Miracle-dz/UniFAR-FTUs huggingface dataset:
The spectral localizer for semifinite spectral triples

The notion of spectral localizer is extended to pairings with semifinite spectral triples. By a spectral flow argument, any semifinite index pairing is shown to be equal to the signature of the spectral localizer. As an application, a formula for the weak invariants of topological insulators is derived. This provides a new approach to their numerical evaluation.
Dissipative Dynamics of Non-Interacting Fermion Systems and Conductivity

In this paper, Non-Equilibrium Steady State induced by electric field and the conductivity of non-interacting fermion systems under the dissipative dynamics is discussed. The dissipation is taken into account within a framework of the quantum dynamical semigroup introduced by Davies (1977). We obtain a formula of the conductivity for the stationary state, which is applicable to arbitrary potentials. Our formula gives a justification of an adiabatic factor which is often introduced in practical calculation using Kubo formula. In addition the conductivity of crystals (i.e. periodic potentials) is also discussed.
Formal Models of the OSPF Routing Protocol

We present three formal models of the OSPF routing protocol. The first two are formalised in the timed process algebra T-AWN, which is not only tailored to routing protocols, but also specifies protocols in pseudo-code that is easily readable. The difference between the two models lies in the level of detail (level of abstraction). From the more abstract model we then generate the third model. It is based on networks of timed automata and can be executed in the model checker Uppaal.
Learning Lines with Ordinal Constraints

We study the problem of finding a mapping $f$ from a set of points into the real line, under ordinal triple constraints. An ordinal constraint for a triple of points $(u,v,w)$ asserts that $|f(u)-f(v)|<|f(u)-f(w)|$. We present an approximation algorithm for the dense case of this problem. Given an instance that admits a solution that satisfies $(1-\varepsilon)$-fraction of all constraints, our algorithm computes a solution that satisfies $(1-O(\varepsilon^{1/8}))$-fraction of all constraints, in time $O(n^7) + (1/\varepsilon)^{O(1/\varepsilon^{1/8})} n$.
Desempenho de alevinos de tilapia do Nilo submetidos a diferentes niveis de inclusao do farelo de algodao

With the objective of evaluating the Nile tilapia fingerlings performance submitted to different levels of cottonseed meal (CM) inclusion (0,00; 20,00; 40,00; 60,00 and 80,00%) in rations with 30,00% of digestible protein and 3000 kcal/kg of digestible energy, the experiment was carried out in the Laboratorio de Aquicultura/UEM, 250 Nile tilapia fingerlings, were used, sexually reverted, divided in 25 tanks with 150 L, in a completely randomized design with five treatments and five replications. The evaluated variables were diary weight gain (DWG), feed gain (FG), protein efficiency rate (PE), survival (S), hepatic-somatic index (HSI), condition factor (CF), dry matter (DM), protein in the carcass percentage (PC) and fat in the carcass percentage (FP). The medium values of temperature, dissolved oxygen, electrical conductivity and pH during the experimental period, were 26,93 ± 0,29oC; 5,47 ± 0,86 mg/L; 245,80 ± 54,29 µS/cm and 7,78 ± 0,19, respectively. The WG and PE presented linear decrease when the CM was included in the ration, the FG and HSI presented linear increase. The DM, PC and WG presented quadratic effect with point maxim to 45,25, 47,84 and 45,15, respectively. The S and CF didn’t present significative differences (P>0,05). Concluded that the cottonseed meal influences in a negative way the Nile tilapia fingerlings performance.
R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge

We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context, which could include shared commonsense or world knowledge between the speaker and the listener. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on the commonsense knowledge generates sarcasm of higher quality. Human evaluation shows that our system generates sarcasm better than humans 34% of the time, and better than a reinforced hybrid baseline 90% of the time.
X-raying winds in distant quasars: The first high-redshift wind duty cycle

Aims. Theoretical models of wind-driven feedback from active galactic nuclei (AGN) often identify ultra-fast outflows as being the main agent in the generation of galaxy-sized outflows, which are possibly the main actors in establishing so-called AGN-galaxy co-evolution. Ultra-fast outflows are well characterized in local AGN but much less is known in quasars at the cosmic time when star formation and AGN activity peaked (z ≃ 1–3). It is therefore necessary to search for evidence of ultra-fast outflows in high-z sources to test wind-driven AGN feedback models. Methods. Here we present a study of Q2237+030, the Einstein Cross, a quadruply-imaged radio-quiet lensed quasar located at z = 1.695. We performed a systematic and comprehensive temporally and spatially resolved X-ray spectral analysis of all the available Chandra and XMM-Newton data (as of September 2019). Results. We find clear evidence for spectral variability, possibly due to absorption column density (or covering fraction) variability intrinsic to the source. For the first time in this quasar, we detect a fast X-ray wind outflowing at vout ≃ 0.1c that would be powerful enough (Ėkin ≃ 0.1 Lbol) to significantly affect the evolution of the host galaxy. We report also on the possible presence of an even faster component of the wind (vout ∼ 0.5c). For the first time in a high-z quasar, given the large sample and long time interval spanned by the analyzed X-ray data, we are able to roughly estimate the wind duty cycle as ≃0.46 (0.31) at 90% (95%) confidence level. Finally, we also confirm the presence of a Fe Kα emission line with variable energy, which we discuss in the light of microlensing effects as well as considering our findings on the source.
Out-of-Sample Representation Learning for Multi-Relational Graphs

