Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Structure From Motion for UAV Images via Weighted Connected Dominating Set

Incremental structure from motion (ISfM) has been widely used for unmanned aerial vehicle (UAV) image orientation. Its efficiency, however, decreases dramatically due to iterative bundle adjustment (BA). Although the divide-and-conquer strategy has been used for efficiency improvement, cluster merging becomes difficult or depends on seriously designed common image poses or 3-D points. This article proposes an algorithm to extract the global model for cluster merging and designs a parallel ISfM solution to achieve efficient and accurate image orientation. First, based on vocabulary tree retrieval, match pairs are selected to construct an undirected weighted match graph, whose edge weights are calculated by considering both the number and distribution of feature matches. Second, an algorithm termed weighted connected dominating set (WCDS) is designed to achieve the simplification of the match graph and build the global model, which incorporates the edge weight in the graph vertex selection and enables the successful reconstruction of the global model. Third, the match graph is simultaneously divided into compact and nonoverlapped clusters. After parallel reconstruction, cluster merging is conducted with the aid of the global model. Finally, using three UAV datasets that are captured by classical oblique and recent optimized views photogrammetry, the validation of the proposed solution is verified through comprehensive analysis and comparison. The experimental results demonstrate that the proposed parallel ISfM can achieve 17.4 times efficiency improvement and comparative orientation accuracy. In absolute BA, the geo-referencing accuracy is approximately 2.0 and 3.0 times the ground sampling distance (GSD) value in the horizontal and vertical directions, respectively. For parallel ISfM, the proposed solution is a more reliable alternative.
Documentation-as-code for Interface Control Document Management in Systems of Systems: a Technical Action Research Study

The architecting of Systems of Systems (SoS), that is, of systems that emerge from the cooperation of multiple independent constituent systems, is a topic of increasing interest in both industry and academia. However, recent empirical studies revealed what seems to be an overlooked aspect of the architecting of SoS that is linked to major integration and operational issues: the interplay between the various disciplines involved in such an architecting process. This aspect becomes particularly relevant for the management of the interfaces between the SoS constituents, where such disciplines inevitably meet. In this paper, we present the results of the ﬁrst cycle of a Technical Action Research (TAR) study conducted in cooperation between the authors and a group of practitioners involved in the long-running architecting process of a large-scale radio astronomy SoS project. This TAR is aimed at exploring potential improvements of the document-centered interface management approach currently followed in this project by adopting elements of the documentation-as-code philosophy, which is widely adopted in the domain of software systems. As a result, a working proof-of-concept of an ICD (Interface Control Document) management approach was developed by the researchers and evaluated by the practitioners. The results of the study and the corresponding lessons learned are reported in this work.
Explanatory causal effects for model agnostic explanations

This paper studies the problem of estimating the contributions of features to the prediction of a speciﬁc instance by a machine learning model and the overall contribution of a feature to the model. The causal effect of a feature (variable) on the predicted outcome reﬂects the contribution of the feature to a prediction very well. A challenge is that most existing causal effects cannot be estimated from data without a known causal graph. In this paper, we deﬁne an explanatory causal effect based on a hypothetical ideal experiment. The deﬁnition brings several beneﬁts to model agnostic explanations. First, explanations are transparent and have causal meanings. Second, the explanatory causal effect estimation can be data driven. Third, the causal effects provide both a local explanation for a speciﬁc prediction and a global explanation showing the overall importance of a feature in a predictive model. We further propose a method using individual and combined variables based on explanatory causal effects for explanations. We show the deﬁnition and the method work with experiments on some real-world data sets.
Efficient Adaptive Federated Optimization of Federated Learning for IoT

—The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communi- cation capabilities have motivated intelligent applications empow-ered by artiﬁcial intelligence. The classical artiﬁcial intelligence algorithms require centralized data collection and processing which are challenging in realistic intelligent IoT applications due to growing data privacy concerns and distributed datasets. Federated Learning (FL) has emerged as a distributed privacy- preserving learning framework that enables IoT devices to train global model through sharing model parameters. However, in- efﬁciency due to frequent parameters transmissions signiﬁcantly reduce FL performance. Existing acceleration algorithms consist of two main type including local update considering trade-offs between communication and computation and parameter compression considering trade-offs between communication and precision. Jointly considering these two trade-offs and adap- tively balancing their impacts on convergence have remained unresolved. To solve the problem, this paper proposes a novel efﬁcient adaptive federated optimization (EAFO) algorithm to improve efﬁciency of FL, which minimizes the learning error via jointly considering two variables including local update and parameter compression and enables FL to adaptively adjust the two variables and balance trade-offs among computation, communication and precision. The experiment results illustrate that comparing with state-of-the-art algorithms, the proposed EAFO can achieve higher accuracies faster.
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth

Obtaining scalable algorithms for hierarchical agglomerative clustering (HAC) is of significant interest due to the massive size of real-world datasets. At the same time, efficiently parallelizing HAC is difficult due to the seemingly sequential nature of the algorithm. In this paper, we address this issue and present ParHAC, the first efficient parallel HAC algorithm with sublinear depth for the widely-used average-linkage function. In particular, we provide a $(1+\epsilon)$-approximation algorithm for this problem on $m$ edge graphs using $\tilde{O}(m)$ work and poly-logarithmic depth. Moreover, we show that obtaining similar bounds for exact average-linkage HAC is not possible under standard complexity-theoretic assumptions. We complement our theoretical results with a comprehensive study of the ParHAC algorithm in terms of its scalability, performance, and quality, and compare with several state-of-the-art sequential and parallel baselines. On a broad set of large publicly-available real-world datasets, we find that ParHAC obtains a 50.1x speedup on average over the best sequential baseline, while achieving quality similar to the exact HAC algorithm. We also show that ParHAC can cluster one of the largest publicly available graph datasets with 124 billion edges in a little over three hours using a commodity multicore machine.
On the Generalizability and Predictability of Recommender Systems

While other areas of machine learning have seen more and more automation, designing a high-performing recommender system still requires a high level of human effort. Furthermore, recent work has shown that modern recommender system algorithms do not always improve over well-tuned baselines. A natural follow-up question is,"how do we choose the right algorithm for a new dataset and performance metric? "In this work, we start by giving the first large-scale study of recommender system approaches by comparing 18 algorithms and 100 sets of hyperparameters across 85 datasets and 315 metrics. We find that the best algorithms and hyperparameters are highly dependent on the dataset and performance metric, however, there are also strong correlations between the performance of each algorithm and various meta-features of the datasets. Motivated by these findings, we create RecZilla, a meta-learning approach to recommender systems that uses a model to predict the best algorithm and hyperparameters for new, unseen datasets. By using far more meta-training data than prior work, RecZilla is able to substantially reduce the level of human involvement when faced with a new recommender system application. We not only release our code and pretrained RecZilla models, but also all of our raw experimental results, so that practitioners can train a RecZilla model for their desired performance metric: https://github.com/naszilla/reczilla.
Chat, Shift and Perform: Bridging the Gap between Task-oriented and Non-task-oriented Dialog Systems

We propose CASPER (ChAt, Shift and PERform), a novel dialog system consisting of three types of dialog models: chatter , shifter , and performer . Shifter, which is designed for topic switching, enables a seamless ﬂow of dialog from open-domain chat- to task-oriented dialog. In a user study, CASPER gave a better impression in terms of naturalness of response, lack of forced topic switching, and satisfaction compared with a baseline dialog system trained in an end-to-end manner. In an ablation study, we found that naturalness of response, dialog satisfaction, and task-elicitation rate improved compared with when shifter was re-moved from CASPER, indicating that topic shift with shifter supports the introduction of natural task-oriented dialog.
Reinforcement Learning under Partial Observability Guided by Learned Environment Models

In practical applications, we can rarely assume full observability of a system's environment, despite such knowledge being important for determining a reactive control system's precise interaction with its environment. Therefore, we propose an approach for reinforcement learning (RL) in partially observable environments. While assuming that the environment behaves like a partially observable Markov decision process with known discrete actions, we assume no knowledge about its structure or transition probabilities. Our approach combines Q-learning with IoAlergia, a method for learning Markov decision processes (MDP). By learning MDP models of the environment from episodes of the RL agent, we enable RL in partially observable domains without explicit, additional memory to track previous interactions for dealing with ambiguities stemming from partial observability. We instead provide RL with additional observations in the form of abstract environment states by simulating new experiences on learned environment models to track the explored states. In our evaluation, we report on the validity of our approach and its promising performance in comparison to six state-of-the-art deep RL techniques with recurrent neural networks and fixed memory.
Improved binary solution for the gamma-ray binary 1FGL J1018.6-5856

The gamma-ray binary 1FGL J1018.6-5856 consists of an O6V((f)) type star and an unknown compact object, and shows orbitally modulated emission from radio to very high energy gamma rays. The X-ray light curve shows a maximum around the same phase as the GeV emission, but also a secondary maximum between phases φ = 0.2 − 0.6. A clear solution to the binary system is important for understanding the emission mechanisms occurring within the system. In order to improve on the existing binary solution, we undertook radial velocity measurements of the optical companion using the Southern African Large Telescope, as well as analysed publicly available X-ray and GeV gamma-ray data. A search for periodicity in Fermi-LAT data found an orbital period of P = 16.5507 ± 0.0004 d. The best fit solution to the radial velocities, held at this new period, finds the system to be more eccentric than previous observations, e = 0.531 ± 0.033 with a longitude of periastron of 151.2 ± 5.1○, and a larger mass function f = 0.00432 ± 0.00077 M⊙. We propose that the peak in the X-ray and gamma-ray light curves around phase 0 are due to the observation of the confined shock formed between the pulsar and stellar wind pointing towards the observer. The secondary increase in the X-ray flux is due to the interaction of multiple randomly oriented stellar wind clumps/pulsar wind interactions around apastron.
Putting Flat $\Lambda$CDM In The (Redshift) Bin

