cssi/SciSciGPT-SciSciNet huggingface dataset:
Putting a bug in ML: The moth olfactory network learns to read MNIST.

Abstract We seek to (i) characterize the learning architectures exploited in biological neural networks for training on very few samples, and (ii) port these algorithmic structures to a machine learning context. The moth olfactory network is among the simplest biological neural systems that can learn, and its architecture includes key structural elements and mechanisms widespread in biological neural nets, such as cascaded networks, competitive inhibition, high intrinsic noise, sparsity, reward mechanisms, and Hebbian plasticity. These structural biological elements, in combination, enable rapid learning. MothNet is a computational model of the moth olfactory network, closely aligned with the moth’s known biophysics and with in vivo electrode data collected from moths learning new odors. We assign this model the task of learning to read the MNIST digits. We show that MothNet successfully learns to read given very few training samples (1–10 samples per class). In this few-samples regime, it outperforms standard machine learning methods such as nearest-neighbors, support-vector machines, and neural networks (NNs), and matches specialized one-shot transfer-learning methods but without the need for pre-training. The MothNet architecture illustrates how algorithmic structures derived from biological brains can be used to build alternative NNs that may avoid the high training data demands of many current engineered NNs.
First-Principles Calculation of the Structure of Mercury

Mercury has perhaps the strangest behavior of any of the metals. Although the other metals in column IIB have an hep ground state, mercury’s ground state is the body centered tetragonal βHg phase. The most common phase of mercury is the rhombohedral αHg phase, which is stable from 79K to the melting point and meta-stable below 79K. Another rhombohedral phase, γHg, is believed to exist at low temperatures. First-principles calculations are used to study the energetics of the various phases of mercury. Even when partial spin-orbit effects are included, the calculations indicate that the hexagonal close packed structure is the ground state. It is suggested that a better treatment of the spinorbit interaction might alter this result.
Factor analysis for spectral estimation

Power spectrum estimation is an important tool in many applications, such as the whitening of noise. The popular multitaper method enjoys significant success, but fails for short signals with few samples. We propose a statistical model where a signal is given by a random linear combination of fixed, yet unknown, stochastic sources. Given multiple such signals, we estimate the subspace spanned by the power spectra of these fixed sources. Projecting individual power spectrum estimates onto this subspace increases estimation accuracy. We provide accuracy guarantees for this method and demonstrate it on simulated and experimental data from cryo-electron microscopy.
A Unified Welfare Analysis of Government Policies

We conduct a comparative welfare analysis of 133 historical policy changes over the past half-century in the United States, focusing on policies in social insurance, education and job training, taxes and cash transfers, and in-kind transfers. For each policy, we use existing causal estimates to calculate both the benefit that each policy provides its recipients (measured as their willingness to pay) and the policy’s net cost, inclusive of long-term impacts on the government’s budget. We divide the willingness to pay by the net cost to the government to form each policy’s Marginal Value of Public Funds, or its “MVPF”. Comparing MVPFs across policies provides a unified method of assessing their impact on social welfare. Our results suggest that direct investments in low-income children’s health and education have historically had the highest MVPFs, on average exceeding 5. Many such policies have paid for themselves as governments recouped the cost of their initial expenditures through additional taxes collected and reduced transfers. We find large MVPFs for education and health policies amongst children of all ages, rather than observing diminishing marginal returns throughout childhood. We find smaller MVPFs for policies targeting adults, generally between 0.5 and 2. Expenditures on adults have exceeded this MVPF range in particular if they induced large spillovers on children. We relate our estimates to existing theories of optimal government policy and we discuss how the MVPF provides lessons for the design of future research.
Topology attack and defense for graph neural networks: An optimization perspective

Graph neural networks (GNNs) which apply the deep neural networks to graph data have achieved significant performance for the task of semi-supervised node classification. However, only few work has addressed the adversarial robustness of GNNs. In this paper, we first present a novel gradient-based attack method that facilitates the difficulty of tackling discrete graph data. When comparing to current adversarial attacks on GNNs, the results show that by only perturbing a small number of edge perturbations, including addition and deletion, our optimization-based attack can lead to a noticeable decrease in classification performance. Moreover, leveraging our gradient-based attack, we propose the first optimization-based adversarial training for GNNs. Our method yields higher robustness against both different gradient based and greedy attack methods without sacrificing classification accuracy on original graph.
Learning User Preferences via Reinforcement Learning with Spatial Interface Valuing

