Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying active and passive stresses in adherent cells

To understand cell migration, it is crucial to gain knowledge on how cells exert and integrate forces on/from their environment. A quantity of prime interest for biophysicists interested in cell movements modeling is the intracellular stresses. Up to now, three different methods have been proposed to calculate it, they are all in the regime of the thin plate approximation. Two are based on solving the mechanical equilibrium equation inside the cell material (Monolayer Stress Microscopy, and Bayesian Inference Stress Microscopy) and one based on the continuity of displacement at the cell/ substrate interface (Intracellular Stress Microscopy). We show here using 3D FEM modeling that these techniques do not calculate the same quantities (as was previously assume), the first techniques calculate the sum of the active and passive stress within the cell, whereas the last one only calculate the passive component. Combining these techniques should in principle permit to access to the active stress alone.
Snowmass2021 Cosmic Frontier White Paper: Puzzling Excesses in Dark Matter Searches and How to Resolve Them

Intriguing signals with excesses over expected backgrounds have been observed in many astrophysical and terrestrial settings, which could potentially have a dark matter origin. Astrophysical excesses include the Galactic Center GeV gamma-ray excess detected by the Fermi Gamma-Ray Space Telescope, the AMS antiproton and positron excesses, and the 511 and 3.5 keV X-ray lines. Direct detection excesses include the DAMA/LIBRA annual modulation signal, the XENON1T excess, and low-threshold excesses in solid state detectors. We discuss avenues to resolve these excesses, with actions the field can take over the next several years.
Towards Semantic Search for Community Question Answering for Mortgage Officers

Community Question Answering (CQA) has gained increasing popularity in many domains. Mortgage is a complex and dynamic industry, and a flexible and efficient CQA platform can potentially enhance the quality of service for mortgage officers significantly. We have built a dynamic CQA platform with a state of the art semantic search engine based on recent Natural Language Processing (NLP) techniques to dynamically and collectively capture and transfer the maturity and tribal knowledge of the more experienced workforce to less experienced ones. The search engine allows for both keyword and natural language queries and is based on a fine-tuned domain-adapted Sentence-BERT encoder linearly composed with a TF-IDF vectorizer, and reciprocal-rank fused with a BM25 vectorizer. Domain adaptation and fine-tuning is based on publicly available mortgage corpora. Evaluation is performed on an internally annotated dataset using standard information retrieval metrics such as normalized discounted cumulative gain (nDCG), precision/recall at n, mean reciprocal rank, and mean average precision (MAP). The results indicate that our hybrid, fine-tuned, domain-adapted search engine is a more effective approach in responding to the information needs of our mortgage officers compared to traditional search techniques. We aim to publish the internally-annotated evaluation and training datasets in the near future.
Do DNNs trained on Natural Images acquire Gestalt Properties?

Under some circumstances, humans tend to perceive individual elements as a group or ‘whole’. This has been widely investigated for more than a century by the school of Gestalt Psychology, which formulated several laws of perceptual grouping. Recently, Deep Neural Networks (DNNs) trained on natural images have been proposed as compelling models of human vision based on reports that they learn internal representations similar to the primate ventral visual stream and show similar patterns of errors in object classiﬁcation tasks. That is, DNNs often perform well on brain and behavioral benchmarks. Here we compared human and DNN responses in discrimination judgments that assess a range of Gestalt organization principles (Pomerantz et al., 1977; Pomerantz and Portillo, 2011). Amongst the DNNs tested we selected models that perform well on the Brain-Score benchmark (Schrimpf et al., 2018). We found that network trained on natural images exhibited sensitivity to shapes at the last stage of classiﬁcation, which in some cases matched humans responses. When shape familiarity was controlled for (by using dot patterns that would not resemble shapes) we found the networks were insensitive to the standard Gestalt principles of proximity, orientation, and linearity, which have been shown to have a strong and robust eﬀect on humans. This shows that models that perform well on behavioral and brain benchmarks nevertheless miss fundamental principles of human vision.
Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech

Training a robust Automatic Speech Recognition (ASR) system for children's speech recognition is a challenging task due to inherent differences in acoustic attributes of adult and child speech and scarcity of publicly available children's speech dataset. In this paper, a novel segmental spectrum warping and perturbations in formant energy are introduced, to generate a children-like speech spectrum from that of an adult's speech spectrum. Then, this modified adult spectrum is used as augmented data to improve end-to-end ASR systems for children's speech recognition. The proposed data augmentation methods give 6.5% and 6.1% relative reduction in WER on children dev and test sets respectively, compared to the vocal tract length perturbation (VTLP) baseline system trained on Librispeech 100 hours adult speech dataset. When children's speech data is added in training with Librispeech set, it gives a 3.7 % and 5.1% relative reduction in WER, compared to the VTLP baseline system.
ProtagonistTagger - a Tool for Entity Linkage of Persons in Texts from Various Languages and Domains

