Miracle-dz/UniFAR-FTUs huggingface dataset:
PHEW: Paths with higher edge-weights give "winning tickets" without training data

Sparse neural networks have generated substantial interest recently because they can be more efficient in learning and inference, without any significant drop in performance. The "lottery ticket hypothesis" has showed the existence of such sparse subnetworks at initialization. Given a fully-connected initialized architecture, our aim is to find such "winning ticket" networks, without any training data. We first show the advantages of forming input-output paths, over pruning individual connections, to avoid bottlenecks in gradient propagation. Then, we show that Paths with Higher Edge-Weights (PHEW) at initialization have higher loss gradient magnitude, resulting in more efficient training. Selecting such paths can be performed without any data. We empirically validate the effectiveness of the proposed approach against pruning-before-training methods on CIFAR10, CIFAR100 and Tiny-ImageNet for VGG-Net and ResNet. PHEW achieves significant improvements on the current state-of-the-art methods at 10\%, 5\% and 2\% network density. We also evaluate the structural similarity relationship between PHEW networks and pruned networks constructed through Iterated Magnitude Pruning (IMP), concluding that the former belong in the family of winning tickets networks.
Effects of precession versus instabilities on the jets of GRS 1758–258

Aims. The prototypical microquasar GRS 1758–258 exhibits large-scale morphological changes in radio maps over time which have been attributed to the rise of instabilities. Here, we investigate whether these effects could be attributed to jet precession instead. Methods. We used new and archival radio maps to fit a kinematic jet precession model. The value of the parameters thus obtained were analysed in order to get constraints on the physical properties of the GRS 1758–258 system. Their consistency with different theories of the origins for the jet precession, such as Lense–Thirring effect and tidal torques induced by the secondary star, has previously been studied. Alternatively, we also assessed the possibility that observations are compatible with eventual jet instabilities. Results. The new radio data presented here confirm that the large-scale radio morphology of GRS 1758–258 is changing over time. Our study shows that the 18.48 day period could plausibly be ascribed to precession, thus implying a reinterpretation of assumptions made for the orbital period to date. However, the observed structural changes in radio jets cannot be easily attributed to jet precession according to our analysis. In contrast, the growth of instabilities certainly appears to be a more realistic explanation of these effects.
Defense-guided Transferable Adversarial Attacks

Though deep neural networks perform challenging tasks excellently, they are susceptible to adversarial exmaples, which mislead classifiers by applying human-imperceptible perturbations on clean inputs. Under the query-free black-box scenario, adversarial examples are hard to transfer to unknown models, and several methods have been proposed with low transferability. To settle such issue, we design a max-min framework inspired by input transformations, which are benificial to both the adversarial attack and defense. Explicitly, we decrease loss values with affline transformations as a defense in the minimum procedure, and then increase loss values with the momentum iterative algorithm as an attack in the maximum procedure. To further promote transferability, we determine transformed values with the max-min theory. Extensive experiments on Imagenet demonstrate that our defense-guided transferable attacks achieve impressive increase on transferability. Experimentally, our best black-box attack fools normally trained models at an 85.3% attack success rate and adversarially trained models at a 40.43% attack success rate on average, respectively. Additionally, we provide elucidative insights on the improvement of transferability, and our method is expected to be a benchmark for assessing the robustness of deep models.
The Effects of Fatty Acids on the Molecular Circadian Clock in Immortalized, Clonal Hypothalamic Neurons

Diets high in saturated fatty acids are associated with the development of circadian dysregulation, obesity, and type 2 diabetes mellitus. Conversely, unsaturated fatty acids are now known to improve insulin sensitivity, reduce weight gain, and alleviate obesity-induced inflammation. The aforementioned effects of saturated and unsaturated fatty acids have also been identified in the hypothalamus; however, there is a paucity of studies regarding the role of unsaturated fatty acids in circadian rhythms. Therefore, a novel cell model was established to examine the effects of omega-3 fatty acids on circadian rhythms in hypothalamic neurons. The mHypoE-37 cell line expresses Bmal1, Per2, and Rev-erbα in a circadian manner. The saturated fatty acid, palmitate, was found to induce circadian dysregulation of the mHypoE-37 neurons, whereas the unsaturated fatty acid, docosahexaenoic acid, protected against palmitate-induced circadian changes. These studies are the first to identify the potential for unsaturated fatty acids to protect the circadian system.
A Very Compact Embedded CNN Processor Design Based on Logarithmic Computing