Interactive Machine Learning is concerned with creating systems that operate in environments alongside humans to achieve a task. A typical use is to extend or amplify the capabilities of a human in cognitive or physical ways, requiring the machine to adapt to the users’ intentions and preferences. Often, this takes the form of a human operator providing some type of feedback to the user, which can be explicit feedback, implicit feedback, or a combination of both. Explicit feedback, such as through a mouse click, carries a high cognitive load. The focus of this study is to extend the current state of the art in interactive machine learning by demonstrating that agents can learn a human user’s behavior and adapt to preferences with a reduced amount of explicit human feedback in a mixed feedback setting. The learning agent perceives a value of its own behavior from hand gestures given via a spatial interface. This feedback mechanism is termed Spatial Interface Valuing. This method is evaluated experimentally in a simulated environment for a grasping task using a robotic arm with variable grip settings. Preliminary results indicate that learning agents using spatial interface valuing can learn a value function mapping spatial gestures to expected future rewards much more quickly as compared to those same agents just receiving explicit feedback, demonstrating that an agent perceiving feedback from a human user via a spatial interface can serve as an effective complement to existing approaches.
The altica project: reframing the formative basin of mexico

The Altica Project that began in 2014 is an important step in addressing over two decades of limited problem-oriented research at Formative sites in the Basin of Mexico. Through this research, we demonstrate that Altica is the earliest known settled village in the Teotihuacan Valley and one of the only first-farming village sites in the Basin of Mexico that has not been engulfed by the urban sprawl of Mexico City. It represents the northward spread of settled village life in a time that people of the region were transitioning from a mobile foraging lifestyle to one more reliant on food production. Despite its small size and remote location, Altica was an important part of Early and Middle Formative exchange networks, as it imported multiple types of goods from distant places in Mesoamerica and may have played a role in the early trade of Otumba obsidian. This paper leads the Special Section by framing the research project and outlining its major goals. Survey and excavation results are introduced. Finally, we preface each of the presentations to situate them within the framework. Altica spans a transitional period in Mesoamerica that began around 1000 cal b.c. We establish Altica as a proxy to understand broader Mesoamerican processes at that time.
Cigarette prices and driving fatalities among youths

Abstract Deaths from motor vehicle crashes are a leading cause of unintentional deaths in the United States. This paper investigates the effect of increases in cigarette taxes and prices following the Master Settlement Agreement (MSA) on non-alcohol and alcohol-related motor vehicle fatalities among youths. I find that increases in cigarette taxes and prices are associated with a reduction in non-alcohol related accidents between 1998 and 2006 among 16-to-20 year olds.
Informal Employment and Worker's Well-Being in the Russian Federation

This paper finds that informal workers are more likely to have inferior work conditions, but do not necessarily report worse subjective well-being. Starting with lower wages, but also with less regularity of hours and paid vacation, informal workers have higher incidence of envelope payments than formal workers but not of hazardous or unstable jobs. After controlling for work conditions, informal workers do not have statistically significantly lower job satisfaction and under no specification are informal workers more likely to self-assess worse health than formal workers. Finally, there is some association between informal employment and household poverty and life satisfaction, but it is not robust to changes in econometric specification or sample composition. The authors conclude that the evidence indicates that informal employment in the Russian Federation is mostly a problem of labor productivity and the design of the social protection system, but worsening wages and some association between informality and household poverty indicate that informality may also be a social equity problem.
Concordance and Discordance in Myeloma Bone Marrow Studies

e19508Background: Multiple myeloma (MM) diagnostic costs, including bone marrow (BM) studies: (flow (F), aspirate (manual differential) (A) and biopsy (Bx)) are increasing. BM studies already have ...
BEACHES: Beamspace Channel Estimation for Multi-Antenna mmWave Systems and Beyond

Massive multi-antenna millimeter wave (mmWave) and terahertz wireless systems promise high-bandwidth communication to multiple user equipments in the same time-frequency resource. The high path loss of wave propagation at such frequencies and the fine-grained nature of beamforming with massive antenna arrays necessitates accurate channel estimation to fully exploit the advantages of such systems. In this paper, we propose BEAmspace CHannel EStimation (BEACHES), a low-complexity channel estimation algorithm for multi-antenna mmWave systems and beyond. BEACHES leverages the fact that wave propagation at high frequencies is directional, which enables us to denoise the (approximately) sparse channel state information in the beamspace domain. To avoid tedious parameter selection, BEACHES includes a computationally-efficient tuning stage that provably minimizes the mean-square error of the channel estimate in the large-antenna limit. To demonstrate the efficacy of BEACHES, we provide simulation results for line-of-sight (LoS) and non-LoS mmWave channel models.
Moments of the weighted Cantor measures

