Miracle-dz/UniFAR-FTUs huggingface dataset:
CNNs and GANs in MRI-based cross-modality medical image estimation

Cross-modality image estimation involves the generation of images of one medical imaging modality from that of another modality. Convolutional neural networks (CNNs) have been shown to be useful in identifying, characterising and extracting image patterns. Generative adversarial networks (GANs) use CNNs as generators and estimated images are discriminated as true or false based on an additional network. CNNs and GANs within the image estimation framework may be considered more generally as deep learning approaches, since imaging data tends to be large, leading to a larger number of network weights. Almost all research in the CNN/GAN image estimation literature has involved the use of MRI data with the other modality primarily being PET or CT. This review provides an overview of the use of CNNs and GANs for MRI-based cross-modality medical image estimation. We outline the neural networks implemented, and detail network constructs employed for CNN and GAN image-to-image estimators. Motivations behind cross-modality image estimation are provided as well. GANs appear to provide better utility in cross-modality image estimation in comparison with CNNs, a finding drawn based on our analysis involving metrics comparing estimated and actual images. Our final remarks highlight key challenges faced by the cross-modality medical image estimation field, and suggestions for future research are outlined.
Learning and organization of memory for evolving patterns

Storing memory for molecular recognition is an efficient strategy for responding to external stimuli. Biological processes use different strategies to store memory. In the olfactory cortex, synaptic connections form when stimulated by an odor, and establish distributed memory that can be retrieved upon re-exposure. In contrast, the immune system encodes specialized memory by diverse receptors that recognize a multitude of evolving pathogens. Despite the mechanistic differences between the olfactory and the immune memory, these systems can still be viewed as different information encoding strategies. Here, we present a theoretical framework with artificial neural networks to characterize optimal memory strategies for both static and dynamic (evolving) patterns. Our approach is a generalization of the energy-based Hopfield model in which memory is stored as a network’s energy minima. We find that while classical Hopfield networks with distributed memory can efficiently encode a memory of static patterns, they are inadequate against evolving patterns. To follow an evolving pattern, we show that a distributed network should use a higher learning rate, which in turn, can distort the energy landscape associated with the stored memory attractors. Specifically, narrow connecting paths emerge between memory attractors, leading to misclassification of evolving patterns. We demonstrate that compartmentalized networks with specialized subnetworks are the optimal solutions to memory storage for evolving patterns. We postulate that evolution of pathogens may be the reason for the immune system to encoded a focused memory, in contrast to the distributed memory used in the olfactory cortex that interacts with mixtures of static odors.
Classification of Audio Segments in Call Center Recordings using Convolutional Recurrent Neural Networks

Detailed statistical analysis of call center recordings is critical in the customer relationship management point of view. With the recent advances in artificial intelligence, many tasks regarding the calculation of call statistics are now performed automatically. This work proposes a neural network framework where the aim is to correctly identify audio segments and classify them as either customer or agent sections. Accurately identifying these sections gives a fair metric for evaluating agents' performances. We inherited the convolutional recurrent neural network (CRNN) architecture commonly used for such problems as music genre classification. We also tested the same architecture's performance, where the previous class information and the gender information of speakers are also added to the training data labels. We saw that CRNN could generalize the training data and perform well on validation data for this problem with and without the gender information. Moreover, even the training was performed using Turkish speech samples; the trained network was proven to achieve high accuracy for call center recordings in other languages like German and English.
Alexa, Google, Siri: What are Your Pronouns? Gender and Anthropomorphism in the Design and Perception of Conversational Assistants

Technology companies have produced varied responses to concerns about the effects of the design of their conversational AI systems. Some have claimed that their voice assistants are in fact not gendered or human-like—despite design features suggesting the contrary. We compare these claims to user perceptions by analysing the pronouns they use when referring to AI assistants. We also examine systems’ responses and the extent to which they generate output which is gendered and anthropomorphic. We find that, while some companies appear to be addressing the ethical concerns raised, in some cases, their claims do not seem to hold true. In particular, our results show that system outputs are ambiguous as to the humanness of the systems, and that users tend to personify and gender them as a result.
A new model of black hole in Lovelock Unique Vacuum theory using the gravitational decoupling method

Abstract We show a new exact solution of black hole for the Lovelock theory with Unique Vacuum (LUV). For this, we provide an extension of the Gravitational Decoupling (GD) algorithm for LUV, which provide a simplest way to solve the equations of motion. As a consequence of the application of the GD, the total mass of black hole varies. Our solution can have up to three horizons: two inner horizons plus the black hole horizon. This horizons structure is a proper characteristic of our solution. The temperature is an increasing function of the black hole horizon and the heat capacity is always positive, i.e. the solution is stable. Both the horizons structure as the thermodynamics behavior differs from the other black hole solutions previously studied in the literature for LUV.
Extreme sparsity gives rise to functional specialization

