Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting Embodied Energy Paths from Input–Output Tables: Towards an Input–Output-based Hybrid Energy Analysis Method

Embodied energy is defined as the energy consumed in all activities necessary to support a process, including upstream processes. The Leontief inverse input-output (IO) matrix gives results that are practically complete, because of the aggregation of direct and indirect requirements, but which are also unreliable, because of inherent assumptions. Although accurate for the system boundary considered, process analysis results are incomplete relative to the pure IO system boundary. Attempts to combine process and IO analysis tend to be based on process analysis data. The system boundary is still significantly incomplete—although not as incomplete as for pure process analysis. An IO-based hybrid analysis technique that requires the extraction of particular paths from the direct IO matrix has been developed. The potential for embodied energy paths to be used as the basis for a hybrid analysis of the Australian residential building sector is discussed. The results indicate that less than three-quarters of the total embodied energy of this sector is likely to be able to be validated, because of the complexity of the embodied energy paths.
Two phase equilibrium in charged topological dilaton AdS black hole

In this paper we discuss the phase transition of the charged topological dilaton AdS black holes by Maxwell equal area law. Using Maxwell equal area law we found the border of the region of two phase coexistence in P − v diagrams and analyze the parameters which affect the extent of the region. We also plot the P − T phase diagram and derive the Clapeyron equation for the black hole, and investigate the phase change latent heat. The results show the phase transition characteristic is similar to that of usual non-gravity thermodynamic systems.
Correlation between thermodynamical stabilities of metal borohydrides and cation electronegativites: First-principles calculations and experiments

Note: Times Cited: 251 Reference EPFL-ARTICLE-206012doi:10.1103/PhysRevB.74. 045126View record in Web of Science URL: ://WOS:000239426800043 Record created on 2015-03-03, modified on 2017-05-12
Thermal annealing effect on ZnO surface acoustic wave-based ultraviolet light sensors on glass substrates

Surface acoustic wave (SAW) based ultraviolet (UV) light sensors have a high sensitivity and have been extensively studied and explored for application. However, all of them were made of piezoelectric (PE) bulk materials or PE thin films on crystalline substrates such as Si and sapphire. This paper reports the fabrication of ZnO thin film SAW UV-light sensors on glass substrates and the effect of post-deposition thermal annealing on the sensing performance. It was found that annealing at temperatures higher than 300 °C can improve the properties of ZnO films and the sensing performance of the UV-sensors remarkably. When the ZnO film annealed at 400 °C was used for sensors, the UV light induced resonant frequency shift increased more than 20 times with the response speed reduced to less than 2.4 s, much better than those made on ZnO films with lower temperature annealing.
Multibump homoclinic solutions to a centre equilibrium in a class of autonomous Hamiltonian systems

We consider an autonomous Hamiltonian system of fourth-order that has a centre with non-semi-simple imaginary eigenvalues and such that some coefficient of the corresponding normal form at the centre is (strictly) negative. Firstly, we prove the existence of two-dimensional stable and unstable manifolds to the centre, made of orbits converging polynomially to the equilibrium. Then we show that the existence of a homoclinic orbit that is the transverse intersection of the stable and unstable manifolds, implies the existence of an infinite number of `multibump' homoclinic solutions. In particular the topological entropy of the system is positive. Our approach relies partially on the calculus of variations.
Designing universal causal deep learning models: The geometric (Hyper)transformer

Several problems in stochastic analysis are defined through their geometry, and preserving that geometric structure is essential to generating meaningful predictions. Nevertheless, how to design principled deep learning (DL) models capable of encoding these geometric structures remains largely unknown. We address this open problem by introducing a universal causal geometric DL framework in which the user specifies a suitable pair of metric spaces $\mathscr{X}$ and $\mathscr{Y}$ and our framework returns a DL model capable of causally approximating any ``regular'' map sending time series in $\mathscr{X}^{\mathbb{Z}}$ to time series in $\mathscr{Y}^{\mathbb{Z}}$ while respecting their forward flow of information throughout time. Suitable geometries on $\mathscr{Y}$ include various (adapted) Wasserstein spaces arising in optimal stopping problems, a variety of statistical manifolds describing the conditional distribution of continuous-time finite state Markov chains, and all Fr\'{e}chet spaces admitting a Schauder basis, e.g. as in classical finance. Suitable spaces $\mathscr{X}$ are compact subsets of any Euclidean space. Our results all quantitatively express the number of parameters needed for our DL model to achieve a given approximation error as a function of the target map's regularity and the geometric structure both of $\mathscr{X}$ and of $\mathscr{Y}$. Even when omitting any temporal structure, our universal approximation theorems are the first guarantees that H\"{o}lder functions, defined between such $\mathscr{X}$ and $\mathscr{Y}$ can be approximated by DL models.
Masked Contrastive Pre-Training for Efficient Video-Text Retrieval

