cssi/SciSciGPT-SciSciNet huggingface dataset:
Model-order reduction of lumped parameter systems via fractional calculus

Abstract This study investigates the use of fractional order differential models to simulate the dynamic response of non-homogeneous discrete systems and to achieve efficient and accurate model order reduction. The traditional integer order approach to the simulation of non-homogeneous systems dictates the use of numerical solutions and often imposes stringent compromises between accuracy and computational performance. Fractional calculus provides an alternative approach where complex dynamical systems can be modeled with compact fractional equations that not only can still guarantee analytical solutions, but can also enable high levels of order reduction without compromising on accuracy. Different approaches are explored in order to transform the integer order model into a reduced order fractional model able to match the dynamic response of the initial system. Analytical and numerical results show that, under certain conditions, an exact match is possible and the resulting fractional differential models have both a complex and frequency-dependent order of the differential operator. The implications of this type of approach for both model order reduction and model synthesis are discussed.
Knowledge Base Relation Detection via Multi-View Matching

Relation detection is a core component for Knowledge Base Question Answering (KBQA). In this paper, we propose a knowledge base (KB) relation detection model based on multi-view matching, which utilizes useful information extracted from questions and KB. The matching inside each view is through multiple perspectives to compare two input texts thoroughly. All these components are trained in an end-to-end neural network model. Experiments on SimpleQuestions and WebQSP yield state-of-the-art results on relation detection.
Providing secrecy with lattice codes

Recent results have shown that lattice codes can be used to construct good channel codes, source codes and physical layer network codes for Gaussian channels. On the other hand, for Gaussian channels with secrecy constraints, efforts to date rely on random codes. In this work, we provide a tool to bridge these two areas so that the secrecy rate can be computed when lattice codes are used. In particular, we address the problem of bounding equivocation rates under nonlinear modulus operation that is present in lattice encoders/decoders. The technique is then demonstrated in two Gaussian channel examples: (1) a Gaussian wiretap channel with a cooperative jammer, and (2) a multi-hop line network from a source to a destination with untrusted intermediate relay nodes from whom the information needs to be kept secret. In both cases, lattice codes are used to facilitate cooperative jamming. In the second case, interestingly, we demonstrate that a non-vanishing positive secrecy rate is achievable regardless of the number of hops.
Learning to Branch

Tree search algorithms, such as branch-and-bound, are the most widely used tools for solving combinatorial and nonconvex problems. For example, they are the foremost method for solving (mixed) integer programs and constraint satisfaction problems. Tree search algorithms recursively partition the search space to find an optimal solution. In order to keep the tree size small, it is crucial to carefully decide, when expanding a tree node, which question (typically variable) to branch on at that node in order to partition the remaining space. Numerous partitioning techniques (e.g., variable selection) have been proposed, but there is no theory describing which technique is optimal. We show how to use machine learning to determine an optimal weighting of any set of partitioning procedures for the instance distribution at hand using samples from the distribution. We provide the first sample complexity guarantees for tree search algorithm configuration. These guarantees bound the number of samples sufficient to ensure that the empirical performance of an algorithm over the samples nearly matches its expected performance on the unknown instance distribution. This thorough theoretical investigation naturally gives rise to our learning algorithm. Via experiments, we show that learning an optimal weighting of partitioning procedures can dramatically reduce tree size, and we prove that this reduction can even be exponential. Through theory and experiments, we show that learning to branch is both practical and hugely beneficial.
Quadratic fields with cyclic 2-class groups

Abstract Text For any integer k ⩾ 1 , we show that there are infinitely many complex quadratic fields whose 2-class groups are cyclic of order 2 k . The proof combines the circle method with an algebraic criterion for a complex quadratic ideal class to be a square. Video For a video summary of this paper, please click here or visit http://www.youtube.com/watch?v=H49W8Wyl47o .
Regularity of the free boundary for a Bernoulli-type parabolic problem with variable coefficients

Abstract In this work the regularity of the free boundary for a class of parabolic free boundary problems with variable coefficients is studied. Under the main hypothesis that the free boundary is Lipschitz, the main result is that the free boundary is in fact C 1 , α surfaces, thereby implying that the viscosity solution is in fact classical in this case.
Key Capacity for Product Sources With Application to Stationary Gaussian Processes

We show that for product sources, rate splitting is optimal for secret key agreement using limited one-way communication between two terminals. This yields an alternative proof of the tensorization property of a strong data processing inequality originally studied by Erkip and Cover and amended recently by Anantharam et al. We derive a water-filling solution of the communication-rate–key-rate tradeoff for a wide class of discrete memoryless vector Gaussian sources which subsumes the case without an eavesdropper. Moreover, we derive an explicit formula for the maximum secret key per bit of communication for all discrete memoryless vector Gaussian sources using a tensorization property and a variation on the enhanced channel technique of Weingarten et al. Finally, a one-shot information spectrum achievability bound for key generation is proved from which we characterize the communication-rate–key-rate tradeoff for stationary Gaussian processes.
An information-theoretic measure of dependency among variables in large datasets