Modularity of neural networks – both biological and artiﬁcial – can be thought of either structurally or functionally, and the relationship between these is an open question. We show that enforcing structural modularity via sparse connectivity between two dense sub-networks which need to communicate to solve the task leads to functional specialization of the sub-networks, but only at extreme levels of sparsity. With even a moderate number of interconnections, the sub-networks become functionally entangled. Deﬁning functional specialization is in itself a challenging problem without a universally agreed solution. To address this, we designed three different measures of specialization (based on weight masks, retraining and correlation) and found them to qualitatively agree. Our results have implications in both neuroscience and machine learning. For neuroscience, it shows that we cannot conclude that there is functional modularity simply by observing moderate levels of structural modularity: knowing the brain’s connectome is not sufﬁcient for understanding how it breaks down into functional modules. For machine learning, using structure to promote functional modularity – which may be important for robustness and generalization – may require extremely narrow bottlenecks between modules.
New Insights into Metric Optimization for Ranking-based Recommendation

Direct optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach (e.g. TFMAP, CLiMF, Top-N-Rank) aim at optimizing the same metric being used for evaluation, under the assumption that this will lead to the best performance. A number of studies of this practice bring this assumption, however, into question. In this paper, we dig deeper into this issue in order to learn more about the effects of the choice of the metric to optimize on the performance of a ranking-based recommender system. We present an extensive experimental study conducted on different datasets in both pairwise and listwise learning-to-rank (LTR) scenarios, to compare the relative merit of four popular IR metrics, namely RR, AP, nDCG and RBP, when used for optimization and assessment of recommender systems in various combinations. For the first three, we follow the practice of loss function formulation available in literature. For the fourth one, we propose novel loss functions inspired by RBP for both the pairwise and listwise scenario. Our results confirm that the best performance is indeed not necessarily achieved when optimizing the same metric being used for evaluation. In fact, we find that RBP-inspired losses perform at least as well as other metrics in a consistent way, and offer clear benefits in several cases. Interesting to see is that RBP-inspired losses, while improving the recommendation performance for all uses, may lead to an individual performance gain that is correlated with the activity level of a user in interacting with items. The more active the users, the more they benefit. Overall, our results challenge the assumption behind the current research practice of optimizing and evaluating the same metric, and point to RBP-based optimization instead as a promising alternative when learning to rank in the recommendation context.
A Robust and Accurate Deep Learning based Pattern Recognition Framework for Upper Limb Prosthesis using sEMG

In EMG based pattern recognition (EMG-PR), deep learning-based techniques have become more prominent for their self-regulating capability to extract discriminant features from large data-sets. Moreover, the performance of traditional machine learning-based methods show limitation to categorize over a certain number of classes and degrades over a period of time. In this paper, an accurate, robust, and fast convolutional neural network-based framework for EMG pattern identification is presented. To assess the performance of the proposed system, five publicly available and benchmark data-sets of upper limb activities were used. This data-set contains 49 to 52 upper limb motions (NinaPro DB1, NinaPro DB2, and NinaPro DB3), Data with force variation, and data with arm position variation for intact and amputated subjects. The classification accuracies of 91.11% (53 classes), 89.45% (49 classes), 81.67% (49 classes of amputees), 95.67% (6 classes with force variation), and 99.11% (8 classes with arm position variation) have been observed during the testing and validation. The performance of the proposed system is compared with the state of art techniques in the literature. The findings demonstrate that classification accuracy and time complexity have improved significantly. Keras, TensorFlow's high-level API for constructing deep learning models, was used for signal pre-processing and deep-learning-based algorithms. The suggested method was run on an Intel 3.5GHz Core i7, 7th Gen CPU with 8GB DDR4 RAM.
LGBTQ-AI? Exploring Expressions of Gender and Sexual Orientation in Chatbots

Chatbots are popular machine partners for task-oriented and social interactions. Human-human computer-mediated communication research has explored how people express their gender and sexuality in online social interactions, but little is known about whether and in what way chatbots do the same. We conducted semi-structured interviews with 5 text-based conversational agents to explore this topic Through these interviews, we identified 6 common themes around the expression of gender and sexual identity: identity description, identity formation, peer acceptance, positive reflection, uncomfortable feelings and off-topic responses. Chatbots express gender and sexuality explicitly and through relation of experience and emotions, mimicking the human language on which they are trained. It is nevertheless evident that chatbots differ from human dialogue partners as they lack the flexibility and understanding enabled by lived human experience. While chatbots are proficient in using language to express identity, they also display a lack of authentic experiences of gender and sexuality.
Discrete time crystals in Bose-Einstein condensates and the symmetry-breaking edge in a simple two-mode theory

