cssi/SciSciGPT-SciSciNet huggingface dataset:
DP-colorings of hypergraphs

Classical problems in hypergraph coloring theory are to estimate the minimum number of edges, $m_2(r)$ (respectively, $m^\ast_2(r)$), in a non-$2$-colorable $r$-uniform (respectively, $r$-uniform and simple) hypergraph. The best currently known bounds are \[c \cdot \sqrt{r/\log r} \cdot 2^r \,\leqslant\, m_2(r) \,\leqslant\, C \cdot r^2 \cdot 2^r \qquad \text{and} \qquad c' \cdot r^{-\varepsilon} \cdot 4^r \,\leqslant\, m_2^\ast(r) \,\leqslant\, C' \cdot r^4 \cdot 4^r,\] for any fixed $\varepsilon > 0$ and some $c$, $c'$, $C$, $C' > 0$ (where $c'$ may depend on $\varepsilon$). In this paper we consider the same problems in the context of DP-coloring (also known as correspondence coloring), which is a generalization of list coloring introduced by Dvořak and Postle and related to local conflict coloring studied independently by Fraigniaud, Heinrich, and Kosowski. Let $\tilde{m}_2(r)$ (respectively, $\tilde{m}^\ast_2(r)$) denote the minimum number of edges in a non-$2$-DP-colorable $r$-uniform (respectively, $r$-uniform and simple) hypergraph. By definition, $\tilde{m}_2(r) \leqslant m_2(r)$ and $\tilde{m}^\ast_2(r)\leqslant m^\ast_2(r)$. While the proof of the bound $m^\ast_2(r) = \Omega( r^{-3} 4^r)$ due to Erdős and Lovasz also works for $\tilde{m}^\ast_2(r)$, we show that the trivial lower bound $\tilde{m}_2(r) \geqslant 2^{r-1}$ is asymptotically tight, i.e., $\tilde{m}_2(r) \leqslant (1 + o(1))2^{r-1}$. On the other hand, when $r \geqslant 2$ is even, we prove that the lower bound $\tilde{m}_2(r) \geqslant 2^{r-1}$ is not sharp, i.e., $\tilde{m}_2(r) \geqslant 2^{r-1}+1$. Whether this result holds for any odd values of $r$ remains an open problem. Nevertheless, we conjecture that the difference $\tilde{m}_2(r) - 2^{r-1}$ can be arbitrarily large.
Simulation-Based Reachability Analysis for Nonlinear Systems Using Componentwise Contraction Properties

A shortcoming of existing reachability approaches for nonlinear systems is the poor scalability with the number of continuous state variables. To mitigate this problem we present a simulation-based approach where we first sample a number of trajectories of the system and next establish bounds on the convergence or divergence between the samples and neighboring trajectories that are not explicitly simulated. We compute these bounds using contraction theory and reduce the conservatism by partitioning the state vector into several components and analyzing contraction properties separately in each direction. Among other benefits this allows us to analyze the effect of constant but uncertain parameters by treating them as state variables and partitioning them into a separate direction. We next present a numerical procedure to search for weighted norms that yield a prescribed contraction rate, which can be incorporated in the reachability algorithm to adjust the weights to minimize the growth of the reachable set. The proposed reachability method is illustrated with examples, including a magnetic resonance imaging application.
Assessment of the Choice Behavior Under Cued Conditions (CBUCC) paradigm as a measure of motivation to smoke under laboratory conditions

BACKGROUND AND AIMS The Choice Behavior under Cued Conditions (CBUCC) task uses three indices of tobacco use (consumption, money spent to access a cigarette and latency to reach for a cigarette) to assess motivation to smoke under laboratory conditions. Initial research with this procedure has shown that it can evince cue-specific craving and differential responding for smoking versus a neutral cue. This study aimed to replicate these findings and assess the interaction of cue-specific craving and behavior with abstinence prior to testing. DESIGN A mixed repeated-measures between-groups factorial design was used. Participants attended a morning laboratory session in which they were randomized to remain abstinent or smoke as usual (between-groups factor) and returned in the afternoon to complete CBUCC. In this, participants were exposed to 40 experimental trials. In each trial they were exposed to a cigarette or water cue behind a movable glass door (repeated-measures factor). SETTING University at Buffalo, New York, USA. PARTICIPANTS Participants were 106 daily non-treatment-seeking cigarette smokers, data from 102 were used. MEASUREMENTS On each of 40 trials, participants rated cigarette craving, and behavioral measures from the CBUCC (money spent, latency to access the cue, puff duration) were recorded. FINDINGS Craving and CBUCC behavioral measures showed high internal reliability across trials (Cronbach alphas ranged from 0.88 to 0.98). Craving and money spent were higher in trials with the cigarette cue than the water cue (F(1100) = 45.49, P < 0.001 and F(1100) = 116.26, P < 0.001). Other CBUCC measures did not show a significant effect of cue type. The difference in spending between cigarette and water cues was larger for abstinent participants than non-abstinent participants (F(1100) = 5.0, P = 0.03). Other CBUCC measures did not show a significant interaction between abstinence and cue type. Craving on smoking trials was significantly correlated with cigarette spending (r = 0.54, P < 0.001) in the non-abstinence condition but not in the abstinence condition. CONCLUSIONS Craving and 'money spent' in the Choice Behavior under Cued Conditions task (CBUCC) appears to be responsive to cigarette versus water cues, and money spent appears to show greater difference in responsiveness to cigarette than water cues after abstinence.
Lusternik–Schnirelmann theory and closed Reeb orbits

