Miracle-dz/UniFAR-FTUs huggingface dataset:
Micronucleus as a Non-invasive Biomarker – A Review

Oral Cancer has a remarkably high incidence worldwide and a significant decrease in its mortality and morbidity rates has been established if it is diagnosed in early stages. There has been always a strong need to develop new, objective, non-invasive methods for its early detection. Micronucleus has come up in the recent past as non-invasive biomarker for diagnosis of not only malignant and pre-malignant lesions but also many other significant diseases. Micronucleus (“MN") is defined as microscopically visible, round or oval cytoplasmic chromatin mass next to the nucleus. Micronuclei Review Article Dev et al.; JAMPS, 6(4): 1-9, 2016; Article no.JAMPS. 24136 2 (“MNi”) originate from aberrant mitoses and consist of eccentric chromosomes, chromatid fragments or whole chromosomes that have failed to be incorporated into the daughter nuclei during mitosis. The MN assay has been widely accepted as an in vitro genotoxicity test and a biomarker assay for genotoxic exposure and effect in humans. An attempt has been made to review the related studies, utilizing micronucleus assay of buccal cells as a novel marker of genotoxicity in head and neck region.
Instabilities of quantum critical metals in the limit $N_f\rightarrow0$

We study a model in 2+1 dimensions composed of a Fermi surface of $N_f$ flavors of fermions coupled to scalar fluctuations near quantum critical points (QCPs). The $N_f\rightarrow0$ limit allows us to non-perturbatively calculate the long-range behavior of fermion correlation functions. We use this to calculate charge, spin and pair susceptibilities near different QCPs at zero and finite temperatures, with zero and finite order parameter gaps. While fluctuations smear out the fermionic quasiparticles, we find QCPs where the overall effect of fluctuations leads to enhanced pairing. We also find QCPs where the fluctuations induce spin and charge density wave instabilities for a finite interval of order parameter fluctuation gaps at $T=0$. We restore a subset of the diagrams suppressed in the $N_f\rightarrow0$ limit, all diagrams with internal fermion loops with at most 2 vertices, and find that this does not change the long-range behavior of correlators except right at the QCPs.
A Hardware-Aware Heuristic for the Qubit Mapping Problem in the NISQ Era

Due to several physical limitations in the realization of quantum hardware, today's quantum computers are qualified as noisy intermediate-scale quantum (NISQ) hardware. NISQ hardware is characterized by a small number of qubits (50 to a few hundred) and noisy operations. Moreover, current realizations of superconducting quantum chips do not have the ideal all-to-all connectivity between qubits but rather at most a nearest-neighbor connectivity. All these hardware restrictions add supplementary low-level requirements. They need to be addressed before submitting the quantum circuit to an actual chip. Satisfying these requirements is a tedious task for the programmer. Instead, the task of adapting the quantum circuit to a given hardware is left to the compiler. In this article, we propose a hardware-aware (HA) mapping transition algorithm that takes the calibration data into account with the aim to improve the overall fidelity of the circuit. Evaluation results on IBM quantum hardware show that our HA approach can outperform the state of the art, both in terms of the number of additional gates and circuit fidelity.
Topological boundaries of connected graphs and Coxeter groups.

We introduce and study certain topological spaces associated with connected rooted graphs. These spaces reflect combinatorial and order theoretic properties of the underlying graph and relate in the case of hyperbolic graphs to Gromov's hyperbolic compactification [32]. They are particularly tractable in the case of Cayley graphs of finite rank Coxeter groups. In that context we speak of the compactification and the boundary of the Coxeter group. They also appear in [13] (see also [54]) and [53]. As it turns out, the canonical action of the group on its Cayley graph induces a natural action on the compactification and the boundary. We prove its amenability, we characterize when the compactification is small at infinity and we study classes of Coxeter groups for which the action is a topological boundary action in the sense of Furstenberg. The second part of the paper deals with the applications of our results to the study of (Iwahori) Hecke algebras. These are certain deformations of group algebras of Coxeter groups. We first study embeddings of Hecke $C^{\ast}$-algebras and prove property Akemann-Ostrand for a certain class of Hecke-von Neumann algebras. Lastly, we make use of results that are widely related to Kalantar-Kennedy their approach to the $C^{\ast}$-simplicity problem [49] to study the simplicity and injective envelopes of operator algebras associated with Hecke algebras.
A Self-supervised Approach for Semantic Indexing in the Context of COVID-19 Pandemic

