Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study on the Relationship Between the Number of Coordinated Views and Visual Analysis

Coordinated Multiple views (CMVs) are a visualization technique that simultaneously presents multiple visualizations in separate but linked views. There are many studies that report the advantages (e.g., usefulness for finding hidden relationships) and disadvantages (e.g., cognitive load) of CMVs. But little empirical work exists on the impact of the number of views on visual anlaysis results and processes, which results in uncertainty in the relationship between the view number and visual anlaysis. In this work, we aim at investigating the relationship between the number of coordinated views and users analytic processes and results. To achieve the goal, we implemented a CMV tool for visual anlaysis. We also provided visualization duplication in the tool to help users easily create a desired number of visualization views on-the-fly. We conducted a between-subject study with 44 participants, where we asked participants to solve five analytic problems using the visual tool. Through quantitative and qualitative analysis, we discovered the positive correlation between the number of views and analytic results. We also found that visualization duplication encourages users to create more views and to take various analysis strategies. Based on the results, we provide implications and limitations of our study.
Learning Theory of Mind via Dynamic Traits Attribution

Machine learning of Theory of Mind (ToM) is essential to build social agents that co-live with humans and other agents. This capacity, once acquired, will help machines infer the mental states of others from observed contextual action trajectories, enabling future prediction of goals, intention, actions and successor representations. The underlying mechanism for such a prediction remains unclear, however. Inspired by the observation that humans often infer the character traits of others, then use it to explain behaviour, we propose a new neural ToM architecture that learns to generate a latent trait vector of an actor from the past trajectories. This trait vector then multiplicatively modulates the prediction mechanism via a `fast weights' scheme in the prediction neural network, which reads the current context and predicts the behaviour. We empirically show that the fast weights provide a good inductive bias to model the character traits of agents and hence improves mindreading ability. On the indirect assessment of false-belief understanding, the new ToM model enables more efficient helping behaviours.
OutCast: Outdoor Single‐image Relighting with Cast Shadows

We propose a relighting method for outdoor images. Our method mainly focuses on predicting cast shadows in arbitrary novel lighting directions from a single image while also accounting for shading and global effects such the sun light color and clouds. Previous solutions for this problem rely on reconstructing occluder geometry, e. g. , using multi‐view stereo, which requires many images of the scene. Instead, in this work we make use of a noisy off‐the‐shelf single‐image depth map estimation as a source of geometry. Whilst this can be a good guide for some lighting effects, the resulting depth map quality is insufficient for directly ray‐tracing the shadows. Addressing this, we propose a learned image space ray‐marching layer that converts the approximate depth map into a deep 3D representation that is fused into occlusion queries using a learned traversal. Our proposed method achieves, for the first time, state‐of‐the‐art relighting results, with only a single image as input. For supplementary material visit our project page at: dgriffiths.uk/outcast.
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction

Given the ubiquitous existence of graph-structured data, learning the representations of nodes for the downstream tasks ranging from node classification, link prediction to graph classification is of crucial importance. Regarding missing link inference of diverse networks, we revisit the link prediction techniques and identify the importance of both the structural and attribute information. However, the available techniques either heavily count on the network topology which is spurious in practice, or cannot integrate graph topology and features properly. To bridge the gap, we propose a bicomponent structural and attribute learning framework (BSAL) that is designed to adaptively leverage information from topology and feature spaces. Specifically, BSAL constructs a semantic topology via the node attributes and then gets the embeddings regarding the semantic view, which provides a flexible and easy-to-implement solution to adaptively incorporate the information carried by the node attributes. Then the semantic embedding together with topology embedding are fused together using attention mechanism for the final prediction. Extensive experiments show the superior performance of our proposal and it significantly outperforms baselines on diverse research benchmarks.
Lightyear: Using Modularity to Scale BGP Control Plane Verification

Current network control plane verification tools cannot scale to large networks because of the complexity of jointly reasoning about the behaviors of all network nodes. We present a modular approach to control plane verification, where end-to-end network properties are verified via a set of purely local checks on individual nodes and edges. The approach targets verification of reachability properties for BGP configurations, and provides guarantees in the face of arbitrary external route announcements and, for some properties, arbitrary node/link failures. We have proven the approach correct and implemented it in a tool Lightyear. Experimentally we show Lightyear scales dramatically better than prior control plane verifiers. Further, Lightyear has been used for six months to verify properties of a major cloud provider network containing hundreds of routers and tens of thousands of edges, finding and fixing bugs in the process. To our knowledge no prior control-plane verification tool has been shown to scale to that size and complexity. Our modular approach also makes it easy to localize configuration errors and enables incremental re-verification.
First light and reionisation epoch simulations (FLARES) V: The redshift frontier