We develop a variant of Lusternik–Schnirelmann theory for the shift operator in equivariant Floer and symplectic homology. Our key result is that the spectral invariants are strictly decreasing under the action of the shift operator when periodic orbits are isolated. As an application, we prove new multiplicity results for simple closed Reeb orbits on the standard contact sphere, the unit cotangent bundle to the sphere and some other contact manifolds. We also show that the lower Conley–Zehnder index enjoys a certain recurrence property and revisit and reprove from a different perspective a variant of the common jump theorem of Long and Zhu. This is the second, combinatorial ingredient in the proof of the multiplicity results.
Affordable remote monitoring of plant growth in facilities using Raspberry Pi computers

Premise Environmentally controlled facilities, such as growth chambers, are essential tools for experimental research. Automated, low-cost, remote-monitoring hardware can greatly improve both reproducibility and maintenance. Methods and Results Using a Raspberry Pi computer, open-source software, environmental sensors, and a camera, we developed Growth Monitor pi (GMpi), a cost-effective system for monitoring growth chamber conditions. Coupled with our software, GMPi_Pack, our setup automates sensor readings, photography, and alerts when conditions fall out of range. Conclusions GMpi offers access to environmental data logging, improving reproducibility of experiments and reinforcing the stability of controlled environmental facilities. The device is also flexible and scalable, allowing researchers the ability to customize and expand GMpi for their own needs.
Sampling-Based Polytopic Trees for Approximate Optimal Control of Piecewise Affine Systems

Piecewise affine (PWA) systems are widely used to model highly nonlinear behaviors such as contact dynamics in robot locomotion and manipulation. Existing control techniques for PWA systems have computational drawbacks, both in offline design and online implementation. In this paper, we introduce a method to obtain feedback control policies and a corresponding set of admissible initial conditions for discrete-time PWA systems such that all the closed-loop trajectories reach a goal polytope, while a cost function is optimized. The idea is conceptually similar to LQR-trees [1], which consists of 3 steps: (1) open-loop trajectory optimization, (2) feedback control for computation of “funnels” of states around trajectories, and (3) repeating (1) and (2) in a way that the funnels are grown backward from the goal in a tree fashion and fill the state-space as much as possible. We show PWA dynamics can be exploited to combine step (1) and (2) into a single step that is tackled using mixed-integer convex programming, which makes the method suitable for dealing with hard constraints. Illustrative examples on contact-based dynamics are presented.
Dosing time matters.

Abstract Trainees in medicine are taught to diagnose and administer treatment as needed; time-of-day is rarely considered. Yet accumulating evidence shows that ∼half of human genes and physiologic functions follow daily rhythms. Circadian medicine aims to incorporate knowledge of these rhythms to enhance diagnosis and treatment. Interest in this approach goes back at least six decades, but the path to the clinic has been marked by starts, stops, and ambiguity. How do we move the field forward to impact clinical practice? To gain insight into successful strategies, we studied the results of more than 100 human trials that evaluated time-of-administration of drugs.
NONTHERMAL PARTICLES AND RADIATION PRODUCED BY CLUSTER MERGER SHOCKS

Abstract : We have developed a numerical model for the temporal evolution of particle and photon spectra resulting from nonthermal processes at the shock fronts formed in merging clusters of galaxies. Fermi acceleration is approximated by injecting power-law distributions of particles during a merger event, subject to constraints on maximum particle energies. We consider synchrotron, bremsstrahlung, Compton, and Coulomb processes for the electrons, nuclear, photomeson, and Coulomb processes for the protons, and knock-on electron production during the merging process. The broadband radio through gamma-ray emission radiated by nonthermal protons and primary and secondary electrons is calculated both during and after the merger event. Using ROSAT observations to establish typical parameters for the matter density profile of clusters of galaxies, we find that typical merger shocks are weak and accelerate particles with relatively soft spectra. We consider the prospects for detecting nonthermal radio and gamma-ray emission from clusters of galaxies and implications for the origin of ultra-high-energy cosmic rays and the diffuse gamma-ray background. Our results suggest that only a few of the isotropically distributed unidentified EGRET sources are due to shocks formed in cluster mergers and that only a minor contribution to the diffuse extragalactic gamma-ray background can originate from cluster merger shocks. Cluster merger shocks can accelerate protons to < 10(19) eV for the standard parameters considered here. We predict that GLAST will detect several cluster mergers, and depending on the mean magnetic fields in the intracluster medium, the Low Frequency Array could detect anywhere from several to several hundred.
Disrupted mechanobiology links the molecular and cellular phenotypes in familial dilated cardiomyopathy.