The pandemic has accelerated the pace at which COVID-19 scientific papers are published. In addition, the process of manually assigning semantic indexes to these papers by experts is even more time-consuming and overwhelming in the current health crisis. Therefore, there is an urgent need for automatic semantic indexing models which can effectively scale-up to newly introduced concepts and rapidly evolving distributions of the hyperfocused related literature. In this research, we present a novel semantic indexing approach based on the state-of-the-art self-supervised representation learning and transformer encoding exclusively suitable for pandemic crises. We present a case study on a novel dataset that is based on COVID-19 papers published and manually indexed in PubMed. Our study shows that our self-supervised model outperforms the best performing models of BioASQ Task 8a by micro-F1 score of 0.1 and LCA-F score of 0.08 on average. Our model also shows superior performance on detecting the supplementary concepts which is quite important when the focus of the literature has drastically shifted towards specific concepts related to the pandemic. Our study sheds light on the main challenges confronting semantic indexing models during a pandemic, namely new domains and drastic changes of their distributions, and as a superior alternative for such situations, propose a model founded on approaches which have shown auspicious performance in improving generalization and data efficiency in various NLP tasks. We also show the joint indexing of major Medical Subject Headings (MeSH) and supplementary concepts improves the overall performance.
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospect

Given the growing amount of industrial data spaces worldwide, deep learning solutions have become popular for predictive maintenance, which monitor assets to optimise maintenance tasks. Choosing the most suitable architecture for each use-case is complex given the number of examples found in literature. This work aims at facilitating this task by reviewing state-of-the-art deep learning architectures, and how they integrate with predictive maintenance stages to meet industrial companies' requirements (i.e. anomaly detection, root cause analysis, remaining useful life estimation). They are categorised and compared in industrial applications, explaining how to fill their gaps. Finally, open challenges and future research paths are presented.
Periodic Eruptive Variability of the Isolated Pre-main-sequence Star V347 Aurigae

V347 Aurigae is associated with the small dark cloud L1438 and appears to be an isolated pre-main-sequence star located at distance d ≈ 200 pc. Multi-epoch, archival photometry reveals periodic brightness variations with amplitude V ≈ 2.0 mag occurring on timescales of ∼160 days that have persisted for decades. Regular-cadence, optical imaging of the source with the Zwicky Transient Facility shows that a small reflection nebula illuminated by V347 Aur also fluctuates in brightness, at times fading completely. Multi-epoch, Keck/HIRES data suggests the presence of two distinct spectral components: a prominent emission-line-dominated spectrum with a heavily veiled continuum correlated with the bright photometric state, and an M-type absorption line spectrum associated with quiescence. All spectra exhibit strong Balmer and He i line emission, consistent with accretion, as well as high velocity emission arising from the forbidden transitions of [O i], [N ii], and [S ii] that are generally associated with collimated jets and disk winds. There is no evidence in existing high-dispersion spectroscopy or high-resolution imaging for binarity of V347 Aur. The repeating outburst events are possibly linked to accretion instabilities induced by an undetected companion or a structure within the circumstellar disk that periodically increases the mass accretion rate. V347 Aur is perhaps analogous to an EXor-type variable, though more regularly recurring.
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation

Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult to find huge annotated corpora for non-English languages. The recent rise of the transformer models in Natural Language Processing allows to achieve unparalleled performances in many tasks, but these models need a consequent quantity of text to adapt to the tweet domain. We propose the use of a multilingual transformer model, that we pre-train over English tweets on which we apply data-augmentation using automatic translation to adapt the model to non-English languages. Our experiments in French, Spanish, German and Italian suggest that the proposed technique is an efficient way to improve the results of the transformers over small corpora of tweets in a non-English language.
Parameter estimation in FACS-seq enables high-throughput characterization of phenotypic heterogeneity

Phenotypic heterogeneity is a most fascinating property of a population of cells, which shows the differences among individuals even with the same genetic background and extracellular environmental conditions. However, the lack of high-throughput analysis of phenotypic diversity has limited our research progress. To deal with it, we constructed a novel parameter estimation method in FACS-seq, a commonly used experimental framework, to achieve simultaneous characterization of thousands of variants in a library. We further demonstrated the model's ability in estimating the expression properties of each variant, which we believe can help to decipher the mechanisms of phenotypic heterogeneity.
Attention augmented differentiable forest for tabular data

Differentiable forest is an ensemble of decision trees with full differentiability. Its simple tree structure is easy to use and explain. With full differentiability, it would be trained in the end-to-end learning framework with gradient-based optimization method. In this paper, we propose tree attention block(TAB) in the framework of differentiable forest. TAB block has two operations, squeeze and regulate. The squeeze operation would extract the characteristic of each tree. The regulate operation would learn nonlinear relations between these trees. So TAB block would learn the importance of each tree and adjust its weight to improve accuracy. Our experiment on large tabular dataset shows attention augmented differentiable forest would get comparable accuracy with gradient boosted decision trees(GBDT), which is the state-of-the-art algorithm for tabular datasets. And on some datasets, our model has higher accuracy than best GBDT libs (LightGBM, Catboost, and XGBoost). Differentiable forest model supports batch training and batch size is much smaller than the size of training set. So on larger data sets, its memory usage is much lower than GBDT model. The source codes are available at this https URL.
No Data? No Problem! A Search-Based Recommendation System With Cold Starts