JWST is set to transform many areas of astronomy, one of the most exciting is the expansion of the redshift frontier to z > 10. In its first year alone JWST should discover hundreds of galaxies, dwarfing the handful currently known. To prepare for these powerful observational constraints, we use the First Light And Reionisation Epoch (Flares) simulations to predict the physical and observational properties of the z > 10 population of galaxies accessible to JWST. This is the first time such predictions have been made using a hydrodynamical model validated at low redshift. Our predictions at z = 10 are broadly in agreement with current observational constraints on the far-UV luminosity function and UV continuum slope β, though the observational uncertainties are large. We note tension with recent constraints z ∼ 13 from Harikane et al. (2021) - compared to these constraints, Flares predicts objects with the same space density should have an order of magnitude lower luminosity, though this is mitigated slightly if dust attenuation is negligible in these systems. Our predictions suggest that in JWST’s first cycle alone, around 600 galaxies should be identified at z > 10, with the first small samples available at z > 13.
Learned Monocular Depth Priors in Visual-Inertial Initialization

Visual-inertial odometry (VIO) is the pose estimation backbone for most AR/VR and autonomous robotic systems today, in both academia and industry. However, these systems are highly sensitive to the initialization of key parameters such as sensor biases, gravity direction, and metric scale. In practical scenarios where high-parallax or variable acceleration assumptions are rarely met (e.g. hovering aerial robot, smartphone AR user not gesticulating with phone), classical visual-inertial initialization formulations often become ill-conditioned and/or fail to meaningfully converge. In this paper we target visual-inertial initialization specifically for these low-excitation scenarios critical to in-the-wild usage. We propose to circumvent the limitations of classical visual-inertial structure-from-motion (SfM) initialization by incorporating a new learning-based measurement as a higher-level input. We leverage learned monocular depth images (mono-depth) to constrain the relative depth of features, and upgrade the mono-depths to metric scale by jointly optimizing for their scales and shifts. Our experiments show a significant improvement in problem conditioning compared to a classical formulation for visual-inertial initialization, and demonstrate significant accuracy and robustness improvements relative to the state-of-the-art on public benchmarks, particularly under low-excitation scenarios. We further extend this improvement to implementation within an existing odometry system to illustrate the impact of our improved initialization method on resulting tracking trajectories.
Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection

We study short-term prediction of wind speed and wind power (every 10 minutes up to 4 hours ahead). Accurate forecasts for these quantities are crucial to mitigate the negative effects of wind farms' intermittent production on energy systems and markets. We use machine learning to combine outputs from numerical weather prediction models with local observations. The former provide valuable information on higher scales dynamics while the latter gives the model fresher and location-specific data. So as to make the results usable for practitioners, we focus on well-known methods which can handle a high volume of data. We study first variable selection using both a linear technique and a nonlinear one. Then we exploit these results to forecast wind speed and wind power still with an emphasis on linear models versus nonlinear ones. For the wind power prediction, we also compare the indirect approach (wind speed predictions passed through a power curve) and the indirect one (directly predict wind power).
Discovery of a 500 au Protobinary in the Massive Prestellar Core G11.92–0.61 MM2