Familial dilated cardiomyopathy (DCM) is a leading cause of sudden cardiac death and a major indicator for heart transplant. The disease is frequently caused by mutations of sarcomeric proteins; however, it is not well understood how these molecular mutations lead to alterations in cellular organization and contractility. To address this critical gap in our knowledge, we studied the molecular and cellular consequences of a DCM mutation in troponin-T, ΔK210. We determined the molecular mechanism of ΔK210 and used computational modeling to predict that the mutation should reduce the force per sarcomere. In mutant cardiomyocytes, we found that ΔK210 not only reduces contractility but also causes cellular hypertrophy and impairs cardiomyocytes’ ability to adapt to changes in substrate stiffness (e.g., heart tissue fibrosis that occurs with aging and disease). These results help link the molecular and cellular phenotypes and implicate alterations in mechanosensing as an important factor in the development of DCM.
Quantum algorithms for jet clustering

Identifying jets formed in high-energy particle collisions requires solving optimization problems over potentially large numbers of final-state particles. In this work, we consider the possibility of using quantum computers to speed up jet clustering algorithms. Focusing on the case of electron-positron collisions, we consider a well-known event shape called thrust whose optimum corresponds to the most jetlike separating plane among a set of particles, thereby defining two hemisphere jets. We show how to formulate thrust both as a quantum annealing problem and as a Grover search problem. A key component of our analysis is the consideration of realistic models for interfacing classical data with a quantum algorithm. With a sequential computing model, we show how to speed up the well-known $O({N}^{3})$ classical algorithm to an $O({N}^{2})$ quantum algorithm, including the $O(N)$ overhead of loading classical data from $N$ final-state particles. Along the way, we also identify a way to speed up the classical algorithm to $O({N}^{2}\mathrm{log}N)$ using a sorting strategy inspired by the siscone jet algorithm, which has no natural quantum counterpart. With a parallel computing model, we achieve $O(N\mathrm{log}N)$ scaling in both the classical and quantum cases. Finally, we consider the generalization of these quantum methods to other jet algorithms more closely related to those used for proton-proton collisions at the Large Hadron Collider.
The Higher Power of Religiosity Over Personality on Political Ideology

Two streams of research, culture war and system justification, have proposed that religious orientations and personality, respectively, play critical roles in political orientations. There has been only limited work integrating these two streams. This integration is now of increased importance given the introduction of behavior-genetic frameworks into our understanding of why people differ politically. Extant research has largely considered the influence of personality as heritable and religiosity as social, but this view needs reconsideration as religiosity is also genetically influenced. Here we integrate these domains and conduct multivariate analyses on twin samples in the U.S. and Australia to identify the relative importance of genetic, environmental, and cultural influences. First, we find that religiosity’s role on political attitudes is more heritable than social. Second, religiosity accounts for more genetic influence on political attitudes than personality. When including religiosity, personality’s influence is greatly reduced. Our results suggest religion scholars and political psychologists are partially correct in their assessment of the “culture wars”—religiosity and ideology are closely linked, but their connection is grounded in genetic predispositions.
Differential tractography as a track-based biomarker for neuronal injury.

Abstract Diffusion MRI tractography has been used to map the axonal structure of the human brain, but its ability to detect neuronal injury is yet to be explored. Here we report differential tractography, a new type of tractography that utilizes repeat MRI scans and a novel tracking strategy to map the exact segment of fiber pathways with a neuronal injury. We examined differential tractography on multiple sclerosis, Huntington’s disease, amyotrophic lateral sclerosis, and epileptic patients. The results showed that the affected pathways shown by differential tractography matched well with the unique clinical symptoms of the patients, and the false discovery rate of the findings could be estimated using a sham setting to provide a reliability measurement. This novel approach enables a quantitative and objective method to monitor neuronal injury in individuals, allowing for diagnostic and prognostic evaluation of brain diseases.
Engagement With Air Quality Information: Stated Versus Revealed Preferences:

Air pollution has a significant impact on health but is often invisible to the naked eye. Real-time air quality information can help people take action to protect their health. However, little is k...
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation

Existing Rademacher complexity bounds for neural networks rely only on norm control of the weight matrices and depend exponentially on depth via a product of the matrix norms. Lower bounds show that this exponential dependence on depth is unavoidable when no additional properties of the training data are considered. We suspect that this conundrum comes from the fact that these bounds depend on the training data only through the margin. In practice, many data-dependent techniques such as Batchnorm improve the generalization performance. For feedforward neural nets as well as RNNs, we obtain tighter Rademacher complexity bounds by considering additional data-dependent properties of the network: the norms of the hidden layers of the network, and the norms of the Jacobians of each layer with respect to all previous layers. Our bounds scale polynomially in depth when these empirical quantities are small, as is usually the case in practice. To obtain these bounds, we develop general tools for augmenting a sequence of functions to make their composition Lipschitz and then covering the augmented functions. Inspired by our theory, we directly regularize the network’s Jacobians during training and empirically demonstrate that this improves test performance.
Posttraumatic stress disorder and the social brain: Affect-related disruption of the default and mirror networks.