Recommendation systems are essential ingredients in producing matches between products and buyers. Despite their ubiquity, they face two important challenges. First, they are data-intensive, a feature that precludes sophisticated recommendations by some types of sellers, including those selling durable goods. Second, they often focus on estimating fixed evaluations of products by consumers while ignoring state-dependent behaviors identified in the Marketing literature. We propose a recommendation system based on consumer browsing behaviors, which bypasses the "cold start" problem described above, and takes into account the fact that consumers act as "moving targets," behaving differently depending on the recommendations suggested to them along their search journey. First, we recover the consumers' search policy function via machine learning methods. Second, we include that policy into the recommendation system's dynamic problem via a Bellman equation framework. When compared with the seller's own recommendations, our system produces a profit increase of 33%. Our counterfactual analyses indicate that browsing history along with past recommendations feature strong complementary effects in value creation. Moreover, managing customer churn effectively is a big part of value creation, whereas recommending alternatives in a forward-looking way produces moderate effects.
In vitro measurements of oxygen consumption rates in hTERT-RPE cells exposed to low levels of red light

Exposure to 2.88 J/cm2 of red light induces an adaptive response against a lethal pulse of 2.0 μm laser radiation in hTERT-RPE cells in vitro, but not in a knockdown mutant for vascular endothelial growth factor c (VEGF-C). The generally accepted initiation sequence for photobiomodulation is that absorption of red light by cytochome c oxidase (CCOX) of the electron transport chain increases the binding affinity of CCOX for O2 vs. nitric oxide (NO). This results in displacement of NO by O2 in the active site of CCOX, thereby increasing cellular respiration and intracellular ATP. We've previously reported that red-light exposure induces a small, but consistently reproducible, increase in NO levels in these cells. But the relative importance of NO and oxidative phosphorylation is unclear because little is known about the relative contributions of NO and ATP to the response. However, if NO dissociation from CCOX actually increases oxidative phosphorylation, one should see a corresponding increase in oxygen consumption. A Seahorse Extracellular Flux Analyzer was used to measure oxygen consumption rates (OCR) in normal and mutant cells as a proxy for oxidative phosphorylation. Both basal respiration and maximum respiration rates in normal cells are significantly higher than in the mutant. The normal cells have a significant amount of “excess capacity,” whereas the VEGF-C(KD) have little or none. The OCR in exposed normal cells is lower than in unexposed cells when measured immediately after exposure. The exposures used for these experiments had no effect on the OCR in mutant cells.
Improving the efficiency of spectral features extraction by structuring the audio files

The extraction of spectral features from a music clip is a computationally expensive task. As in order to extract accurate features, we need to process the clip for its whole length. This preprocessing task creates a large overhead and also makes the extraction process slower. We show how formatting a dataset in a certain way, can help make the process more efficient by eliminating the need for processing the clip for its whole duration, and still extract the features accurately. In addition, we discuss the possibility of defining set generic durations for analyzing a certain type of music clip while training. And in doing so we cut down the need of processing the clip duration to just 10% of the global average.
Optimal Gradient Compression for Distributed and Federated Learning

Communicating information, like gradient vectors, between computing nodes in distributed and federated learning is typically an unavoidable burden, resulting in scalability issues. Indeed, communication might be slow and costly. Recent advances in communication-efficient training algorithms have reduced this bottleneck by using compression techniques, in the form of sparsification, quantization, or low-rank approximation. Since compression is a lossy, or inexact, process, the iteration complexity is typically worsened; but the total communication complexity can improve significantly, possibly leading to large computation time savings. In this paper, we investigate the fundamental trade-off between the number of bits needed to encode compressed vectors and the compression error. We perform both worst-case and average-case analysis, providing tight lower bounds. In the worst-case analysis, we introduce an efficient compression operator, Sparse Dithering, which is very close to the lower bound. In the average-case analysis, we design a simple compression operator, Spherical Compression, which naturally achieves the lower bound. Thus, our new compression schemes significantly outperform the state of the art. We conduct numerical experiments to illustrate this improvement.
Blow-up formulae for twisted cohomologies with supports

We study twisted cohomologies with paracompactifying families of supports. The Kunneth theorems, Leray-Hirsch theorems and self-intersection formulae are established. Based on these results, we eventually give explicit expressions of complex blow-up formulae for twisted Dolbeault cohomology on arbitrary complex manifolds and the ones of generalized blow-ups formulae for twisted de Rham cohomology on arbitrary oriented smooth manifolds. These expressions are induced by the morphisms of (simple or double) complexes of spaces of forms and currents rather than just the maps between cohomologies, which help us to obtain the corresponding results for twisted Bott-Chern, Aeppli cohomologies and hypercohomologies of truncated twisted holomorphic de Rham complexes.
Batch Normalization Increases Adversarial Vulnerability: Disentangling Usefulness and Robustness of Model Features