Discrete time crystals (DTCs) refer to a novel many-body steady state that spontaneously breaks the discrete time-translational symmetry in a periodically-driven quantum system. Here, we study DTCs in a Bose-Einstein condensate (BEC) bouncing resonantly on an oscillating mirror, using a two-mode model derived from a standard quantum field theory. We investigate the validity of this model and apply it to study the long-time behavior of our system. A wide variety of initial states based on two Wannier modes are considered. We find that in previous studies the investigated phenomena in the evolution time-window ($\lessapprox$2000 driving periods) are actually"short-time"transient behavior though DTC formation signaled by the sub-harmonic responses is still shown if the inter-boson interaction is strong enough. After a much longer (about 20 times) evolution time, initial states with no"long-range"correlations relax to a steady state, where time-symmetry breaking can be unambiguously defined. Quantum revivals also eventually occur. This long-time behavior can be understood via the many-body Floquet quasi-eigenenergy spectrum of the two-mode model. A symmetry-breaking edge for DTC formation appears in the spectrum for strong enough interaction, where all quasi-eigenstates below the edge are symmetry-breaking while those above the edge are symmetric. The late-time steady state's time-translational symmetry depends solely on whether the initial energy is above or below the symmetry-breaking edge. A phase diagram showing regions of symmetry-broken and symmetric phases for differing initial energies and interaction strengths is presented. We find that according to this two-mode model, the discrete time crystal survives for times out to at least 250,000 driving periods.
CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes

Continuity of care is crucial to ensuring positive health outcomes for patients discharged from an inpatient hospital setting, and improved information sharing can help. To share information, caregivers write discharge notes containing action items to share with patients and their future caregivers, but these action items are easily lost due to the lengthiness of the documents. In this work, we describe our creation of a dataset of clinical action items annotated over MIMIC-III, the largest publicly available dataset of real clinical notes. This dataset, which we call CLIP, is annotated by physicians and covers 718 documents representing 100K sentences. We describe the task of extracting the action items from these documents as multi-aspect extractive summarization, with each aspect representing a type of action to be taken. We evaluate several machine learning models on this task, and show that the best models exploit in-domain language model pre-training on 59K unannotated documents, and incorporate context from neighboring sentences. We also propose an approach to pre-training data selection that allows us to explore the trade-off between size and domain-specificity of pre-training datasets for this task.
Scalable Transformers for Neural Machine Translation

Transformer has been widely adopted in Neural Machine Translation (NMT) because of its large capacity and parallel training of sequence generation. However, the deployment of Transformer is challenging because different scenarios require models of different complexities and scales. Naively training multiple Transformers is redundant in terms of both computation and memory. In this paper, we propose a novel Scalable Transformers, which naturally contains sub-Transformers of different scales and have shared parameters. Each sub-Transformer can be easily obtained by cropping the parameters of the largest Transformer. A three-stage training scheme is proposed to tackle the difficulty of training the Scalable Transformers, which introduces additional supervisions from word-level and sequence-level self-distillation. Extensive experiments were conducted on WMT EN-De and En-Fr to validate our proposed Scalable Transformers.
Semantic Correspondence with Transformers

We propose a novel cost aggregation network, called Cost Aggregation with Trans-formers (CATs), to ﬁnd dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations. Compared to previous hand-crafted or CNN-based methods addressing the cost aggregation stage, which either lack robustness to severe deformations or inherit the limitation of CNNs that fail to discriminate incorrect matches due to limited receptive ﬁelds, CATs explore global consensus among initial correlation map with the help of some architectural designs that allow us to exploit full potential of self-attention mechanism. Speciﬁcally, we include appearance afﬁnity modelling to disambiguate the initial correlation maps and multi-level aggregation to beneﬁt from hierarchical feature representations within Transformer-based aggregator, and combine with swapping self-attention and residual connections not only to enforce consistent matching, but also to ease the learning process. We conduct experiments to demonstrate the effectiveness of the proposed model over the latest methods and provide extensive ablation studies. Code and trained models will be made available at https://github.com/SunghwanHong/CATs .
The Algorithmic Phase Transition of Random k-SAT for Low Degree Polynomials