The maximal information coefficient (MIC), which measures the amount of dependence between two variables, is able to detect both linear and non-linear associations. However, computational cost grows rapidly as a function of the dataset size. In this paper, we develop a computationally efficient approximation to the MIC that replaces its dynamic programming step with a much simpler technique based on the uniform partitioning of data grid. A variety of experiments demonstrate the quality of our approximation.
A characterization of boundary representations of positive matrices in the Hardy space via the Abel product

Abstract Spectral measures give rise to a natural harmonic analysis on the unit disc via a boundary representation of a positive matrix arising from a spectrum of the measure. We consider in this paper the reverse: for a positive matrix in the Hardy space of the unit disc we consider which measures, if any, yield a boundary representation of the positive matrix. We prove a characterization of those representing measures via a matrix identity by introducing a new operator product called the Abel Product.
Exploring patterns of demand in bike sharing systems via replicated point process models

Understanding patterns of demand is fundamental for fleet management of bike sharing systems. We analyse data from the Divvy system of the city of Chicago. We show that the demand for bicycles can be modelled as a multivariate temporal point process, with each dimension corresponding to a bike station in the network. The availability of daily replications of the process enables non‐parametric estimation of the intensity functions, even for stations with low daily counts, and straightforward estimation of pairwise correlations between stations. These correlations are then used for clustering, revealing different patterns of bike usage.
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling.

Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition. In this work, we propose an attention-based neural network model for joint intent detection and slot filling, both of which are critical steps for many speech understanding and dialog systems. Unlike in machine translation and speech recognition, alignment is explicit in slot filling. We explore different strategies in incorporating this alignment information to the encoder-decoder framework. Learning from the attention mechanism in encoder-decoder model, we further propose introducing attention to the alignment-based RNN models. Such attentions provide additional information to the intent classification and slot label prediction. Our independent task models achieve state-of-the-art intent detection error rate and slot filling F1 score on the benchmark ATIS task. Our joint training model further obtains 0.56% absolute (23.8% relative) error reduction on intent detection and 0.23% absolute gain on slot filling over the independent task models.
Torsion Subgroups of CM Elliptic Curves over Odd Degree Number Fields

Let GCM(d) denote the collection of groups (up to isomorphism) that appear as the torsion subgroup of a CM elliptic curve over a degree d number field. We completely determine GCM(d) for odd integers d and deduce a number of statistical theorems about the behavior of torsion subgroups of CM elliptic curves. Here are three examples: (1) For each odd d, the set of natural numbers d′ with GCM(d ′) = GCM(d) possesses a well-defined, positive asymptotic density. (2) Let TCM(d) = maxG∈GCM(d) #G; under the Generalized Riemann Hypothesis, ( 12e π )2/3 ≤ lim sup d→∞ d odd TCM(d) (d log log d)2/3 ≤ ( 24e π )2/3 . (3) For each > 0, we have #GCM(d) d for all odd d; on the other hand, for each A > 0, we have #GCM(d) > (log d) A for infinitely many odd d.
Between Linearizability and Quiescent Consistency - Quantitative Quiescent Consistency.

Linearizability is the de facto correctness criterion for concurrent data structures. Unfortunately, linearizability imposes a performance penalty which scales linearly in the number of contending threads. Quiescent consistency is an alternative criterion which guarantees that a concurrent data structure behaves correctly when accessed sequentially. Yet quiescent consistency says very little about executions that have any contention. We define quantitative quiescent consistency (QQC), a relaxation of lineariz- ability where thedegree of relaxation is proportional to the degree of contention. When quiescent, no relaxation is allowed, and therefore QQC refines quiescent consistency, unlike other proposed relaxations of linearizability. We show that high performance counters and stacks designed to satisfy quiescent consistency continue to satisfy QQC. The precise assumptions under which QQC holds pro- vides fresh insight on these structures. To demonstrate the robustness of QQC, we provide three natural characterizations and prove compositionality.
Positive definite functions on the unit sphere and integrals of Jacobi polynomials

It is shown that the integrals of the Jacobi polynomials \begin{equation*}%\label{eq:Fn^J} \int_0^t (t-\theta)^\delta P_n^{(\alpha-\frac12,\beta-\frac12)}(\cos \theta) \left(\sin \tfrac{\theta}2\right)^{2 \alpha} \left(\cos \tfrac{\theta}2\right)^{2 \beta} d\theta > 0 \end{equation*} for all $t \in (0,\pi]$ and $n \in \mathbb{N}$ if $\delta \ge \alpha + 1$ for $\alpha,\beta \in \mathbb{N}_0$ and $\max\{\alpha,\beta\} > 0$. This proves a conjecture on the integral of the Gegenbauer polynomials in \cite{BCX} that implies the strictly positive definiteness of the function $\theta \mapsto (t - \theta)_+^\delta$ on the unit sphere $\mathbb{S}^{d-1}$ for $\delta \ge \lceil \frac{d}{2}\rceil$ and the Polya criterion for positive definite functions on the sphere for all dimensions. Moreover, the positive definiteness of the function $\theta \mapsto (t - \theta)_+^\delta$ is also established on the compact two-point homogeneous spaces.
Denoising linear models with permuted data

