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January 12, 2015

  • Date:19TuesdayDecember 2023

    From Randomness to Function: de novo Proteins as a Source of Molecular and Cellular Innovation

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    Time
    11:00 - 12:00
    Location
    Nella and Leon Benoziyo Building for Biological Sciences
    LecturerDr. Idan Frumkin
    Life Sciences, Massachusetts Institute of Technology (MIT), USA
    Organizer
    Department of Biomolecular Sciences
    Contact
    AbstractShow full text abstract about How do novel genes emerge? While new genes often evolve from...»
    How do novel genes emerge? While new genes often evolve from older ones, novelty can also originate from random sequences in a process termed "de novo gene birth". The functions of such de novo genes and how they integrate into complex cellular systems are poorly understood. By screening a library of 100 million random proteins with no sequence similarity to existing proteins, we identified thousands of functional proteins promoting E. coli survival against a toxin or a bacterial virus. Using genetic and biochemical tools, we characterized selected random proteins and revealed they integrate into pre-existing cellular pathways to mitigate cellular threats. Our work provides a mechanistic basis for understanding how de novo gene birth can yield functional proteins that effectively benefit cells evolving under stress.

    Lecture
  • Date:20WednesdayDecember 2023

    "Shedding light on the dark matter of viral proteomes to advance our understanding of antiviral immunity"

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    Time
    10:00 - 12:00
    Location
    Nella and Leon Benoziyo Building for Biological Sciences
    LecturerDr. Shira Weingarten-Gabbay
    Broad Institute of MIT and Harvard
    Organizer
    Department of Molecular Cell Biology
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    Lecture
  • Date:21ThursdayDecember 2023

    Geometric Functional Analysis and Probability Seminar

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    Time
    13:30 - 14:30
    Title
    Gaps of Fourier Quasicrystals and Lee-Yang Polynomials
    Location
    Jacob Ziskind Building
    LecturerLior Alon
    MIT
    Organizer
    Department of Mathematics
    Contact
    AbstractShow full text abstract about The concept of "quasi-periodic" sets, functions, a...»
    The concept of "quasi-periodic" sets, functions, and measures is

    prevalent in diverse mathematical fields such as Mathematical Physics,

    Fourier Analysis, and Number Theory. The Poisson summation formula provides a “Fourier characterization” for discrete periodic sets, saying that the Fourier transform of the counting measure of a discrete periodic set is also a counting measure of a discrete periodic set. Fourier Quasicrystals (FQ) generalize this notion of periodicity: a counting measure of a discrete set is called a Fourier quasicrystal (FQ) if its Fourier transform is also a discrete atomic measure, together with some growth condition.   

     

    Recently Kurasov and Sarnak provided a method for construction of one-dimensional counting measures which are FQ (motivated by quantum graphs) using the torus zero sets of multivariate Lee-Yang polynomials. In this talk, I will show that the Kurasov-Sarnak construction generates all FQ counting measures in 1D.

     

    A discrete set on the real line is fully described by the gaps between consecutive points. A discrete periodic set has finitely many gaps. We show that a non-periodic FQ has uncountably many gaps, with a well-defined gap distribution. This distribution is given explicitly in terms of an ergodic dynamical system induced from irrational flow on the  torus.

     

    The talk is aimed at a broad audience, no prior knowledge in the field is assumed.

