Miracle-dz/UniFAR-FTUs huggingface dataset:
Counterfactual Phenotyping with Censored Time-to-Events

Estimation of treatment efficacy of real-world clinical interventions involves working with continuous time-to-event outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring. Counterfactual reasoning in such scenarios requires decoupling the effects of confounding physiological characteristics that affect baseline survival rates from the effects of the interventions being assessed. In this paper, we present a latent variable approach to model heterogeneous treatment effects by proposing that an individual can belong to one of latent clusters with distinct response characteristics. We show that this latent structure can mediate the base survival rates and help determine the effects of an intervention. We demonstrate the ability of our approach to discover actionable phenotypes of individuals based on their treatment response on multiple large randomized clinical trials originally conducted to assess appropriate treatment strategies to reduce cardiovascular risk.
Observations of Instability-driven Nanojets in Coronal Loops

The recent discovery of nanojets by Antolin et al. represents magnetic reconnection in a braided field, thus clearly identifying reconnection-driven nanoflares. Due to their small scale (500 km in width, 1500 km in length) and short timescales (<15 s), it is unclear how pervasive nanojets are in the solar corona. In this paper, we present Interface Region Imaging Spectrograph and Solar Dynamics Observatory observations of nanojets found in multiple coronal structures, namely, in a coronal loop powered by a blowout jet, and in two other coronal loops with coronal rain. In agreement with previous findings, we observe that nanojets are accompanied by small nanoflare-like intensity bursts in the (E)UV, have velocities of 150–250 km s−1 and occur transversely to the field line of origin, which is sometimes observed to split. However, we find a variety of nanojet directions in the plane transverse to the loop axis. These nanojets are found to have kinetic and thermal energies within the nanoflare range, and often occur in clusters. In the blowout jet case study, the Kelvin–Helmholtz instability (KHI) is directly identified as the reconnection driver. For the other two loops, we find that both, KHI and Rayleigh–Taylor instability (RTI) are likely to be the drivers. However, we find that KHI and RTI are each more likely in one of the other two cases. These observations of nanojets in a variety of structures and environments support nanojets being a general result of reconnection that are driven here by dynamic instabilities.
Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses

Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of a media item remains poorly understood. In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analysis to study how the auditory and visual modalities contribute to the perceived emotion of media. The data were collected by presenting music videos to participants in three conditions: music, visual, and audiovisual. Participants annotated the music videos for valence and arousal over time, as well as the overall emotion conveyed. We present detailed descriptive statistics for key measures in the dataset and the results of feature importance analyses for each condition. Finally, we propose a novel transfer learning architecture to train Predictive models Augmented with Isolated modality Ratings (PAIR) and demonstrate the potential of isolated modality ratings for enhancing multimodal emotion recognition. Our results suggest that perceptions of arousal are influenced primarily by auditory information, while perceptions of valence are more subjective and can be influenced by both visual and auditory information. The dataset is made publicly available.
On the Effectiveness of Adversarial Training against Backdoor Attacks

Although adversarial training (AT) is regarded as a potential defense against backdoor attacks, AT and its variants have only yielded unsatisfactory results or have even inversely strengthened backdoor attacks. The large discrepancy between expectations and reality motivates us to thoroughly evaluate the effectiveness of AT against backdoor attacks across various settings for AT and backdoor attacks. We find that the type and budget of perturbations used in AT are important, and AT with common perturbations is only effective for certain backdoor trigger patterns. Based on these empirical findings, we present some practical suggestions for backdoor defense, including relaxed adversarial perturbation and composite AT. This work not only boosts our confidence in AT's ability to defend against backdoor attacks but also provides some important insights for future research.
Topological Universality of the Art Gallery Problem

We prove that any compact semi-algebraic set is homeomorphic to the solution space of some art gallery problem. Previous works have established similar universality theorems, but holding only up to homotopy equivalence, rather than homeomorphism, and prior to this work, the existence of art galleries even for simple spaces such as the M\"obius strip or the three-holed torus were unknown. Our construction relies on an elegant and versatile gadget to copy guard positions with minimal overhead. It is simpler than previous constructions, consisting of a single rectangular room with convex slits cut out from the edges. We show that both the orientable and non-orientable surfaces of genus $n$ admit galleries with only $O(n)$ vertices.
A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges

Long-range (LoRa) technology is most widely used for enabling low-power wide area networks (WANs) on unlicensed frequency bands. Despite its modest data rates, it provides extensive coverage for low-power devices, making it an ideal communication system for many Internet of Things (IoT) applications. In general, LoRa is considered as the physical layer, whereas LoRaWAN is the medium access control (MAC) layer of the LoRa stack that adopts a star topology to enable communication between multiple end devices (EDs) and the network gateway. The chirp spread spectrum modulation deals with LoRa signal interference and ensures long-range communication. At the same time, the adaptive data rate mechanism allows EDs to dynamically alter some LoRa features, such as the spreading factor (SF), code rate, and carrier frequency to address the time variance of communication conditions in dense networks. Despite the high LoRa connectivity demand, LoRa signals interference and concurrent transmission collisions are major limitations. Therefore, to enhance LoRaWAN capacity, the LoRa Alliance released many LoRaWAN versions, and the research community has provided numerous solutions to develop scalable LoRaWAN technology. Hence, we thoroughly examine LoRaWAN scalability challenges and state-of-the-art solutions in both the physical and MAC layers. These solutions primarily rely on SF, logical, and frequency channel assignment, whereas others propose new network topologies or implement signal processing schemes to cancel the interference and allow LoRaWAN to connect more EDs efficiently. A summary of the existing solutions in the literature is provided at the end of the paper, describing the advantages and disadvantages of each solution and suggesting possible enhancements as future research directions.
Brain representation of perceptual stimuli at different levels of awareness

This article questions the widespread assumption that there are brain representations that will always remain unconscious in the sense of being « inaccessible to an individual's awareness under any circumstances ». This implies that some part of the knowledge generated by the brain is once and for always excluded from an individual's consciousness and, therefore, from being communicated to the outside world. This standpoint neglects the possibility that the human brain might have a capacity for generating meta-representations of non-conscious knowledge contents at a given moment in time through context-sensitive adaptive learning, and is somewhat difficult to reconcile with experimental findings showing that initially subliminal targets can be made available to awareness, or « break through » to supraliminal levels of processing, when they are embedded in the appropriate perceptual object context. Functional properties of neural network architectures inspired by the functional organization of the primate cortex are able to explain how a human brain could generate this kind of perceptual learning. Signals or knowledge processed outside awareness can be made available to awareness through adaptive resonance of bottom-up and top-down signal exchanges in massively parallel neural network architectures, in other words, on the basis of statistically significant signal matches in the domain of time and in the domain of contents.
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Data augmentation is commonly applied to improve performance of deep learning by enforcing the knowledge that certain transformations on the input preserve the output. Currently, the data augmentation parameters are chosen by human effort and costly cross-validation, which makes it cumbersome to apply to new datasets. We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network. Our approach relies on phrasing data augmentation as an invariance in the prior distribution on the functions of a neural network, which allows us to learn it using Bayesian model selection. This has been shown to work in Gaussian processes, but not yet for deep neural networks. We propose a differentiable Kronecker-factored Laplace approximation to the marginal likelihood as our objective, which can be optimised without human supervision or validation data. We show that our method can successfully recover invariances present in the data, and that this improves generalisation and data efficiency on image datasets.
Hierarchical Perceiver

General perception systems such as Perceivers can process arbitrary modalities in any combination and are able to handle up to a few hundred thousand inputs. They achieve this generality by using exclusively global attention operations. This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video. In this paper, we show that some degree of locality can be introduced back into these models, greatly improving their efficiency while preserving their generality. To scale them further, we introduce a self-supervised approach that enables learning dense low-dimensional positional embeddings for very large signals. We call the resulting model a Hierarchical Perceiver (HiP). In sum our contributions are: 1) scaling Perceiver-type models to raw high-resolution images and audio+video, 2) showing the feasibility of learning 1M+ positional embeddings from scratch using masked auto-encoding, 3) demonstrating competitive performance on raw data from ImageNet, AudioSet, PASCAL VOC, ModelNet40 and Kinetics datasets with the same exact, unchanged model and without specialized preprocessing or any tokenization.
Improving traffic safety and drivers' behavior in reduced visibility conditions