We consider the multivariate linear regression model with shuffled data and additive noise, which arises in various correspondence estimation and matching problems. We focus on the denoising problem and characterize the minimax error rate up to logarithmic factors. We also analyze the performance of two versions of a computationally efficient estimator that are consistent for a large range of input parameters. Finally, we provide an exact algorithm for the noiseless problem and demonstrate its performance on an image point-cloud matching task. Our analysis also extends to datasets with missing data.
Distributed Weighted Matching via Randomized Composable Coresets

Maximum weight matching is one of the most fundamental combinatorial optimization problems with a wide range of applications in data mining and bioinformatics. Developing distributed weighted matching algorithms is challenging due to the sequential nature of efficient algorithms for this problem. In this paper, we develop a simple distributed algorithm for the problem on general graphs with approximation guarantee of $2+\varepsilon$ that (nearly) matches that of the sequential greedy algorithm. A key advantage of this algorithm is that it can be easily implemented in only two rounds of computation in modern parallel computation frameworks such as MapReduce. We also demonstrate the efficiency of our algorithm in practice on various graphs (some with half a trillion edges) by achieving objective values always close to what is achievable in the centralized setting.
Driver hand localization and grasp analysis: A vision-based real-time approach

Extracting hand regions and their grasp information from images robustly in real-time is critical for occupants' safety and in-vehicular infotainment applications. It must however, be noted that naturalistic driving scenes suffer from rapidly changing illumination and occlusion. This is aggravated by the fact that hands are highly deformable objects, and change in appearance frequently. This work addresses the task of accurately localizing driver hands and classifying the grasp state of each hand. We use a fast ConvNet to first detect likely hand regions. Next, a pixel-based skin classifier that takes into account the global illumination changes is used to refine the hand detections and remove false positives. This step generates a pixel-level mask for each hand. Finally, we study each such masked regions and detect if the driver is grasping the wheel, or in some cases a mobile phone. Through evaluation we demonstrate that our method can outperform state-of-the-art pixel based hand detectors, while running faster (at 35 fps) than other deep ConvNet based frameworks even for grasp analysis. Hand mask cues are shown to be crucial when analyzing a set of driver hand gestures (wheel/mobile phone grasp and no-grasp) in naturalistic driving settings. The proposed detection and localization pipeline hence can act as a general framework for real-time hand detection and gesture classification.
On groups, slow heat kernel decay yields Liouville property and sharp entropy bounds

Let $\mu$ be a symmetric probability measure of finite entropy on a group $G$. We show that if $-\log \mu^{(2n)}(id)=o(n^{1/2})$, then the pair $(G,\mu)$ has the Liouville property (all bounded $\mu$-harmonic functions on $G$ are constant). Furthermore, if $-\log \mu^{(2n)}(id)=O(n^{\beta})$ where $\beta\in(0,1/2)$, then the entropy of the $n$-fold convolution power $\mu^{(n)}$ satisfies $H(\mu^{(n)})=O\left(n^{\frac{\beta}{1-\beta}}\right)$. This improves earlier results of Gournay and of Saloff-Coste and the second author. We extend the bounds to transitive graphs and illustrate their sharpness on a family of groups.
INVARIANTS OF CLOSED BRAIDS VIA COUNTING SURFACES

A Gauss diagram is a simple, combinatorial way to present a link. It is known that any Vassiliev invariant may be obtained from a Gauss diagram formula that involves counting subdiagrams of certain combinatorial types. In this paper we present simple formulas for an infinite family of invariants in terms of counting surfaces of a certain genus and number of boundary components in a Gauss diagram associated with a closed braid. We then identify the resulting invariants with partial derivatives of the HOMFLY-PT polynomial.
Motives over simplicial schemes

This paper was written as a part of [8] and is intended primarily to provide the definitions and results concerning motives over simplicial schemes, which are used in the proof of the Bloch-Kato conjecture.
Paying for procurement: analysis of landman compensation surveys

ABSTRACTUtilizing a unique time series of cross-sectional surveys, we analyse the labour market for professional landman services to establish the factors affecting compensation during a recent per...
A Linear-Time Algorithm for Radius-Optimally Augmenting Paths in a Metric Space

Let P be a path graph of n vertices embedded in a metric space. We consider the problem of adding a new edge to P to minimize the radius of the resulting graph. Previously, a similar problem for minimizing the diameter of the graph was solved in \(O(n\log n)\) time. To the best of our knowledge, the problem of minimizing the radius has not been studied before. In this paper, we present an O(n) time algorithm for the problem, which is optimal.
Formal Policy Learning from Demonstrations for Reachability Properties