     

    Based on joint works with Alex Cohen and Cynthia Vinzant.
    Lecture
  • Date:26TuesdayDecember 2023

    The structure of protein complexes underlies co-translational assembly

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    Time
    14:00 - 15:00
    Location
    Gerhard M.J. Schmidt Lecture Hall
    Organizer
    Department of Chemical and Structural Biology
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    Lecture
  • Date:27WednesdayDecember 2023

    Membrane dynamics during giant vesicle secretion

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    Time
    10:00 - 11:00
    Location
    Arthur and Rochelle Belfer Building for Biomedical Research
    LecturerProf. Benny Shilo
    Dept of Molecular Genetics
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    Lecture
  • Date:27WednesdayDecember 2023

    Foundations of Computer Science Seminar

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    Time
    11:15 - 12:15
    Title
    Explicit Codes for Poly-Size Circuits and Functions that are Hard to Sample on Low Entropy Distributions
    Location
    Jacob Ziskind Building
    LecturerJad Silbak
    Northeastern University
    Organizer
    Department of Computer Science and Applied Mathematics
    Contact
    AbstractShow full text abstract about Guruswami and Smith (J. ACM 2016) considered codes for chann...»
    Guruswami and Smith (J. ACM 2016) considered codes for channels that are computationally bounded which modify at most a p-fraction of the bits of the codeword. This class of channels is significantly stronger than Shannon’s binary symmetric channel which flips each bit independently with probability p, but weaker than Hamming’s channels which may flip any p-fraction of bits and are computationally unbounded.

    Recently, there has been a growing body of work that aims to construct codes against channels that are computationally bounded (e.g., bounded memory channels, channels that are poly-size circuits). In this work, we consider bounded size channels and construct codes that:
    - Achieve an optimal rate of 1-H(p) (matching the rate of binary symmetric channels, and beating the rate of Hamming channels).
    - Are explicit, assuming E does not have a size 2^Ω(n) nondeterministic circuits.

    Achieving these codes implies circuit lower bounds (and therefore explicit constructions need to be based on hardness assumptions). This result builds on the recent result by Shaltiel and Silbak (FOCS 2022) that gave a randomized Monte-Carlo construction, rather than explicit codes.

    A key component in our codes (that we believe to be of independent interest) is a new complexity theoretic notion of hard to sample functions (HTS).
    Loosely speaking, a function f is HTS for circuits of size n^c, if for every randomized circuit A of size n^c that samples a distribution (X,Y) such that X has sufficiently large min-entropy, it holds that Pr[Y=f(X)] is small.
    This notion is inspired by a related notion introduced by Viola (SICOMP 2012) in which X is the uniform distribution and is similar in flavor to the definition of multi-collision-resistant hash functions. Building on classical works on “hardness amplification” (and using many additional tools and ideas from pseudorandomness) we construct HTS functions under hardness assumptions.

    This is a joint work with Ronen Shaltiel.
    Lecture
  • Date:27WednesdayDecember 2023

    LS Luncheon

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    Time
    12:00 - 14:00
    Title
    The tumor micro(b)environment and its effects on response to therapy
    Location
    Nella and Leon Benoziyo Building for Biological Sciences
    LecturerProf. Ravid Straussman
    Dept of Molecular Cell Biology
    Contact
    Lecture
  • Date:28ThursdayDecember 2023

    Vision and AI

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    Time
    12:15 - 13:15
    Title
    A theory of Unsupervised Translation Motivated by Understanding whale communication
    Location
    Jacob Ziskind Building
    LecturerShafi Goldwasser
    Simons Institute, UC Berkeley
    Organizer
    Department of Computer Science and Applied Mathematics
    Contact
    AbstractShow full text abstract about Neural networks are capable of translating between languages...»
    Neural networks are capable of translating between languages—in some cases even between two languages where there is little or no access to parallel translations, in what is known as Unsupervised Machine Translation (UMT). Given this progress, it is intriguing to ask whether machine learning tools can ultimately enable understanding animal communication, particularly that of highly intelligent animals. We propose a theoretical framework for analyzing UMT when no parallel translations are available and when it cannot be assumed that the source and target corpora address related subject domains or posses similar linguistic structure. 
    Lecture
  • Date:28ThursdayDecember 2023