Moreover, two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data is advantageous for predicting VR crashes; LDs or AVIs. Thus, this research attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two Expressways (SR 408 and SR 417). Also, it investigates which data is better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression using the historical VR crashes, LDs and AVI data. Regarding models estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5-10 minute prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5-10 minute prior to the crash time, affected the likelihood of VR crash occurrence. Argument concerning which traffic data (LDs or AVI) is better for predicting VR crashes is also provided and discussed. ; The results revealed that gender, age, road type, visibility condition, and familiarity with VSL signs were the significant factors affecting the likelihood of reducing speed following CMS/VSL instructions in reduced visibility conditions. Other objectives of this survey study were to determine the content of messages that would achieve the best perceived safety and drivers' compliance and to examine the best way to improve safety during these adverse visibility conditions. The results indicated that "Caution-fog ahead-reduce speed" was the best message and using CMS and VSL signs together was the best way to improve safety during such inclement weather situations. In addition, this research aimed to thoroughly examine drivers' responses under low visibility conditions and quantify the impacts and values of various factors found to be related to drivers' compliance and drivers' satisfaction with VSL and CMS instructions in different visibility and traffic conditions. To achieve these goals, Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM) approaches were adopted. The results revealed that drivers' satisfaction with VSL/CMS was the most significant factor that positively affected drivers' compliance with advice or warning messages displayed on VSL/CMS signs under different fog conditions followed by driver factors. Moreover, it was found that roadway type affected drivers' compliance to VSL instructions under medium and heavy fog conditions. Furthermore, drivers' familiarity with VSL signs and driver factors were the significant factors affecting drivers' satisfaction with VSL/CMS advice under…
Canonical Portfolios: Optimal Asset and Signal Combination

This paper presents a novel framework for analyzing the optimal asset and signal combination problem. Our approach builds upon the dynamic portfolio selection problem introduced by Brandt and Santa-Clara (2006) and consists of two stages. First, we reformulate their original investment problem into a tractable one that allows us to derive a closed-form expression for the optimal portfolio policy that is scalable to large cross-sectional financial applications. Second, we recast the problem of selecting a portfolio of correlated assets and signals into selecting a set of uncorrelated managed portfolios through the lens of Canonical Correlation Analysis of Hotelling (1936). The new investment environment of uncorrelated managed portfolios offers unique economic insights into the joint correlation structure of our optimal portfolio policy. We also operationalize our theoretical framework to bridge the gap between theory and practice, showcasing the improved performance of our proposed method over natural competing benchmarks.
An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems

Express systems play important roles in modern major cities. Couriers serving for the express system pick up packages in certain areas of interest (AOI) during a specific time. However, future pick-up requests vary significantly with time. While the assignment results are generally static without changing with time. Using the historical pick-up request number to conduct AOI assignment (or pick-up request assignment) for couriers is thus unreasonable. Moreover, even we can first predict future pick-up requests and then use the prediction results to conduct the assignments, this kind of two-stage method is also impractical and trivial, and exists some drawbacks, such as the best prediction results might not ensure the best clustering results. To solve these problems, we put forward an intelligent end-to-end predict-then-optimize clustering method to simultaneously predict the future pick-up requests of AOIs and assign AOIs to couriers by clustering. At first, we propose a deep learning-based prediction model to predict order numbers on AOIs. Then a differential constrained K-means clustering method is introduced to cluster AOIs based on the prediction results. We finally propose a one-stage end-to-end predict-then-optimize clustering method to assign AOIs to couriers reasonably, dynamically, and intelligently. Results show that this kind of one-stage predict-then-optimize method is beneficial to improve the performance of optimization results, namely the clustering results. This study can provide critical experiences for predict-and-optimize related tasks and intelligent assignment problems in express systems.
Movies2Scenes: Learning Scene Representations Using Movie Similarities

Automatic understanding of movie-scenes is an important problem with multiple downstream applications including video-moderation, search and recommendation. The long-form nature of movies makes labeling of movie scenes a laborious task, which makes applying end-to-end supervised approaches for understanding movie-scenes a challenging problem. Directly applying state-of-the-art visual representations learned from large-scale image datasets for movie-scene understanding does not prove to be effective given the large gap between the two domains. To address these challenges, we propose a novel contrastive learning approach that uses commonly available sources of movie-information (e.g., genre, synopsis, more-like-this information) to learn a general-purpose scene-representation. Using a new dataset ( MovieCL30K ) with 30 , 340 movies, we demonstrate that our learned scene-representation surpasses existing state-of-the-art results on eleven downstream tasks from multiple datasets [50][25][39]. To further show the effectiveness of our scene-representation, we introduce another new dataset ( MCD ) focused on large-scale video-moderation with 44 , 581 clips containing sex , violence , and drug - use
Surface-tension-driven evolution of a viscoplastic liquid coating the interior of a cylindrical tube