Based on the seminal work of Hutchinson, we investigate properties of {\em $\alpha$-weighted Cantor measures} whose support is a fractal contained in the unit interval. Here, $\alpha$ is a vector of nonnegative weights summing to $1$, and the corresponding weighted Cantor measure $\mu^\alpha$ is the unique Borel probability measure on $[0,1]$ satisfying $ \mu^\alpha(E) = \sum_{ n=0 }^{N-1} \alpha_n\mu^\alpha( \varphi_n^{-1}(E) )$ where $\varphi_n: x\mapsto (x+n)/N$. In Sections 1 and 2 we examine several general properties of the measure $\mu^\alpha$ and the associated Legendre polynomials in $L_{\mu^\alpha}^2[0,1]$. In Section 3, we (1) compute the Laplacian and moment generating function of $\mu^\alpha$, (2) characterize precisely when the moments $I_m = \int_{[0,1]}x^m\,d\mu^\alpha$ exhibit either polynomial or exponential decay, and (3) describe an algorithm which estimates the first $m$ moments within uniform error $\varepsilon$ in $O( (\log\log(1/\varepsilon))\cdot m\log m )$. We also state analogous results in the natural case where $\alpha$ is {\em palindromic} for the measure $\nu^{\alpha}$ attained by shifting $\mu^{\alpha}$ to $[-1/2,1/2]$.
A Local Perspective on the Edge Removal Problem

The edge removal problem studies the loss in network coding rates that results when a network communication edge is removed from a given network. It is known, for example, that in networks restricted to linear coding schemes and networks restricted to Abelian group codes, removing an edge e∗ with capacity R e∗ reduces the achievable rate on each source by no more than R e∗ . In this work, we seek to uncover larger families of encoding functions for which the edge removal statement holds. We take a local perspective: instead of requiring that all network encoding functions satisfy certain restrictions (e.g., linearity), we limit only the function carried on the removed edge e∗. Our central results give sufficient conditions on the function carried by edge e∗ in the code used to achieve a particular rate vector under which we can demonstrate the achievability of a related rate vector once e∗ is removed.
1D Eyewear: Peripheral, Hidden LEDs and Near-Eye Holographic Displays for Unobtrusive Augmentation

1D Eyewear introduces digital designs where 1D arrays of LEDs and pre-recorded holographic symbols, enables minimal head-worn displays that can be embedded in normal-looking eyeglasses frames. We developed a set of optical designs (transmissive, reflective, and steerable), and a proof-of-concept optics implementation of a self-contained symbology display. To enable very compact symbol generation, we use computer-generated holograms (CGHs) to encode diffraction gratings, which project a pre-recorded static image when illuminated with coherent light.
A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks

In this era of machine learning models, their functionality is being threatened by adversarial attacks. In the face of this struggle for making artificial neural networks robust, finding a model, resilient to these attacks, is very important. In this work, we present, for the first time, a comprehensive analysis of the behavior of more bio-plausible networks, namely Spiking Neural Network (SNN) under state-of-the-art adversarial tests. We perform a comparative study of the accuracy degradation between conventional VGG-9 Artificial Neural Network (ANN) and equivalent spiking network with CIFAR-10 dataset in both whitebox and blackbox setting for different types of single-step and multi-step FGSM (Fast Gradient Sign Method) attacks. We demonstrate that SNNs tend to show more resiliency compared to ANN under blackbox attack scenario. Additionally, we find that SNN robustness is largely dependent on the corresponding training mechanism. We observe that SNNs trained by spike-based backpropagation are more adversarially robust than the ones obtained by ANN-to-SNN conversion rules in several whitebox and blackbox scenarios. Finally, we also propose a simple, yet, effective framework for crafting adversarial attacks from SNNs. Our results suggest that attacks crafted from SNNs following our proposed method are much stronger than those crafted from ANNs.
Promoting innovation in biophotonics to economical growth

Submission for Biophotonics Challenges - Research Frontiers vs. Biomedical Applications in the Real World and Commercialization
Returns to Option Strategies Following Class Action Lawsuits

Turmoil and uncertainty confront firms when they are named as defendants in class action lawsuits. In this article, we consider whether option markets interpret the implications of these dramatic corporate events for mid-to-long term performance. In particular, we consider relatively simple, long, volatility-based combined option positions. We find consistent, positive, and frequently significant returns to option straddle and strangle positions held from six months to 1.5 years after a firm is targeted in a class action. This may be indicative of under appreciation, in the option markets, for the dichotomous nature of firm stock price performance as the class action proceeds toward a resolution. TOPICS:Legal/regulatory/public policy, performance measurement, volatility measures, derivatives, options Key Findings • Long, volatility-based straddle and strangle option positions are a potentially profitable strategy if stock markets fail to appreciate the dichotomous nature of price movements after class action lawsuits begin. • Straddles and strangles are consistently profitable for holding periods between six months and 1.5 years if created after the initial filing of a class action lawsuit. • The median time from the initial filing of a class action lawsuit until resolution is 717 days.
Simple Distances for Trajectories via Landmarks

We develop a new class of distances for trajectories, based on the distance to a set of landmarks. These distances easily and interpretably map objects to a Euclidean space, are simple to compute, and perform well in data analysis tasks. For trajectories, they match and in some cases significantly out-perform all state-of-the-art other metrics, can effortlessly be used in k-means clustering, and directly plugged into approximate nearest neighbor approaches which immediately out-perform the best recent advances in trajectory similarity search by several orders of magnitude. These distances do not require complicated alignment (common in trajectory case). We show reasonable and often simple conditions under which these distances are metrics.
Independent control of the thermodynamic and kinetic properties of aptamer switches.