Named entities recognition (NER) and disambiguation (NED) can add semantic context to the recognized named entities in texts. Named entity linkage in texts, regardless of a domain, provides links between the entities mentioned in unstructured texts and individual instances of real-world objects. In this poster, we present a tool - protagonistTagger - for person NER and NED in texts. The tool was tested on texts extracted from classic English novels and Polish Internet news. The tool's performance (both precision and recall) fluctuates between 78% and even 88%.
Detection of Electromagnetic Signal Injection Attacks on Actuator Systems

An actuator is a device that converts electricity into another form of energy, typically physical movement. They are absolutely essential for any system that needs to impact or modify the physical world, and are used in millions of systems of all sizes, all over the world, from cars and spacecraft to factory control systems and critical infrastructure. An actuator is a “dumb device” that is entirely controlled by the surrounding electronics, e.g., a microcontroller, and thus cannot authenticate its control signals or do any other form of processing. The problem we look at in this paper is how the wires that connect an actuator to its control electronics can act like antennas, picking up electromagnetic signals from the environment. This makes it possible for a remote attacker to wirelessly inject signals (energy) into these wires to bypass the controller and directly control the actuator. To detect such attacks, we propose a novel detection method that allows the microcontroller to monitor the control signal and detect attacks as a deviation from the intended value. We have managed to do this without requiring the microcontroller to sample the signal at a high rate or run any signal processing. That makes our defense mechanism practical and easy to integrate into existing systems. Our method is general and applies to any type of actuator (provided a few basic assumptions are met), and can deal with adversaries with arbitrarily high transmission power. We implement our detection method on two different practical systems to show its generality, effectiveness, and robustness.
Meso-scale controlled motion for a microfluidic drop ejector.

The objective of this LDRD was to develop a uniquely capable, novel droplet solution based manufacturing system built around a new MEMS drop ejector. The development all the working subsystems required was completed, leaving the integration of these subsystems into a working prototype still left to accomplish. This LDRD report will focus on the three main subsystems: (1) MEMS drop ejector--the MEMS ''sideshooter'' effectively ejected 0.25 pl drops at 10 m/s, (2) packaging--a compact ejector package based on a modified EMDIP (Electro-Microfluidic Dual In-line Package--SAND2002-1941) was fabricated, and (3) a vision/stage system allowing precise ejector package positioning in 3 dimensions above a target was developed.
Stability and Power-Gain Invariants of Linear Twoports

It is shown that the stability of a linear twoport is invariant under arbitrary lossless terminations, under interchange of input and output, and under "immittance substitution," a transformation group involving the arbitrary interchanging of impedance and admittance formulations at both ports. The quantity k = \frac {2 Re (\gamma_{11}) Re (\gamma_{22}) - Re(\gamma_{12} \gamma_{21})} {|\gamme_{12} \gamma_{21}|} (where the \gamma may be any of the conventional immittance z, y, or hybrid h, g matrix parameters) is the simplest invariant under these transformations, and describes uniquely the degree of stability, provided Re(\gamma_{11}), Re(\gamma_{22}) \geq 0; the larger k is, the greater the stability, and in particular k = 1 defines the boundary between unconditional and conditional stability. The quantity k is thus the basic invariant stability factor. Its definition is also extended to include the effect of terminating immittances, which may be padding resistances or source and load immittances, or both. Certain power-gain functions, including the maximum available power gain, are shown to be invariant under immittance substitution, and k is identified as a function of ratios between them, where they exist. This provides a fundamental way of determining k, apart from calculating it from matrix parameters, and indicates that it is a measure of an inherent physical property.
Network Bandwidth Allocation Problem For Cloud Computing

Cloud computing enables ubiquitous, convenient, and on-demand network access to a shared pool of computing resources. Cloud computing technologies create tremendous commercial values in various areas, while many scientific challenges have arisen accordingly. The process of transmitting data through networks is characterized by some distinctive characteristics such as nonlinear, nonconvex and even noncontinuous cost functions generated by pricing schemes, periodically updated network topology, as well as replicable data within network nodes. Because of these characteristics, data transfer scheduling is a very challenging problem both engineeringly and scientifically. On the other hand, the cost for bandwidth is a major component of the operating cost for cloud providers, and thus how to save bandwidth cost is extremely important for them to supply service with minimized cost. We propose the Network Bandwidth Allocation (NBA) problem for cloud computing and formulate it as an integer programming model on a high level, with which more comprehensive and rigorous scientific studies become possible. We also show that the NBA problem captures some of the major cloud computing scenarios including the content delivery network (CDN), the live video delivery network (LVDN), the real-time communication network (RTCN), and the cloud wide area network (Cloud-WAN).
Optimal Aggregation Strategies for Social Learning Over Graphs

