Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of ASIC-based signal readout electronics for LEGEND-1000

LEGEND, the Large Enriched Germanium Experiment for Neutrinoless ββ Decay, is a ton-scale experimental program to search for neutrinoless double beta (0νββ) decay in the isotope 76Ge with an unprecedented sensitivity. Building on the success of the low-background 76Ge-based GERDA and \textsc{Majorana Demonstrator} experiments, the LEGEND collaboration is targeting a signal discovery sensitivity beyond 1028 yr on the decay half-life with approximately 10 t⋅yr of exposure. Signal readout electronics in close proximity to the detectors plays a major role in maximizing the experiment's discovery sensitivity by reducing electronic noise and improving pulse shape analysis capabilities for the rejection of backgrounds. However, the proximity also poses unique challenges for the radiopurity of the electronics. Application-specific integrated circuit (ASIC) technology allows the implementation of the entire charge sensitive amplifier (CSA) into a single low-mass chip while improving the electronic noise and reducing the power consumption. In this work, we investigated the properties and electronic performance of a commercially available ASIC CSA, the XGLab CUBE preamplifier, together with a p-type point contact high-purity germanium detector. We show that low noise levels and excellent energy resolutions can be obtained with this readout. Moreover, we demonstrate the viability of pulse shape discrimination techniques for reducing background events.
The Effect of Different Portions of the Sunlight Spectrum on Ethylene Evolution in Peach (Prunus persica) Apices

Exposure of peach plants to the blue plus far-red (B/FR) portions of the sunlight spectrum caused a rapid rise in ethylene evolution from their apices. Two days were enough to produce a significant rise in ethylene evolution relative to blue without far-red or to neutral shade. Maximal level of ethylene evolution in the B/FR light, more than eight times that of the blue or the neutral shade, was reached after four days of exposure. A higher endogenous ethylene content was also found under B/FR relative to blue or to neutral shade conditions. The level of ethylene evolution from peach apices was correlated with their arrested growth as observed a few days later. Exposure of peach plants to dense leaf shade, under the canopy of a big avocado tree, enhanced ethylene evolution from their apices, relative to unfiltered sunlight and to neutral shade. It was suggested that the rise in ethylene evolution in both B/FR and leaf shade conditions resulted from a high far-red: red ratio. Ethylene was further suggested to act as a mediator of photomorphogenetic regulation of vegetative development in far-red-rich tree shade.
Evolution of Dust and Water Ice in Cometary Comae by Radiative Torques

Comets provide unique information about the physical and chemical properties of the environment in which the solar system was formed. Understanding how cometary dust and ice evolve under the effect of sunlight is essential for constraining nuclear structure and triggering the mechanism of comet activity. In this paper, we first study the rotational disruption of dust grains lifted by outgassing from comet nuclei by radiative torques (RATs). We find that composite grains could be rapidly disrupted into small fragments by the RAT disruption mechanism. We then study rotational desorption of ice grains by RATs and find that icy grains could be desorbed from large heliocentric distances, beyond the sublimation radius of water at . We also calculate the production rate of water vapor versus the heliocentric distance of comets due to rotational desorption. Our results could explain the variation of dust properties and the presence of small grains frequently observed from cometary comae. Finally, we suggest that the activity of distant comets could be triggered by rotational disruption of grains and desorption of water-ice grains at large heliocentric distances.
A Second-Order Lower Bound for Globally Optimal 2D Registration

The problem of planar registration consists in finding the transformation that better aligns two point sets. In our setting, the search domain is the set of planar rigid transformations and the objective function is the sum of the distances between each point of the transformed source set and the destination set. We consider a Branch and Bound (BnB) method for finding the globally optimal solution. The algorithm recursively splits the search domain into boxes and computes an upper and a lower bound for the minimum value of the restricted problem. The main contribution of this work is the introduction of a novel lower bound, the relaxation bound, which corresponds to the solution of a concave relaxation of the objective function based on the linearization of the distance. In the BnB we also employ the so called cheap bound, equal to to the sum of the minimum distances between each point of source point set, transformed according to current box, and all the candidate points in the destination point set. We prove, both theoretically and practically, that the novel relaxation bound dominates the cheap bound over small boxes. More precisely, from the theoretical point of view, we prove that the relaxation bound is a second-order approximation of the minimum value, i.e., its distance from the minimum value decreases quadratically with respect to the diameter of the box (see Theorem 1), while the cheap bound is a first-order one (see Proposition 3). From the practical point of view, we show through different computational experiments that the addition of the relaxation bound considerably enhances the performance of the BnB algorithm, compensating the higher cost of its computation with respect to the cheap bound with a strong reduction of the number of BnB nodes to be explored.
A Study of Deep Learning Colon Cancer Detection in Limited Data Access Scenarios