Molecular switches that change their conformation upon target binding offer powerful capabilities for biotechnology and synthetic biology. Aptamers are useful as molecular switches because they offer excellent binding properties, undergo reversible folding, and can be engineered into many nanostructures. Unfortunately, the thermodynamic and kinetic properties of the aptamer switches developed to date are intrinsically coupled, such that high temporal resolution can only be achieved at the cost of lower sensitivity or high background. Here, we describe a design strategy that decouples and enables independent control over the thermodynamics and kinetics of aptamer switches. Starting from a single aptamer, we create an array of aptamer switches with effective dissociation constants ranging from 10 μM to 40 mM and binding kinetics ranging from 170 ms to 3 s. Our strategy is broadly applicable to other aptamers, enabling the development of switches suitable for a diverse range of biotechnology applications. Aptamer switches are promising biotechnological tools but coupling of their affinity and temporal response limits their versatility. Here, the authors developed an intramolecular strand-displacement strategy that allows for independent fine-tuning of thermodynamics and kinetics of aptamer switches.
Socially Conscious Decision-Making

For individually motivated agents to work collaboratively to satisfy shared goals, they must make decisions about actions and intentions that take into account their commitments to group activities. This paper examines the role of social consciousness in the process of reconciling intentions to do group-related actions with other, conflicting intentions. We operationalize the notion of social consciousness and provide a first attempt to formally add social consciousness to a cooperative decision-making model. We define a measure of social consciousness; describe its incorporation into the SPIRE experimental system, a simulation environment that allows the process of intention reconciliation in team contexts to be studied; and present results of several experiments that investigate the interaction in decision-making of measures of group and individual good. In particular, we investigate the effect of varying levels of social consciousness on the utility of the group and the individuals it comprises. A key finding is that an intermediate level of social consciousness yields better results in certain circumstances than an extreme commitment. We suggest preliminary principles for designers of collaborative agents based on the results.
Detecting mapping spaces

We show if $A$ is a finite CW-complex such that algebraic theories detect mapping spaces out of $A$, then $A$ has the homology type of a wedge of spheres of the same dimension. Furthermore, if $A$ is simply connected then $A$ has the homotopy type of a wedge of spheres.
Aegis: a context-aware security framework for smart home systems

Our everyday lives are expanding fast with the introduction of new Smart Home Systems (SHSs). Today, a myriad of SHS devices and applications are widely available to users and have already started to re-define our modern lives. Smart home users utilize the apps to control and automate such devices. Users can develop their own apps or easily download and install them from vendor-specific app markets. App-based SHSs offer many tangible benefits to our lives, but also unfold diverse security risks. Several attacks have already been reported for SHSs. However, current security solutions consider smart home devices and apps individually to detect malicious actions rather than the context of the SHS as a whole. The existing mechanisms cannot capture user activities and sensor-device-user interactions in a holistic fashion. To address these issues, in this paper, we introduce Aegis, a novel context-aware security framework to detect malicious behavior in a SHS. Specifically, Aegis observes the states of the connected smart home entities (sensors and devices) for different user activities and usage patterns in a SHS and builds a contextual model to differentiate between malicious and benign behavior. We evaluated the efficacy and performance of Aegis in multiple smart home settings (i.e., single bedroom, double bedroom, duplex) with real-life users performing day-to-day activities and real SHS devices. We also measured the performance of Aegis against five different malicious behaviors. Our detailed evaluation shows that Aegis can detect malicious behavior in SHS with high accuracy (over 95%) and secure the SHS regardless of the smart home layout, device configuration, installed apps, and enforced user policies. Finally, Aegis achieves minimum overhead in detecting malicious behavior in SHS, ensuring easy deployability in real-life smart environments.
Induced transglottal pressure variations during voicing

The response of the vibratory pattern of the vocal folds to externally induced changes of supraglottal pressure was measured. Tracings of the variation of F0 after a step change in pressure indicated that a pressure variation similar to a single bipolar cycle of an 8–10 Hz sinusoid is fast enough to vary the average transglottal and/or intraglottal pressure without stimulating compensatory action during the induced pressure pulse and slow enough to allow the vocal fold oscillatory behavior to respond to the change in pressure during each half of the pulse. An example is presented of the shape of the glottal airflow pulse during a continuous 10Hz sinusoidal induced pressure variation, as derived by inverse filtering oral airflow.
Clathrin Adaptor AP1B Controls Adenovirus Infectivity of Epithelial Cells

