Miracle-dz/UniFAR-FTUs huggingface dataset:
Drosophila menthol sensitivity and the Precambrian origins of transient receptor potential-dependent chemosensation

Transient receptor potential (TRP) cation channels are highly conserved, polymodal sensors which respond to a wide variety of stimuli. Perhaps most notably, TRP channels serve critical functions in nociception and pain. A growing body of evidence suggests that transient receptor potential melastatin (TRPM) and transient receptor potential ankyrin (TRPA) thermal and electrophile sensitivities predate the protostome–deuterostome split (greater than 550 Ma). However, TRPM and TRPA channels are also thought to detect modified terpenes (e.g. menthol). Although terpenoids like menthol are thought to be aversive and/or harmful to insects, mechanistic sensitivity studies have been largely restricted to chordates. Furthermore, it is unknown if TRP-menthol sensing is as ancient as thermal and/or electrophile sensitivity. Combining genetic, optical, electrophysiological, behavioural and phylogenetic approaches, we tested the hypothesis that insect TRP channels play a conserved role in menthol sensing. We found that topical application of menthol to Drosophila melanogaster larvae elicits a Trpm- and TrpA1-dependent nocifensive rolling behaviour, which requires activation of Class IV nociceptor neurons. Further, in characterizing the evolution of TRP channels, we put forth the hypotheses that three previously undescribed TRPM channel clades (basal, αTRPM and βTRPM), as well as TRPs with residues critical for menthol sensing, were present in ancestral bilaterians. This article is part of the Theo Murphy meeting issue ‘Evolution of mechanisms and behaviour important for pain’.
An assay for chemical nociception in Drosophila larvae

Chemically induced nociception has not yet been studied intensively in genetically tractable models. Hence, our goal was to establish a Drosophila assay that can be used to study the cellular and molecular/genetic bases of chemically induced nociception. Drosophila larvae exposed to increasing concentrations of hydrochloric acid (HCl) produced an increasingly intense aversive rolling response. HCl (0.5%) was subthreshold and provoked no response. All classes of peripheral multidendritic (md) sensory neurons (classes I–IV) are required for full responsiveness to acid, with class IV making the largest contribution. At the cellular level, classes IV, III and I showed increases in calcium following acid exposure. In the central nervous system, Basin-4 second-order neurons are the key regulators of chemically induced nociception, with a slight contribution from other types. Finally, chemical nociception can be sensitized by tissue damage. Subthreshold HCl provoked chemical allodynia in larvae 4 h after physical puncture wounding. Pinch wounding and UV irradiation, which do not compromise the cuticle, did not cause chemical allodynia. In sum, we developed a novel assay to study chemically induced nociception in Drosophila larvae. This assay, combined with the high genetic resolving power of Drosophila, should improve our basic understanding of fundamental mechanisms of chemical nociception. This article is part of the Theo Murphy meeting issue ‘Evolution of mechanisms and behaviour important for pain’.
The neuroscience of vision and pain: evolution of two disciplines

Research in the neuroscience of pain perception and visual perception has taken contrasting paths. The contextual and the social aspects of pain judgements predisposed pain researchers to develop computational and functional accounts early, while vision researchers tended to simple localizationist or descriptive approaches first. Evolutionary thought was applied to distinct domains, such as game-theoretic approaches to cheater detection in pain research, versus vision scientists' studies of comparative visual ecologies. Both fields now contemplate current motor or decision-based accounts of perception, particularly predictive coding. Vision researchers do so without the benefit of earlier attention to social and motivational aspects of vision, while pain researchers lack a comparative behavioural ecology of pain, the normal incidence and utility of responses to tissue damage. Hybrid hypotheses arising from predictive coding as used in both domains are applied to some perplexing phenomena in pain perception to suggest future directions. The contingent and predictive interpretation of complex sensations, in such domains as ‘runner's high’, multiple cosmetic procedures, self-harm and circadian rhythms in pain sensitivity is one example. The second, in an evolutionary time frame, considers enhancement of primary perception and expression of pain in social species, when expressions of pain might reliably elicit useful help. This article is part of the Theo Murphy meeting issue ‘Evolution of mechanisms and behaviour important for pain’.
Modeling the Pharmacogenomics of Depression