Many important problems can be formulated as reasoning in multi-relational graphs. Representation learning has proved extremely effective for transductive reasoning, in which one needs to make new predictions for already observed entities. This is true for both attributed graphs (where each entity has an initial feature vector) and non-attributed graphs(where the only initial information derives from known relations with other entities). For out-of-sample reasoning, where one needs to make predictions for entities that were unseen at training time, much prior work considers attributed graph. However, this problem has been surprisingly left unexplored for non-attributed graphs. In this paper, we introduce the out-of-sample representation learning problem for non-attributed multi-relational graphs, create benchmark datasets for this task, develop several models and baselines, and provide empirical analyses and comparisons of the proposed models and baselines.
Embarrassingly Simple Unsupervised Aspect Extraction

We present a simple but effective method for aspect identification in sentiment analysis. Our unsupervised method only requires word embeddings and a POS tagger, and is therefore straightforward to apply to new domains and languages. We introduce Contrastive Attention (CAt), a novel single-head attention mechanism based on an RBF kernel, which gives a considerable boost in performance and makes the model interpretable. Previous work relied on syntactic features and complex neural models. We show that given the simplicity of current benchmark datasets for aspect extraction, such complex models are not needed. The code to reproduce the experiments reported in this paper is available at https://github.com/clips/cat.
The Problem of Fragmented Occlusion in Object Detection

Object detection in natural environments is still a very challenging task, even though deep learning has brought a tremendous improvement in performance over the last years. A fundamental problem of object detection based on deep learning is that neither the training data nor the suggested models are intended for the challenge of fragmented occlusion. Fragmented occlusion is much more challenging than ordinary partial occlusion and occurs frequently in natural environments such as forests. A motivating example of fragmented occlusion is object detection through foliage which is an essential requirement in green border surveillance. This paper presents an analysis of state-of-the-art detectors with imagery of green borders and proposes to train Mask R-CNN on new training data which captures explicitly the problem of fragmented occlusion. The results show clear improvements of Mask R-CNN with this new training strategy (also against other detectors) for data showing slight fragmented occlusion.
Evaluating Stochastic Rankings with Expected Exposure

We introduce the concept of expected exposure as the average attention ranked items receive from users over repeated samples of the same query. Furthermore, we advocate for the adoption of the principle of equal expected exposure: given a fixed information need, no item should receive more or less expected exposure than any other item of the same relevance grade. We argue that this principle is desirable for many retrieval objectives and scenarios, including topical diversity and fair ranking. %Leveraging user models from existing retrieval metrics, we propose a general evaluation methodology based on expected exposure and draw connections to related metrics in information retrieval evaluation. Importantly, this methodology relaxes classic information retrieval assumptions, allowing a system, in response to a query, to produce a distribution over rankings instead of a single fixed ranking. We study the behavior of the expected exposure metric and stochastic rankers across a variety of information access conditions, including ad hoc retrieval and recommendation. %We believe that measuring and optimizing expected exposure metrics using randomization opens a new area for retrieval algorithm development and progress.
Concept-based Abstraction-Aggregation Network for Interpretable Document Classiﬁcation

Using attention weights to identify information that is important for models’ decision making is a popular approach to interpret attention-based neural networks, which is commonly realized via creating a heat-map for each single document based on attention weights. However, this interpretation method is fragile. In this paper, we propose a corpus-level explanation approach, which aims to capture causal relationships between keywords and model predictions via learning importance of keywords for predicted labels across a training corpus based on attention weights. Using this idea as the fundamental building block, we further propose a concept-based explanation method that can automatically learn higher level concepts and their importance to model prediction task. Our concept-based explanation method is built upon a novel Abstraction-Aggregation Network, which can automatically cluster important keywords during an end-to-end training process. We apply these methods to the document classiﬁcation task and show that they are powerful in extracting semantically meaningful keywords and concepts. Our consistency analysis results based on an attention-based Na ¨ ıve Bayes Classiﬁer also demonstrate these keywords and concepts are important for model predictions.
Proximal Gradient Methods with Adaptive Subspace Sampling