In this paper, we propose a very compact embedded CNN processor design based on a modified logarithmic computing method using very low bit-width representation. Our high-quality CNN processor can easily fit into edge devices. For Yolov2, our processing circuit takes only 0.15 mm2 using TSMC 40 nm cell library. The key idea is to constrain the activation and weight values of all layers uniformly to be within the range [-1, 1] and produce low bit-width logarithmic representation. With the uniform representations, we devise a unified, reusable CNN computing kernel and significantly reduce computing resources. The proposed approach has been extensively evaluated on many popular image classification CNN models (AlexNet, VGG16, and ResNet-18/34) and object detection models (Yolov2). The hardware-implemented results show that our design consumes only minimal computing and storage resources, yet attains very high accuracy. The design is thoroughly verified on FPGAs, and the SoC integration is underway with promising results. With extremely efficient resource and energy usage, our design is excellent for edge computing purposes.
AstroSat soft X-ray observations of the symbiotic recurrent nova V3890 Sgr during its 2019 outburst

Two long AstroSat Soft X-ray Telescope observations were taken of the third recorded outburst of the Symbiotic Recurrent Nova, V3890 Sgr. The first observing run, 8.1-9.9 days after the outburst, initially showed a stable intensity level with a hard X-ray spectrum that we attribute to shocks between the nova ejecta and the pre-existing stellar companion. On day 8.57, the first, weak, signs appeared of Super Soft Source (SSS) emission powered by residual burning on the surface of the White Dwarf. The SSS emission was observed to be highly variable on time scales of hours. After day 8.9, the SSS component was more stable and brighter. In the second observing run, on days 15.9-19.6 after the outburst, the SSS component was even brighter but still highly variable. The SSS emission was observed to fade significantly during days 16.8-17.8 followed by re-brightening. Meanwhile the shock component was stable leading to increase in hardness ratio during the period of fading. AstroSat and XMM-Newton observations have been used to study the spectral properties of V3890 Sgr to draw quantitative conclusions even if their drawback is model-dependence. We used the xspec to fit spectral models of plasma emission, and the best fits are consistent with the elemental abundances being lower during the second observing run compared to the first for spectra >1 keV. The SSS emission is well fit by non-local thermal equilibrium model atmosphere used for white dwarfs. The resulting spectral parameters, however, are subject to systematic uncertainties such as completeness of atomic data.
Logic Guided Genetic Algorithms

We present a novel Auxiliary Truth enhanced Genetic Algorithm (GA) that uses logical or mathematical constraints as a means of data augmentation as well as to compute loss (in conjunction with the traditional MSE), with the aim of increasing both data efficiency and accuracy of symbolic regression (SR) algorithms. Our method, logic-guided genetic algorithm (LGGA), takes as input a set of labelled data points and auxiliary truths (ATs) (mathematical facts known a priori about the unknown function the regressor aims to learn) and outputs a specially generated and curated dataset that can be used with any SR method. Three key insights underpin our method: first, SR users often know simple ATs about the function they are trying to learn. Second, whenever an SR system produces a candidate equation inconsistent with these ATs, we can compute a counterexample to prove the inconsistency, and further, this counterexample may be used to augment the dataset and fed back to the SR system in a corrective feedback loop. Third, the value addition of these ATs is that their use in both the loss function and the data augmentation process leads to better rates of convergence, accuracy, and data efficiency. We evaluate LGGA against state-of-the-art SR tools, namely, Eureqa and TuringBot on 16 physics equations from "The Feynman Lectures on Physics" book. We find that using these SR tools in conjunction with LGGA results in them solving up to 30.0% more equations, needing only a fraction of the amount of data compared to the same tool without LGGA, i.e., resulting in up to a 61.9% improvement in data efficiency.
AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines

In this paper, we present AISHELL-3, a large-scale and high-fidelity multi-speaker Mandarin speech corpus which could be used to train multi-speaker Text-to-Speech (TTS) systems. The corpus contains roughly 85 hours of emotion-neutral recordings spoken by 218 native Chinese mandarin speakers. Their auxiliary attributes such as gender, age group and native accents are explicitly marked and provided in the corpus. Accordingly, transcripts in Chinese character-level and pinyin-level are provided along with the recordings. We present a baseline system that uses AISHELL-3 for multi-speaker Madarin speech synthesis. The multi-speaker speech synthesis system is an extension on Tacotron-2 where a speaker verification model and a corresponding loss regarding voice similarity are incorporated as the feedback constraint. We aim to use the presented corpus to build a robust synthesis model that is able to achieve zero-shot voice cloning. The system trained on this dataset also generalizes well on speakers that are never seen in the training process. Objective evaluation results from our experiments show that the proposed multi-speaker synthesis system achieves high voice similarity concerning both speaker embedding similarity and equal error rate measurement. The dataset, baseline system code and generated samples are available online.
Learning Occupancy Function from Point Clouds for Surface Reconstruction

Implicit function based surface reconstruction has been studied for a long time to recover 3D shapes from point clouds sampled from surfaces. Recently, Signed Distance Functions (SDFs) and Occupany Functions are adopted in learning-based shape reconstruction methods as implicit 3D shape representation. This paper proposes a novel method for learning occupancy functions from sparse point clouds and achieves better performance on challenging surface reconstruction tasks. Unlike the previous methods, which predict point occupancy with fully-connected multi-layer networks, we adapt the point cloud deep learning architecture, Point Convolution Neural Network (PCNN), to build our learning model. Specifically, we create a sampling operator and insert it into PCNN to continuously sample the feature space at the points where occupancy states need to be predicted. This method natively obtains point cloud data's geometric nature, and it's invariant to point permutation. Our occupancy function learning can be easily fit into procedures of point cloud up-sampling and surface reconstruction. Our experiments show state-of-the-art performance for reconstructing With ShapeNet dataset and demonstrate this method's well-generalization by testing it with McGill 3D dataset \cite{siddiqi2008retrieving}. Moreover, we find the learned occupancy function is relatively more rotation invariant than previous shape learning methods.
Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning

The future of mobility-as-a-Service (Maas)should embrace an integrated system of ride-hailing, street-hailing and ride-sharing with optimised intelligent vehicle routing in response to a real-time, stochastic demand pattern. We aim to optimise routing policies for a large fleet of vehicles for street-hailing services, given a stochastic demand pattern in small to medium-sized road networks. A model-based dispatch algorithm, a high performance model-free reinforcement learning based algorithm and a novel hybrid algorithm combining the benefits of both the top-down approach and the model-free reinforcement learning have been proposed to route the \emph{vacant} vehicles. We design our reinforcement learning based routing algorithm using proximal policy optimisation and combined intrinsic and extrinsic rewards to strike a balance between exploration and exploitation. Using a large-scale agent-based microscopic simulation platform to evaluate our proposed algorithms, our model-free reinforcement learning and hybrid algorithm show excellent performance on both artificial road network and community-based Singapore road network with empirical demands, and our hybrid algorithm can significantly accelerate the model-free learner in the process of learning.
Learning Loss for Test-Time Augmentation

