cssi/SciSciGPT-SciSciNet huggingface dataset:
People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error

Will people use self-driving cars, virtual doctors, and other algorithmic decision-makers if they outperform humans? The answer depends on the uncertainty inherent in the decision domain. We propose that people have diminishing sensitivity to forecasting error and that this preference results in people favoring riskier (and often worse-performing) decision-making methods, such as human judgment, in inherently uncertain domains. In nine studies (N = 4,820), we found that (a) people have diminishing sensitivity to each marginal unit of error that a forecast produces, (b) people are less likely to use the best possible algorithm in decision domains that are more unpredictable, (c) people choose between decision-making methods on the basis of the perceived likelihood of those methods producing a near-perfect answer, and (d) people prefer methods that exhibit higher variance in performance (all else being equal). To the extent that investing, medical decision-making, and other domains are inherently uncertain, people may be unwilling to use even the best possible algorithm in those domains.
Unique axon-to-soma signaling pathways mediate dendritic spine loss and hyper-excitability post-axotomy

Axon damage may cause axon regeneration, retrograde synapse loss, and hyper-excitability, all of which affect recovery following acquired brain injury. While axon regeneration is studied extensively, less is known about signaling mediating retrograde synapse loss and hyper-excitability, especially in long projection pyramidal neurons. To investigate intrinsic injury signaling within neurons, we used an in vitro microfluidic platform that models dendritic spine loss and delayed hyper-excitability following remote axon injury. Our data show that sodium influx and reversal of sodium calcium exchangers (NCXs) at the site of axotomy, mediate dendritic spine loss following axotomy. In contrast, sodium influx and NCX reversal alone are insufficient to cause retrograde hyper-excitability. We found that calcium release from axonal ER is critical for the induction of hyper-excitability and inhibition loss. These data suggest that synapse loss and hyper-excitability are uncoupled responses following axon injury. Further, axonal ER may play a critical and underappreciated role in mediating retrograde hyper-excitability within the CNS.
Deformation and stresses in solid-state composite battery cathodes

Abstract Differences in thermal contraction/expansion and volume changes during discharge/charge cycles lead to internal stresses that ultimately cause degradation of solid-state composite cathodes and hinder the realization of their practical applications. We employ the smoothed boundary method to solve the mechanical equilibrium equation for the residual stresses induced by cooling from the sintering temperature and by (de)lithiation in polycrystalline composite microstructures that are similar to realistic solid-state composite cathodes. The overall deformations of the composite slabs under these fabrication and operation conditions are also evaluated. The effects of cathode thickness and selections of different cathode materials on the resulting residual stresses and deformations are examined. We find that the (de)lithiation stresses during cycling are more than twice the thermal residual stresses after sintering. Furthermore, the maximum (de)lithiation and thermal residual stresses are sensitive to the cathode thickness only when the cathode-layer thickness is comparable to that of the electrolyte separator layer. We also investigate the impact of the lithium site fraction of the cathode particles prior to sintering on the cycling stresses and deformations, which may pave the path toward an approach to mitigating mechanically induced degradation.
Synoptic classification of a ten-year record of 500 mb weather maps for the western United States

When used to classify a ten-year sample of 500 mb pressure surfaces over the western United States, the Kirchhofer sun-of-squares map classification procedure produces self-consistent results. These results are strongly biased by the significance criteria used to make the classification. The selection of criteria must be based on subjective considerations. When criteria were used such that 80% of the 3527-map sample could be characterized, twelve synoptic types were defined, with 42% of the maps grouped in the three largest categories.
Informative Path Planning with Local Penalization for Decentralized and Asynchronous Swarm Robotic Search

Decentralized swarm robotic solutions to searching for targets that emit a spatially varying signal promise task parallelism, time efficiency, and fault tolerance. It is, however, challenging for swarm algorithms to offer scalability and efficiency, while preserving mathematical insights into the exhibited behavior. A new decentralized search method (called Bayes-Swarm), founded on batch Bayesian Optimization (BO) principles, is presented here to address these challenges. Unlike swarm heuristics approaches, Bayes-Swarm decouples the knowledge generation and task planning process, thus preserving insights into the emergent behavior. Key contributions lie in: 1) modeling knowledge extraction over trajectories, unlike in BO; 2) time-adaptively balancing exploration/exploitation and using an efficient local penalization approach to account for potential interactions among different robots' planned samples; and 3) presenting an asynchronous implementation of the algorithm. This algorithm is tested on case studies with bimodal and highly multimodal signal distributions. Up to 76 times better efficiency is demonstrated compared to an exhaustive search baseline. The benefits of exploitation/exploration balancing, asynchronous planning, and local penalization, and scalability with swarm size, are also demonstrated.
Co-rank of weakly parafree $3$-manifold groups