    Geometric Functional Analysis and Probability Seminar

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    Time
    13:30 - 14:30
    Title
    Persistence of stationary Gaussian fields with spectral singularity: from universality to entropic repulsion
    Location
    Jacob Ziskind Building
    LecturerOhad Noy Feldheim
    HUJI
    Organizer
    Department of Mathematics
    Contact
    AbstractShow full text abstract about We study the persistence event ${f(x)>0 for all x in D}$ ...»
    We study the persistence event ${f(x)>0 for all x in D}$ for stationary Gaussian fields on d dimensional Euclidean space, with a spectral singularity at the origin of order $alpha$, i.e. such that their spectral density at the origin behaves like $|lambda|^{-alpha}$. Our main result is that, under certain mild regularity conditions, the probability of persistence on a large ball $D=B(T)$ decays at a universal log-asymptotic rate of $m (d-alpha)(log T) capa(B(T))$, where $capa(B(T))$ is the capacity of $B(T)$ with respect to the field, and $m$ is the mass of the absolutely continuous component of its spectrum. This generalises a result of Bolthausen, Deuschel and Zeitouni for the Gaussian free field (GFF) on $^d$, $d ge 3$, to a wide class of Gaussian fields with spectral singularity.  Using this estimate, we establish an `entropic repulsion’ phenomenon,  showing that the field conditioned to persist is tending in distribution to an independent copy of the unconditioned process, shifted by a constant level of $sqrt{m (d-alpha)(log T)}$.

     

    This work is the first to introduce the notions of capacity and equilibrium potential to the spectral theory of persistence. In the course of the talk I will explain these notions, provide an overview of the proof and present the role of these notions in improving the precision of persistence probability estimates. I will also try to put this work in the context of current study of rare events of stationary Gaussian processes. No prior knowledge of the subject is assumed.

     

    Joint work with Naomi Feldheim from Bar-Ilan and Stephen Muirhead from Melbourne.
    Lecture
  • Date:31SundayDecember 2023

    The Clore Center for Biological Physics

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    Time
    13:15 - 14:15
    Title
    Erasing information fast and cheap --- How to approach Landauer’s bound?
    Location
    Edna and K.B. Weissman Building of Physical Sciences
    Organizer
    Clore Center for Biological Physics
    Contact
    AbstractShow full text abstract about The celebrated Landauer bound is the fundamental universal c...»
    The celebrated Landauer bound is the fundamental universal cost of computation: there must be dissipation of at least kBTlog2 per erasure of one bit. This fundamental bound is reached when the erasure protocol is performed in the slow quasi-static limit. Generally, the faster the erasure protocol, the more dissipation is generated.
    In this talk, I will present two approaches that challenge this view. First, it will be shown that by the use of a conserved quantity in the system, one can bypass Liouville’s theorem and perform erasure at zero energetic cost. The second approach that will be discussed is considering a system that is weakly coupled to the environment. In that case, one can design an erasure procedure that does not scale with its operation time.
    Lecture
  • Date:02TuesdayJanuary 2024

    A paradigm shift in GPCR recruitment and activity: GPCR Voltage Dependence Controls Neuronal Plasticity and Behavior

