Miracle-dz/UniFAR-FTUs huggingface dataset:
High entropy materials as brazing filler metals for thermoelectric devices

Brazing is a 5000 year-old joining process. Components are joined by heating them above the liquidus of a filler metal placed between them which forms a metallurgical bond as it solidifies. Brazed joints are relatively high strength and are often electrically conductive. As materials applications evolve, ever more is demanded of filler metals and hence applications arise in which current filler metals cannot perform sufficiently. In these cases, development of new custom-designed filler metals becomes the means of progress. The aim of this work is to design filler metals to replace ISO17672:Ag-155 when joining skutterudite thermoelectrics to copper within automotive heat-recovery devices. To do this, the class of High Entropy Alloys is explored as a source of new filler metal compositions. Initial work here reported concludes that Ra=0. 09μm is the optimum surface roughness for wetting of Ag-155 on copper. EDX data demonstrates that excessive silver diffusion is the principal inadequacy of filler metal Ag-155; leading to the conclusion that the replacement filler metal should not contain silver. Replacement filler metals are designed and explored. They can form low void percentage (<15%) metallurgical bonds between copper and nickel, contain 4 phases and melt between 528-681⁰C, with a characteristic constituent diffusion distance through nickel <70% that of filler metal Ag-155. Brazed joints formed with these fillers have lower bond strength than joints formed with Ag-155, but showed no clear decline in strength after thermal cycling; although the joint failure rate reached 40% within 20-30 cycles. Contact resistance of joints between skutterudite thermoelectrics brazed with these alloys was measured as 2.02×10−5Ωcm-2, approximately half that of joints formed with Ag-155 and below the target maximum contact resistance of 5.0×10−5Ωcm-2 Overall this work demonstrates the potential of some HEAs as filler metals and provides insights into designing HEA systems to fill niche filler metal applications.
Multi Modal Adaptive Normalization for Audio to Video Generation

Speech-driven facial video generation has been a complex problem due to its multi-modal aspects namely audio and video domain. The audio comprises lots of underlying features such as expression, pitch, loudness, prosody(speaking style) and facial video has lots of variability in terms of head movement, eye blinks, lip synchronization and movements of various facial action units along with temporal smoothness. Synthesizing highly expressive facial videos from the audio input and static image is still a challenging task for generative adversarial networks. In this paper, we propose a multi-modal adaptive normalization(MAN) based architecture to synthesize a talking person video of arbitrary length using as input: an audio signal and a single image of a person. The architecture uses the multi-modal adaptive normalization, keypoint heatmap predictor, optical flow predictor and class activation map[58] based layers to learn movements of expressive facial components and hence generates a highly expressive talking-head video of the given person. The multi-modal adaptive normalization uses the various features of audio and video such as Mel spectrogram, pitch, energy from audio signals and predicted keypoint heatmap/optical flow and a single image to learn the respective affine parameters to generate highly expressive video. Experimental evaluation demonstrates superior performance of the proposed method as compared to Realistic Speech-Driven Facial Animation with GANs(RSDGAN) [53], Speech2Vid [10], and other approaches, on multiple quantitative metrics including: SSIM (structural similarity index), PSNR (peak signal to noise ratio), CPBD (image sharpness), WER(word error rate), blinks/sec and LMD(landmark distance). Further, qualitative evaluation and Online Turing tests demonstrate the efficacy of our approach.
Carbon nanotube tip melting with vacuum breakdown in cold cathode

Failure of patterned multiwalled carbon nanotubes during field emission (FE) was systematically studied at different fields using an indigenous FE setup. Here, the findings are reported from the experimental observation of the degradation of carbon nanotube (CNT) based field emitters over a silicon substrate. The CNTs were grown on the patterned silicon substrate using chemical vapor deposition (CVD) and plasma-enhanced CVD (PECVD) techniques. Scanning electron microscopy (SEM) was employed to observe the effect of different fields over CNTs. The observed current density of 28mA∕cm2 at a field of 5V∕μm from CNTs grown via CVD before giving a high-field treatment remained almost the same until the substrate started melting. Similar observations were made in CNTs grown via PECVD. SEM images clearly reveal that the high-field treatment resulted in melting of silicon substrate at a certain point; at some other points, etching of silicon substrate was also observed. The authors attribute these observations to .
Large hypergraphs without tight cycles

