Miracle-dz/UniFAR-FTUs huggingface dataset:
Super-resolution hyper-spectral imaging for the direct visualization of local bandgap heterogeneity

Optical hyperspectral imaging based on absorption and scattering of photons at the visible and adjacent frequencies denotes one of the most informative and inclusive characterization methods in material research. Unfortunately, restricted by the diffraction limit of light, it is unable to resolve the nanoscale inhomogeneity in light-matter interactions, which is diagnostic of the local modulation in material structure and properties. Moreover, many nanomaterials have highly anisotropic optical properties that are outstandingly appealing yet hard to characterize through conventional optical methods. Therefore, there has been a pressing demand in the diverse fields including electronics, photonics, physics, and materials science to extend the optical hyperspectral imaging into the nanometer length scale. In this work, we report a super-resolution hyperspectral imaging technique that simultaneously measures optical absorption and scattering spectra with the illumination from a tungsten-halogen lamp. We demonstrated sub-5 nm spatial resolution in both visible and near-infrared wavelengths (415 to 980 nm) for the hyperspectral imaging of strained single-walled carbon nanotubes (SWNT) and reconstructed true-color images to reveal the longitudinal and transverse optical transition-induced light absorption and scattering in the SWNTs. This is the first time transverse optical absorption in SWNTs were clearly observed experimentally. The new technique provides rich near-field spectroscopic information that had made it possible to analyze the spatial modulation of band-structure along a single SWNT induced through strain engineering.
A gaze driven fast-forward method for first-person videos

The growing data sharing and life-logging cultures are driving an unprecedented increase in the amount of unedited First-Person Videos. In this paper, we address the problem of accessing relevant information in First-Person Videos by creating an accelerated version of the input video and emphasizing the important moments to the recorder. Our method is based on an attention model driven by gaze and visual scene analysis that provides a semantic score of each frame of the input video. We performed several experimental evaluations on publicly available First-Person Videos datasets. The results show that our methodology can fast-forward videos emphasizing moments when the recorder visually interact with scene components while not including monotonous clips.
Fishing suitability maps: helping fishermen reduce discards

Discards poses a serious problem for the productivity and sustainability of European fisheries and thus is an important fisheries and ecological management issue to be solved. In this paper we present a statistical tool, based on the random forest technique, that aims to reduce the negative ecological impact of fishing by providing fishermen with near-real-time maps of a fishing suitability index based on haul-by-haul catch and discard rates, indicating the most suitable areas for fishing. These easy-to-interpret maps are to be accessible to users via an online geoportal. Observer data from the Spanish discard sampling program from 2004 to 2008 for several species caught in the Cantabrian Sea (ICES area VIIIc) were used to illustrate the random forest approach. Results in the case study varied among species and seasons, with better results achieved for balanced datasets, such as those for economically valuable target species with segregated life stages. We discuss how this online tool could be useful for .
Prevention of dietary hypercholesterolemia in rats using sunflower-oil-fried sardines. Effects on cholesterol and serum enzymes

The acceptability, hypocholesterolemic effect, and possible harmfulness of cholesterol-enriched diets containing different kinds of sunflower-oil-fried sardines were studied in growing Wistar rats. Group 1 was fed a diet confining casein and sunflower oil. For group 2 the only source of protein and fat was a mixture of fried sardines from the first and second fryings in sunflower oil. Group 3 received a mixture of sardines from the 8th to 10th fryings in sunflower oil. Weight gain and food acceptability were lower in group 3
The Structure of U.S. College Networks on Facebook

Anecdotally, social connections made in university have life-long impact. Yet knowledge of social networks formed in college remains episodic, due in large part to the difficulty and expense involved in collecting a suitable dataset for comprehensive analysis. To advance and systematize insight into college social networks, we describe a dataset of the largest online social network platform used by college students in the United States. We combine de-identified and aggregated Facebook data with College Scorecard data, campus-level information provided by U.S. Department of Education, to produce a dataset covering the 2008-2015 entry year cohorts for 1,159 U.S. colleges and universities, spanning 7.6 million students. To perform the difficult task of comparing these networks of different sizes we develop a new methodology. We compute features over sampled ego-graphs, train binary classifiers for every pair of graphs, and operationalize distance between graphs as predictive accuracy. Social networks of different year cohorts at the same school are structurally more similar to one another than to cohorts at other schools. Networks from similar schools have similar structures, with the public/private and graduation rate dimensions being the most distinguishable. We also relate school types to specific outcomes. For example, students at private schools have larger networks that are more clustered and with higher homophily by year. Our findings may help illuminate the role that colleges play in shaping social networks which partly persist throughout people's lives.
Backtracking linesearch for conditional gradient sliding