BACKGROUND Social cognitive impairments, specifically in mentalizing and emotion recognition, are common and debilitating symptoms of posttraumatic stress disorder (PTSD). Despite this, little is known about the neurobiology of these impairments, as there are currently no published neuroimaging investigations of social inference in PTSD. METHODS Trauma-exposed veterans with and without PTSD (n = 20 each) performed the Why/How social inference task during functional magnetic resonance imaging (fMRI). Patients with PTSD had two fMRI sessions, between which they underwent affect labeling training. We probed the primary networks of the "social brain"-the default mode network (DMN) and mirror neuron system (MNS)-by examining neural activity evoked by mentalizing and action identification prompts, which were paired with emotional and nonemotional targets. RESULTS Hyperactivation to emotional stimuli differentiated PTSD patients from controls, correlated with symptom severity, and predicted training outcomes. Critically, these effects were nonsignificant or marginal for nonemotional stimuli. Results were generally consistent throughout DMN and MNS. Unexpectedly, effects were nonsignificant in core affect regions, but robust in regions that overlap with the dorsal attention, ventral attention, and frontoparietal control networks. CONCLUSIONS The array of social cognitive processes subserved by DMN and MNS appear to be inordinately selective for emotional stimuli in PTSD. However, core affective processes do not appear to be the primary instigators of such selectivity. Instead, we propose that affective attentional biases may instigate widespread affect-selectivity throughout the social brain. Affect labeling training may inhibit such biases. These accounts align with numerous reports of affect-biased attentional processes in PTSD.
Serial exposure to ethanol drinking and methamphetamine enhances glutamate excitotoxicity

A significant comorbidity exists between alcohol and methamphetamine (Meth) abuse but the neurochemical consequences of this co-abuse are unknown. Alcohol and Meth independently and differentially affect glutamatergic transmission but the unique effects of their serial exposure on glutamate signaling in mediating damage to dopamine neurons are unknown. Sprague-Dawley rats had intermittent voluntary access to 10% ethanol (EtOH) every other day and water over 28 days and were then administered a binge injection regimen of Meth or saline. EtOH drinking decreased the glutamate aspartate transporter and increased basal extracellular concentrations of glutamate within the striatum when measured after the last day of drinking. Ceftriaxone is known to increase the expression and/or activity of glutamate transporters in the brain and prevented both the decreases in glutamate aspartate transporter and the increases in basal extracellular glutamate when administered during EtOH drinking. EtOH drinking also exacerbated the acute increases in extracellular glutamate observed upon Meth exposure, the subsequent increases in spectrin proteolysis, and the long-term decreases in dopamine content in the striatum, all of which were attenuated by ceftriaxone administration during EtOH drinking only. These results implicate EtOH-induced increases in extracellular glutamate and corresponding decreases in glutamate uptake as mechanisms that contribute to the vulnerability produced by EtOH drinking and the unique neurotoxicity observed after serial exposure to Meth that is not observed with either drug alone. Open Science: This manuscript was awarded with the Open Materials Badge For more information see: https://cos.io/our-services/open-science-badges/.
A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition

Modern Automatic Speech Recognition (ASR) systems rely on distributed deep learning to for quick training completion. To enable efficient distributed training, it is imperative that the training algorithms can converge with a large mini-batch size. In this work, we discovered that Asynchronous Decentralized Parallel Stochastic Gradient Descent (ADPSGD) can work with much larger batch size than commonly used Synchronous SGD (SSGD) algorithm. On commonly used public SWB-300 and SWB-2000 ASR datasets, ADPSGD can converge with a batch size 3X as large as the one used in SSGD, thus enable training at a much larger scale. Further, we proposed a Hierarchical-ADPSGD (H-ADPSGD) system in which learners on the same computing node construct a super learner via a fast allreduce implementation, and super learners deploy ADPSGD algorithm among themselves. On a 64 Nvidia V100 GPU cluster connected via a 100Gb/s Ethernet network, our system is able to train SWB-2000 to reach a 7.6% WER on the Hub5-2000 Switchboard (SWB) test-set and a 13.2% WER on the Call-home (CH) test-set in 5.2 hours. To the best of our knowledge, this is the fastest ASR training system that attains this level of model accuracy for SWB-2000 task to be ever reported in the literature.
Holomorphic quadratic differentials on graphs and the chromatic polynomial

Abstract We study “holomorphic quadratic differentials” on graphs. We relate them to the reactive power in an LC circuit, and also to the chromatic polynomial of a graph. Specifically, we show that the chromatic polynomial χ of a graph G , at negative integer values, can be evaluated as the degree of a certain rational mapping, arising from the defining equations for a holomorphic quadratic differential. This allows us to give an explicit integral expression for χ ( − k ) .
Two classes of modular $p$-Stanley sequences