ABSTRACT Pharmacogenomics aims to better understand the pharmacological response of a drug with respect to genetic variation. Essential to the delivery of better health care is the use of pharmacogenomics knowledge to answer questions about therapeutic, pharmacological or genetic aspects. In this preliminary work, we present a knowledge base designed with semantic web technologies capable of capturing essential aspects including genes, gene variants, SNPs, drugs, measures and outcomes, as well as gene-drug interactions and drug treatments. We populate the knowledge base from a primary source (PharmGKB), and augment it with literature curated pharmacogenomics knowledge of depression. We demonstrate the use of OWL 1.1 role chains to facilitate question answering and pharmacogenomics knowledge discovery. Categories and Subject Descriptors H.1 [Models and Principles]: OWL ontology H.4 [Information Systems Applications] I.2. 4 [Knowledge Representation Formalisms and Methods] J.2 [Physical Sciences and Engineering] J.3 [Life and Medical Sciences]: Biology and genetics
An Evalutation of Programming Language Models' performance on Software Defect Detection

This dissertation presents an evaluation of several language models on software defect datasets. A language Model (LM) "can provide word representation and probability indication of word sequences as the core component of an NLP system. " Language models for source code are specified for tasks in the software engineering field. While some models are directly the NLP ones, others contain structural information that is uniquely owned by source code. Software defects are defects in the source code that lead to unexpected behaviours and malfunctions at all levels. This study provides an original attempt to detect these defects at three different levels (syntactical, algorithmic and general) We also provide a tool chain that researchers can use to reproduce the experiments. We have tested the different models against different datasets, and performed an analysis over the results. Our original attempt to deploy bert, the state-of-the-art model for multitasks, leveled or outscored all other models compared.
Deep Reinforcement Learning for Smart Home Energy Management

In this article, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, nonshiftable power demand, outdoor temperature, and electricity price), and temporally coupled operational constraints, it is very challenging to design an optimal energy management algorithm for scheduling heating, ventilation, and air conditioning systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management algorithm based on deep deterministic policy gradients. It is worth mentioning that the proposed algorithm does not require the prior knowledge of uncertain parameters and building the thermal dynamics model. The simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed algorithm.
Risk-sensitive safety specifications for stochastic systems using Conditional Value-at-Risk

This paper proposes a safety analysis method that facilitates a tunable balance between worst-case and risk-neutral perspectives. First, we define a risk-sensitive safe set to specify the degree of safety attained by a stochastic system. This set is defined as a sublevel set of the solution to an optimal control problem that is expressed using the Conditional Value-at-Risk (CVaR) measure. This problem does not satisfy Bellman's Principle, thus our next contribution is to show how risk-sensitive safe sets can be under-approximated by the solution to a CVaR-Markov Decision Process. Our third and fourth contributions are to show that a value iteration algorithm solves the reduced problem and enables tractable policy synthesis for a class of linear systems. Fifth, we develop a realistic numerical example of a stormwater system to show that our approach can be applied to non-linear systems. Finally, we compare the CVaR criterion to the exponential disutility criterion. The latter allocates control effort evenly across the cost distribution to reduce variance, while the CVaR criterion focuses control effort on a given worst-case quantile--where it matters most for safety.
Recursive Calculation of the Optical Response of Multicomponent Metamaterials

A recursive computational procedure is developed to efficiently calculate the macroscopic dielectric function of multicomponent metamaterials of arbitrary geometry and composition within the long wavelength approximation. Although the microscopic response of the system might correspond to non‐Hermitian operators, a representation of the microscopic fields and of the response is developed, and an appropriate metric that makes all operators symmetric is introduced. This allows one to use a modified Haydock recursion, introducing complex Haydock coefficients that allow an efficient computation of the macroscopic response and the microscopic fields. The procedure is tested by comparing the results with analytical ones in simple systems and verifying whether they obey a generalized multicomponent Keller's and the Mortola and Steffe's theorems for four‐component metallic and dielectric systems.
Asynchronous Decentralized Successive Convex Approximation.

We study decentralized asynchronous multiagent optimization over networks, modeled as static (possibly directed) graphs. The optimization problem consists of minimizing a (possibly nonconvex) smooth function--the sum of the agents' local costs--plus a convex (possibly nonsmooth) regularizer, subject to convex constraints. Agents can perform their local computations as well as communicate with their immediate neighbors at any time, without any form of coordination or centralized scheduling; furthermore, when solving their local subproblems, they can use outdated information from their neighbors. We propose the first distributed asynchronous algorithm, termed ASY-DSCA, that converges at an R-linear rate to the optimal solution of convex problems whose objective function satisfies a general error bound condition; this condition is weaker than the more frequently used strong convexity, and it is satisfied by several empirical risk functions that are not strongly convex; examples include LASSO and logistic regression problems. When the objective function is nonconvex, ASY-DSCA converges to a stationary solution of the problem at a sublinear rate.
Two Birds, One Stone: A Simple, Unified Model for Text Generation from Structured and Unstructured Data