We present a modification of the conditional gradient sliding (CGS) method that was originally developed in \cite{lan2016conditional}. While the CGS method is a theoretical breakthrough in the theory of projection-free first-order methods since it is the first that reaches the theoretical performance limit, in implementation it requires the knowledge of the Lipschitz constant of the gradient of the objective function $L$ and the number of total gradient evaluations $N$. Such requirements imposes difficulties in the actual implementation, not only because that it can be difficult to choose proper values of $L$ and $N$ that satisfies the conditions for convergence, but also since conservative choices of $L$ and $N$ can deteriorate the practical numerical performance of the CGS method. Our proposed method, called the conditional gradient sliding method with linesearch (CGS-ls), does not require the knowledge of either $L$ and $N$, and is able to terminate early before the theoretically required number of iterations. While more practical in numerical implementation, the theoretical performance of our proposed CGS-ls method is still as good as that of the CGS method. We present numerical experiments to show the efficiency of our proposed method in practice.
Accurate transferable polarization model derived from the monomer electron density

Force field have for decades proven to be an indispensable tool for molecular simulations which are out of reach for ab-initio methods. Recent efforts to improve the accuracy of these simulations have focused on the inclusion of many-body interactions in force fields. In this regard, we propose a transferable inducible dipole model which requires only the monomer electron density as input, without the need for atom type specific parameters. Slater dipoles are introduced, the widths of which are derived from the ab-initio monomer density. An additional exchange-repulsion interaction is introduced in our model, originating from the overlap of the delocalized dipoles with other dipoles and the ground state electron density. This interaction has previously been neglected in point dipole models, as the lack of spatial extent of the dipoles prevents the inclusion of an overlap term. The inclusion of this interaction is shown to significantly improve the prediction of three-body energies. Our model is incorporated in a previously proposed non-covalent force field and is benchmarked on interaction energies of dimers contained in the hsg and hbc6 datasets. Furthermore, we demonstrate the transferability of our model to the condensed phase of water, and to the interaction of CO$_2$ and H$_2$O molecules with the ZIF-8 metal-organic framework. The inherent transferability of our model makes it widely applicable to systems like the aforementioned metal-organic frameworks, where no specifically fitted parameters for polarization models are available in the literature.
A virtual element-based flux recovery on quadtree

In this paper, we introduce a simple local flux recovery for $\mathcal{Q}_k$ finite element of a scalar coefficient diffusion equation on quadtree meshes, with no restriction on the irregularities of hanging nodes. The construction requires no specific ad hoc tweaking for hanging nodes on $l$-irregular ($l\geq 2$) meshes thanks to the adoption of some novel ideas borrowed from virtual element families. The rectangular elements with hanging nodes are treated as polygons as in the flux recovery context. An efficient a posteriori error estimator is then constructed based on the recovered flux projected to a space with simpler structure, and its reliability is proved under common assumptions, both of which are further verified in numerics.
Foreshadowing the Benefits of Incidental Supervision

Learning theory mostly addresses the standard learning paradigm, assuming the availability of complete and correct supervision signals for large amounts of data. However, in practice, machine learning researchers and practitioners acquire and make use of a range of {\em incidental supervision} signals that only have statistical associations with the gold supervision. This paper addresses the question: {\em Can one quantify models' performance when learning with such supervision signals, without going through an exhaustive experimentation process with various supervision signals and learning protocols? } To quantify the benefits of various incidental supervision signals, we propose a unified PAC-Bayesian Informativeness measure (PABI), characterizing the reduction in uncertainty that incidental supervision signals provide. We then demonstrate PABI's use in quantifying various types of incidental signals such as partial labels, noisy labels, constraints, cross-domain signals, and some combinations of these. Experiments on named entity recognition and question answering show that PABI correlates well with learning performance, providing a promising way to determine, ahead of learning, which supervision signals would be beneficial.
J1110+4817 – a compact symmetric object candidate revisited