We consider the problem of learning structured, closed-loop policies (feedback laws) from demonstrations in order to control under-actuated robotic systems, so that formal behavioral specifications such as reaching a target set of states are satisfied. Our approach uses a “counterexample-guided” iterative loop that involves the interaction between a policy learner, a demonstrator and a verifier. The learner is responsible for querying the demonstrator in order to obtain the training data to guide the construction of a policy candidate. This candidate is analyzed by the verifier and either accepted as correct, or rejected with a counterexample. In the latter case, the counterexample is used to update the training data and further refine the policy.The approach is instantiated using receding horizon model-predictive controllers (MPCs) as demonstrators. Rather than using regression to fit a policy to the demonstrator actions, we extend the MPC formulation with the gradient of the cost-to-go function evaluated at sample states in order to constrain the set of policies compatible with the behavior of the demonstrator. We demonstrate the successful application of the resulting policy learning schemes on two case studies and we show how simple, formally-verified policies can be inferred starting from a complex and unverified nonlinear MPC implementations. As a further benefit, the policies are many orders of magnitude faster to implement when compared to the original MPCs.
Spin-State Ordering in Metal-Based Compounds Using the Localized Active Space Self-Consistent Field Method.

Quantitatively accurate calculations for spin-state ordering in transition-metal complexes typically demand a robust multiconfigurational treatment. The poor scaling of such methods with increasing...
An Optical Tweezer Array of Ultracold Molecules

Ultracold molecules have important applications that range from quantum simulation and computation to precision measurements probing physics beyond the Standard Model. Optical tweezer arrays of laser-cooled molecules, which allow control of individual particles, offer a platform for realizing this full potential. In this work, we report on creating an optical tweezer array of laser-cooled calcium monofluoride molecules. This platform has also allowed us to observe ground-state collisions of laser-cooled molecules both in the presence and absence of near-resonant light.
CRISPR/Cas9-based editing of a sensitive transcriptional regulatory element to achieve cell type-specific knockdown of the NEMO scaffold protein

The use of alternative promoters for the cell type-specific expression of a given mRNA/protein is a common cell strategy. NEMO is a scaffold protein required for canonical NF-κB signaling. Transcription of the NEMO gene is primarily controlled by two promoters: one (promoter B) drives NEMO transcription in most cell types and the second (promoter D) is largely responsible for NEMO transcription in liver cells. Herein, we have used a CRISPR/Cas9-based approach to disrupt a core sequence element of promoter B, and this genetic editing essentially eliminates expression of NEMO mRNA and protein in 293T human kidney cells. By cell subcloning, we have isolated targeted 293T cell lines that express no detectable NEMO protein, have defined genomic alterations at promoter B, and do not support activation of canonical NF-κB signaling in response to treatment with tumor necrosis factor. Nevertheless, non-canonical NF-κB signaling is intact in these NEMO-deficient cells. Expression of ectopic wild-type NEMO, but not certain human NEMO disease mutants, in the edited cells restores downstream NF-κB signaling in response to tumor necrosis factor. Targeting of the promoter B element does not substantially reduce NEMO expression (from promoter D) in the human SNU-423 liver cancer cell line. Thus, we have created a strategy for selectively eliminating cell type-specific expression from an alternative promoter and have generated 293T cell lines with a functional knockout of NEMO. The implications of these findings for further studies and for therapeutic approaches to target canonical NF-κB signaling are discussed.
Quickest Search for a Change Point

This paper considers a sequence of random variables that undergo periods of transient changes at an unknown set of time instants, referred to as transient change-points. The objective is to constantly monitor the sequence in order to detect one of the change-points subject to a hard constraint on the detection delay, while in parallel, the rate of false alarms is controlled. This setting is fundamentally different from the conventional change-point detection problems, in which there exists at most one change-point that can be either persistent or transient. In this paper, the exact optimal decision rules are characterized. Furthermore, it is shown that in the special case that the objective is detecting a transient change-point at exactly the instant that a change occurs (i.e., no detection delay), the test reduces to the well-known Shewhart test. Numerical evaluations are also provided to assess the performance of the decision rules.
A low-cost and high-precision scanning electrochemical microscope built with open source tools

Abstract A low-cost Scanning Electrochemical Microscope (SECM) was built with a 0.6 pA current measurement capability potentiostat and submicron resolution motorized stage, using open source software and hardware tools. The high performance potentiostat with a Python graphical user interface was built based on an open source project. Stepper motors, Arduino controller, a manual XY micromanipulator stage, 3D printed couplers and gears were used in building the motorized stage. An open source motor control software was used for moving the motorized stage with high precision. An inverted microscope was utilized for viewing a standard microelectrode while scanning. The setup was tested in the formation of a map of electrochemical signals from an array of pores on a parafilm membrane.
Fast Authentication from Aggregate Signatures with Improved Security