Batch normalization (BN) has been widely used in modern deep neural networks (DNNs) due to fast convergence. BN is observed to increase the model accuracy while at the cost of adversarial robustness. We conjecture that the increased adversarial vulnerability is caused by BN shifting the model to rely more on non-robust features (NRFs). Our exploration finds that other normalization techniques also increase adversarial vulnerability and our conjecture is also supported by analyzing the model corruption robustness and feature transferability. With a classifier DNN defined as a feature set $F$ we propose a framework for disentangling $F$ robust usefulness into $F$ usefulness and $F$ robustness. We adopt a local linearity based metric, termed LIGS, to define and quantify $F$ robustness. Measuring the $F$ robustness with the LIGS provides direct insight on the feature robustness shift independent of usefulness. Moreover, the LIGS trend during the whole training stage sheds light on the order of learned features, i.e. from RFs (robust features) to NRFs, or vice versa. Our work analyzes how BN and other factors influence the DNN from the feature perspective. Prior works mainly adopt accuracy to evaluate their influence regarding $F$ usefulness, while we believe evaluating $F$ robustness is equally important, for which our work fills the gap.
Controlling a CyberOctopus Soft Arm with Muscle-like Actuation

This paper presents an application of the energy shaping methodology to control a flexible, elastic Cosserat rod model of a single octopus arm. The novel contributions of this work are two-fold: (i) a control-oriented modeling of the anatomically realistic internal muscular architecture of an octopus arm; and (ii) the integration of these muscle models into the energy shaping control methodology. The control-oriented modeling takes inspiration in equal parts from theories of nonlinear elasticity and energy shaping control. By introducing a stored energy function for muscles, the difficulties associated with explicitly solving the matching conditions of the energy shaping methodology are avoided. The overall control design problem is posed as a bilevel optimization problem. Its solution is obtained through iterative algorithms. The methodology is numerically implemented and demonstrated in a full-scale dynamic simulation environment Elastica. Two bio-inspired numerical experiments involving the control of octopus arms are reported.
Contour Primitive of Interest Extraction Network Based on One-Shot Learning for Object-Agnostic Vision Measurement

Image contour based vision measurement is widely applied in robot manipulation and industrial automation. It is appealing to realize object-agnostic vision system, which can be conveniently reused for various types of objects. We propose the contour primitive of interest extraction network (CPieNet) based on the one-shot learning framework. First, CPieNet is featured by that its contour primitive of interest (CPI) output, a designated regular contour part lying on a specified object, provides the essential geometric information for vision measurement. Second, CPieNet has the one-shot learning ability, utilizing a support sample to assist the perception of the novel object. To realize lower-cost training, we generate support-query sample pairs from unpaired online public images, which cover a wide range of object categories. To obtain single-pixel wide contour for precise measurement, the Gabor-filters based non-maximum suppression is designed to thin the raw contour. For the novel CPI extraction task, we built the Object Contour Primitives dataset using online public images, and the Robotic Object Contour Measurement dataset using a camera mounted on a robot. The effectiveness of the proposed methods is validated by a series of experiments.
The Impact of Daily Torpor on Energy Requirements in the Djungarian Hamster, Phodopus sungorus

Continuous records of body temperature (Tb) and oxygen consumption were used to investigate the influence of daily torpor on energy requirements in Djungarian hamsters (Phodopus sungorus) exposed to ambient temperatures (Ta's) between O° C and 20° C During torpor Tb was lowered to values between 14.0 °C and 31.3°C for variable periods (0.3-9.2 h; mean, 5.4 ± 0.23 h). Average daily metabolic rates (ADMRs) were inversely related to Ta in both hamsters exhibiting torpor and animals staying normothermic and can be predicted from their linear relation to mean daily (Ta − Tb)-gradients. The torpor-induced reduction of ADMRs below normothermic values was directly related to torpor depth and duration, withprolonged torpor episodes (>4 h) causing a 20.2% ± 0.9% saving in energy. Thus, daily torpor reduces metabolic costs to a level approaching the resting metabolic rate of normothermic hamsters (24% below ADMR) and helps compensate for the energetic costs of locomotor activity. Individuals with a high tendency to enter torpor also save substantial amounts of energy by lowering average metabolic rates during the normothermic phase of the day. Calculations based on these data show that daily torpor in Djungarian hamsters may serve to allow the continuation of foraging at low Ta's during winter in the Siberian steppe.