Consider a set $A$ with no $p$-term arithmetic progressions for $p$ prime. The $p$-Stanley sequence of a set $A$ is generated by greedily adding successive integers that do not create a $p$-term arithmetic progression. For $p>3$ prime, we give two distinct constructions for $p$-Stanley sequences which have a regular structure and satisfy certain conditions in order to be modular $p$-Stanley sequences, a set of particularly nice sequences defined by Moy and Rolnick which always have a regular structure. Odlyzko and Stanley conjectured that the 3-Stanley sequence generated by $\{0,n\}$ only has a regular structure if $n=3^k$ or $n=2\cdot 3^k$. For $p>3$ we find a substantially larger class of integers $n$ such that the $p$-Stanley sequence generated from $\{0,n\}$ is a modular $p$-Stanley sequence and numerical evidence given by Moy and Rolnick suggests that these are the only $n$ for which the $p$-Stanley sequence generated by $\{0,n\}$ is a modular $p$-Stanley sequence. Our second class is a generalization of a construction of Rolnick for $p=3$ and is thematically similar to the analogous construction by Rolnick.
Collusion Resistant Trace-and-Revoke for Arbitrary Identities from Standard Assumptions

A traitor tracing scheme is a multi-user public-key encryption scheme where each user in the system holds a decryption key that is associated with the user’s identity. Using the public key, a content distributor can encrypt a message to all of the users in the system. At the same time, if a malicious group of users combine their respective decryption keys to build a “pirate decoder,” there is an efficient tracing algorithm that the content distributor can use to identify at least one of the keys used to construct the decoder. A trace-and-revoke scheme is an extension of a standard traitor tracing scheme where there is an additional key-revocation mechanism that the content distributor can use to disable the decryption capabilities of compromised keys. Namely, during encryption, the content distributor can encrypt a message with respect to a list of revoked users such that only non-revoked users can decrypt the resulting ciphertext.
Time-dependent polynomials with one multiple root and new solvable dynamical systems

A time-dependent monic polynomial in the z variable with N distinct roots such that exactly one root has multiplicity m¯≥2 is considered. For k = 1, 2, the kth derivatives of the N roots are expressed in terms of the derivatives of order j ≤ k of the first N coefficients of the polynomial and of the derivatives of order j ≤ k − 1 of the roots themselves. These relations are utilized to construct new classes of algebraically solvable first order systems of ordinary differential equations as well as N-body problems. Multiple examples of solvable isochronous (all solutions are periodic with the same period) 2- and 3-body problems are provided.A time-dependent monic polynomial in the z variable with N distinct roots such that exactly one root has multiplicity m¯≥2 is considered. For k = 1, 2, the kth derivatives of the N roots are expressed in terms of the derivatives of order j ≤ k of the first N coefficients of the polynomial and of the derivatives of order j ≤ k − 1 of the roots themselves. These relations are utilized to construct new classes of algebraically solvable first order systems of ordinary differential equations as well as N-body problems. Multiple examples of solvable isochronous (all solutions are periodic with the same period) 2- and 3-body problems are provided.
A Quaternion-Based Certifiably Optimal Solution to the Wahba Problem With Outliers

The Wahba problem, also known as rotation search, seeks to find the best rotation to align two sets of vector observations given putative correspondences, and is a fundamental routine in many computer vision and robotics applications. This work proposes the first polynomial-time certifiably optimal approach for solving the Wahba problem when a large number of vector observations are outliers. Our first contribution is to formulate the Wahba problem using a Truncated Least Squares (TLS) cost that is insensitive to a large fraction of spurious correspondences. The second contribution is to rewrite the problem using unit quaternions and show that the TLS cost can be framed as a Quadratically-Constrained Quadratic Program (QCQP). Since the resulting optimization is still highly non-convex and hard to solve globally, our third contribution is to develop a convex Semidefinite Programming (SDP) relaxation. We show that while a naive relaxation performs poorly in general, our relaxation is tight even in the presence of large noise and outliers. We validate the proposed algorithm, named QUASAR (QUAternion-based Semidefinite relAxation for Robust alignment), in both synthetic and real datasets showing that the algorithm outperforms RANSAC, robust local optimization techniques, global outlier-removal procedures, and Branch-and-Bound methods. QUASAR is able to compute certifiably optimal solutions (i.e. the relaxation is exact) even in the case when 95% of the correspondences are outliers.
Robust control of linear time-varying systems with constraints

Despite a rich and complete theory developed for robust control of linear systems, little work has been done for robust control of linear systems with constraints. In this paper, a synthesis method to design a model predictive controller which optimizes robust performance is proposed for a stable linear time-varying discrete-time system represented by a finite impulse response model. In the absence of constraints, we show that with this method robust bounded-input-bounded-output stability of the resulting closed-loop system is guaranteed. Both necessary and sufficient conditions for robust global asymptotic stability, i.e. offset free tracking for all plants in the set, are stated. Furthermore, robust global asymptotic stability is preserved for a class of asymptotically constant disturbances entering at the plant output. These results hold for any uncertainty description expressed in the time-domain. However, there is a trade-off between the generality of the uncertainty description and the computational complexity of the resulting optimization problem. For a broad class of uncertainty descriptions, we show that the optimization problem can be cast as a linear program of moderate size.
A high-affinity human PD-1/PD-L2 complex informs avenues for small-molecule immune checkpoint drug discovery.