Adaptive social learning is a useful tool for studying distributed decision-making problems over graphs. This paper investigates the effect of combination policies on the performance of adaptive social learning strategies. Using large-deviation analysis, it first derives a bound on the steady-state error probability and characterizes the optimal selection for the Perron eigenvectors of the combination policies. It subsequently studies the effect of the combination policy on the transient behavior of the learning strategy by estimating the adaptation time in the low signal-to-noise ratio regime. In the process, it is discovered that, interestingly, the influence of the combination policy on the transient behavior is insignificant, and thus it is more critical to employ policies that enhance the steady-state performance. The theoretical conclusions are illustrated by means of computer simulations.
Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference

Optimal control under uncertainty is a prevailing challenge for many reasons. One of the critical difficulties lies in producing tractable solutions for the underlying stochastic optimization problem. We show how advanced approximate inference techniques can be used to handle the statistical approximations principled and practically by framing the control problem as a problem of input estimation. Analyzing the Gaussian setting, we present an inference-based solver that is effective in stochastic and deterministic settings and was found to be superior to popular baselines on nonlinear simulated tasks. We draw connections that relate this inference formulation to previous approaches for stochastic optimal control and outline several advantages that this inference view brings due to its statistical nature.
M ar 2 02 2 Quantum correlations preparation assisted by a steering Maxwell demon

A Maxwell demon can reduce the entropy of a quantum system by performing measurements on its environment. This avoids the disturbance to average energy of the system. We study the preparations of quantum correlations from a thermal qubit and an auxiliary qubit, assisted by a demon who obtains information of the system qubit from measurements on its environment. The demon can affect the postmeasured states of system by choosing different measurements, which establishes the relationships between quantum steering and other correlations in the thermodynamic framework. We present the optimal protocols for creating mutual information, entanglement and Bell-nonlocality, the maximums of which depend monotonically upon the extractable work in a similar process. These maximal correlations are found to relate exactly to the steerable boundary of the system-environment state with maximally mixed marginals. We also present upper bounds of the prepared correlations by utilizing classical environment-system correlation, which can be regarded as steering-type inequalities bounding the correlations created with the aid of classical demons.
A combined approach to the analysis of speech conversations in a contact center domain

The ever more accurate search for deep analysis in customer data is a really strong technological trend nowadays, quite appealing to both private and public companies. This is particularly true in the contact center domain, where speech analytics is an extremely powerful methodology for gaining insights from unstructured data, coming from customer and human agent conversations. In this work, we describe an experimentation with a speech analytics process for an Italian contact center, that deals with call recordings extracted from inbound or outbound flows. First, we illustrate in detail the development of an in-house speech-to-text solution, based on Kaldi framework, and evaluate its performance (and compare it to Google Cloud Speech API). Then, we evaluate and compare different approaches to the semantic tagging of call transcripts, ranging from classic regular expressions to machine learning models based on ngrams and logistic regression, and propose a combination of them, which is shown to provide a consistent benefit. Finally, a decision tree inducer, called J48S, is applied to the problem of tagging. Such an algorithm is natively capable of exploiting sequential data, such as texts, for classification purposes. The solution is compared with the other approaches and is shown to provide competitive classification performances, while generating highly interpretable models and reducing the complexity of the data preparation phase. The potential operational impact of the whole process is thoroughly examined.
A Survey of Generative Adversarial Networks for Synthesizing Structured Electronic Health Records

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particularly, Generative Adversarial Networks (GANs) show great promise in generating synthetic EHR data by learning underlying data distributions while achieving excellent performance and addressing these challenges. This work aims to survey the major developments in various applications of GANs for EHRs and provides an overview of the proposed methodologies. For this purpose, we combine perspectives from healthcare applications and machine learning techniques in terms of source datasets and the fidelity and privacy evaluation of the generated synthetic datasets. We also compile a list of the metrics and datasets used by the reviewed works, which can be utilized as benchmarks for future research in the field. We conclude by discussing challenges in GANs for EHRs development and proposing recommended practices. We hope that this work motivates novel research development directions in the intersection of healthcare and machine learning.
KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling

Currently, Medical Subject Headings (MeSH) are manually assigned to every biomedical article published and subsequently recorded in the PubMed database to facilitate retrieving relevant information. With the rapid growth of the PubMed database, large-scale biomedical document indexing becomes increasingly important. MeSH indexing is a challenging task for machine learning, as it needs to assign multiple labels to each article from an extremely large hierachically organized collection. To address this challenge, we propose KenMeSH, an end-to-end model that combines new text features and a dynamic knowledge-enhanced mask attention that integrates document features with MeSH label hierarchy and journal correlation features to index MeSH terms. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures.
Post-Newtonian Jeans Equation for Stationary and Spherically Symmetrical Self-Gravitating Systems