Many applications in machine learning or signal processing involve nonsmooth optimization problems. This nonsmoothness brings a low-dimensional structure to the optimal solutions. In this paper, we propose a randomized proximal gradient method harnessing this underlying structure. We introduce two key components: (i) a random subspace proximal gradient algorithm; and (ii) an identification-based sampling of the subspaces. Their interplay brings a significant performance improvement on typical learning problems in terms of dimensions explored.
Extending Multilingual BERT to Low-Resource Languages

Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. However, this success is focused only on the top 104 languages in Wikipedia it was trained on. In this paper, we propose a simple but effective approach to extend M-BERT E-MBERT so it can benefit any new language, and show that our approach aids languages that are already in M-BERT as well. We perform an extensive set of experiments with Named Entity Recognition (NER) on 27 languages, only 16 of which are in M-BERT, and show an average increase of about 6% F1 on M-BERT languages and 23% F1 increase on new languages. We release models and code at http://cogcomp.org/page/publication_view/912.
KoParadigm: A Korean Conjugation Paradigm Generator

Korean is a morphologically rich language. Korean verbs change their forms in a fickle manner depending on tense, mood, speech level, meaning, etc. Therefore, it is challenging to construct comprehensive conjugation paradigms of Korean verbs. In this paper we introduce a Korean (verb) conjugation paradigm generator, dubbed KoParadigm. To the best of our knowledge, it is the first Korean conjugation module that covers all contemporary Korean verbs and endings. KoParadigm is not only linguistically well established, but also computationally simple and efficient. We share it via PyPi.
VD-BERT: A Unified Vision and Dialog Transformer with BERT

Visual dialog is a challenging vision-language task, where a dialog agent needs to answer a series of questions through reasoning on the image content and dialog history. Prior work has mostly focused on various attention mechanisms to model such intricate interactions. By contrast, in this work, we propose VD-BERT, a simple yet effective framework of unified vision-dialog Transformer that leverages the pretrained BERT language models for Visual Dialog tasks. The model is unified in that (1) it captures all the interactions between the image and the multi-turn dialog using a single-stream Transformer encoder, and (2) it supports both answer ranking and answer generation seamlessly through the same architecture. More crucially, we adapt BERT for the effective fusion of vision and dialog contents via visually grounded training. Without the need of pretraining on external vision-language data, our model yields new state of the art, achieving the top position in both single-model and ensemble settings (74.54 and 75.35 NDCG scores) on the visual dialog leaderboard.
Thermal evolution of protoplanetary disks: from β-cooling to decoupled gas and dust temperatures

Aims: We explore the long-term evolution of young protoplanetary disks with different approaches to computing the thermal structure determined by various cooling and heating processes in the disk and its surroundings. Methods: Numerical hydrodynamics simulations in the thin-disk limit were complemented with three thermal evolution schemes: a simplified $\beta$-cooling approach with and without irradiation, in which the rate of disk cooling is proportional to the local dynamical time, a fiducial model with equal dust and gas temperatures calculated taking viscous heating, irradiation, and radiative cooling into account, and also a more sophisticated approach allowing decoupled dust and gas temperatures. Results: We found that the gas temperature may significantly exceed that of dust in the outer regions of young disks thanks to additional compressional heating caused by the infalling envelope material in the early stages of disk evolution and slow collisional exchange of energy between gas and dust in low-density disk regions. The outer envelope however shows an inverse trend with the gas temperatures dropping below that of dust. The global disk evolution is only weakly sensitive to temperature decoupling. Nevertheless, separate dust and gas temperatures may affect the chemical composition, dust evolution, and disk mass estimates. Constant-$\beta$ models without stellar and background irradiation fail to reproduce the disk evolution with more sophisticated thermal schemes because of intrinsically variable nature of the $\beta$-parameter. Constant-$\beta$ models with irradiation can better match the dynamical and thermal evolution, but the agreement is still incomplete. Conclusions: Models allowing separate dust and gas temperatures are needed when emphasis is placed on the chemical or dust evolution in protoplanetary disks, particularly in sub-solar metallicity environments.
Distributed Projected Subgradient Method for Weakly Convex Optimization

The stochastic subgradient method is a widely-used algorithm for solving large-scale optimization problems arising in machine learning. Often these problems are neither smooth nor convex. Recently, Davis et al. [1-2] characterized the convergence of the stochastic subgradient method for the weakly convex case, which encompasses many important applications (e.g., robust phase retrieval, blind deconvolution, biconvex compressive sensing, and dictionary learning). In practice, distributed implementations of the projected stochastic subgradient method (stoDPSM) are used to speed-up risk minimization. In this paper, we propose a distributed implementation of the stochastic subgradient method with a theoretical guarantee. Specifically, we show the global convergence of stoDPSM using the Moreau envelope stationarity measure. Furthermore, under a so-called sharpness condition, we show that deterministic DPSM (with a proper initialization) converges linearly to the sharp minima, using geometrically diminishing step-size. We provide numerical experiments to support our theoretical analysis.
Intake of omega-3 long chain polyunsaturated fatty acid (n-3 LCPUFA) among Australian children (2-16 years): in depth nutrient and food analysis