Adenoviruses invading the organism via normal digestive or respiratory routes require the Coxsackie-adenovirus receptor (CAR) to infect the epithelial barrier cells. Because CAR is a component of tight junctions and the basolateral membrane and is normally excluded from the apical membrane, most epithelia are resistant to adenoviruses. However, we discovered that a specialized epithelium, the retinal pigment epithelium (RPE), anomalously expressed CAR at the apical surface and was highly susceptible to adenovirus infection. These properties of RPE cells correlated with the absence of the epithelial-specific clathrin adaptor AP1B. Furthermore, knockdown of this basolateral sorting adaptor in adenovirus-resistant MDCK cells promoted apical localization of CAR and increased dramatically Adenovirus infectivity. Targeting assays showed that AP1B is required for accurate basolateral recycling of CAR after internalization. AP1B knock down MDCK cells missorted CAR from recycling endosomes to the apical surface. In summary, we have characterized the cellular machinery responsible for normal sorting of an adenovirus receptor and illustrated how tissue-specific variations in such machinery result in drastic changes in tissue-susceptibility to adenoviruses.
The degree of a q-holonomic sequence is a quadratic quasi-polynomial

A sequence of rational functions in a variable $q$ is $q$-holonomic if it satisfies a linear recursion with coefficients polynomials in $q$ and $q^n$. We prove that the degree of a $q$-holonomic sequence is eventually a quadratic quasi-polynomial, and that the leading term satisfies a linear recursion relation with constant coefficients. Our proof uses differential Galois theory (adapting proofs regarding holonomic $D$-modules to the case of $q$-holonomic $D$-modules) combined with the Lech-Mahler-Skolem theorem from number theory. En route, we use the Newton polygon of a linear $q$-difference equation, and introduce the notion of regular-singular $q$-difference equation and a WKB basis of solutions of a linear $q$-difference equation at $q=0$. We then use the Skolem-Mahler-Lech theorem to study the vanishing of their leading term. Unlike the case of $q=1$, there are no analytic problems regarding convergence of the WKB solutions. Our proofs are constructive, and they are illustrated by an explicit example.
Decomposed Algorithms for Speed and Parameter Estimation in Induction Machines

Abstract This paper presents different decomposed algorithms for speed and parameter estimation in induction machines, and compares their performance using experimental data. It is shown that decompositions matched to the parameter coupling structure lead to the fastest convergence. Also, the conditioning of the problem is studied using componentwise condition numbers and it is shown that a significant improvement in convergence speed and a reduction of problem sensitivity are achieved by fixing the stator resistance to a prior estimate.
Heroin type, injecting behavior, and HIV transmission. A simulation model of HIV incidence and prevalence.

Author(s): Bobashev, Georgiy; Mars, Sarah; Murphy, Nicholas; Dreisbach, Clinton; Zule, William; Ciccarone, Daniel | Abstract: Background and aimsUsing mathematical modeling to illustrate and predict how different heroin source-forms: "black tar" (BTH) and powder heroin (PH) can affect HIV transmission in the context of contrasting injecting practices. By quantifying HIV risk by these two heroin source-types we show how each affects the incidence and prevalence of HIV over time. From 1997 to 2010 PH reaching the United States was manufactured overwhelmingly by Colombian suppliers and distributed in the eastern states of the United States. Recently Mexican cartels that supply the western U.S. states have started to produce PH too, replacing Colombian distribution to the east. This raises the possibility that BTH in the western U.S. may be replaced by PH in the future.DesignWe used an agent-based model to evaluate the impact of use of different heroin formulations in high- and low-risk populations of persons who inject drugs (PWID) who use different types of syringes (high vs. low dead space) and injecting practices. We obtained model parameters from peer-reviewed publications and ethnographic research.ResultsHeating of BTH, additional syringe rinsing, and subcutaneous injection can substantially decrease the risk of HIV transmission. Simulation analysis shows that HIV transmission risk may be strongly affected by the type of heroin used. We reproduced historic differences in HIV prevalence and incidence. The protective effect of BTH is much stronger in high-risk compared with low-risk populations. Simulation of future outbreaks show that when PH replaces BTH we expect a long-term overall increase in HIV prevalence. In a population of PWID with mixed low- and high-risk clusters we find that local HIV outbreaks can occur even when the overall prevalence and incidence are low. The results are dependent on evidence-supported assumptions.ConclusionsThe results support harm-reduction measures focused on a reduction in syringe sharing and promoting protective measures of syringe rinsing and drug solution heating.
Collective dynamics at RHIC

We discuss the strong collective flow effects, 〈v 2〉 and 〈β t 〉, observed at RHIC (√ s NN=130 GeV). We argue that part of the collectivity could have already developed at partonic stage. To ‘see’ the partonic effects directly, measurements of multi-strange baryons like Ξ, Ω and charmed particles like J/ψ transverse momentum distributions are important. They provide one of the possible keys to the discovery of collective modes with the partonic (quarks and gluons) degrees of freedom.
Validation of continuous measures of peer social interaction with self- and teacher-reports of friendship and social engagement.