We present a simple yet effective end-to-end Video-language Pre-training (VidLP) framework, Masked Contrastive Video-language Pretraining (MAC), for video-text retrieval tasks. Our MAC aims to reduce video representation's spatial and temporal redundancy in the VidLP model by a mask sampling mechanism to improve pre-training efficiency. Comparing conventional temporal sparse sampling, we propose to randomly mask a high ratio of spatial regions and only feed visible regions into the encoder as sparse spatial sampling. Similarly, we adopt the mask sampling technique for text inputs for consistency. Instead of blindly applying the mask-then-prediction paradigm from MAE, we propose a masked-then-alignment paradigm for efficient video-text alignment. The motivation is that video-text retrieval tasks rely on high-level alignment rather than low-level reconstruction, and multimodal alignment with masked modeling encourages the model to learn a robust and general multimodal representation from incomplete and unstable inputs. Coupling these designs enables efficient end-to-end pre-training: reduce FLOPs (60% off), accelerate pre-training (by 3x), and improve performance. Our MAC achieves state-of-the-art results on various video-text retrieval datasets, including MSR-VTT, DiDeMo, and ActivityNet. Our approach is omnivorous to input modalities. With minimal modifications, we achieve competitive results on image-text retrieval tasks.
MMBench: Benchmarking End-to-End Multi-modal DNNs and Understanding Their Hardware-Software Implications

The explosive growth of various types of big data and advances in AI technologies have catalyzed a new type of workloads called multi-modal DNNs. Multi-modal DNNs are capable of interpreting and reasoning about information from multiple modalities, making them more applicable to real-world AI scenarios. In recent research, multi-modal DNNs have outperformed the best uni-modal DNN in a wide range of distributed computing applications from traditional multimedia systems to emerging autonomous edge systems. However, despite their importance and superiority, very limited research attention has been devoted to understand the characteristics of multi-modal DNNs and their implications on current computing software/hardware platforms. Existing benchmarks either target uni-modal DNNs or only focus on the algorithm characteristics of multi-modal DNNs. There lacks representative benchmark suites that provide comprehensive system and architecture level analysis of multi-modal networks.To advance the understanding of these multi-modal DNN workloads and facilitate related research, we present MM-Bench, an open-source, end-to-end benchmark suite consisting of a set of real-world multi-modal DNN workloads with relevant performance metrics for evaluation. We then use MMBench to conduct an in-depth analysis on the characteristics of multi-modal DNNs. We demonstrate their unique characteristics of clear multi-stage execution, frequent synchronization and high heterogeneity, which distinguish them from conventional uni-modal DNNs. Finally, we conduct a case study and extend our benchmark to edge devices. We hope that our work can provide insights for future software/hardware design and optimization to underpin multi-modal DNNs on both cloud and edge computing platforms.
Risk-Adaptive Approaches to Learning and Decision Making: A Survey

Uncertainty is prevalent in engineering design, statistical learning, and decision making broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to address uncertainty by formulating and solving conservative optimization models expressed using measures of risk and related concepts. We survey the rapid development of risk measures over the last quarter century. From their beginning in financial engineering, we recount the spread to nearly all areas of engineering and applied mathematics. Solidly rooted in convex analysis, risk measures furnish a general framework for handling uncertainty with significant computational and theoretical advantages. We describe the key facts, list several concrete algorithms, and provide an extensive list of references for further reading. The survey recalls connections with utility theory and distributionally robust optimization, points to emerging applications areas such as fair machine learning, and defines measures of reliability.
ObjectStitch: Generative Object Compositing

Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results. Furthermore, annotating training data pairs for compositing requires substantial manual effort from professionals, and is hardly scalable. Thus, with the recent advances in generative models, in this work, we propose a self-supervised framework for object compositing by leveraging the power of conditional diffusion models. Our framework can hollistically address the object compositing task in a unified model, transforming the viewpoint, geometry, color and shadow of the generated object while requiring no manual labeling. To preserve the input object's characteristics, we introduce a content adaptor that helps to maintain categorical semantics and object appearance. A data augmentation method is further adopted to improve the fidelity of the generator. Our method outperforms relevant baselines in both realism and faithfulness of the synthesized result images in a user study on various real-world images.
Relation-aware Language-Graph Transformer for Question Answering