A number of researchers have recently questioned the necessity of increasingly complex neural network (NN) architectures. In particular, several recent papers have shown that simpler, properly tuned models are at least competitive across several NLP tasks. In this work, we show that this is also the case for text generation from structured and unstructured data. We consider neural table-to-text generation and neural question generation (NQG) tasks for text generation from structured and unstructured data, respectively. Table-to-text generation aims to generate a description based on a given table, and NQG is the task of generating a question from a given passage where the generated question can be answered by a certain sub-span of the passage using NN models. Experimental results demonstrate that a basic attention-based seq2seq model trained with the exponential moving average technique achieves the state of the art in both tasks. Code is available at https://github.com/h-shahidi/2birds-gen.
In-Hand Sliding Regrasp With Spring-Sliding Compliance and External Constraints

We investigate in-hand regrasping by pushing an object against an external constraint and allowing sliding at the fingertips. Each fingertip is modeled as attached to a multidimensional spring mounted to a position-controlled anchor. Spring compliance maps contact forces to spring compressions, ensuring the fingers remain in contact, and sliding “compliance” governs the relationship between sliding motions and tangential contact forces. A spring-sliding compliant regrasp is achieved by controlling the finger anchor motions. We derive the fingertip sliding mechanics for multifingered sliding regrasps and analyze robust regrasping conditions in the presence of finger contact wrench uncertainties. The results are verified in simulation and experiment with a two-fingered sliding regrasp designed to maximize robustness of the operation.
sZoom: A Framework for Automatic Zoom into High Resolution Surveillance Videos

Current cameras are capable of recording high resolution video. While viewing on a mobile device, a user can manually zoom into this high resolution video to get more detailed view of objects and activities. However, manual zooming is not suitable for surveillance and monitoring. It is tiring to continuously keep zooming into various regions of the video. Also, while viewing one region, the operator may miss activities in other regions. In this paper, we propose sZoom, a framework to automatically zoom into a high resolution surveillance video. The proposed framework selectively zooms into the sensitive regions of the video to present details of the scene, while still preserving the overall context required for situation assessment. A multi-variate Gaussian penalty is introduced to ensure full coverage of the scene. The method achieves near real-time performance through a number of timing optimizations. An extensive user study shows that, while watching a full HD video on a mobile device, the system enhances the security operator's efficiency in understanding the details of the scene by 99% on the average compared to a scaled version of the original high resolution video. The produced video achieved 46% higher ratings for usefulness in a surveillance task.
Pixel-Level Dense Prediction without Decoder

Pixel-level dense prediction tasks such as keypoint estimation are dominated by encoder-decoder structures, where the decoder as a vital component is complex and computationally intensive. In contrast, we propose a fully decoding-free pixel-level dense prediction network called FlatteNet, in which the high dimensional tensor outputted by the backbone network is directly flattened to fit the desired output resolution. The proposed FlatteNet is end-to-end differentiable. By removing the decoder unit, FlatteNet requires much fewer parameters and lower computational complexity. We empirically demonstrate the effectiveness of the proposed network through competitive results in human pose estimation on MPII, semantic segmentation on PASCAL-Context, and object detection on PASCAL VOC. We hope that the proposed FlatteNet can serve as a simple and strong alternative of current mainstream decoder-based pixel-level dense prediction networks.
Efficient Learning of Distributed Linear-Quadratic Controllers

In this work, we propose a robust approach to design distributed controllers for unknown-but-sparse linear and time-invariant systems. By leveraging modern techniques in distributed controller synthesis and structured linear inverse problems as applied to system identification, we show that near-optimal distributed controllers can be learned with sub-linear sample complexity and computed with near-linear time complexity, both measured with respect to the dimension of the system. In particular, we provide sharp end-to-end guarantees on the stability and the performance of the designed distributed controller and prove that for sparse systems, the number of samples needed to guarantee robust and near optimal performance of the designed controller can be significantly smaller than the dimension of the system. Finally, we show that the proposed optimization problem can be solved to global optimality with near-linear time complexity by iteratively solving a series of small quadratic programs.
Extended Einstein diffusion-mobility equation for two-dimensional Schr\"odinger-type quantum materials

We present the exact analytical equation of diffusion-mobility for two-dimensional (2D) Schrodinger type transport systems, from molecules to materials. The density of electronic states in such Schrodinger systems pertains to the 2D non-relativistic carrier dynamics. We implement the Gaussian function into carrier density derivation; accordingly we develop the electronic compressibility and diffusion-mobility for both the generic and the degenerate Fermi systems. This model is originally developed from generalized Einstein relation, along with concern about the thermodynamic effects on many-body interactions. The effect of interactions is included through the imperfect Fermi-gas entropy function. Our extended model explains the cooperative behavior of thermal and electronic counterparts on diffusion-mobility in disordered systems at wide temperature range. Using earlier experimental and theoretical results, we have shown the validity of our extended Einstein model for different 2D degenerate systems. The results validate the original Einstein equation at certain sets of temperature and chemical potential values for different Gaussian variances. Beyond those combinations, the deviation is observed. At very low temperature, the diffusion-mobility depends only on chemical potential, which is the extended Einstein equation for ideal quantum materials.
Dipole oscillation of a trapped Bose-Fermi-mixture gas in collisionless and hydrodynamic regimes