Data augmentation has been actively studied for robust neural networks. Most of the recent data augmentation methods focus on augmenting datasets during the training phase. At the testing phase, simple transformations are still widely used for test-time augmentation. This paper proposes a novel instance-level test-time augmentation that efficiently selects suitable transformations for a test input. Our proposed method involves an auxiliary module to predict the loss of each possible transformation given the input. Then, the transformations having lower predicted losses are applied to the input. The network obtains the results by averaging the prediction results of augmented inputs. Experimental results on several image classification benchmarks show that the proposed instance-aware test-time augmentation improves the model's robustness against various corruptions.
A Cluster-Matching-Based Method for Video Face Recognition

Face recognition systems are present in many modern solutions and thousands of applications in our daily lives. However, current solutions are not easily scalable, especially when it comes to the addition of new targeted people. We propose a cluster-matching-based approach for face recognition in video. In our approach, we use unsupervised learning to cluster the faces present in both the dataset and targeted videos selected for face recognition. Moreover, we design a cluster matching heuristic to associate clusters in both sets that is also capable of identifying when a face belongs to a non-registered person. Our method has achieved a recall of 99.435% and a precision of 99.131% in the task of video face recognition. Besides performing face recognition, it can also be used to determine the video segments where each person is present.
Observing the propagation of shear stress in strongly interacting metallic Fermi liquids

A highlight of Fermi-liquid phenomenology, as explored in neutral $^3$He, is the observation that in the collisionless regime shear stress propagates as if one is dealing with the transverse phonon of a solid. The existence of this $"$transverse zero sound$"$ requires that the quasiparticle mass enhancement exceeds a critical value. Could such a propagating shear stress also exist in strongly correlated electron systems? Despite some noticeable differences with the neutral case in the Galilean continuum, we arrive at the verdict that transverse zero sound should be generic. We present an experimental setup that should be exquisitely sensitive in this regard: the transmission of terahertz radiation through a thin slab of heavy-fermion material will be strongly enhanced at low temperature and accompanied by giant oscillations, which reflect the interference between light itself and the $"$material photon$"$ being the actual manifestation of transverse zero sound in the charged Fermi liquid.
Potassium and Calcium Nitrate Ameliorates the Adverse Effect of Nacl on In Vitro Induced Tomato (Lycopersicon Esculentum Mill.)

An in vitro tissue culture experiments were carried out to investigate the effects of supplementary potassium and calcium nitrate applied to basal media on morphogenesis of saline stressed tomato (Lycopersicon esculentum Mill. Cv. Omdurman). Shoot tip explants were cultured on MS media salinized with NaCl and supplemented with KNO3 and Ca (NO3)2. Treatments consist of four NaCl concentrations (0.0, 50, 75 and 100 mM) in combination with either KNO3 ( 0.0, 5, 10, 15 and 20 mM) or Ca (NO3)2 (0.0, 5, 10, 15 and 20 mM). Salinity significantly reduced the root growth, shoot growth and whole plant growth. Root growth was more reduced by the presence of NaCl in growth media than shoot growth. Application of supplemental calcium nitrate (20 mM) and potassium nitrate (15 mM) resulted in mitigation of the harmful effect of NaCl on tomato growth. However Ca (NO3)2 at 20 mM has negative effect on tomato shoot tip rooting %. In non salinized media, the best root and shoot growth was observed at 10 mM Ca (NO3)2. These findings suggest that the additions of potassium and calcium nitrate can ameliorate the negative effect of salinity on the growth of tomato.
Scalable Bottom-Up Hierarchical Clustering

Bottom-up algorithms such as the classic hierarchical agglomerative clustering, are highly effective for hierarchical as well as flat clustering. However, the large number of rounds and their sequential nature limit the scalability of agglomerative clustering. In this paper, we present an alternative round-based bottom-up hierarchical clustering, the Sub-Cluster Component Algorithm (SCC), that scales gracefully to massive datasets. Our method builds many sub-clusters in parallel in a given round and requires many fewer rounds -- usually an order of magnitude smaller than classic agglomerative clustering. Our theoretical analysis shows that, under a modest separability assumption, SCC will contain the optimal flat clustering. SCC also provides a 2-approx solution to the DP-means objective, thereby introducing a novel application of hierarchical clustering methods. Empirically, SCC finds better hierarchies and flat clusterings even when the data does not satisfy the separability assumption. We demonstrate the scalability of our method by applying it to a dataset of 30 billion points and showing that SCC produces higher quality clusterings than the state-of-the-art.
Faithful Euclidean Distance Field From Log-Gaussian Process Implicit Surfaces