An $r$-uniform tight cycle of length $\ell>r$ is a hypergraph with vertices $v_1,\dots,v_\ell$ and edges $\{v_i,v_{i+1},\dots,v_{i+r-1}\}$ (for all $i$), with the indices taken modulo $\ell$. It was shown by Sudakov and Tomon that for each fixed $r\geq 3$, an $r$-uniform hypergraph on $n$ vertices which does not contain a tight cycle of any length has at most $n^{r-1+o(1)}$ hyperedges, but the best known construction (with the largest number of edges) only gives $\Omega(n^{r-1})$ edges. In this note we prove that, for each fixed $r\geq 3$, there are $r$-uniform hypergraphs with $\Omega(n^{r-1}\log n/\log\log n)$ edges which contain no tight cycles, showing that the $o(1)$ term in the exponent of the upper bound is necessary.
SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection

In this paper we describe the SOREL-20M (Sophos/ReversingLabs-20 Million) dataset: a large-scale dataset consisting of nearly 20 million files with pre-extracted features and metadata, high-quality labels derived from multiple sources, information about vendor detections of the malware samples at the time of collection, and additional ``tags'' related to each malware sample to serve as additional targets. In addition to features and metadata, we also provide approximately 10 million ``disarmed'' malware samples -- samples with both the optional\_headers. subsystem and file\_header. machine flags set to zero -- that may be used for further exploration of features and detection strategies. We also provide Python code to interact with the data and features, as well as baseline neural network and gradient boosted decision tree models and their results, with full training and evaluation code, to serve as a starting point for further experimentation.
Forecasting Daily Primary Three-Hour Net Load Ramps in the CAISO System

The deepening penetration of variable energy resources creates unprecedented challenges for system operators (SOs). An issue that merits special attention is the precipitous net load ramps, which require SOs to have flexible capacity at their disposal so as to maintain the supply-demand balance at all times. In the judicious procurement and deployment of flexible capacity, a tool that forecasts net load ramps may be of great assistance to SOs. To this end, we propose a methodology to forecast the magnitude and start time of daily primary three-hour net load ramps. We perform an extensive analysis so as to identify the factors that influence net load and draw on the identified factors to develop a forecasting methodology that harnesses the long short-term memory model. We demonstrate the effectiveness of the proposed methodology on the CAISO system using comparative assessments with selected benchmarks based on various evaluation metrics.
AffectON: Incorporating Affect Into Dialog Generation

Due to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry (e.g., How are you? ) might induce responses with different affects depending on the affective state of the conversational partner(s) and the context of the conversation. Yet, most dialog systems do not consider affect as constitutive aspect of response generation. In this article, we introduce AffectON, an approach for generating affective responses during inference. For generating language in a targeted affect, our approach leverages a probabilistic language model and an affective space. AffectON is language model agnostic, since it can work with probabilities generated by any language model (e.g., sequence-to-sequence models, neural language models, n-grams). Hence, it can be employed for both affective dialog and affective language generation. We experimented with affective dialog generation and evaluated the generated text objectively and subjectively. For the subjective part of the evaluation, we designed a custom user interface for rating and provided recommendations for the design of such interfaces. The results, both subjective and objective demonstrate that our approach is successful in pulling the generated language toward the targeted affect, with little sacrifice in syntactic coherence.
Crystallographic phase stabilities and electronic structures in AgNbO3 by first-principles calculation