Compact symmetric objects (CSOs) are radio-emitting active galactic nuclei (AGNs) typically with a double-lobed radio structure confined to within 1 kpc. CSOs represent the earliest evolutionary phase of jetted AGNs. Some of them may eventually evolve into large-scale extended double sources, while others stall within the host galaxy and die out, depending on the longevity of nuclear activity, the jet power, and parameters of the surrounding galactic environment. Studying CSOs is a useful tool for understanding the evolution of the galaxies and the interactions between the jets and the medium of the host galaxy. Based on milliarcsec-resolution imaging observations using very long baseline interferometry (VLBI), it is not always straightforward to distinguish between a compact double-lobed or a core–jet structure. The quasar J1110+4817 was considered a CSO candidate in the literature earlier, but because of the lack of clear evidence, it could not be securely classified as a CSO. Here we present a comprehensive analysis of archival multifrequency VLBI observations combined with accurate Gaia optical astrometric information. Lower frequency VLBI images reveal an extended radio feature nearly perpendicular to the main structural axis of the source, apparently emanating from the brighter northern feature, which is rare among the known CSOs. While the presence of a binary AGN system cannot be fully excluded, the most plausible explanation is that J1110+4817 is a CSO.
Parking packet payload with P4

Network Function (NF) deployments suffer from poor link goodput, because popular NFs such as firewalls process only packet headers while receiving and transmitting complete packets. As a result, unnecessary packet payloads needlessly consume link bandwidth. We introduce PayloadPark, which improves goodput by temporarily parking packet payloads in the stateful memory of dataplane programmable switches. PayloadPark forwards only packet headers to NF servers, thereby saving bandwidth between the switch and the NF server. PayloadPark is a transparent in-network optimization that complements existing approaches for optimizing NF performance on end-hosts. We prototyped PayloadPark on a Barefoot Tofino ASIC using the P4 language. Our prototype, when deployed on a top-of-rack switch, can service up to 8 NF servers using less than 40% of the on-chip memory resources. The prototype improves goodput by 10-26% for a Firewall → NAT NF chain and reduces PCIe bandwidth load by 2-58%. With workloads that have datacenter network traffic characteristics, PayloadPark provides a 13% goodput gain with a Firewall → NAT → LB NF chain, without latency penalty. In this scenario, we can further increase the goodput gain to 28% by using packet recirculation.
Surveillance, Stigma & Sociotechnical Design for HIV

Online dating and hookup platforms have fundamentally changed people’s day-to-day practices of sex and love — but exist in tension with older social and medicolegal norms. This is particularly the case for people with HIV, who are frequently stigmatized, surveilled, ostracized, and incarcerated because of their status. Efforts to make intimate platforms “work” for HIV frequently focus on user-to-user interactions and disclosure of one’s HIV status but elide both the structural forces at work in regulating sex and the involvement of the state in queer lives. In an effort to foreground these forces and this involvement, we analyze the approaches that intimate platforms have taken in designing for HIV disclosure through a content analysis of 50 current platforms. We argue that the implicit reinforcement of stereotypes about who HIV is or is not a concern for, along with the failure to consider state practices when designing for data disclosure, opens up serious risks for HIV-positive and otherwise marginalized people. While we have no panacea for the tension between disclosure and risk, we point to bottom-up, communal, and queer approaches to design as a way of potentially making that tension easier to safely navigate.
The influence of floral symmetry and pollination systems on flower size variation

We compared the amount of variation in flower size between autogamous and insect-pollinated species to examine the hypothesis that pollinator-mediated selection stabilizes flower size in plant populations. One would expect the flower size variation to be larger in selfing species that are less affected by pollinator-mediated stabilizing selection than in insect-pollinated species. The results of phylogenetic comparisons between autogamous and insect-pollinated flowers supported the pollinator-mediated stabilizing selection hypothesis, although the non-phylogenetic comparison did not. According to our results, we discuss the factors influencing the flower size variation.
Physics Regularized Gaussian Processes

We consider incorporating incomplete physics knowledge, expressed as differential equations with latent functions, into Gaussian processes (GPs) to improve their performance, especially for limited data and extrapolation. While existing works have successfully encoded such knowledge via kernel convolution, they only apply to linear equations with analytical Green's functions. The convolution can further restrict us from fusing physics with highly expressive kernels, e.g., deep kernels. To overcome these limitations, we propose Physics Regularized Gaussian Process (PRGP) that can incorporate both linear and nonlinear equations, does not rely on Green's functions, and is free to use arbitrary kernels. Specifically, we integrate the standard GP with a generative model to encode the differential equation in a principled Bayesian hybrid framework. For efficient and effective inference, we marginalize out the latent variables and derive a simplified model evidence lower bound (ELBO), based on which we develop a stochastic collapsed inference algorithm. Our ELBO can be viewed as a posterior regularization objective. We show the advantage of our approach in both simulation and real-world applications.
Uniphore's submission to Fearless Steps Challenge Phase-2