We present high-resolution (≲160 au) Atacama Large Millimeter/submillimeter Array (ALMA) 1.3 mm observations of the high-mass prestellar core candidate G11.92−0.61 MM2, which reveal that this source is in fact a protobinary system with a projected separation of 505 au. The binary components, MM2E and MM2W, are compact (radii <140 au) sources within the partially optically thick dust emission with α 0.9 cm−1.3 mm = 2.47–2.94. The 1.3 mm brightness temperatures, T b = 68.4/64.6 K for MM2E/MM2W, imply internal heating and minimum luminosities L * > 24.7 L ⊙ for MM2E and L * > 12.6 L ⊙ for MM2W. The compact sources are connected by a “bridge” of lower-surface-brightness dust emission and lie within more extended emission that may correspond to a circumbinary disk. The circumprotostellar gas mass, estimated from ∼0. ″2 resolution VLA 0.9 cm observations assuming optically thin emission, is 6.8 ± 0.9 M ⊙. No line emission is detected toward MM2E and MM2W in our high-resolution 1.3 mm ALMA observations. The only line detected is 13CO J = 2–1, in absorption against the 1.3 mm continuum, which likely traces a layer of cooler molecular material surrounding the protostars. We also report the discovery of a highly asymmetric bipolar molecular outflow that appears to be driven by MM2E and/or MM2W in new deep, ∼0. ″5 resolution (1685 au) ALMA 0.82 mm observations. This outflow, traced by low-excitation CH3OH emission, indicates ongoing accretion onto the protobinary system. Overall, the super-Alfvénic models of Mignon-Risse et al. agree well with the observed properties of the MM2E/MM2W protobinary, suggesting that this system may be forming in an environment with a weak magnetic field.
Residual Mixture of Experts

Mixture of Experts (MoE) is able to scale up vision transformers effectively. However, it requires prohibiting computation resources to train a large MoE transformer. In this paper, we propose Residual Mixture of Experts (RMoE), an efficient training pipeline for MoE vision transformers on downstream tasks, such as segmentation and detection. RMoE achieves comparable results with the upper-bound MoE training, while only introducing minor additional training cost than the lower-bound non-MoE training pipelines. The efficiency is supported by our key observation: the weights of an MoE transformer can be factored into an input-independent core and an input-dependent residual. Compared with the weight core, the weight residual can be efficiently trained with much less computation resource, e.g., finetuning on the downstream data. We show that, compared with the current MoE training pipeline, we get comparable results while saving over 30% training cost. When compared with state-of-the-art non- MoE transformers, such as Swin-T / CvT-13 / Swin-L, we get +1.1 / 0.9 / 1.0 mIoU gain on ADE20K segmentation and +1.4 / 1.6 / 0.6 AP gain on MS-COCO object detection task with less than 3% additional training cost.
Detection of Stellar-like Abundance Anomalies in the Slow Solar Wind

The elemental composition of the Sun’s hot atmosphere, the corona, shows a distinctive pattern that is different from the underlying surface or photosphere. Elements that are easy to ionize in the chromosphere are enhanced in abundance in the corona compared to their photospheric values. A similar pattern of behavior is often observed in the slow-speed (<500 km s−1) solar wind and in solar-like stellar coronae, while a reversed effect is seen in M dwarfs. Studies of the inverse effect have been hampered in the past because only unresolved (point-source) spectroscopic data were available for these stellar targets. Here we report the discovery of several inverse events observed in situ in the slow solar wind using particle-counting techniques. These very rare events all occur during periods of high solar activity that mimic conditions more widespread on M dwarfs. The detections allow a new way of connecting the slow wind to its solar source and are broadly consistent with theoretical models of abundance variations due to chromospheric fast-mode waves with amplitudes of 8–10 km s−1, sufficient to accelerate the solar wind. The results imply that M-dwarf winds are dominated by plasma depleted in easily ionized elements and lend credence to previous spectroscopic measurements.
Runtime Prevention of Deserialization Attacks

Untrusted deserialization exploits, where a serialised object graph is used to achieve denial-of-service or arbitrary code execution, have become so prominent that they were introduced in the 2017 OWASP Top 10. In this paper, we present a novel and lightweight approach for runtime prevention of deserialization attacks using Markov chains. The intuition behind our work is that the features and ordering of classes in malicious object graphs make them distinguishable from benign ones. Preliminary results indeed show that our approach achieves an Fl-score of 0.94 on a dataset of 264 serialised payloads, collected from an industrial Java BE application server and a repository of deserialization exploits. ACM Reference Format: François Gauthier and Sora Bae. 2022. Runtime Prevention of Deserialization Attacks. In New Ideas and Emerging Results (ICSE-NIER’22), May 21–29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3510455.3512786
Detection of Tool based Edited Images from Error Level Analysis and Convolutional Neural Network

Image Forgery is a problem of image forensics and its detection can be leveraged using Deep Learning. In this paper we present an approach for identification of authentic and tampered images done using image editing tools with Error Level Analysis and Convolutional Neural Network. The process is performed on CASIA ITDE v2 dataset and trained for 50 and 100 epochs respectively. The respective accuracies of the training and validation sets are represented using graphs.
Congestion Mitigation in Unbalanced Residential Networks with OPF-based Demand Management

