among chalcogens, the basic features of Te compounds cannot be deduced straightforwardly from the properties of their lighter analogues, S and Se. The modeled Te-based NFAs presented interesting features like stronger intra- and intermolecular interactions induced by a distinctive secondary bond effect between the end acceptor moiety and the outer chalcogen atom. This design strategy resulted in stiffer molecules with red-shifted absorption spectra and less susceptible to degradation verified through stress tests and vibrational spectra analysis. Besides that, a weakened exciton binding energy has been found, opening the possibility of blends with a lower driving force. Our results shed light on several aspects of selenation and telluration of traditional NFAs, providing valuable insights into the possible consequences for OSCs applications.
Relação entre indicadores de gestão pública e de desenvolvimento dos municípios brasileiros

The objective of this research was to evaluate the relationship between governance indicators (IFGF - Fiscal Management FIRJAN Index) and the development indicators of Brazilian municipalities (IFDM - Municipal Development FIRJAN Index). Annual measurement data were used, arranged in a panel, and considered the years 2006 to 2011. Brazilian cities were seen to have average levels of moderate development and municipal management indicators in difficulty criticism, excluding debt cost and personnel expenses, the two variables that were most closely monitored by the Fiscal Responsibility Law over time. It was found that there are seasonal and cyclical changes in municipal public investments, whose trajectories coincide with periods before elections. The results of the panel data regressions showed that municipal development was explained in 33.75% of the cases by all the indicators of municipal management with municipal GDP.