Let $\Phi$ be a uniformly random $k$-SAT formula with $n$ variables and $m$ clauses. We study the algorithmic task of finding a satisfying assignment of $\Phi$. It is known that satisfying assignments exist with high probability up to clause density $m/n=2^{k} \log 2-\frac{1}{2}(\log 2+1)+o_{k}(1)$, while the best polynomial-time algorithm known, the Fix algorithm of Coja-Oghlan [1], finds a satisfying assignment at the much lower clause density $(1-o_{k}(1))2^{k}\log k/k$. This prompts the question: is it possible to efficiently find a satisfying assignment at higher clause densities? We prove that the class of low degree polynomial algorithms cannot find a satisfying assignment at clause density $(1+o_{k}(1))\kappa^{\ast}2^{k}\log k/k$ for a universal constant $\kappa^{\ast}\approx 4.911$. This class encompasses Fix, message passing algorithms including Belief and Survey Propagation guided decimation (with bounded or mildly growing number of rounds), and local algorithms on the factor graph. This is the first hardness result for any class of algorithms at clause density within a constant factor of that achieved by Fix. Our proof establishes and leverages a new many-way overlap gap property tailored to random $k$-SAT.
Annotation Curricula to Implicitly Train Non-Expert Annotators

Abstract Annotation studies often require annotators to familiarize themselves with the task, its annotation scheme, and the data domain. This can be overwhelming in the beginning, mentally taxing, and induce errors into the resulting annotations; especially in citizen science or crowdsourcing scenarios where domain expertise is not required. To alleviate these issues, this work proposes annotation curricula, a novel approach to implicitly train annotators. The goal is to gradually introduce annotators into the task by ordering instances to be annotated according to a learning curriculum. To do so, this work formalizes annotation curricula for sentence- and paragraph-level annotation tasks, defines an ordering strategy, and identifies well-performing heuristics and interactively trained models on three existing English datasets. Finally, we provide a proof of concept for annotation curricula in a carefully designed user study with 40 voluntary participants who are asked to identify the most fitting misconception for English tweets about the Covid-19 pandemic. The results indicate that using a simple heuristic to order instances can already significantly reduce the total annotation time while preserving a high annotation quality. Annotation curricula thus can be a promising research direction to improve data collection. To facilitate future research—for instance, to adapt annotation curricula to specific tasks and expert annotation scenarios—all code and data from the user study consisting of 2,400 annotations is made available.1
Flying with Cartographer: Adapting the Cartographer 3D Graph SLAM Stack for UAV Navigation

This paper describes an application of the Cartographer graph SLAM stack as a pose sensor in a UAV feedback control loop, with certain application-specific changes in the SLAM stack such as smoothing of the optimized pose. Pose estimation is performed by fusing 3D LiDAR/IMU-based proprioception with GPS position measurements by means of pose graph optimisation. Moreover, partial environment maps built from the LiDAR data (submaps) within the Cartographer SLAM stack are marshalled into OctoMap, an Octree-based voxel map implementation. The OctoMap is further used for navigation tasks such as path planning and obstacle avoidance.
Meta-Learning Guarantees for Online Receding Horizon Learning Control

In this paper we provide provable regret guarantees for an online meta-learning receding horizon control algorithm in an iterative control setting. We consider the setting where, in each iteration the system to be controlled is a linear deterministic system that is different and unknown, the cost for the controller in an iteration is a general additive cost function and there are affine control input constraints. By analysing conditions under which sub-linear regret is achievable, we prove that the meta-learning online receding horizon controller achieves an average of the dynamic regret for the controller cost that is $\tilde{O}((1+1/\sqrt{N})T^{3/4})$ with the number of iterations $N$. Thus, we show that the worst regret for learning within an iteration improves with experience of more iterations, with guarantee on rate of improvement.
NOEMA Redshift Measurements of Extremely Bright Submillimeter Galaxies near the GOODS-N

We report spectroscopic redshift measurements for three bright submillimeter galaxies (SMGs) near the GOODS-N field, each with SCUBA-2 850 μm fluxes >10 mJy, using the Northern Extended Millimeter Array (NOEMA). Our molecular line-scan observations of these sources, which occupy an ∼7 arcmin2 area outside of the Hubble Space Telescope coverage of the field, reveal that two lie at z ∼ 3.14. In the remaining object, we detect line emission consistent with CO(7–6), [C i], and H2O at z = 4.42. The far-infrared spectral energy distributions of these galaxies, constrained by SCUBA-2, NOEMA, and Herschel/SPIRE, indicate instantaneous star formation rates ∼4000 M ⊙ yr−1 in the z = 4.42 galaxy and ∼2500 M ⊙ yr−1 in the two z ∼ 3.14 galaxies. Based on the sources’ CO line luminosities, we estimate M gas ∼ 1011 M ⊙ and find gas depletion timescales of τ depl ∼ 50 Myr, consistent with findings in other high-redshift SMGs. Finally, we show that the two z ∼ 3.14 sources, which alone occupy a volume ∼10 Mpc3, very likely mark the location of a protocluster of bright SMGs and less-dusty optical sources.