—This paper proposes a novel congestion mitigation strategy for low voltage residential feeders in which the rising power demand due to the electriﬁcation of the transport and heating systems leads to congestion problems. The strategy is based on requiring residential customers to limit their demand for a certain amount of time in exchange for economic beneﬁts. The main novelty of the method consists of combining a thorough representation of the network physics with advanced constraints that ensure the comfort of residential users, in a scalable manner that suits real systems. The mitigation strategy is presented from a DSO perspective, and takes the form of contracts between users and system operator. The focus on user comfort aims to make the contracts appealing, encouraging users to voluntarily enroll in the proposed mitigation scheme. The presented solution is implemented as a mixed-integer multi-period optimal power ﬂow problem which relies on a linearized three-phase power ﬂow formulation. Calculations on 100 real-life distribution feeders are performed, to analyze the congestion-relieving potential of several possible system operator- user contracts. From a planning perspective, the results can help the system operator deﬁne contractual terms that make a speciﬁc congestion mitigation scheme effective and viable. From an operational perspective, the same calculations can be used to optimally schedule power reduction on a day-ahead basis.
Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability

In this paper, we study learning in probabilistic domains where the learner may receive incorrect labels but can improve the reliability of labels by repeatedly sampling them. In such a setting, one faces the problem of whether the fixed budget for obtaining training examples should rather be used for obtaining all different examples or for improving the label quality of a smaller number of examples by re-sampling their labels. We motivate this problem in an application to compare the strength of poker hands where the training signal depends on the hidden community cards, and then study it in depth in an artificial setting where we insert controlled noise levels into the MNIST database. Our results show that with increasing levels of noise, resampling previous examples becomes increasingly more important than obtaining new examples, as classifier performance deteriorates when the number of incorrect labels is too high. In addition, we propose two different validation strategies; switching from lower to higher validations over the course of training and using chi-square statistics to approximate the confidence in obtained labels.
Private measures, random walks, and synthetic data

Differential privacy is a mathematical concept that provides an information-theoretic security guarantee. While differential privacy has emerged as a de facto standard for guaranteeing privacy in data sharing, the known mechanisms to achieve it come with some serious limitations. Utility guarantees are usually provided only for a fixed, a priori specified set of queries. Moreover, there are no utility guarantees for more complex - but very common - machine learning tasks such as clustering or classification. In this paper we overcome some of these limitations. Working with metric privacy, a powerful generalization of differential privacy, we develop a polynomial-time algorithm that creates a private measure from a data set. This private measure allows us to efficiently construct private synthetic data that are accurate for a wide range of statistical analysis tools. Moreover, we prove an asymptotically sharp min-max result for private measures and synthetic data for general compact metric spaces. A key ingredient in our construction is a new superregular random walk, whose joint distribution of steps is as regular as that of independent random variables, yet which deviates from the origin logarithmicaly slowly.
Sheaf semantics of termination-insensitive noninterference

We propose a new sheaf semantics for secure information flow over a space of abstract behaviors, based on synthetic domain theory: security classes are open/closed partitions, types are sheaves, and redaction of sensitive information corresponds to restricting a sheaf to a closed subspace. Our security-aware computational model satisfies termination-insensitive noninterference automatically, and therefore constitutes an intrinsic alternative to state of the art extrinsic/relational models of noninterference. Our semantics is the latest application of Sterling and Harper's recent re-interpretation of phase distinctions and noninterference in programming languages in terms of Artin gluing and topos-theoretic open/closed modalities. Prior applications include parametricity for ML modules, the proof of normalization for cubical type theory by Sterling and Angiuli, and the cost-aware logical framework of Niu et al. In this paper we employ the phase distinction perspective twice: first to reconstruct the syntax and semantics of secure information flow as a lattice of phase distinctions between"higher"and"lower"security, and second to verify the computational adequacy of our sheaf semantics vis-\`a-vis an extension of Abadi et al.'s dependency core calculus with a construct for declassifying termination channels.
Integral, mean and covariance of the simplex-truncated multivariate normal distribution

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution. In this paper, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution. For computations in low-dimensional distributions, the semi-analytical method is fast and thus should be considered. As the dimension increases, the Gessner et al. method becomes the only practically efficient approach of the methods tested here.