Digitization of histopathology slides has led to several advances, from easy data sharing and collaborations to the development of digital diagnostic tools. Deep learning (DL) methods for classification and detection have shown great potential, but often require large amounts of training data that are hard to collect, and annotate. For many cancer types, the scarceness of data creates barriers for training DL models. One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming. DL-based tools can allow faster diagnosis, with potentially increased quality. Unfortunately, due to the sparsity of tumor cells, annotating this type of data demands a high level of effort from pathologists. Using weak annotations from slide-level images have shown great potential, but demand access to a substantial amount of data as well. In this study, we investigate mitigation strategies for limited data access scenarios. Particularly, we address whether it is possible to exploit mutual structure between tissues to develop general techniques, wherein data from one type of cancer in a particular tissue could have diagnostic value for other cancers in other tissues. Our case is exemplified by a DL model for metastatic colon cancer detection in lymph nodes. Could such a model be trained with little or even no lymph node data? As alternative data sources, we investigate 1) tumor cells taken from the primary colon tumor tissue, and 2) cancer data from a different organ (breast), either as is or transformed to the target domain (colon) using Cycle-GANs. We show that the suggested approaches make it possible to detect cancer metastasis with no or very little lymph node data, opening up for the possibility that existing, annotated histopathology data could generalize to other domains.
Tensor Clustering with Planted Structures: Statistical Optimality and Computational Limits

This paper studies the statistical and computational limits of high-order clustering with planted structures. We focus on two clustering models, constant high-order clustering (CHC) and rank-one higher-order clustering (ROHC), and study the methods and theory for testing whether a cluster exists (detection) and identifying the support of cluster (recovery). Specifically, we identify the sharp boundaries of signal-to-noise ratio for which CHC and ROHC detection/recovery are statistically possible. We also develop the tight computational thresholds: when the signal-to-noise ratio is below these thresholds, we prove that polynomial-time algorithms cannot solve these problems under the computational hardness conjectures of hypergraphic planted clique (HPC) detection and hypergraphic planted dense subgraph (HPDS) recovery. We also propose polynomial-time tensor algorithms that achieve reliable detection and recovery when the signal-to-noise ratio is above these thresholds. Both sparsity and tensor structures yield the computational barriers in high-order tensor clustering. The interplay between them results in significant differences between high-order tensor clustering and matrix clustering in literature in aspects of statistical and computational phase transition diagrams, algorithmic approaches, hardness conjecture, and proof techniques. To our best knowledge, we are the first to give a thorough characterization of the statistical and computational trade-off for such a double computational-barrier problem. Finally, we provide evidence for the computational hardness conjectures of HPC detection and HPDS recovery.
Sequential Universal Modeling for Non-Binary Sequences with Constrained Distributions

Sequential probability assignment and universal compression go hand in hand. We propose sequential probability assignment for non-binary (and large alphabet) sequences with empirical distributions whose parameters are known to be bounded within a limited interval. Sequential probability assignment algorithms are essential in many applications that require fast and accurate estimation of the maximizing sequence probability. These applications include learning, regression, channel estimation and decoding, prediction, and universal compression. On the other hand, constrained distributions introduce interesting theoretical twists that must be overcome in order to present efficient sequential algorithms. Here, we focus on universal compression for memoryless sources, and present precise analysis for the maximal minimax and the average minimax for constrained distributions. We show that our sequential algorithm based on modified Krichevsky-Trofimov (KT) estimator is asymptotically optimal up to $O(1)$ for both maximal and average redundancies. This paper follows and addresses the challenge presented in \cite{stw08} that suggested "results for the binary case lay the foundation to studying larger alphabets".
ScriptWriter: Narrative-Guided Script Generation

It is appealing to have a system that generates a story or scripts automatically from a storyline, even though this is still out of our reach. In dialogue systems, it would also be useful to drive dialogues by a dialogue plan. In this paper, we address a key problem involved in these applications - guiding a dialogue by a narrative. The proposed model ScriptWriter selects the best response among the candidates that fit the context as well as the given narrative. It keeps track of what in the narrative has been said and what is to be said. A narrative plays a different role than the context (i.e., previous utterances), which is generally used in current dialogue systems. Due to the unavailability of data for this new application, we construct a new large-scale data collection GraphMovie from a movie website where end- users can upload their narratives freely when watching a movie. Experimental results on the dataset show that our proposed approach based on narratives significantly outperforms the baselines that simply use the narrative as a kind of context.
The mergers in Abell 2256: displaced gas and its connection to the radio-emitting plasma