An attempt to derive signer-efficient digital signatures from aggregate signatures was made in a signature scheme referred to as Structure-free Compact Rapid Authentication (SCRA) (IEEE TIFS 2017). In this paper, we first mount a practical universal forgery attack against the NTRU instantiation of SCRA by observing only 8161 signatures. Second, we propose a new signature scheme (\(\texttt {FAAS}\)), which transforms any single-signer aggregate signature scheme into a signer-efficient scheme. We show two efficient instantiations of \(\texttt {FAAS}\), namely, \(\texttt {FAAS}\hbox {-}{} \texttt {NTRU}\) and \(\texttt {FAAS}\hbox {-}{} \texttt {RSA}\), both of which achieve high computational efficiency. Our experiments confirmed that \(\texttt {FAAS}\) schemes achieve up to 100\(\times \) faster signature generation compared to their underlying schemes. Moreover, \(\texttt {FAAS}\) schemes eliminate some of the costly operations such as Gaussian sampling, rejection sampling, and exponentiation at the signature generation that are shown to be susceptible to side-channel attacks. This enables \(\texttt {FAAS}\) schemes to enhance the security and efficiency of their underlying schemes. Finally, we prove that \(\texttt {FAAS}\) schemes are secure (in random oracle model), and open-source both our attack and \(\texttt {FAAS}\) implementations for public testing purposes.
Sequence specificity despite intrinsic disorder: How a disease-associated Val/Met polymorphism rearranges tertiary interactions in a long disordered protein.

The role of electrostatic interactions and mutations that change charge states in intrinsically disordered proteins (IDPs) is well-established, but many disease-associated mutations in IDPs are charge-neutral. The Val66Met single nucleotide polymorphism (SNP) in precursor brain-derived neurotrophic factor (BDNF) is one of the earliest SNPs to be associated with neuropsychiatric disorders, and the underlying molecular mechanism is unknown. Here we report on over 250 μs of fully-atomistic, explicit solvent, temperature replica-exchange molecular dynamics (MD) simulations of the 91 residue BDNF prodomain, for both the V66 and M66 sequence. The simulations were able to correctly reproduce the location of both local and non-local secondary structure changes due to the Val66Met mutation, when compared with NMR spectroscopy. We find that the change in local structure is mediated via entropic and sequence specific effects. We developed a hierarchical sequence-based framework for analysis and conceptualization, which first identifies “blobs” of 4-15 residues representing local globular regions or linkers. We use this framework within a novel test for enrichment of higher-order (tertiary) structure in disordered proteins; the size and shape of each blob is extracted from MD simulation of the real protein (RP), and used to parameterize a self-avoiding heterogenous polymer (SAHP). The SAHP version of the BDNF prodomain suggested a protein segmented into three regions, with a central long, highly disordered polyampholyte linker separating two globular regions. This effective segmentation was also observed in full simulations of the RP, but the Val66Met substitution significantly increased interactions across the linker, as well as the number of participating residues. The Val66Met substitution replaces β-bridging between V66 and V94 (on either side of the linker) with specific side-chain interactions between M66 and M95. The protein backbone in the vicinity of M95 is then free to form β-bridges with residues 31-41 near the N-terminus, which condenses the protein. A significant role for Met/Met interactions is consistent with previously-observed non-local effects of the Val66Met SNP, as well as established interactions between the Met66 sequence and a Met-rich receptor that initiates neuronal growth cone retraction.
Regular session — Energy management…

This session aims to discuss on various aspects in management of energy such as: • Smart grids; • Pricing Strategy of the Social Commerce; • Engineering data; • Models for a fair relief distribution; • …
Beam Acquisition and Training in Millimeter Wave Networks With Narrowband Pilots

This paper studies initial beam acquisition in a millimeter wave network consisting of multiple access points (APs) and mobile devices. A training protocol for joint estimation of transmit and receive beams is presented with a general frame structure consisting of an initial access sub-frame followed by data transmission sub-frames. During the initial subframe, APs and mobiles sweep through a set of beams and determine the best transmit and receive beams via a handshake. All pilot signals are narrowband (tones), and the mobiles are distinguished by their assigned pilot frequencies. Both non-coherent and coherent beam estimation methods based on, respectively, power detection and maximum likelihood (ML) are presented. To avoid exchanging information about beamforming vectors between APs and mobiles, a local maximum likelihood (LML) algorithm is also presented. An efficient fast Fourier transform implementation is proposed for ML and LML to achieve high-resolution. A system-level optimization is performed in which the frame length, training time, and training bandwidth are selected to maximize a rate objective taking into account blockage and mobility. Simulation results based on a realistic network topology are presented to compare the performance of different estimation methods and training codebooks, and demonstrate the effectiveness of the proposed protocol.
Convex Stochastic Dominance in Bayesian Localization, Filtering, and Controlled Sensing POMDPs