Recall that a group is called large if it has a finite index subgroup which surjects onto a non-abelian free group. By work of Agol and Cooper-Long-Reid, most 3-manifold groups are large; in particular, the fundamental groups of hyperbolic 3-manifolds are large. In previous work, the first author gave examples of closed, hyperbolic 3-manifolds with arbitrarily large first homology rank but whose fundamental groups do not surject onto a non-abelian free group. We call a group very large if it surjects onto a non-abelian free group. In this paper, we consider the question of whether the groups of homology handlebodies - which are very close to being free - are very large. We show that the fundamental group of W. Thurston's tripus manifold, is not very large; it is known to be weakly parafree by Stallings' Theorem and large by the work of Cooper-Long-Reid since the tripus is a hyperbolic manifold with totally geodesic boundary. It is still unknown if a 3-manifold group that is weakly parafree of rank at least 3 must be very large. However, we more generally consider the co-rank of the fundamental group, also known as the cut number of the manifold. For each positive integer g we construct a homology handlebody Y_g of genus g whose group has co-rank equal to r(g), where r(g)=g/2 for g even and r(g)=(g+1)/2 for g odd. That is, these groups are weakly parafree of rank g and surject onto a free group of rank roughly half of g but no larger.
Improved Modeling of Peptide-Protein Binding Through Global Docking and Accelerated Molecular Dynamics Simulations.

Peptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play a key role in cellular signaling, protein trafficking, immunology, and oncology. However, it is challenging to predict peptide-protein binding with conventional computational modeling approaches, due to slow dynamics and high peptide flexibility. Here, we present a prototype of the approach which combines global peptide docking using ClusPro PeptiDock and all-atom enhanced simulations using Gaussian accelerated molecular dynamics (GaMD). For three distinct model peptides, the lowest backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures obtained from PeptiDock were 3.3-4.8 A, being medium quality predictions according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. GaMD simulations refined the peptide-protein complex structures with significantly reduced peptide backbone RMSDs of 0.6-2.7 A, yielding two high quality (sub-angstrom) and one medium quality models. Furthermore, the GaMD simulations identified important low-energy conformational states and revealed the mechanism of peptide binding to the target proteins. Therefore, PeptiDock+GaMD is a promising approach for exploring peptide-protein interactions.
An Extended Game–Theoretic Model for Aggregate Lane Choice Behavior of Vehicles at Traffic Diverges with a Bifurcating Lane

Road network junctions, such as merges and diverges, often act as bottlenecks that initiate and exacerbate congestion. More complex junction configurations lead to more complex driver behaviors, resulting in aggregate congestion patterns that are more difficult to predict and mitigate. In this paper, we discuss diverge configurations where vehicles on some lanes can enter only one of the downstream roads, but vehicles on other lanes can enter one of several downstream roads. Counterintuitively, these bifurcating lanes, rather than relieve congestion (by acting as a versatile resource that can serve either downstream road as the demand changes), often cause enormous congestion due to lane changing. We develop an aggregate lane–changing model for this situation that is expressive enough to model drivers’ choices and the resultant congestion, but simple enough to easily analyze. We use a game–theoretic framework to model the aggregate lane choice behavior of selfish vehicles as a Wardrop equilibrium (an aggregate type of Nash equilibrium). We then establish the existence and uniqueness of this equilibrium. We explain how our model can be easily calibrated using simulation data or real data, and we present results showing that our model successfully predicts the aggregate behavior that emerges from widely–used behavioral lane–changing models. Our model’s expressiveness, ease of calibration, and accuracy may make it a useful tool for mitigating congestion at these complex diverges.
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings

Convolutional neural networks (CNNs) have been increasingly deployed to edge devices. Hence, many efforts have been made towards efficient CNN inference on resource-constrained platforms. This paper attempts to explore an orthogonal direction: how to conduct more energy-efficient training of CNNs, so as to enable on-device training? We strive to reduce the energy cost during training, by dropping unnecessary computations, from three complementary levels: stochastic mini-batch dropping on the data level; selective layer update on the model level; and sign prediction for low-cost, low-precision back-propagation, on the algorithm level. Extensive simulations and ablation studies, with real energy measurements from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training. For example, when training ResNet-74 on CIFAR-10, we achieve aggressive energy savings of >90% and >60%, while incurring a top-1 accuracy loss of only about 2% and 1.2%, respectively. When training ResNet-110 on CIFAR-100, an over 84% training energy saving is achieved without degrading inference accuracy.
Aggregate volatility risk: International evidence

Abstract Using a procedure analogous to that of Ang et al. (2006), this paper documents that aggregate volatility risk does not appear to be priced in European equity markets. Specifically, based on the 2002–2016 period (for which European stock return data is available), the price of aggregate volatility risk is not statistically different from zero. Analysis based on GARCH-class and high-frequency intraday data models support these results. Consequently, contrary to what has been reported in some studies that examine U.S. data, whether aggregate volatility risk is priced in equity markets is an open question.
Random Attractor for Stochastic Hindmarsh–Rose Equations with Additive Noise