Silver niobate (AgNbO3) assumes several phases that crystallise in the perovskite structure similar to sodium niobate (NaNbO3). In order to investigate the phase stability of AgNbO3, the formation enthalpies of both physically the realised (real) cubic , tetragonal (P4/mbm) and orthorhombic (Cmcm, Pbcm) phases as well as the virtual phases, the orthorhombic (Pc21 b) and rhombohedral (R3cR) phases, which are observed in NaNbO3, have been obtained using a plane-wave pseudopotential method. Although the orthorhombic (Pbcm) phase has the lowest formation enthalpy among the real phases, the virtual rhombohedral (R3cR) phase has the lowest symmetry as well as the lowest formation enthalpy among all phases. We thus speculate that the rhombohedral (R3cR) phase may be stable at extremely low temperatures provided that it is accessible kinetically. In addition, the electronic structures of the various phases of AgNbO3 are calculated within the generalised gradient approximation with the Perdew–Burke–Ernzerhof correction (GGA-PBE). The valence band top was found to consist of predominately localised Ag 4d and O 2p orbitals constituting the O 2p–Nb 4d bonding orbital, while the bottom of the conduction band was found to mainly consist of the anti-bonding orbital of Nb 4d–O 2p under the Ag 5s orbital.
Regularizing Recurrent Neural Networks via Sequence Mixup

In this paper, we extend a class of celebrated regularization techniques originally proposed for feed-forward neural networks, namely Input Mixup (Zhang et al., 2017) and Manifold Mixup (Verma et al., 2018), to the realm of Recurrent Neural Networks (RNN). Our proposed methods are easy to implement and have a low computational complexity, while leverage the performance of simple neural architectures in a variety of tasks. We have validated our claims through several experiments on real-world datasets, and also provide an asymptotic theoretical analysis to further investigate the properties and potential impacts of our proposed techniques. Applying sequence mixup to BiLSTM-CRF model (Huang et al., 2015) to Named Entity Recognition task on CoNLL-2003 data (Sang and De Meulder, 2003) has improved the F-1 score on the test stage and reduced the loss, considerably.
Filtering DDoS Attacks from Unlabeled Network Traffic Data Using Online Deep Learning

DDoS attacks are simple, effective, and still pose a significant threat even after more than two decades. Given the recent success in machine learning, it is interesting to investigate how we can leverage deep learning to filter out application layer attack requests. There are challenges in adopting deep learning solutions due to the ever-changing profiles, the lack of labeled data, and constraints in the online setting. Offline unsupervised learning methods can sidestep these hurdles by learning an anomaly detector N from the normal-day traffic N. However, anomaly detection does not exploit information acquired during attacks, and their performance typically is not satisfactory. In this paper, we propose two approaches that utilize both the historic N and the mixture M traffic obtained during attacks, consisting of unlabeled requests. First, our proposed approach, inspired by statistical methods, extends an unsupervised anomaly detector N to solve the problem using estimated conditional probability distributions. We adopt transfer learning to apply N on N and M separately and efficiently, combining the results to obtain an online learner. Second, we formulate a specific loss function more suited for deep learning and use iterative training to solve it in the online setting. On publicly available datasets, such as the CICIDS2017, our online learners achieve an average of 90.6% accuracy rates compared to the baseline detection method, which achieves around 60.0% accuracy. In the offline setting, our approaches on unlabeled data achieve competitive accuracy compared to classifiers trained on labeled data.
Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding

Though beneficial for encouraging the visual question answering (VQA) models to discover the underlying knowledge by exploiting the input–output correlation beyond image and text contexts, the existing knowledge VQA data sets are mostly annotated in a crowdsource way, e.g., collecting questions and external reasons from different users via the Internet. In addition to the challenge of knowledge reasoning, how to deal with the annotator bias also remains unsolved, which often leads to superficial overfitted correlations between questions and answers. To address this issue, we propose a novel data set named knowledge-routed visual question reasoning for VQA model evaluation. Considering that a desirable VQA model should correctly perceive the image context, understand the question, and incorporate its learned knowledge, our proposed data set aims to cut off the shortcut learning exploited by the current deep embedding models and push the research boundary of the knowledge-based visual question reasoning. Specifically, we generate the question–answer pair based on both the visual genome scene graph and an external knowledge base with controlled programs to disentangle the knowledge from other biases. The programs can select one or two triplets from the scene graph or knowledge base to push multistep reasoning, avoid answer ambiguity, and balance the answer distribution. In contrast to the existing VQA data sets, we further imply the following two major constraints on the programs to incorporate knowledge reasoning. First, multiple knowledge triplets can be related to the question, but only one knowledge relates to the image object. This can enforce the VQA model to correctly perceive the image instead of guessing the knowledge based on the given question solely. Second, all questions are based on different knowledge, but the candidate answers are the same for both the training and test sets. We make the testing knowledge unused during training to evaluate whether a model can understand question words and handle unseen combinations. Extensive experiments with various baselines and state-of-the-art VQA models are conducted to demonstrate that there still exists a big gap between the model with and without groundtruth supporting triplets when given the embedded knowledge base. This reveals the weakness of the current deep embedding models on the knowledge reasoning problem.
Analyzing Large and Sparse Tensor Data using Spectral Low-Rank Approximation

Information is extracted from large and sparse data sets organized as 3-mode tensors. Two methods are described, based on best rank-(2,2,2) and rank-(2,2,1) approximation of the tensor. The first method can be considered as a generalization of spectral graph partitioning to tensors, and it gives a reordering of the tensor that clusters the information. The second method gives an expansion of the tensor in sparse rank-(2,2,1) terms, where the terms correspond to graphs. The low-rank approximations are computed using an efficient Krylov-Schur type algorithm that avoids filling in the sparse data. The methods are applied to topic search in news text, a tensor representing conference author-terms-years, and network traffic logs.
The nature of bright C-complex asteroids

Most C-complex asteroids have albedo values less than 0.1, but there are some high-albedo (bright) C-complex asteroids with albedo values exceeding 0.1. To reveal the nature and origin of bright C-complex asteroids, we conducted spectroscopic observations of the asteroids at visible and near-infrared wavelengths. As a result, the bright B-, C-, and Ch-type (Bus) asteroids, which are subclasses of the Bus C-complex, are classified as DeMeo C-type asteroids with concave curvature, B-, Xn-, and K-type asteroids. Analogue meteorites and material (CV/CK chondrites, enstatite chondrites/achondrites, and salts) associated with these spectral types of asteroids are thought to be composed of minerals and material exposed to high temperatures. A comparison of the results obtained in this study with the SDSS photometric data suggests that salts may have occurred in the parent bodies of 24 Themis and 10 Hygiea, as well as 2 Pallas. The bright C-complex asteroids in other C-complex families were likely caused by impact heating. Bright C-complex asteroids that do not belong to any families are likely to be impact-metamorphosed carbonaceous chondrites, CV/CK chondrites, or enstatite chondrites/achondrites.
Decimated Framelet System on Graphs and Fast G-Framelet Transforms

Graph representation learning has many real-world applications, from super-resolution imaging, 3D computer vision to drug repurposing, protein classification, social networks analysis. An adequate representation of graph data is vital to the learning performance of a statistical or machine learning model for graph-structured data. In this paper, we propose a novel multiscale representation system for graph data, called decimated framelets, which form a localized tight frame on the graph. The decimated framelet system allows storage of the graph data representation on a coarse-grained chain and processes the graph data at multi scales where at each scale, the data is stored at a subgraph. Based on this, we then establish decimated G-framelet transforms for the decomposition and reconstruction of the graph data at multi resolutions via a constructive data-driven filter bank. The graph framelets are built on a chain-based orthonormal basis that supports fast graph Fourier transforms. From this, we give a fast algorithm for the decimated G-framelet transforms, or FGT, that has linear computational complexity O(N) for a graph of size N. The theory of decimated framelets and FGT is verified with numerical examples for random graphs. The effectiveness is demonstrated by real-world applications, including multiresolution analysis for traffic network, and graph neural networks for graph classification tasks.
Mapping the Timescale Organization of Neural Language Models