This paper provides conditions on the observation probability distribution in Bayesian localization and optimal filtering so that the conditional mean estimate satisfies convex stochastic dominance. Convex dominance allows us to compare the unconditional mean square error between two optimal Bayesian state estimators over arbitrary time horizons instead of using brute force Monte-Carlo computations. The proof uses two key ideas from microeconomics, namely, integral precision dominance and aggregation of single crossing. The convex dominance result is then used to give sufficient conditions so that the optimal policy of a controlled sensing two-state partially observed Markov decision process (POMDP) is lower bounded by a myopic policy. Numerical examples are presented where the Shannon capacity of the observation distribution using one sensor dominates that of another, and convex dominance holds but Blackwell dominance does not hold. These illustrate the usefulness of the main result in localization, filtering and controlled sensing applications.
FGF23 and Associated Disorders of Phosphate Wasting

Fibroblast growth factor 23 (FGF23), one of the endocrine fibroblast growth factors, is a principal regulator in the maintenance of serum phosphorus concentration. Binding to its cofactor αKlotho and a fibroblast growth factor receptor is essential for its activity. Its regulation and interaction with other factors in the bone-parathyroid-kidney axis is complex. FGF23 reduces serum phosphorus concentration through decreased reabsorption of phosphorus in the kidney and by decreasing 1,25 dihydroxyvitamin D (1,25(OH)2D) concentrations. Various FGF23-mediated disorders of renal phosphate wasting share similar clinical and biochemical features. The most common of these is X-linked hypophosphatemia (XLH). Additional disorders of FGF23 excess include autosomal dominant hypophosphatemic rickets, autosomal recessive hypophosphatemic rickets, fibrous dysplasia, and tumor-induced osteomalacia. Treatment is challenging, requiring careful monitoring and titration of dosages to optimize effectiveness and to balance side effects. Conventional therapy for XLH and other disorders of FGF23-mediated hypophosphatemia involves multiple daily doses of oral phosphate salts and active vitamin D analogs, such as calcitriol or alfacalcidol. Additional treatments may be used to help address side effects of conventional therapy such as thiazides to address hypercalciuria or nephrocalcinosis, and calcimimetics to manage hyperparathyroidism. The recent development and approval of an anti-FGF23 antibody, burosumab, for use in XLH provides a novel treatment option.
Airborne SAR Focusing in the Presence of Severe Squint Variations

In this work, a time-domain procedure is presented to focus airborne Synthetic Aperture Radar (SAR) data collected in the presence of strong variations of the squint angle during the radar acquisition. The approach is tested on two datasets acquired by two different X-band airborne SAR systems.
Privacy, Utility, and Cognitive Load in Remote Presence Systems

As robotics technology improves, remotely-operated telepresence robots will become more prevalent in homes and businesses, allowing guests, business partners, and contractors to visit and accomplish tasks without being physically present. These devices raise new privacy concerns: a telepresence robot may be used by a remote operator to spy on the local area, or recorded video may be viewed by a third party. Video filtering is one method of reducing spying ability while still allowing the remote operator to perform their task. In this paper, we examine the effects of three different visual conditions (filters) on the remote operator’s ability to discern details while completing a navigation task. We found that applying such filters protected privacy without significantly affecting the operator’s ability to perform the task, and that a depth image filter was the most effective privacy protector. We also found that the cognitive load of driving the robot has a slight privacy-protecting effect.
A probabilistic case for a large missing carbon sink on Mars after 3.5 billion years ago

Abstract Mars has a thin (6 mbar) CO2 atmosphere currently. There is strong evidence for paleolakes and rivers formed by warm climates on Mars, including after 3.5 billion years (Ga) ago, which indicates that a CO2 atmosphere thick enough to permit a warm climate was present at these times. Since Mars no longer has a thick CO2 atmosphere, it must have been lost. One possibility is that Martian CO2 was lost to space. Oxygen escape rates from Mars are high enough to account for loss of a thick CO2 atmosphere, if CO2 was the main source of escaping O. But here, using H isotope ratios, O escape calculations, and quantification of the surface O sinks on Mars, we show for the first time that O escape from Mars after 3.5 Ga must have been predominantly associated with the loss of H2O, not CO2, and therefore it is unlikely that ≥250 mbar Martian CO2 has been lost to space in the last 3.5 Ga, because such results require highly unfavored O loss scenarios. It is possible that the presence of young rivers and lakes on Mars could be reconciled with limited CO2 loss to space if crater chronologies on Mars are sufficiently incorrect that all apparently young rivers and lakes are actually older than 3.5 Ga, or if climate solutions exist for sustained runoff on Mars with atmospheric CO2 pressure
Extended emission and star formation in I Zw 18