For stochastic Hindmarsh–Rose equations with additive noises in the study of neurodynamics, the longtime and global pullback dynamics on a two-dimensional bounded domain is explored in this work. Using the additive transformation and by the sharp uniform estimates, we proved the pullback absorbing and the pullback asymptotically compact characteristics of the Hindmarsh–Rose random dynamical system in the $$L^2$$ Hilbert space. It shows the existence of a random attractor for this random dynamical system.
Decomposing algebraic m-isometric tuples

Abstract We show that any m-isometric tuple of commuting algebraic operators on a Hilbert space can be decomposed as a sum of a spherical isometry and a commuting nilpotent tuple. Our approach applies as well to tuples of algebraic operators that are hereditary roots of polynomials in several variables.
Testing DNN image classifiers for confusion & bias errors

We found that many of the reported erroneous cases in popular DNN image classifiers occur because the trained models confuse one class with another or show biases towards some classes over others. Most existing DNN testing techniques focus on per-image violations, so fail to detect class-level confusions or biases. We developed a testing technique to automatically detect class-based confusion and bias errors in DNN-driven image classification software. We evaluated our implementation, DeepInspect, on several popular image classifiers with precision up to 100% (avg. 72.6%) for confusion errors, and up to 84.3% (avg. 66.8%) for bias errors.
A novel approach in strategic planning of power networks against physical attacks

Abstract The reported work points at developing a practical approach for power transmission planners to secure power networks from potential deliberate attacks. We study the interaction between a system planner (defender) and a rational attacker who threatens the operation of power grid. In addition to the commonly used hardening strategy for protecting the network, a new sort of resource is introduced under the deception concept. Feint and deception are acknowledged as effective tools for misleading the attacker in strategic planning. To this end, the defender deception is mathematically formulated by releasing misinformation about his plan in the shared cognition-based model. To reduce the risk of damage in case of deception failure, preemptive-goal programming is utilized to prioritize the hardening strategy for the vital components. Furthermore, the “value of posturing” is introduced which is the benefits that the deception brings to the system. The problems are formulated as tri-level mixed-integer linear programming and solved by constraint-and-column generation method. Comprehensive simulation studies performed on WSCC 9-bus and IEEE 118-bus systems indicate how the defender will save significant cost from protecting his network with posturing rather than hardening and the proposed approach is a promising development to ensure the secure operation of power networks.
That and There: Judging the Intent of Pointing Actions with Robotic Arms

Collaborative robotics requires effective communication between a robot and a human partner. This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature. These principles are evaluated through studies where English-speaking human subjects view animations of simulated robots instructing pick-and-place tasks. The evaluation distinguishes two classes of pointing actions that arise in pick-and-place tasks: referential pointing (identifying objects) and locating pointing (identifying locations). The study indicates that human subjects show greater flexibility in interpreting the intent of referential pointing compared to locating pointing, which needs to be more deliberate. The results also demonstrate the effects of variation in the environment and task context on the interpretation of pointing. Our corpus, experiments and design principles advance models of context, common sense reasoning and communication in embodied communication.
The Fight against Corruption : Taming Tigers and Swatting Flies

Corruption robs the public of precious resources, distorts the incentives to engage in productive activities, destroys confidence in public institutions, and spurs political instability. It disproportionately harms the poor and vulnerable. In turn, corruption is the result of perverse incentives, concentration of power, and lack of accountability. Countries are not condemned to suffer from corruption. They can break the vicious cycle with a comprehensive approach that tackles country-specific governance gaps. This approach should include streamlining rules and regulations; building a meritocratic and well-paid civil service; promoting transparency in public employment, procurement, and services; enabling citizen voice and government accountability; and enforcing anti-corruption laws and penalties.
Quantifying the burden of exploration and the unfairness of free riding

We consider the multi-armed bandit setting with a twist. Rather than having just one decision maker deciding which arm to pull in each round, we have n different decision makers (agents). In the simple stochastic setting, we show that a "free-riding" agent observing another "self-reliant" agent can achieve just O(1) regret, as opposed to the regret lower bound of Ω(log t) when one decision maker is playing in isolation. This result holds whenever the self-reliant agent's strategy satisfies either one of two assumptions: (1) each arm is pulled at least γ ln t times in expectation for a constant γ that we compute, or (2) the self-reliant agent achieves o(t) realized regret with high probability. Both of these assumptions are satisfied by standard zero-regret algorithms. Under the second assumption, we further show that the free rider only needs to observe the number of times each arm is pulled by the self-reliant agent, and not the rewards realized. In the linear contextual setting, each arm has a distribution over parameter vectors, each agent has a context vector, and the reward realized when an agent pulls an arm is the inner product of that agent's context vector with a parameter vector sampled from the pulled arm's distribution. We show that the free rider can achieve O(1) regret in this setting whenever the free rider's context is a small (in L2-norm) linear combination of other agents' contexts and all other agents pull each arm Ω(log t) times with high probability. Again, this condition on the self-reliant players is satisfied by standard zero-regret algorithms like UCB. We also prove a number of lower bounds.
CD42b Immunostaining as a Marker for Placental Fibrinoid in Normal Pregnancy and Complications.