Immune checkpoint blockade of programmed death-1 (PD-1) by monoclonal antibody drugs has delivered breakthroughs in the treatment of cancer. Nonetheless, small-molecule PD-1 inhibitors could lead to increases in treatment efficacy, safety, and global access. While the ligand-binding surface of apo-PD-1 is relatively flat, it harbors a striking pocket in the murine PD-1/PD-L2 structure. An analogous pocket in human PD-1 may serve as a small-molecule drug target, but the structure of the human complex is unknown. Because the CC' and FG loops in murine PD-1 adopt new conformations upon binding PD-L2, we hypothesized that mutations in these two loops could be coupled to pocket formation and alter PD-1's affinity for PD-L2. Here, we conducted deep mutational scanning in these loops and used yeast surface display to select for enhanced PD-L2 binding. A PD-1 variant with three substitutions binds PD-L2 with an affinity two orders of magnitude higher than that of the wild-type protein, permitting crystallization of the complex. We determined the X-ray crystal structures of the human triple-mutant PD-1/PD-L2 complex and the apo triple-mutant PD-1 variant at 2.0 A and 1.2 A resolution, respectively. Binding of PD-L2 is accompanied by formation of a prominent pocket in human PD-1, as well as substantial conformational changes in the CC' and FG loops. The structure of the apo triple-mutant PD-1 shows that the CC' loop adopts the ligand-bound conformation, providing support for allostery between the loop and pocket. This human PD-1/PD-L2 structure provide critical insights for the design and discovery of small-molecule PD-1 inhibitors.
Control system for the NSTX lithium pellet injector

The lithium pellet injector (LPI) is being developed for the National Spherical Torus Experiment (NSTX). The LPI will inject 'pellets' of various composition into the plasma in order to study wall conditioning, edge impurity transport, liquid limiter simulations, and other areas of research. The control system for the NSTX LPI has incorporated widely used advanced technologies, such as LabVIEW/spl trade/ and PCI bus I/O boards, to create a low-cost control system which is fully integrated into the NSTX computing environment. This paper will present the hardware and software design of the computer control system for the LPI.
Time-variant metasurfaces enable tunable spectral bands of negative extinction

We demonstrate that rapidly switched high-Q metasurfaces enable spectral regions of negative optical extinction.
Fast Poisson solvers for spectral methods

Poisson's equation is the canonical elliptic partial differential equation. While there exist fast Poisson solvers for finite difference and finite element methods, fast Poisson solvers for spectral methods have remained elusive. Here, we derive spectral methods for solving Poisson's equation on a square, cylinder, solid sphere, and cube that have an optimal complexity (up to polylogarithmic terms) in terms of the degrees of freedom required to represent the solution. Whereas FFT-based fast Poisson solvers exploit structured eigenvectors of finite difference matrices, our solver exploits a separated spectra property that holds for our spectral discretizations. Without parallelization, we can solve Poisson's equation on a square with 100 million degrees of freedom in under two minutes on a standard laptop.
Identification and Characterization of Cis-Regulatory Elements for Photoreceptor-Type-Specific Transcription in ZebraFish

Tissue-specific or cell-type-specific transcription of protein-coding genes is controlled by both trans-regulatory elements (TREs) and cis-regulatory elements (CREs). However, it is challenging to identify TREs and CREs, which are unknown for most genes. Here, we describe a protocol for identifying two types of transcription-activating CREs-core promoters and enhancers-of zebrafish photoreceptor type-specific genes. This protocol is composed of three phases: bioinformatic prediction, experimental validation, and characterization of the CREs. To better illustrate the principles and logic of this protocol, we exemplify it with the discovery of the core promoter and enhancer of the mpp5b apical polarity gene (also known as ponli), whose red, green, and blue (RGB) cone-specific transcription requires its enhancer, a member of the rainbow enhancer family. While exemplified with an RGB-cone-specific gene, this protocol is general and can be used to identify the core promoters and enhancers of other protein-coding genes.
Commensurability invariance for abelian splittings of right-angled Artin groups, braid groups and loop braid groups

We prove that if a right-angled Artin group A Γ is abstractly commensurable to a group splitting nontrivially as an amalgam or HNN extension over ℤ n , then A Γ must itself split nontrivially over ℤ k for some k ≤ n . Consequently, if two right-angled Artin groups A Γ and A Δ are commensurable and Γ has no separating k –cliques for any k ≤ n , then neither does Δ , so “smallest size of separating clique” is a commensurability invariant. We also discuss some implications for issues of quasi-isometry. Using similar methods we also prove that for n ≥ 4 the braid group B n is not abstractly commensurable to any group that splits nontrivially over a “free group–free” subgroup, and the same holds for n ≥ 3 for the loop braid group LB n . Our approach makes heavy use of the Bieri–Neumann–Strebel invariant.
AdvectiveNet: An Eulerian-Lagrangian Fluidic Reservoir for Point Cloud Processing