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    Time
    All day
    Location
    Gerhard M.J. Schmidt Lecture Hall
    LecturerProf. Moshe Parnas
    Dept of Physiology and Pharmacology Tel Aviv University
    Organizer
    Department of Brain Sciences
    Contact
    AbstractShow full text abstract about : G-protein coupled receptors (GPCRs) play a paramount role ...»
    : G-protein coupled receptors (GPCRs) play a paramount role in diverse brain functions. Twenty years ago, GPCR activity was shown to be regulated by membrane potential in vitro, but whether the voltage dependence of GPCRs contributes to neuronal coding and behavioral output under physiological conditions in vivo has never been demonstrated. We show in two different processes that muscarinic GPCR mediated neuromodulation in vivo is voltage dependent. First, we show that muscarinic type A receptors (mAChR-A) mediated neuronal potentiation is voltage dependent. This potentiation voltage dependency is abolished in mutant flies expressing a voltage independent receptor. Most important, muscarinic receptor voltage independence caused a strong behavioral effect of increased odor habituation. Second, we show that muscarinic type B receptors (mAChR-B) voltage dependency is required for both efficient and accurate learning and memory. Normally, to prevent non-specific olfactory learning and memory, mAChR-B activity suppress both signals that are required for plasticity. Behavior experiments demonstrate that mAChR-B knockdown impairs olfactory learning by inducing undesired changes to the valence of an odor that was not associated with the reinforcer. On the other hand, mAChR-B voltage dependence prevents mAChR-B to interfere with plasticity in neurons that are required for the learning and memory process. Indeed, generating flies with a voltage independent mAChR-B resulted in impaired learning. Thus, we provide the very first demonstrations of physiological roles for the voltage dependency of GPCRs by demonstrating crucial involvement of GPCR voltage dependence in neuronal plasticity and behavior. As such, our findings create a paradigm shift in our thinking on GPCR recruitment and activity. Together, we suggest that GPCR voltage dependency plays a role in many diverse neuronal functions including learning and memory and may serve as a target for novel drug development.
    Light refreshments before the seminar.
    Lecture
  • Date:02TuesdayJanuary 2024

    Enhanced Growth in Atomic Layer Deposition of Transition Metals: The Role of Surface Diffusion and Nucleation Sites

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    Time
    11:15 - 12:15
    Location
    Gerhard M.J. Schmidt Lecture Hall
    LecturerDr. Amnon Rothman
    Chemical Engineering, Stanford University
    Organizer
    Department of Molecular Chemistry and Materials Science
    Contact
    AbstractShow full text abstract about Noble metal thin films have attracted significant interest o...»
    Noble metal thin films have attracted significant interest owing to their distinctive properties and structures, which make them ideal for applications in microelectronics, catalysis, energy, and photovoltaics. While several parameters influence the properties of these metals for such applications, the deposition process remains a critical factor. Atomic Layer Deposition (ALD) stands out as a prevalent deposition technique due to its surface-sensitive nature. The ALD process is characterized by its self-limiting surface reactions, promoting a layer-by-layer growth mechanism and allowing for precise control over film thickness and conformality. However, challenges arise in achieving continuous, pinhole-free noble metal ALD layers on oxide surfaces, often resulting in low film quality. These challenges can be traced back to the lack of adequate nucleation sites and the poor wettability of the low-surface energy substrates. The research studied the impact of substrate surface functionalization using organometallic molecules, such as trimethylaluminum (TMA) and diethylzinc (DEZ), on the nucleation and growth of Ru layers. The results reveal an enhancement in both nucleation density and the average diameter of the Ru nanoparticles deposited, and these improvements were attributed to an increase in both nucleation sites and elevated surface diffusivity. The latter effect is speculated to result from a reduction in the substrate's surface free energy. The study also examines the influence of substrate surface characteristics, including surface termination and crystallinity, on the nucleation and growth of Ru metal via ALD. The morphologies of the resulting Ru thin films are studied using scanning electron microscopy (SEM), atomic force microscopy (AFM), and grazing incidence small angle x-ray scattering (GISAXS). These analytical results are integrated with an experimental model to elucidate the differences in growth mechanisms observed across substrates. The findings underscore the importance of substrate choice in the ALD process and broaden our understanding of Ru metal growth. This research serves as an important step in optimizing the ALD process for various applications by tailoring substrate selection.
    Lecture
  • Date:03WednesdayJanuary 2024

    Special Guest Seminar: Dr. Sharon Fleischer

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    Time
    10:00 - 12:00
    Title
    Stem cell based cardiac tissue models to study the human heart in health and disease
    Location
    Wolfson Building for Biological Research
    LecturerDr. Sharon Fleischer
    Columbia University
    Organizer
    Department of Molecular Cell Biology
    Contact
    Lecture
  • Date:03WednesdayJanuary 2024