The present study validates a new procedure that combines continuous measures of proximity (Ubisense) and vocalization (LENA) into measures of peer social interaction. The data were collected from ...
A Troubling Trio of Family Law Issues: Contributions from Comparative Law

As an international collection of essays, Marriage, Children, and Family: Modern Challenges and Comparative Law Perspective, reveals the universal nature of challenges every society faces both in encouraging family formation and responding to some of the harms arising from the failure of such formation due to a variety of addictions. Abuse, abandonment, and the sexualization of children are just a few of the harms addresses by the experts. In this brief review, the author encourages readers to compare both jurisprudential and technical differences in national treatment of these difficult family law issues, with a special emphasis on understanding the current approach of the Polish government.
Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM

Energy-efficient simultaneous localization and mapping (SLAM) is crucial for mobile robots exploring unknown environments. The mammalian brain solves SLAM via a network of specialized neurons, exhibiting asynchronous computations and event-based communications, with very low energy consumption. We propose a brain-inspired spiking neural network (SNN) architecture that solves the unidimensional SLAM by introducing spike-based reference frame transformation, visual likelihood computation, and Bayesian inference. We integrated our neuromorphic algorithm to Intel’s Loihi neuromorphic processor, a non-Von Neumann hardware that mimics the brain’s computing paradigms. We performed comparative analyses for accuracy and energy-efficiency between our neuromorphic approach and the GMapping algorithm, which is widely used in small environments. Our Loihi-based SNN architecture consumes 100 times less energy than GMapping run on a CPU while having comparable accuracy in head direction localization and map-generation. These results pave the way for scaling our approach towards active-SLAM alternative solutions for Loihi-controlled autonomous robots.
Fair Allocation through Selective Information Acquisition

Public and private institutions must often allocate scarce resources under uncertainty. Banks, for example, extend credit to loan applicants based in part on their estimated likelihood of repaying a loan. But when the quality of information differs across candidates (e.g., if some applicants lack traditional credit histories), common lending strategies can lead to disparities across groups. Here we consider a setting in which decision makers---before allocating resources---can choose to spend some of their limited budget further screening select individuals. We present a computationally efficient algorithm for deciding whom to screen that maximizes a standard measure of social welfare. Intuitively, decision makers should screen candidates on the margin, for whom the additional information could plausibly alter the allocation. We formalize this idea by showing the problem can be reduced to solving a series of linear programs. Both on synthetic and real-world datasets, this strategy improves utility, illustrating the value of targeted information acquisition in such decisions. Further, when there is social value for distributing resources to groups for whom we have a priori poor information---like those without credit scores---our approach can substantially improve the allocation of limited assets.
Antenatal Screening for Group B Streptococcus in the Setting of Preterm Premature Rupture of Membranes: Empiric versus Culture-based Prophylaxis.

Objective Imperfect culture sensitivity and increase of early onset neonatal sepsis (EONS) risk in preterm neonates raise concern that culture-based intrapartum antibiotic prophylaxis (IAP) may be insufficient after preterm premature rupture of membranes (PPROM). Our objective was to compare rates of EONS after empiric versus culture-based IAP in PPROM. Study Design This retrospective cohort study included women with a singleton gestation and PPROM between 23 and 33 weeks. Outcomes after culture-based IAP were compared with empiric IAP. The primary outcome was EONS. Secondary outcomes included group B streptococcus (GBS) bacteremia, bacteremia, and neonatal GBS infection. Bivariable and multivariable logistic analyses were performed. Results Of the 270 women who met inclusion criteria, 136 (50%) had culture-based IAP of whom 36 (26.5%) were GBS positive. There was no significant difference in bacteremia (2.2 vs. 4.5%, p = 0.30), GBS infection (0.8 vs. 0.7%, p = 1.00), or EONS (11.8 vs. 12.7%, p = 0.82) in infants of women with culture-based IAP compared with empiric IAP. Multivariable analysis confirmed a lack of advantage to empiric versus culture-based IAP in EONS risk (adjusted odds ratio [aOR] = 0.82, 95% confidence interval [CI]: 0.44–1.93). Conclusion In pregnancies complicated by PPROM, infants of women who received culture-based IAP had no significant difference in EONS or GBS infection compared with infants of women with empiric IAP.
Mini Lesions Detection on Diabetic Retinopathy Images via Large Scale CNN Features