Flat $\Lambda$CDM cosmology is specified by two constant fitting parameters at the background level in the late Universe, the Hubble constant $H_0$ and matter density (today) $\Omega_m$. Mathematically, $H_0$ and $\Omega_m$ are either integration constants arising from solving ordinary differential equations or are directly related to integration constants. Seen in this context, if fits of the $\Lambda$CDM model to cosmological probes at different redshifts lead to different $(H_0, \Omega_m)$ parameters, this is a mismatch between mathematics and observation. Here, in mock observational Hubble data (OHD) (geometric probes of expansion history) we demonstrate evolution in distributions of best fit parameters with effective redshift. As a result, considerably different $(H_0, \Omega_m)$ best fits from Planck-$\Lambda$CDM cannot be precluded in high redshift bins. We explore if OHD, Type Ia supernovae and standardisable quasar samples exhibit redshift evolution of best fit $\Lambda$CDM parameters. In all samples, we confirm a decreasing $H_0$ and increasing $\Omega_m$ trend with increasing bin redshift. Through comparison with mocks, we confirm that similar behaviour can arise randomly within the flat $\Lambda$CDM model with probabilities as low as $p = 0.0021$ ($3.1 \, \sigma$). We present complementary profile distribution analysis confirming the shifts in cosmological parameters in high redshift bins. In particular, we identify a redshift range where Planck $(H_0, \Omega_m)$ values are disfavoured at $99.6 \%$ ($2.9 \sigma$) confidence level in a combination of OHD and supernovae data.
Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models

This article explores the natural language generation capabilities of large language models with application to the production of two types of learning resources common in programming courses. Using OpenAI Codex as the large language model, we create programming exercises (including sample solutions and test cases) and code explanations, assessing these qualitatively and quantitatively. Our results suggest that the majority of the automatically generated content is both novel and sensible, and in some cases ready to use as is. When creating exercises we find that it is remarkably easy to influence both the programming concepts and the contextual themes they contain, simply by supplying keywords as input to the model. Our analysis suggests that there is significant value in massive generative machine learning models as a tool for instructors, although there remains a need for some oversight to ensure the quality of the generated content before it is delivered to students. We further discuss the implications of OpenAI Codex and similar tools for introductory programming education and highlight future research streams that have the potential to improve the quality of the educational experience for both teachers and students alike.
LoneSTAR: Analog Beamforming Codebooks for Full-Duplex Millimeter Wave Systems

This work develops LoneSTAR, a novel enabler of full-duplex millimeter wave (mmWave) communication systems through the design of analog beamforming codebooks. LoneSTAR codebooks deliver high beamforming gain and broad coverage while simultaneously reducing the self-interference coupled by transmit and receive beams at a full-duplex mmWave transceiver. Our design framework accomplishes this by tolerating some variability in transmit and receive beamforming gain to strategically shape beams that reject self-interference spatially while accounting for digitally-controlled analog beamforming networks and self-interference channel estimation error. By leveraging the coherence time of the self-interference channel, a mmWave system can use the same LoneSTAR design over many time slots to serve several downlink-uplink user pairs in a full-duplex fashion without the need for additional self-interference cancellation. Compared to those using conventional codebooks, full-duplex mmWave systems employing LoneSTAR codebooks can mitigate higher levels of self-interference, tolerate more cross-link interference, and demand lower SNRs in order to outperform half-duplex operation—all while supporting beam alignment. This makes LoneSTAR a potential standalone solution for enabling simultaneous transmission and reception in mmWave systems, from which it derives its name.
Vision- and tactile-based continuous multimodal intention and attention recognition for safer physical human-robot interaction