In the human brain, sequences of language input are processed within a distributed and hierarchical architecture, in which higher stages of processing encode contextual information over longer timescales. In contrast, in recurrent neural networks which perform natural language processing, we know little about how the multiple timescales of contextual information are functionally organized. Therefore, we applied tools developed in neuroscience to map the "processing timescales" of individual units within a word-level LSTM language model. This timescale-mapping method assigned long timescales to units previously found to track long-range syntactic dependencies, and revealed a new cluster of previously unreported long-timescale units. Next, we explored the functional role of units by examining the relationship between their processing timescales and network connectivity. We identified two classes of long-timescale units: "Controller" units composed a densely interconnected subnetwork and strongly projected to the forget and input gates of the rest of the network, while "Integrator" units showed the longest timescales in the network, and expressed projection profiles closer to the mean projection profile. Ablating integrator and controller units affected model performance at different position of a sentence, suggesting distinctive functions of these two sets of units. Finally, we tested the generalization of these results to a character-level LSTM model. In summary, we demonstrated a model-free technique for mapping the timescale organization in neural network models, and we applied this method to reveal the timescale and functional organization of LSTM language models.
Scalable Psychological Momentum Estimation in Esports

The world of competitive Esports and video gaming has seen and continues to experience steady growth in popularity and complexity. Correspondingly, more research on the topic is being published, ranging from social network analyses to the benchmarking of advanced artificial intelligence systems in playing against humans.In this paper, we present ongoing work on an intelligent agent recommendation engine that suggests actions to players in order to maximise success and enjoyment, both in the space of in-game choices, as well as decisions made around play session timing in the broader context. By leveraging temporal data and appropriate models, we show that a learned representation of player psychological momentum, and of tilt, can be used, in combination with player expertise, to achieve state-of-the-art performance in pre and post-draft win prediction. Our progress toward fulfilling the potential for deriving optimal recommendations is documented.
DPG approximation of eigenvalue problems

In this paper, the discontinuous Petrov--Galerkin approximation of the Laplace eigenvalue problem is discussed. We consider in particular the primal and ultra weak formulations of the problem and prove the convergence together with a priori error estimates. Moreover, we propose two possible error estimators and perform the corresponding a posteriori error analysis. The theoretical results are confirmed numerically and it is shown that the error estimators can be used to design an optimally convergent adaptive scheme.
On the characteristics of fast neutrino flavor instabilities in three-dimensional core-collapse supernova models

We assess the occurrence of fast neutrino flavor instabilities in two three-dimensional state-of-the-art core-collapse supernova simulations performed using a two-moment three-species neutrino transport scheme: one with an exploding 9$\mathrm{M_{\odot}}$ and one with a non-exploding 20$\mathrm{M_{\odot}}$ model. Apart from confirming the presence of fast instabilities occurring within the neutrino decoupling and the supernova pre-shock regions, we detect flavor instabilities in the post-shock region for the exploding model. These instabilities are likely to be scattering-induced. In addition, the failure in achieving a successful explosion in the heavier supernova model seems to seriously hinder the occurrence of fast instabilities in the post-shock region. This is a consequence of the large matter densities behind the stalled or retreating shock, which implies high neutrino scattering rates and thus more isotropic distributions of neutrinos and antineutrinos. Our findings suggest that the supernova model properties and the fate of the explosion can remarkably affect the occurrence of fast instabilities. Hence, a larger set of realistic hydrodynamical simulations of the stellar collapse is needed in order to make reliable predictions on the flavor conversion physics.