The results of sky limited emission-line (H-alpha and forbidden O III 5007A) and line-free (6450 A and 5100 A) continuum imagery of the super-metal-poor blue compact galaxy I Zw 18 are reported. The main body of star formation in the galaxy is surrounded by a 3 kpc diameter extended envelope in H-alpha. In addition, there exist arcs of emission emanating from both central H II regions and winding around to the west, as well as a broader ridge of emission extending northward from the main body. The total mass of ionized material is estimated to be 3 x 10 to the 6th solar masses, or about 5 percent of the mass of the observed H I clouds. Several extended stellar continuum regions exist to the northwest of the main body, the brightest three forming a line along PA = 310 deg out to a 3 kpc distance from the central region of the galaxy. The correlation of the morphology and location of stellar continuum and emission regions with the distribution of H I is discussed. It is calculated that I Zw 18 has had previous, but disconnected, episodes of star formation, possibly from chance collisions of widely separated H Imore » clouds. 17 refs.« less
Singularity formation for a fluid mechanics model with nonlocal velocity

We study a 1D fluid mechanics model with nonlocal velocity. The equation can be viewed as a fractional porous medium flow, a 1D model of the quasi-geostrophic equation, and also a special case of Euler-Alignment system. For strictly positive smooth initial data, global regularity has been proved by Do, Kiselev, Ryzhik and Tan. We construct a family of non-negative smooth initial data so that solution loses $C^1$ regularity. Our result indicates that strict positivity is a critical condition to ensure global regularity of the system. We also extend our construction to the corresponding models in multi-dimensions.
Reorganization of the Primate Dorsal Horn in Response to a Deafferentation Lesion Affecting Hand Function

The loss of sensory input following a spinal deafferentation injury can be debilitating, and this is especially true in primates when the hand is involved. Although significant recovery of function occurs, little is currently understood about the reorganization of the neuronal circuitry, particularly within the dorsal horn. This region receives primary afferent input from the periphery, and cortical input via the somatosensory subcomponent of the corticospinal tract (S1 CST), and is critically important in modulating sensory transmission, both in normal and lesioned states. To determine how dorsal horn circuitry alters to facilitate recovery post-injury, we used an established deafferentation lesion model (dorsal root/dorsal column) in male monkeys to remove sensory input from just the opposing digits (digits 1-3) of one hand. This results in a deficit in fine dexterity that recovers over several months. Electrophysiological mapping, tract tracing, and immunolabeling techniques were combined to delineate specific changes to dorsal horn input circuitry. Our main findings show that (1) there is complementary sprouting of the primary afferent and S1 CST populations into an overlapping region of the reorganizing dorsal horn; (2) S1 CST and primary afferent inputs connect in different ways within this region to facilitate sensory integration; and (3) there is a loss of larger S1 CST terminal boutons in the affected dorsal horn, but no change in the size profile of the spared/sprouted primary afferent terminal boutons post-lesion. Understanding such changes helps to inform new and targeted therapies that best promote recovery.SIGNIFICANCE STATEMENT Spinal injuries that remove sensation from the hand, can be debilitating, though functional recovery does occur. We examined changes to the neuronal circuitry of the dorsal horn in monkeys following a lesion that deafferented three digits of one hand. Little is understood about dorsal horn circuitry, despite the fact that this region loses most of its normal input after such an injury, and is clearly a major focus of reorganization. We found that both the spared primary afferents and somatosensory corticospinal efferents sprouted in an overlapping region of the dorsal horn after injury, and that larger (presumably faster) corticospinal terminals are lost, suggesting a significantly altered cortical modulation of primary afferents. Understanding this changing circuitry is important for designing targeted therapies.
Reducing approximate longest common subsequence to approximate edit distance

Given a pair of n-character strings, the problems of computing their Longest Common Subsequence and Edit Distance have been extensively studied for decades. For exact algorithms, LCS and Edit Distance (with character insertions and deletions) are equivalent; the state of the art running time is (almost) quadratic in n, and this is tight under plausible fine-grained complexity assumptions. But for approximation algorithms the picture is different: there is a long line of works with improved approximation factors for Edit Distance, but for LCS (with binary strings) only a trivial 1/2-approximation was known. In this work we give a reduction from approximate LCS to approximate Edit Distance, yielding the first efficient (1/2 + ϵ)-approximation algorithm for LCS for some constant ϵ > 0.
Unsupervised Traffic Accident Detection in First-Person Videos

Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems. However, most work on video anomaly detection suffers from two crucial drawbacks. First, they assume cameras are fixed and videos have static backgrounds, which is reasonable for surveillance applications but not for vehicle-mounted cameras. Second, they pose the problem as one-class classification, relying on arduously hand-labeled training datasets that limit recognition to anomaly categories that have been explicitly trained. This paper proposes an unsupervised approach for traffic accident detection in first-person (dashboard-mounted camera) videos. Our major novelty is to detect anomalies by predicting the future locations of traffic participants and then monitoring the prediction accuracy and consistency metrics with three different strategies. We evaluate our approach using a new dataset of diverse traffic accidents, AnAn Accident Detection (A3D), as well as another publicly-available dataset. Experimental results show that our approach outperforms the state-of-the-art. Code and the dataset developed in this work are available at: https:llgithub.comlMoonBtvdltad-IROS2019
Context-Dependent Decision Making in a Premotor Circuit.