Employing skin-like tactile sensors on robots enhances both the safety and usability of collaborative robots by adding the capability to detect human contact. Unfortunately, simple binary tactile sensors alone cannot determine the context of the human contact -- whether it is a deliberate interaction or an unintended collision that requires safety manoeuvres. Many published methods classify discrete interactions using more advanced tactile sensors or by analysing joint torques. Instead, we propose to augment the intention recognition capabilities of simple binary tactile sensors by adding a robot-mounted camera for human posture analysis. Different interaction characteristics, including touch location, human pose, and gaze direction, are used to train a supervised machine learning algorithm to classify whether a touch is intentional or not with an F1-score of 86%. We demonstrate that multimodal intention recognition is significantly more accurate than monomodal analyses with the collaborative robot Baxter. Furthermore, our method can also continuously monitor interactions that fluidly change between intentional or unintentional by gauging the user's attention through gaze. If a user stops paying attention mid-task, the proposed intention and attention recognition algorithm can activate safety features to prevent unsafe interactions. We also employ a feature reduction technique that reduces the number of inputs to five to achieve a more generalized low-dimensional classifier. This simplification both reduces the amount of training data required and improves real-world classification accuracy. It also renders the method potentially agnostic to the robot and touch sensor architectures while achieving a high degree of task adaptability.
Short-range forecasts of global precipitation using deep learning-augmented numerical weather prediction

Precipitation governs Earth's hydroclimate, and its daily spatiotemporal fluctuations have major socioeconomic effects. Advances in Numerical weather prediction (NWP) have been measured by the improvement of forecasts for various physical fields such as temperature and pressure; however, large biases exist in precipitation prediction. We augment the output of the well-known NWP model CFSv2 with deep learning to create a hybrid model that improves short-range global precipitation at 1-, 2-, and 3-day lead times. To hybridise, we address the sphericity of the global data by using modified DLWP-CS architecture which transforms all the fields to cubed-sphere projection. Dynamical model precipitation and surface temperature outputs are fed into a modified DLWP-CS (UNET) to forecast ground truth precipitation. While CFSv2's average bias is +5 to +7 mm/day over land, the multivariate deep learning model decreases it to within -1 to +1 mm/day. Hurricane Katrina in 2005, Hurricane Ivan in 2004, China floods in 2010, India floods in 2005, and Myanmar storm Nargis in 2008 are used to confirm the substantial enhancement in the skill for the hybrid dynamical-deep learning model. CFSv2 typically shows a moderate to large bias in the spatial pattern and overestimates the precipitation at short-range time scales. The proposed deep learning augmented NWP model can address these biases and vastly improve the spatial pattern and magnitude of predicted precipitation. Deep learning enhanced CFSv2 reduces mean bias by 8x over important land regions for 1 day lead compared to CFSv2. The spatio-temporal deep learning system opens pathways to further the precision and accuracy in global short-range precipitation forecasts.
An Optimization-Based User Scheduling Framework for mmWave Massive MU-MIMO Systems

We propose a novel user equipment (UE) scheduling framework for millimeter-wave (mmWave) massive multiuser (MU) multiple-input multiple-output (MIMO) wireless systems. Our framework determines (sub)sets of UEs that should transmit simultaneously in a given time slot by approximately solving a nonconvex optimization problem using forward-backward splitting. Our UE scheduling framework is flexible in the sense that it (i) supports a variety of cost functions, including post-equalization mean square error and sum rate, and (ii) enables precise control over the minimum and maximum number of resources the UEs should occupy. We demonstrate the efficacy of our framework using realistic mmWave channel vectors generated with a commercial ray-tracer. We show that our UE scheduler outperforms a range of existing scheduling methods and closely approaches the performance of an exhaustive search.
Quantum Approximation of Normalized Schatten Norms and Applications to Learning

Eﬃcient measures to determine similarity of quantum states, such as the ﬁdelity metric, have been widely studied. In this paper, we address the problem of deﬁning a similarity measure for quantum operations that can be eﬃciently estimated . Given two quantum operations, U 1 and U 2 , represented in their circuit forms, we ﬁrst develop a quantum sampling circuit to estimate the normalized Schatten 2-norm of their diﬀerence ( (cid:107) U 1 − U 2 (cid:107) S 2 ) with precision (cid:15) , using only one clean qubit and one classical random variable . We prove a Poly ( 1 (cid:15) ) upper bound on the sample complexity, which is independent of the size of the quantum system. We then show that such a similarity metric is directly related to a functional deﬁnition of similarity of unitary operations using the conventional ﬁdelity metric of quantum states ( F ): If (cid:107) U 1 − U 2 (cid:107) S 2 is suﬃciently small (e.g. ≤ (cid:15) 1+ √ 2(1 /δ − 1) ) then the ﬁdelity of states obtained by processing the same randomly and uniformly picked pure state, | ψ (cid:105) , is as high as needed ( F ( U 1 | ψ (cid:105) , U 2 | ψ (cid:105) ) ≥ 1 − (cid:15) ) with probability exceeding 1 − δ . We provide example applications of this eﬃcient similarity metric estimation framework to quantum circuit learning tasks, such as ﬁnding the square root of a given unitary operation.