Abstract One mechanism for airway closure in the lung is the surface-tension-driven instability of the mucus layer which lines the airway wall. We study the instability of an axisymmetric layer of viscoplastic Bingham liquid coating the interior of a rigid tube, which is a simple model for an airway that takes into account the yield stress of mucus. An evolution equation for the thickness of the liquid layer is derived using long-wave theory, from which we also derive a simpler thin-film evolution equation. In the thin-film case we show that two branches of marginally yielded static solutions of the evolution equation can be used to both predict the size of the initial perturbation required to trigger instability and quantify how increasing the capillary Bingham number (a parameter measuring yield stress relative to surface tension) reduces the final deformation of the layer. Using numerical solutions of the long-wave evolution equation, we quantify how the critical layer thickness required to form a liquid plug in the tube increases as the capillary Bingham number is increased. We discuss the significance of these findings for modelling airway closure in obstructive conditions such as cystic fibrosis, where the mucus layer is often thicker and has a higher yield stress.
Impact of cultural management on anthracnose severity of annual bluegrass putting green turf

Anthracnose (Colletotrichum cereale Manns) has become an increasingly destructive disease of annual bluegrass (ABG) [Poa annua L. f. reptans (Hausskn) T. Koyama] putting green turf over the past two decades. Cultural management to enhance the playing surface characteristics of putting greens are thought to enhance abiotic stress and predispose turf to anthracnose. Three field trials conducted from 2006 to 2008 evaluated the effects of irrigation, equipment traffic and lightweight rolling, and sand topdressing on anthracnose severity of ABG turf mowed at 3.2 mm. Severe deficit irrigation [40% replacement of reference evapotranspiration (ETo)] increased anthracnose severity during the study. Anthracnose was less severe under greater irrigation quantity. However, irrigating at 100% ETo resulted in disease severity similar to irrigation at 40% ETo by the end of 2006 and 2008. Irrigation to replace 80% ETo typically resulted in the least amount of disease and the best turf quality. Plots receiving turning of mowers and lightweight rollers as well as clean-up mowing (representing the putting green perimeter) had lower disease severity compared to center plots on 6 of 13 dates in 2007 and 2008. Additionally, lightweight rolling reduced anthracnose 2 to 13% compared to non-rolled turf in 2007 and 2008; the heavier sidewinder roller treatment had less disease than the triplex mounted vibratory roller treatment on 4 of 13 rating dates. Initially, turf receiving sand topdressing (0.3 L m-2 wk-1) had a small increase in disease compared to non-topdressed turf during 2007; however, disease decreased as much as 9% by August 2007 and again in 2008 on topdressed plots. Surprisingly, turf receiving foot traffic (5 d wk-1, ~200 rounds d-1) had as much as 27% lower disease severity than non-trafficked turf. This effect was independent of the level of sand topdressing during 2007 and 2008. The treatment combination of weekly topdressing and frequent foot traffic resulted in the best turf quality by the end of both seasons. Overall, the results of these three studies indicate that best management practices should be an integral component of a disease control program intended to reduce anthracnose severity on ABG putting green turf.
In This Apportionment Lottery, the House Always Wins

Apportionment is the problem of distributing h indivisible seats across states in proportion to the states' populations. In the context of the US House of Representatives, this problem has a rich history and is a prime example of interactions between mathematical analysis and political practice. Grimmett [2004] suggested to apportion seats in a randomized way such that each state receives exactly their proportional share qi of seats in expectation (ex ante proportionality) and receives either ↾qi↿ or ⇂qi⇃ many seats ex post (quota). However, there is a vast space of randomized apportionment methods satisfying these two axioms, and so we additionally consider prominent axioms from the apportionment literature. Our main result is a randomized method satisfying quota, ex ante proportionality and house monotonicity — a property that prevents paradoxes when the number of seats changes and which we require to hold ex post. This result is based on a generalization of dependent rounding on bipartite graphs, which we call cumulative rounding and which might be of independent interest, as we demonstrate via applications beyond apportionment. The full version of this paper is available at \urlhttps://arxiv.org/pdf/2202.11061. pdf.