Publications in support of this thesis Rahmawaty S, Charlton K, Lyons-Wall P, Meyer BJ (2010) Dietary intake and food sources of omega-3 long chain EPA, DPA and DHA of Australian children. Proceedings of the Nutrition Society of Australia 34, 6. Rahmawaty S, LyonsWall P, Charlton K, Meyer BJ (2012) Dietary modelling of omega-3 long chain polyunsaturated fatty acid (n-3 LCPUFA): effect of replacement of bread, egg, milk and yogurt with n-3 enriched for these foods on n-3 LCPUFA intake of Australian children. The 2 nd International Food Studies at the University Illinois at Urbana Champaign USA, 4 – 5 October 2012 Rahmawaty S, LyonsWall P, Charlton K, Meyer BJ (2012) Dietary modelling of omega-3 long chain polyunsaturated fatty acid (n-3 LCPUFA). Proceedings of the Nutrition Society of Australia 36, 45.
Slim, Sparse, and Shortcut Networks

Over the recent years, deep learning has become the mainstream data-driven approach to solve many real-world problems in many important areas. Among the successful network architectures, shortcut connections are well established to take the outputs of earlier layers as additional inputs to later layers, which have produced excellent results. Despite the extraordinary power, there remain important questions on the underlying mechanism and associated functionalities regarding shortcuts. For example, why are the shortcuts powerful? How to tune the shortcut topology to optimize the efficiency and capacity of the network model? Along this direction, here we first demonstrate a topology of shortcut connections that can make a one-neuron-wide deep network approximate any univariate function. Then, we present a novel width-bounded universal approximator in contrast to depth-bounded universal approximators. Next we demonstrate a family of theoretically equivalent networks, corroborated by the concerning statistical significance experiments, and their graph spectral characterization, thereby associating the representation ability of neural network with their graph spectral properties. Furthermore, we shed light on the effect of concatenation shortcuts on the margin-based multi-class generalization bound of deep networks. Encouraged by the positive results from the bounds analysis, we instantiate a slim, sparse, and shortcut network (S3-Net) and the experimental results demonstrate that the S3-Net can achieve better learning performance than the densely connected networks and other state-of-the-art models on some well-known benchmarks.
Explicitly Modeling Adaptive Depths for Transformer

The vanilla Transformer conducts a fixed number of computations over all words in a sentence, irrespective of whether they are easy or difficult to learn. In terms of both computational efficiency and ease of learning, it is preferable to dynamically vary the numbers of computations according to the hardness of the input words. However, how to find a suitable estimation for such hardness, then explicitly modeling adaptive computation depths are still not investigated. In this paper, we try to solve this issue, and propose two effective approaches, namely 1) mutual information based estimation and 2) reconstruction loss based estimation, to measure the hardness of learning the representation for a word and determine its computational depth. Results on the classic text classification task (24 datasets in various sizes and domains) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive models. More importantly, our approaches lead to more robust depth-adaptive Transformer models with better interpretability of the depth distribution.
Tower-type bounds for Roth’s theorem with popular differences

Green developed an arithmetic regularity lemma to prove a strengthening of Roth's theorem on arithmetic progressions in dense sets. It states that for every $\epsilon > 0$ there is some $N_0(\epsilon)$ such that for every $N \ge N_0(\epsilon)$ and $A \subset [N]$ with $|A| = \alpha N$, there is some nonzero $d$ such that $A$ contains at least $(\alpha^3 - \epsilon) N$ three-term arithmetic progressions with common difference $d$. We prove that the minimum $N_0(\epsilon)$ in Green's theorem is an exponential tower of 2s of height on the order of $\log(1/\epsilon)$. Both the lower and upper bounds are new. It shows that the tower-type bounds that arise from the use of a regularity lemma in this application are quantitatively necessary.
The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent

The mini-batch stochastic gradient descent (SGD) algorithm is widely used in training machine learning models, in particular deep learning models. We study SGD dynamics under linear regression and two-layer linear networks, with an easy extension to deeper linear networks, by focusing on the variance of the gradients, which is the first study of this nature. In the linear regression case, we show that in each iteration the norm of the gradient is a decreasing function of the mini-batch size $b$ and thus the variance of the stochastic gradient estimator is a decreasing function of $b$. For deep neural networks with $L_2$ loss we show that the variance of the gradient is a polynomial in $1/b$. The results back the important intuition that smaller batch sizes yield lower loss function values which is a common believe among the researchers. The proof techniques exhibit a relationship between stochastic gradient estimators and initial weights, which is useful for further research on the dynamics of SGD. We empirically provide further insights to our results on various datasets and commonly used deep network structures.