Background: There are two types of fibrinoids within the placenta, fibrin-type and matrix-type. The clinical importance of these fibrinoids is poorly understood. Design: Fibrinoid deposits occurrin...
Learning Event-based Height from Plane and Parallax

Event-based cameras are a novel asynchronous sensing modality that provide exciting benefits, such as the ability to track fast moving objects with no motion blur and low latency, high dynamic range, and low power consumption. Given the low latency of the cameras, as well as their ability to work in challenging lighting conditions, these cameras are a natural fit for reactive problems such as fast local structure estimation. In this work, we propose a fast method to perform structure estimation for vehicles traveling in a roughly 2D environment (e.g. in an environment with a ground plane). Our method transfers the method of plane and parallax to events, which, given the homography to a ground plane and the pose of the camera, generates a warping of the events which removes the optical flow for events on the ground plane, while inducing flow for events above the ground plane. We then estimate dense flow in this warped space using a self-supervised neural network, which provides the height of all points in the scene. We evaluate our method on the Multi Vehicle Stereo Event Camera dataset, and show its ability to rapidly estimate the scene structure both at high speeds and in low lighting conditions.
Confined elasticae and the buckling of cylindrical shells

For curves of prescribed length embedded into the unit disc in two dimensions, we obtain scaling results for the minimal elastic energy as the length just exceeds $2\pi$ and in the large length limit. In the small excess length case, we prove convergence to a fourth order obstacle type problem with integral constraint on the real line which we then solve. From the solution, we obtain the first order coefficient $\Theta\approx 37$ in the energy expansion $2\pi + \Theta \delta^{1/3} + o(\delta^{1/3})$ when a curve has length $2\pi + \delta$. We present an application of the scaling result to buckling in two-layer cylindrical shells where we can determine an explicit bifurcation point between compression and buckling in terms of universal constants and material parameters scaling with the thickness of the inner shell.
A statistical approach to optimizing paper spray mass spectrometry parameters.

Rationale Paper spray mass spectrometry (PS-MS) was used to analyze and quantify ampicillin, a hydrophilic compound and frequently utilized antibiotic. Hydrophilic molecules are difficult to analyze via PS-MS due to their strong binding affinity to paper substrates and low ionization efficiency, among other reasons. Methods Solvent and paper parameters were optimized to increase the extraction of ampicillin from the paper substrate. After optimizing these key parameters, a Resolution IV 1/16 fractional factorial design with two center points was employed to screen eight different design parameters simultaneously. Results Pore size, sample volume, and solvent volume were the most significant factors affecting average peak area under the curve (AUC) and the signal-to-blank (S/B) ratio for the 1 μg/mL ampicillin calibrant. After optimizing the key parameters, a linear calibration curve with a range of 0.2 μg/mL to 100 μg/mL was generated (R2 = 0.98) and the limit of detection (LOD) and lower limit of quantification (LLOQ) were calculated to be 0.07 μg/mL and 0.25 μg/mL, respectively. Conclusions The statistical optimization procedure undertaken here increased the mass spectral signal intensity by more than a factor of 40. This statistical method of screening followed by optimization experiments proved faster and more efficient, and produced more drastic improvements than typical one-factor-at-a-time experiments.
Free Bertini’s theorem and applications

The simplest version of Bertini's irreducibility theorem states that the generic fiber of a non-composite polynomial function is an irreducible hypersurface. The main result of this paper is its analog for a free algebra: if $f$ is a noncommutative polynomial such that $f-\lambda$ factors for infinitely many scalars $\lambda$, then there exist a noncommutative polynomial $h$ and a nonconstant univariate polynomial $p$ such that $f=p\circ h$. Two applications of free Bertini's theorem for matrix evaluations of noncommutative polynomials are given. An eigenlevel set of $f$ is the set of all matrix tuples $X$ where $f(X)$ attains some given eigenvalue. It is shown that eigenlevel sets of $f$ and $g$ coincide if and only if $fa=ag$ for some nonzero noncommutative polynomial $a$. The second application pertains quasiconvexity and describes polynomials $f$ such that the connected component of $\{X \text{ tuple of symmetric $n\times n$ matrices}: \lambda I\succ f(X) \}$ about the origin is convex for all natural $n$ and $\lambda>0$. It is shown that such a polynomial is either everywhere negative semidefinite or the composition of a univariate and a convex quadratic polynomial.
Gut-Liver Physiomimetics Reveal Paradoxical Modulation of IBD-Related Inflammation by Short-Chain Fatty Acids