This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics. Our learning architecture jointly defines data in an Eulerian world space, using a static background grid, and a Lagrangian material space, using moving particles. By introducing this Eulerian-Lagrangian representation, we are able to naturally evolve and accumulate particle features using flow velocities generated from a generalized, high-dimensional force field. We demonstrate the efficacy of this system by solving various point cloud classification and segmentation problems with state-of-the-art performance. The entire geometric reservoir and data flow mimic the pipeline of the classic PIC/FLIP scheme in modeling natural flow, bridging the disciplines of geometric machine learning and physical simulation.
OE-CAM: A Hybrid Opto-Electronic Content Addressable Memory

A content addressable memory (CAM) is a type of memory that implements a parallel search engine at its core. A CAM takes as an input a value and outputs the address where this value is stored in case of a match. CAMs are used in a wide range of applications including networking, cashing, neuromorphic associative memories, multimedia, and data analytics. Here, we introduce a novel opto-electronic CAM (OE-CAM) utilizing the integrated silicon photonic platform. In our approach, we explore the performance of an experimental OE-CAM and verify the efficiency of the device at 25 Gbit/s while maintaining the bit integrity under noise conditions. We show that OE-CAM enables a) two orders of magnitude faster search functionality resulting in b) a five orders of magnitude lower power-delay-product compared to CAMs implementations based on other emerging technologies. This remarkable performance potential is achieved by utilizing i) a high parallelism of wavelength-division-multiplexing in the optical domain, combined with ii) 10's of GHz-fast opto-electronic components, packaged in iii) integrated photonics for 10–100's ps-short communication delays. We further verify the upper optical input power limit of this OE-CAM to be given by parasitic nonlinearities inside the silicon waveguides, and the minimal detectable optical power at the back-end photoreceiver's responsivity given channel noise. Such energy-efficient and short-delay OE-CAMs could become a key component of functional photonic-augmented ASICS, co-processors, or smart sensors.
Magnetic correlations in the disordered ferromagnetic alloy Ni-V revealed with small angle neutron scattering

We present small angle neutron scattering (SANS) data collected on polycrystalline Ni1−xVx samples with x ≥ 0.10 with confirmed random atomic distribution. We aim to determine the relevant length scales of magnetic correlations in ferromagnetic samples with low critical temperatures Tc that show signs of magnetic inhomogeneities in magnetization and μSR data. The SANS study reveals signatures of long-range order and coexistence of short-range magnetic correlations in this randomly disordered ferromagnetic alloy. We show the advantages of a polarization analysis in identifying the main magnetic contributions from the dominating nuclear scattering.
Stabilization of coupled Dzyaloshinskii domain walls in fully compensated synthetic anti-ferromagnets

We examine the combined effects of interlayer exchange coupling (IEC) and the interfacial Dzyaloshinskii-Moriya Interaction (DMI) on the structure of magnetic domain walls in fully compensated synthetic anti-ferromagnets (SAFs). Ir-based SAFs with ferromagnetic (FM) layers based on [Pt/(Co/Ni)M]N were characterized by Lorentz transmission electron microscopy (LTEM). The multi-layer design of the individual ferromagnetic layers enables control of the interfacial Dzyaloshinskii-Moriya interaction (via ‘M’) and, in turn, the structure and chirality of domain walls (DWs). We compare the Fresnel-mode LTEM images in SAF designs with only a change in the purported strength of the DMI. The existence of anti-ferromagnetically coupled Dzyaloshinskii domain walls (DWs) in a high DMI SAF is confirmed through application of in-situ perpendicular magnetic field and sample tilt. This conclusion is based on a unique set of conditions required to observe contrast in Fresnel-mode LTEM, which we outline in this document.
Evaluation of Surrogate Models for Multi-fin Flapping Propulsion Systems

The aim of this study is to develop surrogate models for quick, accurate prediction of thrust forces generated through flapping fin propulsion for given operating conditions and fin geometries. Different network architectures and configurations are explored to model the training data separately for the lead fin and rear fin of a tandem fin setup. We progressively improve the data representation of the input parameter space for model predictions. The models are tested on three unseen fin geometries and the predictions validated with computational fluid dynamics (CFD) data. Finally, the orders of magnitude gains in computational performance of these surrogate models, compared to experimental and CFD runs, vs their tradeoff with accuracy is discussed within the context of this tandem fin configuration.
Toward Achieving Formal Guarantees for Human-Aware Controllers in Human-Robot Interactions