We present the results of deep Chandra and XMM–Newton X-ray imaging and spatially resolved spectroscopy of Abell 2256, a nearby (z = 0.058) galaxy cluster experiencing multiple mergers and displaying a rich radio morphology dominated by a large relic. The X-ray data reveal three subclusters: (i) the ‘main cluster’; (ii) the remnant of an older merger in the east of the cluster with an ∼600 kpc-long tail; (iii) a bright, bullet-like, low-entropy infalling system, with a large line-of-sight velocity component. The low-entropy system displays a 250 kpc-long cold front with a break and an intriguing surface brightness decrement. Interestingly, the infalling gas is not co-spatial with bright galaxies and the radio-loud brightest cluster galaxy of the infalling group appears dissociated from the low-entropy plasma by ∼50 kpc in projection, to the south of the eastern edge of the cold front. Assuming that the dark matter follows the galaxy distribution, we predict that it is also significantly offset from the low-entropy gas. Part of the low-frequency radio emission near the cold front might be revived by magnetic field amplification due to differential gas motions. Using analytical models and numerical simulations, we investigate the possibility that the supersonic infall of the subcluster generates a large-scale shock along our line of sight, which can be detected in the X-ray temperature map but is not associated with any clear features in the surface brightness distribution.
Fast and Accurate Langevin Simulations of Stochastic Hodgkin-Huxley Dynamics

Fox and Lu introduced a Langevin framework for discrete-time stochastic models of randomly gated ion channels such as the Hodgkin-Huxley (HH) system. They derived a Fokker-Planck equation with state-dependent diffusion tensor D and suggested a Langevin formulation with noise coefficient matrix S such that SS⊤=D. Subsequently, several authors introduced a variety of Langevin equations for the HH system. In this article, we present a natural 14-dimensional dynamics for the HH system in which each directed edge in the ion channel state transition graph acts as an independent noise source, leading to a 14 × 28 noise coefficient matrix S. We show that (1) the corresponding 14D system of ordinary differential equations is consistent with the classical 4D representation of the HH system; (2) the 14D representation leads to a noise coefficient matrix S that can be obtained cheaply on each time step, without requiring a matrix decomposition; (3) sample trajectories of the 14D representation are pathwise equivalent to trajectories of Fox and Lu's system, as well as trajectories of several existing Langevin models; (4) our 14D representation (and those equivalent to it) gives the most accurate interspike interval distribution, not only with respect to moments but under both the L1 and L∞ metric-space norms; and (5) the 14D representation gives an approximation to exact Markov chain simulations that are as fast and as efficient as all equivalent models. Our approach goes beyond existing models, in that it supports a stochastic shielding decomposition that dramatically simplifies S with minimal loss of accuracy under both voltage- and current-clamp conditions.
Environmental Sound Classification with Parallel Temporal-Spectral Attention

Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, temporal attention mechanisms have been used in CNN to capture the useful information from the relevant time frames for audio classification, especially for weakly labelled data where the onset and offset times of the sound events are not applied. In these methods, however, the inherent spectral characteristics and variations are not explicitly exploited when obtaining the deep features. In this paper, we propose a novel parallel temporal-spectral attention mechanism for CNN to learn discriminative sound representations, which enhances the temporal and spectral features by capturing the importance of different time frames and frequency bands. Parallel branches are constructed to allow temporal attention and spectral attention to be applied respectively in order to mitigate interference from the segments without the presence of sound events. The experiments on three environmental sound classification (ESC) datasets and two acoustic scene classification (ASC) datasets show that our method improves the classification performance and also exhibits robustness to noise.
A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions

Personalization is very powerful in improving the effectiveness of health interventions. Reinforcement learning (RL) algorithms are suitable for learning these tailored interventions from sequential data collected about individuals. However, learning can be very fragile. The time to learn intervention policies is limited as disengagement from the user can occur quickly. Also, in e-Health intervention timing can be crucial before the optimal window passes. We present an approach that learns tailored personalization policies for groups of users by combining RL and clustering. The benefits are two-fold: speeding up the learning to prevent disengagement while maintaining a high level of personalization. Our clustering approach utilizes dynamic time warping to compare user trajectories consisting of states and rewards. We apply online and batch RL to learn policies over clusters of individuals and introduce our self-developed and publicly available simulator for e-Health interventions to evaluate our approach. We compare our methods with an e-Health intervention benchmark. We demonstrate that batch learning outperforms online learning for our setting. Furthermore, our proposed clustering approach for RL finds near-optimal clusterings which lead to significantly better policies in terms of cumulative reward compared to learning a policy per individual or learning one non-personalized policy across all individuals. Our findings also indicate that the learned policies accurately learn to send interventions at the right moments and that the users workout more and at the right times of the day.
Detecting a botnet in a network