Summary Although the association between the microbiome and IBD and liver diseases is known, the cause and effect remain elusive. By connecting human microphysiological systems of the gut, liver, and circulating Treg and Th17 cells, we created a multi-organ model of ulcerative colitis (UC) ex vivo. The approach shows microbiome-derived short-chain fatty acids (SCFAs) to either improve or worsen UC severity, depending on the involvement of effector CD4 T cells. Using multiomics, we found SCFAs increased production of ketone bodies, glycolysis, and lipogenesis, while markedly reducing innate immune activation of the UC gut. However, during acute T cell-mediated inflammation, SCFAs exacerbated CD4+ T cell-effector function, partially through metabolic reprograming, leading to gut barrier disruption and hepatic injury. These paradoxical findings underscore the emerging utility of human physiomimetic technology in combination with systems immunology to study causality and the fundamental entanglement of immunity, metabolism, and tissue homeostasis.
Nanophotonic Inverse Design with SPINS: Software Architecture and Practical Considerations

This paper presents a computational nanophotonic design library for gradient-based optimization called the Stanford Photonic INverse design Software (SPINS). Borrowing the concept of computational graphs, SPINS is a design framework that emphasizes flexibility and reproducible results. By factoring the inverse design process into components that can be swapped out for one another, SPINS enables inverse design practitioners to easily explore different design methodologies. Here, we present the mathematical and architectural details on how to achieve these goals, using the inverse design of a wavelength demultiplexer as a primary example. Using inverse design effectively requires understanding the “control knobs” available to the designer, and, to that end, we also discuss practical considerations and heuristics for effective use of inverse design. In particular, by running inverse design on hundreds of designs of 3D wavelength demultiplexers, this paper explores the landscape of local minima, which leads to insights on the choice of initial conditions.
Towards Assume-Guarantee Profiles for Autonomous Vehicles

Rules or specifications for autonomous vehicles are currently formulated on a case-by-case basis, and put together in a rather ad-hoc fashion. As a step towards eliminating this practice, we propose a systematic procedure for generating a set of supervisory specifications for self-driving cars that are 1) associated with a distributed assume-guarantee structure and 2) characterizable by the notion of consistency and completeness. Besides helping autonomous vehicles make better decisions on the road, the assume-guarantee contract structure also helps address the notion of blame when undesirable events occur. We give several game-theoretic examples to demonstrate applicability of our framework.
The microwave spectrum and structure of propylene oxide

Abstract The r s and r o structural parameters have been determined for propylene oxide from rotational constants for 16 isotopic species. The heavy-atom ( r o ) parameters are : r (C 1 C 2 ) = 1.470(3), r (C 2 C 3 ) = 1.505(2), r (C 1 O) = 1.441(2), r (C 2 O) = 1.435(3) A, ∠ OC 2 C 2 = 59.1(2), ∠OC 2 C 1 = 59.4(1), ∠C 1 OC 2 = 61.5(2), ∠C 1 C 2 C 3 = 121.7(2), ∠C 2 C 3 = 116.1(2)°. The methyl group torsional angles with the ring are: τ(C 1 C 2 C 3 H 9 ) = 24.7(8) and τ(OC 2 C 3 H 9 ) = −44.0°(5). These parameters agree closely with analogous results for ethylene oxide.
Do fiscal rules cause better fiscal balances? A new instrumental variable strategy

Abstract This paper estimates the causal effect of fiscal rules on fiscal balances in a panel of 142 countries over the period 1985–2015. Our instrumental variable strategy exploits the geographical diffusion of fiscal rules across countries. The intuition is that reforms in neighboring countries may affect the adoption of domestic reforms through peer pressure and imitational effects. We find that the mere existence of fiscal rules correlates with lower deficits, but the positive link disappears when endogeneity is correctly addressed. However, when considering the strength of rules through a continuous index of fiscal rules’ design, we show that well-designed rules have a statistically significant impact on fiscal balances. We conduct several robustness tests and show that our results are generally robust and not affected by weak instrument problems.
ANTITRUST DAMAGES IN FINANCIAL MARKETS

I briefly review the standard regression methods used to estimate damages in antitrust actions, and I discuss how these would be applied to cases in financial markets. I consider applications to three different financial market cases. The first is the NASDAQ odd-eighths litigation, where existing antitrust methods closely resemble the analyses published in the academic literature on this issue. The second type of case is bond market antitrust litigation, where the expert faces an additional hurdle because they have to estimate bid-ask spreads. The third type of case is related to the LIBOR manipulation scandal. I discuss why existing methods provide a poor fit for the LIBOR damage calculations. Lastly, I discuss IPO issuance fees as an example of price clustering in financial markets which has not let to antitrust litigation.
Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation

The memristor crossbar array has emerged as an intrinsically suitable matrix computation and low-power acceleration framework for DNN applications. Many techniques such as memristor-based weight pruning and memristor-based quantization have been studied. However, the high accuracy solution for the above techniques is still waiting for unraveling. In this paper, we propose a memristor-based DNN framework which combines both structured weight pruning and quantization by incorporating ADMM algorithm for better pruning and quantization performance. We also discover the non-optimality of the ADMM solution in weight pruning and the unused data path in a structured pruned model. We design a software-hardware co-optimization framework which contains the first proposed Network Purification and Unused Path Removal algorithms targeting on post-processing a structured pruned model after ADMM steps. By taking memristor hardware constraints into our whole framework, we achieve extreme high compression rate with minimum accuracy loss. For quantizing structured pruned model, our framework achieves nearly no accuracy loss after quantizing weights to 8-bit memristor weight representation. We share our models at anonymous link https://bit.ly/2VnMUy0.
Real-Time 3-D Segmentation on An Autonomous Embedded System: using Point Cloud and Camera

Present day autonomous vehicle relies on several sensor technologies for it’s autonomous functionality. The sensors based on their type and mounted-location on the vehicle, can be categorized as: line of sight and non-line of sight sensors and are responsible for the different level of autonomy. These line of sight sensors are used for the execution of actions related to localization, object detection and the complete environment understanding. The surrounding or environment understanding for an autonomous vehicle can be achieved by segmentation. Several traditional and deep learning related techniques providing semantic segmentation for an input from camera is already available, however with the advancement in the computing processor, the progression is on developing the deep learning application replacing traditional methods. This paper presents an approach to combine the input of camera and lidar for semantic segmentation purpose. The proposed model for outdoor scene segmentation is based on the frustum pointnet, and ResNet which utilizes the 3d point cloud and camera input for the 3d bounding box prediction across the moving and non-moving object and thus finally recognizing and understanding the scenario at the point-cloud or pixel level. For real time application the model is deployed on the RTMaps framework with Bluebox (an embedded platform for autonomous vehicle). The proposed architecture is trained with the CITYScpaes and the KITTI dataset.
“Nous étions ici pour durer”: Memorialization and environmental advocacy in Véronique Tadjo’s En Compagnie des Hommes

This article focuses on the ecological issues raised in Veronique Tadjo’s En compagnie des hommes (2017), a novel on the deadliest outbreak of Ebola in Guinea, Liberia and Sierra Leone from 2014 to...
Three Prongs for Prudent Climate Policy

For three decades, advocates for climate change policy have simultaneously emphasized the urgency of taking ambitious actions to mitigate greenhouse gas (GHG) emissions and provided false reassurances of the feasibility of doing so. The policy prescription has relied almost exclusively on a single approach: reduce emissions of carbon dioxide (CO2) and other GHGs. Since 1990, global CO2 emissions have increased 60%, atmospheric CO2 concentrations have raced past 400 ppm, and temperatures increased at an accelerating rate. The one‐prong strategy has not worked. After reviewing emission mitigation's poor performance and low‐probability of delivering on long‐term climate goals, we advance a three‐pronged strategy for mitigating climate change risks: adding adaptation and amelioration—through solar radiation management (SRM)—to the emission mitigation approach. We highlight SRM's potential, at dramatically lower cost than emission mitigation, to play a key role in offsetting warming. We address the moral hazard reservation held by environmental advocates—that SRM would diminish emission mitigation incentives—and posit that SRM deployment might even serve as an “awful action alert” that galvanizes more ambitious emission mitigation. We conclude by emphasizing the value of an iterative act‐learn‐act policy framework that engages all three prongs for limiting climate change damages.
Random Band Matrices in the Delocalized Phase I: Quantum Unique Ergodicity and Universality

Consider $N\times N$ symmetric one-dimensional random band matrices with general distribution of the entries and band width $W \geq N^{3/4+\varepsilon}$ for any $\varepsilon>0$. In the bulk of the spectrum and in the large $N$ limit, we obtain the following results. (i) The semicircle law holds up to the scale $N^{-1+\varepsilon}$ for any $\varepsilon>0$. (ii) The eigenvalues locally converge to the point process given by the Gaussian orthogonal ensemble at any fixed energy. (iii) All eigenvectors are delocalized, meaning their ${\rm L}^\infty$ norms are all simultaneously bounded by $N^{-\frac{1}{2}+\varepsilon}$ (after normalization in ${\rm L}^2$) with overwhelming probability, for any $\varepsilon>0$. (iv )Quantum unique ergodicity holds, in the sense that the local ${\rm L}^2$ mass of eigenvectors becomes equidistributed with overwhelming probability. We extend the mean-field reduction method \cite{BouErdYauYin2017}, which required $W=\Omega(N)$, to the current setting $W \ge N^{3/4+\varepsilon}$. Two new ideas are: (1) A new estimate on the "generalized resolvent" of band matrices when $W \geq N^{3/4+\varepsilon}$. Its proof, along with an improved fluctuation average estimate, will be presented in parts 2 and 3 of this series \cite {BouYanYauYin2018,YanYin2018}. (2) A strong (high probability) version of the quantum unique ergodicity property of random matrices. For its proof, we construct perfect matching observables of eigenvector overlaps and show they satisfying the eigenvector moment flow equation \cite{BouYau2017} under the matrix Brownian motions.
Artificial Intelligence or Intelligence Augmentation? Unravelling the Debate through an Industry-Level Analysis