We propose supervised systems for speech activity detection (SAD) and speaker identification (SID) tasks in Fearless Steps Challenge Phase-2. The proposed systems for both the tasks share a common convolutional neural network (CNN) architecture. Mel spectrogram is used as features. For speech activity detection, the spectrogram is divided into smaller overlapping chunks. The network is trained to recognize the chunks. The network architecture and the training steps used for the SID task are similar to that of the SAD task, except that longer spectrogram chunks are used. We propose a two-level identification method for SID task. First, for each chunk, a set of speakers is hypothesized based on the neural network posterior probabilities. Finally, the speaker identity of the utterance is identified using the chunk-level hypotheses by applying a voting rule. On SAD task, a detection cost function score of 5.96%, and 5.33% are obtained on dev and eval sets, respectively. A top 5 retrieval accuracy of 82.07% and 82.42% are obtained on the dev and eval sets for SID task. A brief analysis is made on the results to provide insights into the miss-classified cases in both the tasks.
Standardised convolutional filtering for radiomics

The Image Biomarker Standardisation Initiative (IBSI) aims to improve reproducibility of radiomics studies by standardising the computational process of extracting image biomarkers (features) from images. We have previously established reference values for 169 commonly used features, created a standard radiomics image processing scheme, and developed reporting guidelines for radiomic studies. However, several aspects are not standardised. Here we present a preliminary version of a reference manual on the use of convolutional image filters in radiomics. Filters, such as wavelets or Laplacian of Gaussian filters, play an important part in emphasising specific image characteristics such as edges and blobs. Features derived from filter response maps have been found to be poorly reproducible. This reference manual forms the basis of ongoing work on standardising convolutional filters in radiomics, and will be updated as this work progresses.
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition

This work studies the problem of learning episodic Markov Decision Processes with known transition and bandit feedback. We develop the first algorithm with a ``best-of-both-worlds'' guarantee: it achieves $\mathcal{O}(log T)$ regret when the losses are stochastic, and simultaneously enjoys worst-case robustness with $\tilde{\mathcal{O}}(\sqrt{T})$ regret even when the losses are adversarial, where $T$ is the number of episodes. More generally, it achieves $\tilde{\mathcal{O}}(\sqrt{C})$ regret in an intermediate setting where the losses are corrupted by a total amount of $C$. Our algorithm is based on the Follow-the-Regularized-Leader method from Zimin and Neu (2013), with a novel hybrid regularizer inspired by recent works of Zimmert et al. (2019a, 2019b) for the special case of multi-armed bandits. Crucially, our regularizer admits a non-diagonal Hessian with a highly complicated inverse. Analyzing such a regularizer and deriving a particular self-bounding regret guarantee is our key technical contribution and might be of independent interest.
Knowledge-Aided Open-Domain Question Answering

Open-domain question answering (QA) aims to find the answer to a question from a large collection of documents.Though many models for single-document machine comprehension have achieved strong performance, there is still much room for improving open-domain QA systems since document retrieval and answer reranking are still unsatisfactory. Golden documents that contain the correct answers may not be correctly scored by the retrieval component, and the correct answers that have been extracted may be wrongly ranked after other candidate answers by the reranking component. One of the reasons is derived from the independent principle in which each candidate document (or answer) is scored independently without considering its relationship to other documents (or answers). In this work, we propose a knowledge-aided open-domain QA (KAQA) method which targets at improving relevant document retrieval and candidate answer reranking by considering the relationship between a question and the documents (termed as question-document graph), and the relationship between candidate documents (termed as document-document graph). The graphs are built using knowledge triples from external knowledge resources. During document retrieval, a candidate document is scored by considering its relationship to the question and other documents. During answer reranking, a candidate answer is reranked using not only its own context but also the clues from other documents. The experimental results show that our proposed method improves document retrieval and answer reranking, and thereby enhances the overall performance of open-domain question answering.
Summarising Big Data: Common GitHub Dataset for Software Engineering Challenges

In open-source software development environments; textual, numerical and relationship-based data generated are of interest to researchers. Various data sets are available for this data, which is frequently used in areas such as software engineering and natural language processing. However, since these data sets contain all the data in the environment, the problem arises in the terabytes of data processing. For this reason, almost all of the studies using GitHub data use filtered data according to certain criteria. In this context, using a different data set in each study makes a comparison of the accuracy of the studies quite difficult. In order to solve this problem, a common dataset was created and shared with the researchers, which would allow us to work on many software engineering problems.