Dipole oscillation is studied in a normal phase of a trapped Bose--Fermi-mixture gas composed of single-species bosons and single-species fermions. Applying the moment method to the linearized Boltzmann equation, we derive a closed set of equations of motion for the center-of-mass position and momentum of both components. By solving the coupled equations, we reveal the behavior of dipole modes in the transition between the collisionless regime and the hydrodynamic regime. We find that two oscillating modes in the collisionless regime have distinct fates in the hydrodynamic regime: one collisionless mode shows a crossover to a hydrodynamic in-phase mode, and the other collisionless mode shows a transition to two purely damped modes. The temperature dependence of these dipole modes are also discussed.
To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?

The condition of the Glomeruli, or filter sacks, in renal Direct Immunofluorescence (DIF) specimens is a critical indicator for diagnosing kidney diseases. A digital pathology system which digitizes a glass histology slide into a Whole Slide Image (WSI) and then automatically detects and zooms in on the glomeruli with a higher magnification objective will be extremely helpful for pathologists. In this paper, using glomerulus detection as the study case, we provide analysis and observations on several important issues to help with the development of Computer Aided Diagnostic (CAD) systems to process WSIs. Large image resolution, large file size, and data scarcity are always challenging to deal with. To this end, we first examine image downsampling rates in terms of their effect on detection accuracy. Second, we examine the impact of image compression. Third, we examine the relationship between the size of the training set and detection accuracy. To understand the above issues, experiments are performed on the state-of-the-art detectors: Faster R-CNN, R-FCN, Mask R-CNN and SSD. Critical findings are observed: (1) The best balance between detection accuracy, detection speed and file size is achieved at 8 times downsampling captured with a 40 χ objective; (2) compression which reduces the file size dramatically, does not necessarily have an adverse effect on overall accuracy; (3) reducing the amount of training data to some extents causes a drop in precision but has a negligible impact on the recall; (4) in most cases, Faster R-CNN achieves the best accuracy in the glomerulus detection task. We show that the image file size of 40 χ WSI images can be reduced by a factor of over 6000 with negligible loss of glomerulus detection accuracy.
Provably convergent acceleration in factored gradient descent with applications in matrix sensing

We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with $\ell_2$-norm loss. The purpose of this work is to study the effects of acceleration in non-convex settings, where provable convergence with acceleration should not be considered a \emph{de facto} property. The technique is applied to matrix sensing problems, for the estimation of a rank $r$ optimal solution $X^\star \in \mathbb{R}^{n \times n}$. Our contributions can be summarized as follows. $i)$ We show that acceleration in factored gradient descent converges at a linear rate; this fact is novel for non-convex matrix factorization settings, under common assumptions. $ii)$ Our proof technique requires the acceleration parameter to be carefully selected, based on the properties of the problem, such as the condition number of $X^\star$ and the condition number of objective function. $iii)$ Currently, our proof leads to the same dependence on the condition number(s) in the contraction parameter, similar to recent results on non-accelerated algorithms. $iv)$ Acceleration is observed in practice, both in synthetic examples and in two real applications: neuronal multi-unit activities recovery from single electrode recordings, and quantum state tomography on quantum computing simulators.
Revisiting the Asymptotic Optimality of RRT*

RRT* is one of the most widely used sampling-based algorithms for asymptotically-optimal motion planning. RRT* laid the foundations for optimality in motion planning as a whole, and inspired the development of numerous new algorithms in the field, many of which build upon RRT* itself. In this paper, we first identify a logical gap in the optimality proof of RRT*, which was developed by Karaman and Frazzoli (2011). Then, we present an alternative and mathematically-rigorous proof for asymptotic optimality. Our proof suggests that the connection radius used by RRT* should be increased from $\gamma {\left( {\frac{{\log n}}{n}} \right)^{1/d}}$ to $\gamma ^{\prime}{\left( {\frac{{\log n}}{n}} \right)^{1/\left( {d + 1} \right)}}$ in order to account for the additional dimension of time that dictates the samples' ordering. Here γ,γ′ are constants, and n, d are the number of samples and the dimension of the problem, respectively.
Extensions of expansive dynamical systems

We characterize and describe the extensions of expansive and Anosov homeomorphisms on compact spaces. As an application we obtain a stability result for extensions of Anosov systems, and show a construction that embeds any expansive system inside an expansive system having the shadowing property for the pseudo orbits of the original space.