The tenet of artificial intelligence (AI) is to use machines to replace humans in performing tasks while the conviction of intelligence augmentation (IA) is to use machines to assist and enhance humans in performing tasks. This study examines whether information technology (IT) substitutes for or complements human labor in the production process. It argues that the relationship between IT and human labor is more nuanced than what has been conceptualized in literature — IT not only can substitute for human labor (the AI effect) but also can complement it (the IA effect); the exact nature depends on the tasks to be performed and the education levels of the employees. We test these predictions using an industry-level dataset covering 60 US industries from 1998 to 2013. Elasticity of substitution and organizational complementarity theory are adopted to examine the substitution/complementarity relationship between IT and labor with different levels of education. The findings reveal co-existence of the AI and the IA effects during the sample period, and that education plays a critical role for workers to benefit from the massive adoption of IT by the US industries.
Bank runs in open economies and the international transmission of panics

Abstract In this paper we extend the bank run literature to an open economy model. We show that a foreign banking system, by raising deposit rates in the presence of a domestic banking panic, may generate sufficient liquid resources to acquire assets sold by the domestic banking system at bargain prices. In this case, foreign depositors will benefit from the domestic panic. We also show that our simple model is able to generate the spreading of panics. Perhaps not surprisingly, the crucial element in determining the propagation of financial crises is the effect of interest rates on savings decisions.
Multiple Endpoints, Multivariate Global Tests†

In clinical trials, multiple endpoints are often used to investigate a treatment effect. The statistical procedures available to analyze multiple endpoints fall into two categories: multivariate global methods and endpoint-specific or P level methods. Multivariate global methods are used to make an omnibus assessment of the efficacy of the treatments, providing an overall P value to reflect whether there are any differences among the treatments on several correlated endpoints. Within multivariate global methods there are two types of tests. The first type involves combining multiple endpoints into a single test statistic, which provides an overall statement to declare whether there are any significant differences among the treatments. The second method arises from Bonferroni-type adjustments. They adjust the univariate observed P values of the individual tests to maintain the familywise type I error rate at a prescribed level, and they provide an overall probability for the trial. Keywords: outcome measure; omnibus; Bonferroni-type; familywise; O'Brien; likelihood ratio test; Follmann; Lauter; risk score test; P value adjustment; Hommel; empirical method
Do Networks Really Work? A Framework for Evaluating Public-Sector Organizational Networks

Although cooperative, interorganizational networks have become a common mechanism for delivery of public services, evaluating their effectiveness is extremely complex and has generally been neglected. To help resolve this problem, we discuss the evaluation of networks of community-based, mostly publicly funded health, human service, and public welfare organizations. Consistent with pressures to perform effectively from a broad range of key stakeholders, we argue that networks must be evaluated at three levels of analysis: community, network, and organization/participant levels. While the three levels are related, each has its own set of effectiveness criteria that must be considered. The article offers a general discussion of network effectiveness, followed by arguments explaining effectiveness criteria and stakeholders at each level of analysis. Finally, the article examines how effectiveness at one level of network analysis may or may not match effectiveness criteria at another level and the extent to which integration across levels may be possible.
Predicting the coefficient of restitution of impacting elastic-perfectly plastic spheres

The current work presents a different methodology for modeling the impact between elasto-plastic spheres. Recent finite element results modeling the static deformation of an elasto-plastic sphere are used in conjunction with equations for the variation of kinetic energy to obtain predictions for the coefficient of restitution. A model is also needed to predict the residual deformation of the sphere during rebound, or unloading, of which several are available and compared in this work. The model predicts that a significant amount of energy will be dissipated in the form of plastic deformation such that as the speed at initial impact increases, the coefficient of restitution decreases. This work also derives a new equation for the initial critical speed which causes initial plastic deformation in the sphere that is different than that shown in previously derived equations and is strongly dependant on Poisson’s Ratio. For impacts occurring above this speed, the coefficient of restitution will be less than a value of one. This work also compares the predictions between several models that make significantly different predictions. The results of the current model also compare well with some existing experimental data. Empirical fits to the results are provided for use as a tool to predict the coefficient of restitution.
Stochastic food prices and slash-and-burn agriculture

This paper explores the interrelationship between poverty, risk, and deforestation by small farmers in the low-income tropics. A nonseparable household model reveals how exogenous shocks to the mean or variance of a food price distribution might affect peasants' incentives to clear forest. The resulting links between food price policy, farmer behavior, and deforestation offer an innovative explanation of the vicious cycle of peasant immiserization and tropical deforestation. An intriguing, testable hypothesis also emerges: that market-oriented reforms that increase the mean and variance of food prices may inadvertently stimulate deforestation in economies in which a sizable proportion of farmers are net buyers.
The taxman cometh: Does tax uncertainty affect corporate cash holdings?