Diabetic retinopathy (DR) is a diabetes complication that affects eyes. DR is a primary cause of blindness in working-age people and it is estimated that 3 to 4 million people with diabetes are blinded by DR every year worldwide. Early diagnosis have been considered an effective way to mitigate such problem. The ultimate goal of our research is to develop novel machine learning techniques to analyze the DR images generated by the fundus camera for automatically DR diagnosis. In this paper, we focus on identifying small lesions on DR fundus images. The results from our analysis, which include the lesion category and their exact locations in the image, can be used to facilitate the determination of DR severity (indicated by DR stages). Different from traditional object detection for natural images, lesion detection for fundus images have unique challenges. Specifically, the size of a lesion instance is usually very small, compared with the original resolution of the fundus images, making them diffcult to be detected. We analyze the lesion-vs-image scale carefully and propose a large-size feature pyramid network (LFPN) to preserve more image details for mini lesion instance detection. Our method includes an effective region proposal strategy to increase the sensitivity. The experimental results show that our proposed method is superior to the original feature pyramid network (FPN) method and Faster RCNN.
A Novel Graphical Lasso Based Approach Towards Segmentation Analysis in Energy Game-Theoretic Frameworks

Energy game-theoretic frameworks have emerged to be a successful strategy to encourage energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number of such frameworks have been introduced over the years which formulate the energy saving process as a competitive game with appropriate incentives for energy efficient players. However, prior works involve an incentive design mechanism which is dependent on knowledge of utility functions for all the players in the game, which is hard to compute especially when the number of players is high, common in energy game-theoretic frameworks. Our research proposes that the utilities of players in such a framework can be grouped together to a relatively small number of clusters, and the clusters can then be targeted with tailored incentives. The key to above segmentation analysis is to learn the features leading to human decision making towards energy usage in competitive environments. We propose a novel graphical lasso based approach to perform such segmentation, by studying the feature correlations in a real-world energy social game dataset. To further improve the explainability of the model, we perform causality study using grangers causality. Proposed segmentation analysis results in characteristic clusters demonstrating different energy usage behaviors. We also present avenues to implement intelligent incentive design using proposed segmentation method.
Improving model-based functional near-infrared spectroscopy analysis using mesh-based anatomical and light-transport models.

Significance: Functional near-infrared spectroscopy (fNIRS) has become an important research tool in studying human brains. Accurate quantification of brain activities via fNIRS relies upon solving computational models that simulate the transport of photons through complex anatomy. Aim: We aim to highlight the importance of accurate anatomical modeling in the context of fNIRS and propose a robust method for creating high-quality brain/full-head tetrahedral mesh models for neuroimaging analysis. Approach: We have developed a surface-based brain meshing pipeline that can produce significantly better brain mesh models, compared to conventional meshing techniques. It can convert segmented volumetric brain scans into multilayered surfaces and tetrahedral mesh models, with typical processing times of only a few minutes and broad utilities, such as in Monte Carlo or finite-element-based photon simulations for fNIRS studies. Results: A variety of high-quality brain mesh models have been successfully generated by processing publicly available brain atlases. In addition, we compare three brain anatomical models—the voxel-based brain segmentation, tetrahedral brain mesh, and layered-slab brain model—and demonstrate noticeable discrepancies in brain partial pathlengths when using approximated brain anatomies, ranging between −1.5 % to 23% with the voxelated brain and 36% to 166% with the layered-slab brain. Conclusion: The generation and utility of high-quality brain meshes can lead to more accurate brain quantification in fNIRS studies. Our open-source meshing toolboxes “Brain2Mesh” and “Iso2Mesh” are freely available at http://mcx.space/brain2mesh.
Exoplanet Phase Curves: Observations and Theory

Phase curves are the best technique to probe the three dimensional structure of exoplanets' atmospheres. In this chapter we first review current exoplanets phase curve observations and the particular challenges they face. We then describe the different physical mechanisms shaping the atmospheric phase curves of highly irradiated tidally locked exoplanets. Finally, we discuss the potential for future missions to further advance our understanding of these new worlds.
Correction of Channel Sounding Clock Drift and Antenna Rotation Effects for mmWave Angular Profile Measurements

Proper characterization of the millimeter-wave (mmWave) propagation channel requires measuring the power angular-delay profile of the channel which includes angle-of-departure and angle-of-arrival of the multipath components (MPCs). In this paper, we first describe in detail our rotating directional antennas-based 28 GHz channel sounder. Then, for this specific sounder class, we describe and address the following two problems in extracting the MPCs from the measurements: 1) For long-distance channel measurements, triggering signal cannot be generated for the TX and the RX using a single clock (SICL). This necessitates the use of separate clocks (SECLs) which introduces a random timing drift between the clocks. 2) As positions of the antennas change during scanning, total distance traveled by the same MPC differs at each measurement. These problems together cause missing some of the MPCs and detecting MPCs that do not exist in reality. We propose an algorithm to correct the clock drift and MPC delay errors due to the rotation of the antennas. We compare the MPCs from the SICL measurement and the corrected SECL measurements using a Hungarian algorithm based MPC matching method. We show that the percentage of the matched MPCs increases from 28.36% to 74.13% after the correction process.
Data-Driven Attack Detection for Linear Systems