The post-Newtonian Jeans equation for stationary self-gravitating systems is derived from the post-Newtonian Boltzmann equation in spherical coordinates. The Jeans equation is coupled with the three Poisson equations from the post-Newtonian theory. The Poisson equations are functions of the energy-momentum tensor components which are determined from the post-Newtonian Maxwell–Jüttner distribution function. As an application, the effect of a central massive black hole on the velocity dispersion profile of the host galaxy is investigated and the influence of the post-Newtonian corrections are determined.
AugShuffleNet: Improve ShuffleNetV2 via More Information Communication

Based on ShufﬂeNetV2, we build a more powerful and efﬁcient model family, termed as AugShuf-ﬂeNets, by introducing higher frequency of cross-layer information communication for better model performance. Evaluated on the CIFAR-10 and CIFAR-100 datasets, AugShufﬂeNet consistently outperforms ShufﬂeNetV2 in terms of accuracy, with less computational cost, fewer parameter count.
A Stochastic Binary Vertex-Triggering Resetting Algorithm for Global Synchronization of Pulse-Coupled Oscillators

In this article, we propose a novel stochastic binary resetting algorithm for networks of pulse-coupled oscillators (or, simply, agents) to reach global synchronization. The algorithm is simple to state: Every agent in a network oscillates at a common frequency. Upon completing an oscillation, an agent generates a Bernoulli random variable to decide whether it sends pulses to all of its out-neighbors or it stays quiet. Upon receiving a pulse, an agent resets its state by following a binary phase update rule. We show that such an algorithm can guarantee global synchronization of the agents almost surely as long as the underlying information flow topology is a rooted directed graph. The proof of the result relies on the use of a stochastic hybrid dynamical system approach. Toward the end of this article, we present numerical demonstrations for the validity of the result and numerical studies about the units of time needed to reach synchronization for networks with various information flow topologies.
Simulating transient noise bursts in LIGO with generative adversarial networks

The noise of gravitational-wave (GW) interferometers limits their sensitivity and impacts the data quality, hindering the detection of GW signals from astrophysical sources. For transient searches, the most problematic are transient noise artifacts, known as glitches, that happen at a rate around $ 1 \text{ min}^{-1}$, and can mimic GW signals. Because of this, there is a need for better modeling and inclusion of glitches in large-scale studies, such as stress testing the pipelines. In this proof-of concept work we employ Generative Adversarial Networks (GAN), a state-of-the-art Deep Learning algorithm inspired by Game Theory, to learn the underlying distribution of blip glitches and to generate artificial populations. We reconstruct the glitch in the time-domain, providing a smooth input that the GAN can learn. With this methodology, we can create distributions of $\sim 10^{3}$ glitches from Hanford and Livingston detectors in less than one second. Furthermore, we employ several metrics to measure the performance of our methodology and the quality of its generations. This investigation will be extended in the future to different glitch classes with the final goal of creating an open-source interface for mock data generation.
Beyond Gaussian processes: Flexible Bayesian modeling and inference for geostatistical processes

This paper proposes a novel family of geostatistical models to account for features that cannot be properly accommodated by traditional Gaussian processes. The family is specified hierarchically and combines the infinite-dimensional dynamics of Gaussian processes with that of any multivariate continuous distribution. This combination is stochastically defined through a latent Poisson process and the new family is called the Poisson-Gaussian Mixture Process - POGAMP. Whilst the attempt of defining geostatistical processes by assigning some arbitrary continuous distribution to be the finite-dimension distributions usually leads to non-valid processes, the finite-dimensional distributions of the POGAMP can be arbitrarily close to any continuous distribution and still define a valid process. Formal results to establish the existence and some important properties of the POGAMP, such as absolute continuity with respect to a Gaussian process measure, are provided. Also, an MCMC algorithm is carefully devised to perform Bayesian inference when the POGAMP is discretely observed in some space domain.
ARL F/A-18 IFOSTP Full Scale Fatigue Test

A unique full scale fatigue test system has been developed by ARL which combines dynamic loading with manoeuvre loading to simulate the flight conditions experienced by an F/A-18 aircraft under normal RAAF and CF operations. The system uses a new air bag loading system rather than the more conventional hydraulic loading system to apply the distributed aerodynamic and inertia loads induced by aircraft manoeuvres. Dynamic buffet conditions experienced in flight are applied by high powered, high displacement electromagnetic shakers.