Question Answering (QA) is a task that entails reasoning over natural language contexts, and many relevant works augment language models (LMs) with graph neural networks (GNNs) to encode the Knowledge Graph (KG) information. However, most existing GNN-based modules for QA do not take advantage of rich relational information of KGs and depend on limited information interaction between the LM and the KG. To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner. Specifically, QAT constructs Meta-Path tokens, which learn relation-centric embeddings based on diverse structural and semantic relations. Then, our Relation-Aware Self-Attention module comprehensively integrates different modalities via the Cross-Modal Relative Position Bias, which guides information exchange between relevant entities of different modalities. We validate the effectiveness of QAT on commonsense question answering datasets like CommonsenseQA and OpenBookQA, and on a medical question answering dataset, MedQA-USMLE. On all the datasets, our method achieves state-of-the-art performance. Our code is available at http://github.com/mlvlab/QAT.
Sonus Texere! Automated Dense Soundtrack Construction for Books using Movie Adaptations

Reading, much like music listening, is an immersive experience that transports readers while taking them on an emotional journey. Listening to complementary music has the potential to amplify the reading experience, especially when the music is stylistically cohesive and emotionally relevant. In this paper, we propose the first fully automatic method to build a dense soundtrack for books, which can play high-quality instrumental music for the entirety of the reading duration. Our work employs a unique text processing and music weaving pipeline that determines the context and emotional composition of scenes in a chapter. This allows our method to identify and play relevant excerpts from the soundtrack of the book's movie adaptation. By relying on the movie composer's craftsmanship, our book soundtracks include expert-made motifs and other scene-specific musical characteristics. We validate the design decisions of our approach through a perceptual study. Our readers note that the book soundtrack greatly enhanced their reading experience, due to high immersiveness granted via uninterrupted and style-consistent music, and a heightened emotional state attained via high precision emotion and scene context recognition.
Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula

ML-based motion planning is a promising approach to produce agents that exhibit complex behaviors, and automatically adapt to novel environments. In the context of autonomous driving, it is common to treat all available training data equally. However, this approach produces agents that do not perform robustly in safety-critical settings, an issue that cannot be addressed by simply adding more data to the training set - we show that an agent trained using only a 10% subset of the data performs just as well as an agent trained on the entire dataset. We present a method to predict the inherent difficulty of a driving situation given data collected from a fleet of autonomous vehicles deployed on public roads. We then demonstrate that this difficulty score can be used in a zero-shot transfer to generate curricula for an imitation-learning based planning agent. Compared to training on the entire unbiased training dataset, we show that prioritizing difficult driving scenarios both reduces collisions by 15% and increases route adherence by 14% in closed-loop evaluation, all while using only 10% of the training data.
Hybrid stars with large strange quark cores

The possible existence of hybrid stars is studied using several multiquark interaction channels. The hadronic phase consists of an equation of state (EoS) with presently accepted nuclear matter properties and the quark model is constrained by the vacuum properties of several light mesons. The dependence of several NS properties on the different quark interactions is analyzed. We show that the present constraints from neutron star observations allow for the existence of hybrid stars with large strangeness content and large quark cores.
A Model-based GNN for Learning Precoding

Learning precoding policies with neural networks enables low complexity online implementation, robustness to channel impairments, and joint optimization with channel acquisition. However, existing neural networks suffer from high training complexity and poor generalization ability when they are used to learn to optimize precoding for mitigating multi-user interference. This impedes their use in practical systems where the number of users is time-varying. In this paper, we propose a graph neural network (GNN) to learn precoding policies by harnessing both the mathematical model and the property of the policies. We first show that a vanilla GNN cannot well-learn pseudo-inverse of channel matrix when the numbers of antennas and users are large, and is not generalizable to unseen numbers of users. Then, we design a GNN by resorting to the Taylor's expansion of matrix pseudo-inverse, which allows for capturing the importance of the neighbored edges to be aggregated that is crucial for learning precoding policies efficiently. Simulation results show that the proposed GNN can well learn spectral efficient and energy efficient precoding policies in single- and multi-cell multi-user multi-antenna systems with low training complexity, and can be well generalized to the numbers of users.
Multimagnon bound states in an easy-axis frustrated ferromagnetic spin chain

The frustrated spin-1/2 chain with weakly anisotropic ferromagnetic nearest-neighbor and antiferromagnetic next-nearest-neighbor exchanges is studied using the scaling estimates of the perturbation theory and numerical calculations. We focus on the excitation spectrum and the low-temperature thermodynamics in the ferromagnetic region of the ground-state phase diagram. It is shown that the excitation spectrum of the model is characterized by the existence of the multimagnon bound states. These excitations determine the low-temperature magnetic susceptibility. The energy of the bound magnon complexes is found and the relation of the considered model to the edge-sharing cuprate ${\text{Li}}_{2}{\text{CuO}}_{2}$ is discussed.
End-to-End Semantic Video Transformer for Zero-Shot Action Recognition