In this letter, we introduce the Log-Gaussian Process Implicit Surface (Log-GPIS), a novel continuous and probabilistic mapping representation suitable for surface reconstruction and local navigation. Our key contribution is the realisation that the regularised Eikonal equation can be simply solved by applying the logarithmic transformation to a GPIS formulation to recover the accurate Euclidean distance field (EDF) and, at the same time, the implicit surface. To derive the proposed representation, Varadhan's formula is exploited to approximate the non-linear Eikonal partial differential equation (PDE) of the EDF by the logarithm of a linear PDE. We show that members of the Matérn covariance family directly satisfy this linear PDE. The proposed approach does not require post-processing steps to recover the EDF. Moreover, unlike sampling-based methods, Log-GPIS does not use sample points inside and outside the surface as the derivative of the covariance allow direct estimation of the surface normals and distance gradients. We benchmarked the proposed method on simulated and real data against state-of-the-art mapping frameworks that also aim at recovering both the surface and a distance field. Our experiments show that Log-GPIS produces the most accurate results for the EDF and comparable results for surface reconstruction and its computation time still allows online operations.
DSC-IIT ISM at WNUT-2020 Task 2: Detection of COVID-19 informative tweets using RoBERTa

Social media such as Twitter is a hotspot of user-generated information. In this ongoing Covid-19 pandemic, there has been an abundance of data on social media which can be classified as informative and uninformative content. In this paper, we present our work to detect informative Covid-19 English tweets using RoBERTa model as a part of the W-NUT workshop 2020. We show the efficacy of our model on a public dataset with an F1-score of 0.89 on the validation dataset and 0.87 on the leaderboard.
CHIP mutations affect the heat shock response differently in human fibroblasts and iPSC-derived neurons

ABSTRACT C-terminus of HSC70-interacting protein (CHIP) encoded by the gene STUB1 is a co-chaperone and E3 ligase that acts as a key regulator of cellular protein homeostasis. Mutations in STUB1 cause autosomal recessive spinocerebellar ataxia type 16 (SCAR16) with widespread neurodegeneration manifesting as spastic-ataxic gait disorder, dementia and epilepsy. CHIP−/− mice display severe cerebellar atrophy, show high perinatal lethality and impaired heat stress tolerance. To decipher the pathomechanism underlying SCAR16, we investigated the heat shock response (HSR) in primary fibroblasts of three SCAR16 patients. We found impaired HSR induction and recovery compared to healthy controls. HSPA1A/B transcript levels (coding for HSP70) were reduced upon heat shock but HSP70 remained higher upon recovery in patient- compared to control-fibroblasts. As SCAR16 primarily affects the central nervous system we next investigated the HSR in cortical neurons (CNs) derived from induced pluripotent stem cells of SCAR16 patients. We found CNs of patients and controls to be surprisingly resistant to heat stress with high basal levels of HSP70 compared to fibroblasts. Although heat stress resulted in strong transcript level increases of many HSPs, this did not translate into higher HSP70 protein levels upon heat shock, independent of STUB1 mutations. Furthermore, STUB1(−/−) neurons generated by CRISPR/Cas9-mediated genome editing from an isogenic healthy control line showed a similar HSR to patients. Proteomic analysis of CNs showed dysfunctional protein (re)folding and higher basal oxidative stress levels in patients. Our results question the role of impaired HSR in SCAR16 neuropathology and highlight the need for careful selection of proper cell types for modeling human diseases. Summary: Cell viability, expression and immunocytochemical analyses reveal that mutations in STUB1 that cause SCAR16 impair the heat-shock response in patient-derived fibroblasts, but not in iPSC-derived cortical neurons.