With the primary objective of human-robot interaction being to support humans’ goals, there exists a need to formally synthesize robot controllers that can provide the desired service. Synthesis techniques have the benefit of providing formal guarantees for specification satisfaction. There is potential to apply these techniques for devising robot controllers whose specifications are coupled with human needs. This paper explores the use of formal methods to construct human-aware robot controllers to support the productivity requirements of humans. We tackle these types of scenarios via human workload-informed models and reactive synthesis. This strategy allows us to synthesize controllers that fulfill formal specifications that are expressed as linear temporal logic formulas. We present a case study in which we reason about a work delivery and pickup task such that the robot increases worker productivity, but not stress induced by high work backlog. We demonstrate our controller using the Toyota HSR, a mobile manipulator robot. The results demonstrate the realization of a robust robot controller that is guaranteed to properly reason and react in collaborative tasks with human partners.
Current Sharing in Multiterminal DC Grids - The Analytical Approach

This paper presents a new analytical approach for dc voltage control in dc grids. The control approach aims at improving the dc voltage droop control by generalized feedback which combines the local voltage signal available at the converter terminals with remote voltage signals at different locations in the dc system by means of communication. The main contribution is an analytical approach to derive the generalized feedback gain allowing to differentiating the system response for different converter outages. The goal of the system response is to share the power, in the event of disconnection of a converter, between the remaining converters according to a predetermined scheme. The local voltage feedback control is used for fast and reliable system response, and remote signals to achieve the goal. The analysis of the feasibility space the power shares is also presented, which is a direct function of the grid layout. Simulation results show the validity of the proposed control scheme.
Protecting Billiard Balls From Collisions

We present a game inspired by research on the possible number of billiard ball collisions in the whole Euclidean space. One player tries to place n static “balls” with zero radius (i.e., points) in a way that will minimize the total number of possible collisions caused by the cue ball. The other player tries to find initial conditions for the cue ball to maximize the number of collisions. The value of the game is $$\sqrt{n}$$ (up to constants). The lower bound is based on the Erdős-Szekeres Theorem. The upper bound may be considered a generalization of the Erdős-Szekeres Theorem.
Understanding the Cost of Achieving the Sustainable Development Goals

This paper presents a review of studies that estimate the cost of achieving the Sustainable Development Goals. Although the Sustainable Development Goals provide useful benchmarks for fiscal authorities and donors, typical cross-country costing exercises can be misleading, for a variety of reasons: double counting, sensitivity to underlying assumptions, downplaying the critical role of policy and institutions in advancing toward the goals, failure to discount costs or consider operation and maintenance costs in a consistent manner, and overlooking the tendency for different types of Sustainable Development Goal–related spending to have distinct effects. Recent costing studies by the World Bank Group have been developed to minimize the drawbacks of earlier studies. The paper also briefly reviews how the World Bank Group engages with stakeholders on the Sustainable Development Goals agenda.
borealis—A generalized global update algorithm for Boolean optimization problems

Optimization problems with Boolean variables that fall into the nondeterministic polynomial (NP) class when cast as decision problems are of fundamental importance in computer science, mathematics, physics and industrial applications. Most notably, solving constraint-satisfaction problems, which are related to spin-glass-like Hamiltonians in physics, remains a difficult numerical task. As such, there has been great interest in designing efficient heuristics to solve these computationally difficult problems. Inspired by parallel tempering Monte Carlo in conjunction with the rejection-free isoenergetic cluster algorithm developed for Ising spin glasses, we present a generalized global update optimization heuristic that can be applied to different NP-complete problems with Boolean variables. The global cluster updates allow for a wide-spread sampling of phase space, thus considerably speeding up optimization. By carefully tuning the pseudo-temperature (needed to randomize the configurations) of the problem, we show that the method can efficiently tackle optimization problems with over-constraints or on topologies with a large site percolation threshold. We illustrate the efficiency of the heuristic on paradigmatic optimization problems, such as the maximum satisfiability problem and the vertex cover problem. Because this physics-based algorithm is an algorithm that searches solutions globally, it performs best for random Max-k-SAT instances.
Evaluation of Phenotyping Errors on Polygenic Risk Score Predictions.

Accurate disease risk prediction is essential in healthcare to provide personalized disease prevention and treatment strategies not only to the patients, but also to the general population. In addition to demographic and environmental factors, advancements in genomic research have revealed that genetics play an important role in determining the susceptibility of diseases. However, for most complex diseases, individual genetic variants are only weakly to moderately associated with the diseases. Thus, they are not clinically informative in determining disease risks. Nevertheless, recent findings suggest that the combined effects from multiple disease-associated variants, or polygenic risk score (PRS), can stratify disease risk similar to that of rare monogenic mutations. The development of polygenic risk score provides a promising tool to evaluate the genetic contribution of disease risk; however, the quality of the risk prediction depends on many contributing factors including the precision of the target phenotypes. In this study, we evaluated the impact of phenotyping errors on the accuracies of PRS risk prediction. We utilized electronic Medical Records and Genomics Network (eMERGE) data to simulate various types of disease phenotypes. For each phenotype, we quantified the impact of phenotyping errors generated from the differential and non-differential mechanism by comparing the prediction accuracies of PRS on the independent testing data. In addition, our results showed that the rate of accuracy degradation depended on both the phenotype and the mechanism of phenotyping error.