We formalize the problem of detecting the presence of a botnet in a network as an hypothesis testing problem where we observe a single instance of a graph. The null hypothesis, corresponding to the absence of a botnet, is modeled as a random geometric graph where every vertex is assigned a location on a $d$-dimensional torus and two vertices are connected when their distance is smaller than a certain threshold. The alternative hypothesis is similar, except that there is a small number of vertices, called the botnet, that ignore this geometric structure and simply connect randomly to every other vertex with a prescribed probability. We present two tests that are able to detect the presence of such a botnet. The first test is based on the idea that botnet vertices tend to form large isolated stars that are not present under the null hypothesis. The second test uses the average graph distance, which becomes significantly shorter under the alternative hypothesis. We show that both these tests are asymptotically optimal. However, numerical simulations show that the isolated star test performs significantly better than the average distance test on networks of moderate size. Finally, we construct a robust scheme based on the isolated star test that is also able to identify the vertices in the botnet.
Concurrency-related Flaky Test Detection in Android apps

Validation of Android apps via testing is difficult owing to the presence of flaky tests. Due to non-deterministic execution environments, a sequence of events (a test) may lead to success or failure in unpredictable ways. In this work, we present an approach and tool FlakeShovel for detecting flaky tests through systematic exploration of event orders. Our key observation is that for a test in a mobile app, there is a testing framework thread which creates the test events, a main User-Interface (UI) thread processing these events, and there may be several other background threads running asynchronously. For any event e whose execution involves potential non-determinism, we localize the earliest (latest) event after (before) which e must happen.We then efficiently explore the schedules between the upper/lower bound events while grouping events within a single statement, to find whether the test outcome is flaky. We also create a suite of subject programs called DroidFlaker to study flaky tests in Android apps. Our experiments on subject-suite DroidFlaker demonstrate the efficacy of our flaky test detection. Our work is complementary to existing flaky test detection tools like Deflaker which check only failing tests. FlakeShovel can detect flaky tests among passing tests, as shown by our approach and experiments.
A deep learning model for noise prediction on near-term quantum devices

We present an approach for a deep-learning compiler of quantum circuits, designed to reduce the output noise of circuits run on a specific device. We train a convolutional neural network on experimental data from a quantum device to learn a hardware-specific noise model. A compiler then uses the trained network as a noise predictor and inserts sequences of gates in circuits so as to minimize expected noise. We tested this approach on the IBM 5-qubit devices and observed a reduction in output noise of 12.3% (95% CI [11.5%, 13.0%]) compared to the circuits obtained by the Qiskit compiler. Moreover, the trained noise model is hardware-specific: applying a noise model trained on one device to another device yields a noise reduction of only 5.2% (95% CI [4.9%, 5.6%]). These results suggest that device-specific compilers using machine learning may yield higher fidelity operations and provide insights for the design of noise models.
COMPARISON OF DISSOCIATION CONSTANTS AND OF RELATIVE EFFICACIES OF SELECTED AGONISTS ACTING ON PARASYMPATHETIC RECEPTORS *

An experimental procedure has recently been introduced by one of us (Furchgott, 1966) for determining the dissociation constants of receptor-agonist complexes, and for estimating the relative efficacies of different agonists for stimulating a response through interaction with a common type of receptor. The procedure is applicable in the case of receptors that can be inactivated in an essentially irreversible manner. Data for response as a function of concentration of an agonist are obtained on the same test preparation before and after irreversible inactivation of a fraction of the receptors for the agonist, and the data are then subjected to a simple theoretical analysis to obtain the dissociation constant of the receptor-agonist complex. If such data are obtained on the same test preparation for two agonists that act on a common receptor, then the ratio of the efficacies of the two agonists may be estimated. Certain beta-haloalkylamines may be used as the pharmacological tools for implementing this procedure, since they can inactivate a variety of receptors in an essentially irreversible manner. Among the receptors susceptible to such inactivation are parasympathetic (muscarinic) receptors. In the present work we used the beta-chloroalkylamine, dibenamine, as the inactivating agent, and our test preparation (parasympathetic effector) was an isolated strip of smooth muscle from the fundus of the rabbit stomach. Several parasympathomimetic (muscarinic) agents were tested, and the values obtained for their dissociation constants with parasympathetic receptors and for their relative efficacies will be reported here. The initial results with this procedure indicate that it will permit for the first time the quantitative determination of the dissociation constants and relative efficacies of agonists which act on parasympathetic receptors. The usefulness of such determinations for a more meaningful analysis of structure-activity relationships will be discussed.