We examine whether firms hold more cash in the face of tax uncertainty. Because of gray areas in the tax law and aggressive tax avoidance, the total amount of tax that a firm will pay is uncertain at the time it files its returns. The tax authorities can challenge and disallow the firm’s tax positions, demanding additional cash tax payments. We hypothesize that firms facing greater tax uncertainty hold cash to satisfy these potential future demands. We find that both domestic firms and multinational firms hold larger cash balances when subject to greater tax uncertainty. In terms of economic significance, we find that the effect of tax uncertainty on cash holdings is comparable to that of repatriation taxes. Our evidence adds to knowledge about the real effects of tax avoidance and provides a tax-based precautionary explanation for why there is such wide variation in cash holdings across firms.
Order of magnitude reasoning

Abstract Order of magnitude reasoning is pervasive. It copes with incomplete quantitative information and reduces the burden of calculations. This paper explores the foundations of this form of commonsense reasoning. Intuitively such reasoning is like using a coarse balance which weighs quantities with a variable level of precision. The coarse equilibrium is captured by order of magnitude equations , and the precision levels are given by order of magnitude scales . The system, Estimates , introduced to solve such equations, captures some basic engineering skills. Two tasks demonstrate the leverage it provides: the diagnosis of analog circuits and the estimation of the acidity of chemical solutions.
Is there a Link Between Foreclosure and Health

We investigate the relationship between foreclosures and hospital visits using data on all foreclosures and all hospital and emergency room visits from four states that were among the hardest hit by the foreclosure crisis. We find that living in a neighborhood with a spike in foreclosures is associated with significant increases in urgent unscheduled visits, including increases in visits for preventable conditions. The estimated relationships cannot be accounted for by increasing unemployment, declines in housing prices, migration, or by people switching from out-patient providers to hospitals.
Measuring the Temporal Stability of Near-Repeat Crime Patterns: A Longitudinal Analysis:

This study investigates the temporal stability of identified near-repeat robbery patterns in Newark, New Jersey. With one noteworthy exception, scholars have yet to explore the temporal stability o...
A separation theorem for joint sensor and actuator scheduling with guaranteed performance bounds

Abstract We study the problem of jointly designing a sparse sensor and actuator schedule for linear dynamical systems while guaranteeing a control/estimation performance that approximates the fully sensed/actuated setting. We further prove a separation principle, showing that the problem can be decomposed into finding sensor and actuator schedules separately. However, it is shown that this problem cannot be efficiently solved or approximated in polynomial, or even quasi-polynomial time for time-invariant sensor/actuator schedules; instead, we develop deterministic polynomial-time algorithms for a time-varying sensor/actuator schedule with guaranteed approximation bounds. Our main result is to provide a polynomial-time joint actuator and sensor schedule that on average selects only a constant number of sensors and actuators at each time step, irrespective of the dimension of the system. The key idea is to sparsify the controllability and observability Gramians while providing approximation guarantees for Hankel singular values. This idea is inspired by recent results in theoretical computer science literature on sparsification.
Transfinite product reduction in fundamental groupoids

Infinite products, indexed by countably infinite linear orders, arise naturally in the context of fundamental groupoids. Such products are called “transfinite” if the index orders are permitted to contain a dense suborder and are called “scattered” otherwise. In this paper, we prove several technical lemmas related to the reduction (i.e., combining of factors) of transfinite products in fundamental groupoids. Applying these results, we show that if the transfinite fundamental group operations are well-defined in a space X with a scattered algebraic 1-wild set $$\mathbf {aw}(X)$$ , then all transfinite fundamental groupoid operations are also well-defined.
HyperSTAR: Task-Aware Hyperparameters for Deep Networks

While deep neural networks excel in solving visual recognition tasks, they require significant effort to find hyperparameters that make them work optimally. Hyperparameter Optimization (HPO) approaches have automated the process of finding good hyperparameters but they do not adapt to a given task (task-agnostic), making them computationally inefficient. To reduce HPO time, we present HyperSTAR (System for Task Aware Hyperparameter Recommendation), a task-aware method to warm-start HPO for deep neural networks. HyperSTAR ranks and recommends hyperparameters by predicting their performance conditioned on a joint dataset-hyperparameter space. It learns a dataset (task) representation along with the performance predictor directly from raw images in an end-to-end fashion. The recommendations, when integrated with an existing HPO method, make it task-aware and significantly reduce the time to achieve optimal performance. We conduct extensive experiments on 10 publicly available large-scale image classification datasets over two different network architectures, validating that HyperSTAR evaluates 50% less configurations to achieve the best performance compared to existing methods. We further demonstrate that HyperSTAR makes Hyperband (HB) task-aware, achieving the optimal accuracy in just 25% of the budget required by both vanilla HB and Bayesian Optimized HB (BOHB).