Summary Cognitive capacities afford contingent associations between sensory information and behavioral responses. We studied this problem using an olfactory delayed match to sample task whereby a sample odor specifies the association between a subsequent test odor and rewarding action. Multi-neuron recordings revealed representations of the sample and test odors in olfactory sensory and association cortex, which were sufficient to identify the test odor as match or non-match. Yet, inactivation of a downstream premotor area (ALM), but not orbitofrontal cortex, confined to the epoch preceding the test odor led to gross impairment. Olfactory decisions that were not context-dependent were unimpaired. Therefore, ALM does not receive the outcome of a match/non-match decision from upstream areas. It receives contextual information—the identity of the sample—to establish the mapping between test odor and action. A novel population of pyramidal neurons in ALM layer 2 may mediate this process.
Inference on a New Class of Sample Average Treatment Effects

We derive new variance formulas for inference on a general class of estimands of causal average treatment effects in a randomized control trial. We generalize the seminal work of Robins and show th...
Joint Information Freshness and Completion Time Optimization for Vehicular Networks

The demand for real-time cloud applications has seen an unprecedented growth over the past decade. These applications require rapidly data transfer and fast computations. This paper considers a scenario where multiple IoT devices update information on the cloud, and request a computation from the cloud at certain times. The time required to complete the request for computation includes the time to wait for computation to start on busy virtual machines, performing the computation, waiting and service in the networking stage for delivering the output to the end user. In this context, the freshness of the information is an important concern and is different from the completion time. This paper proposes novel scheduling strategies for both computation and networking stages. Based on these strategies, the age-of-information (AoI) metric and the completion time are characterized. A convex combination of the two metrics is optimized over the scheduling parameters. The problem is shown to be convex and thus can be solved optimally. Moreover, based on the offline policy, an online algorithm for job scheduling is developed. Numerical results demonstrate significant improvement as compared to the considered baselines.
Demand-Aware Career Path Recommendations: A Reinforcement Learning Approach

A skill’s value depends on dynamic market conditions. To remain marketable, contractors need to keep reskilling themselves continuously. But choosing new skills to learn is an inherently hard task:...
Nested Reinforcement Learning Based Control for Protective Relays in Power Distribution Systems

This paper envisions a new control architecture for the protective relay setting in future power distribution systems. With deepening penetration of distributed energy resources at the end users level, it has been recognized as a key engineering challenge to redesign the protective relays in future distribution systems. The key technical difficulty lies in how to set up the control logic of relays so that they could accurately detect faulty conditions. The performance of traditional protection settings are limited by insufficient fault current either due to current limit of power electronics or high fault impedance. This paper proposes a new nested deep reinforcement learning approach to take advantage of the structural property of distribution networks and develops a new set of training methods for tuning the protective relays.
Optical induction of autophagy via Transcription factor EB (TFEB) reduces pathological tau in neurons

Pathological accumulation of microtubule associated protein tau in neurons is a major neuropathological hallmark of Alzheimer’s disease (AD) and related tauopathies. Several attempts have been made to promote clearance of pathological tau (p-Tau) from neurons. Transcription factor EB (TFEB) has shown to clear p-Tau from neurons via autophagy. However, sustained TFEB activation and autophagy can create burden on cellular bioenergetics and can be deleterious. Here, we modified previously described two-plasmid systems of Light Activated Protein (LAP) from bacterial transcription factor—EL222 and Light Responsive Element (LRE) to encode TFEB. Upon blue-light (465 nm) illumination, the conformation changes in LAP induced LRE-driven expression of TFEB, its nuclear entry, TFEB-mediated expression of autophagy-lysosomal genes and clearance of p-Tau from neuronal cells and AD patient-derived human iPSC-neurons. Turning the blue-light off reversed the expression of TFEB-target genes and attenuated p-Tau clearance. Together, these results suggest that optically regulated TFEB expression unlocks the potential of opto-therapeutics to treat AD and other dementias.
Joint Optimization of Waveform Covariance Matrix and Antenna Selection for MIMO Radar

In this paper, we investigate the problem of jointly optimizing the waveform covariance matrix and the antenna position vector for multiple-input-multiple-output (MIMO) radar systems to approximate a desired transmit beampattern as well as to minimize the cross-correlation of the received signals reflected back from the targets. We formulate the problem as a non-convex program and then propose a cyclic optimization approach to efficiently tackle the problem. We further propose a novel local optimization framework in order to efficiently design the corresponding antenna positions. Our numerical investigations demonstrate a good performance both in terms of accuracy and computational complexity, making the proposed framework a good candidate for real-time radar signal processing applications.