While video action recognition has been an active area of research for several years, zero-shot action recognition has only recently started gaining traction. In this work, we propose a novel end-to-end trained transformer model which is capable of capturing long range spatiotemporal dependencies efﬁciently, contrary to existing approaches which use 3D-CNNs. Moreover, to address a common ambiguity in the existing works about classes that can be considered as previously unseen, we propose a new experimentation setup that satisﬁes the zero-shot learning premise for action recognition by avoiding overlap between the training and testing classes. The proposed approach signiﬁcantly outperforms the state of the arts in zero-shot action recognition in terms of the the top-1 accuracy on UCF-101, HMDB-51 and ActivityNet datasets. The code and proposed experimentation setup are available in GitHub: https://github.com/Secure-and-Intelligent-Systems-Lab/SemanticVideoTransformer ©2022ElsevierLtd.Allrightsreserved
Ghost Condensates and Pure Kinetic k-Essence Condensates in the Presence of Field–Fluid Non-Minimal Coupling in the Dark Sector

In this article, we try to determine the conditions when a ghost field, in conjunction with a barotropic fluid, produces a stable accelerating expansion phase of the universe. It is seen that, in many cases, the ghost field produces a condensate and drives the fluid energy density to zero in the final accelerating phase, but there can be other possibilities. We have shown that a pure kinetic k-essence field (which is not a ghost field) interacting with a fluid can also form an interaction-induced condensate and produce a stable accelerating phase of the universe. In the latter case, the fluid energy density does not vanish in the stable phase.
Device Interoperability for Learned Image Compression with Weights and Activations Quantization

Learning-based image compression has improved to a level where it can outperform traditional image codecs such as HEVC and VVC in terms of coding performance. In addition to good compression performance, device interoperability is essential for a compression codec to be deployed, i.e., encoding and decoding on different CPUs or GPUs should be error-free and with negligible performance reduction. In this paper, we present a method to solve the device interoperability problem of a state-of-the-art image compression network. We implement quantization to entropy networks which output entropy parameters. We suggest a simple method which can ensure cross-platform encoding and decoding, and can be implemented quickly with minor performance deviation, of 0.3% BD-rate, from floating point model results.
STL-Based Synthesis of Feedback Controllers Using Reinforcement Learning

Deep Reinforcement Learning (DRL) has the potential to be used for synthesizing feedback controllers (agents) for various complex systems with unknown dynamics. These systems are expected to satisfy diverse safety and liveness properties best captured using temporal logic. In RL, the reward function plays a crucial role in specifying the desired behaviour of these agents. However, the problem of designing the reward function for an RL agent to satisfy complex temporal logic specifications has received limited attention in the literature. To address this, we provide a systematic way of generating rewards in real-time by using the quantitative semantics of Signal Temporal Logic (STL), a widely used temporal logic to specify the behaviour of cyber-physical systems. We propose a new quantitative semantics for STL having several desirable properties, making it suitable for reward generation. We evaluate our STL-based reinforcement learning mechanism on several complex continuous control benchmarks and compare our STL semantics with those available in the literature in terms of their efficacy in synthesizing the controller agent. Experimental results establish our new semantics to be the most suitable for synthesizing feedback controllers for complex continuous dynamical systems through reinforcement learning.
Test Flakiness' Causes, Detection, Impact and Responses: A Multivocal Review

Flaky tests (tests with non-deterministic outcomes) pose a major challenge for software testing. They are known to cause significant issues such as reducing the effectiveness and efficiency of testing and delaying software releases. In recent years, there has been an increased interest in flaky tests, with research focusing on different aspects of flakiness, such as identifying causes, detection methods and mitigation strategies. Test flakiness has also become a key discussion point for practitioners (in blog posts, technical magazines, etc.) as the impact of flaky tests is felt across the industry. This paper presents a multivocal review that investigates how flaky tests, as a topic, have been addressed in both research and practice. We cover a total of 651 articles (560 academic articles and 91 grey literature articles/posts), and structure the body of relevant research and knowledge using four different dimensions: causes, detection, impact and responses. For each of those dimensions we provide a categorisation, and classify existing research, discussions, methods and tools. With this, we provide a comprehensive and current snapshot of existing thinking on test flakiness, covering both academic views and industrial practices, and identify limitations and opportunities for future research.