This letter studies the attack detection problem in a data-driven and model-free setting, for deterministic systems with linear and time-invariant dynamics. Differently from existing studies that leverage knowledge of the system dynamics to derive security bounds and monitoring schemes, we treat the cases where the system dynamics, as well as the attack strategy and attack location, are unknown. We derive fundamental security limitations as a function of only the observed data and without estimating the system dynamics (in fact, no assumption is made on the identifiability of the system). In particular, (i) we derive detection limitations as a function of the informativity and length of the observation window, (ii) provide a data-driven characterization of undetectable attacks, and (iii) construct a data-driven detection monitor. Surprisingly, our results show that while data-driven monitoring requires a larger observation window to attain attack detection capability, once attained it shares the same limitations as model-based monitoring.
Influences of microcontact shape on the state of a frictional interface

The authors develop a protocol wherein the net real area of contact between solids is held constant, despite its continual evolution in time. They show that local contact geometry influences this evolution, and exposes differences between evolution with time and with changing external loading conditions.
Inhibitory regulation of calcium transients in prefrontal dendritic spines is compromised by a nonsense Shank3 mutation

The SHANK3 gene encodes a postsynaptic scaffold protein in excitatory synapses, and its disruption is implicated in neurodevelopmental disorders such as Phelan-McDermid syndrome, autism spectrum disorder, and schizophrenia. Most studies of SHANK3 in the neocortex and hippocampus have focused on disturbances in pyramidal neurons. However, GABAergic interneurons likewise receive excitatory inputs and presumably would also be a target of constitutive SHANK3 perturbations. In this study, we characterize the prefrontal cortical microcircuit in awake mice using subcellular-resolution two-photon microscopy. We focused on a nonsense R1117X mutation, which leads to truncated SHANK3 and has been linked previously to cortical dysfunction. We find that R1117X mutants have abnormally elevated calcium transients in apical dendritic spines. The synaptic calcium dysregulation is due to a loss of dendritic inhibition via decreased NMDAR currents and reduced firing of dendrite-targeting somatostatin-expressing (SST) GABAergic interneurons. Notably, upregulation of the NMDAR subunit GluN2B in SST interneurons corrects the excessive synaptic calcium signals and ameliorates learning deficits in R1117X mutants. These findings reveal dendrite-targeting interneurons, and more broadly the inhibitory control of dendritic spines, as a key microcircuit mechanism compromised by the SHANK3 dysfunction.
Comparative vector competence of North American Lyme disease vectors

Understanding the drivers of Lyme disease incidence at broad spatial scales is critical for predicting and mitigating human disease risk. Previous studies have identified vector phenology and behavior, host community composition, and landscape features as drivers of variable Lyme disease risk. However, while the Lyme disease transmission cycles in the eastern and western USA involve different vector species (Ixodes scapularis and Ixodes pacificus, respectively), the role of vector-specific differences in transmission efficiency has not been directly examined. By comparing the performance of traits involved in vector competence between these two species, this study aims to identify how vector competence contributes to variable Lyme disease risk. We used a suite of laboratory experiments to compare the performance of traits related to vector competence for the two USA Lyme disease vectors. For each species, we measured the rate of attachment to a common rodent host, the engorgement weight, and the efficiency of pathogen acquisition (host to tick) and pathogen transmission (tick to host) from laboratory mice. In measuring pathogen acquisition and transmission, we used two different pathogen strains, one sympatric with I. scapularis and one sympatric with I. pacificus, to assess the importance of vector-pathogen coevolutionary history in transmission dynamics. We found I. pacificus had significantly higher host attachment success and engorgement weights, but significantly lower pathogen transmission efficiency relative to I. scapularis. Molting success and pathogen acquisition did not differ between these two species. However, pathogen acquisition efficiency was significantly higher for both sympatric vector and pathogen strains than the allopatric pairings. This study identified species-specific vector traits as a potential driver of broad scale variation in Lyme disease risk in the USA. In particular, the exceedingly low rates of pathogen transmission from tick to host observed for I. pacificus may limit Lyme disease transmission efficiency in the western USA. Further, observed variation in pathogen acquisition between sympatric and allopatric vector-pathogen strains indicate that vector-pathogen coevolutionary history may play a key role in transmission dynamics. These findings underscore the need to consider vector traits and vector-pathogen coevolution as important factors governing regional Lyme disease risk.