    Machine Learning and Statistics Seminar

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    Time
    11:15 - 12:15
    Title
    Theoretical Foundations of Neural Networks: Expressiveness, Optimization, and Generalization
    Location
    Jacob Ziskind Building
    Organizer
    Department of Computer Science and Applied Mathematics
    Contact
    AbstractShow full text abstract about In recent years neural networks achieved state-of-the-art re...»
    In recent years neural networks achieved state-of-the-art results in many different applications such as computer vision, natural language processing, and speech recognition. Despite their empirical success, the theoretical understanding of neural networks remains limited. This gap can be seen in all aspects of learning: optimization, generalization, and expressiveness. My thesis aims to address this gap by providing theoretical insights into neural network success, bridging the practical and theoretical aspects. In this talk, I focus on the three following topics:
    (1) Expressiveness: We study the memorization capabilities of neural networks, and provide a construction for memorization of $N$ labeled samples using a network with $ ilde{O}(sqrt{N})$ parameters, which is optimal up to log factors due to known lower bounds. Our analysis underscores the crucial role of depth in achieving near-optimal memorization capacity.
    (2) Optimization: Investigating the effects of mild over-parameterization on the optimization landscape of a simple ReLU network in a well-studied teacher-student setting.  We prove that the optimization landscape changes significantly between exact and over-parameterized regimes. For example, over-parameterization can turn local minima into saddle points in certain cases, making the landscape more favorable.  

    (3) Generalization: Exploring the generalization properties of interpolating 2-layer neural networks in the presence of label noise. We prove that in this overfitting regime, the input dimension plays a major role in the type of overfitting the network achieves. Namely, in dimension 1 the overfitting scales with the magnitude of the noise (tempered overfitting), while in high dimensions it converges to zero with the input dimension (benign overfitting).

     
    Lecture
  • Date:03WednesdayJanuary 2024

    Chemical and Biological Physics Guest seminar

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    Time
    15:00 - 16:00
    Title
    Atomic arrays as programmable quantum processors and sensors
    Location
    Gerhard M.J. Schmidt Lecture Hall
    LecturerDr Ran Finkelstein
    Caltech
    Organizer
    Department of Chemical and Biological Physics
    Contact
    AbstractShow full text abstract about Large arrays of trapped neutral atoms have emerged over the ...»
    Large arrays of trapped neutral atoms have emerged over the past few years as a promising platform for quantum information processing, combining inherent scalability with high-fidelity control and site-resolved readout. In this talk, I will discuss ongoing work with arrays of Alkaline-earth atoms. These divalent atoms offer unique properties stemming largely from their long-lived metastable states, which form the basis of the optical atomic clock. I will describe the design of a universal quantum processor based on clock qubits and its application in quantum metrology, and I will address the challenge of generating and benchmarking highly entangled states in an analog quantum simulator. First, we realize scalable local control of individual clock qubits, which we utilize to extend the Ramsey interrogation time beyond the coherence time of a single atom [1]. To realize a universal quantum processor, we demonstrate record high-fidelity two-qubit entangling gates mediated by Rydberg interactions, which we combine with dynamical reconfiguration to entangle clock probes in GHZ states and perform Ancilla-based detection [2]. We then use the narrow clock transition to measure and remove thermal excitations of atoms in tweezers (a technique known as erasure conversion) and generate hyperentangled states of motion and spin [3]. In the second part of the talk, I will describe a different approach for generating large scale entangled states in an analog quantum simulator configuration [4], including error mitigation [5] and benchmarking of a 60-atom simulator [6]. Together, these show the great promise and the large variety of experiments accessed with this emerging platform. [1] A. Shaw*, R. Finkelstein*, R. Tsai, P. Scholl, T. Yoon, J. Choi, M. Endres, Multi-ensemble metrology by programming local rotations with atom movements, arxiv:2303.16885, Nature Physics in press (2023). [2] R. Finkelstein, R. Tsai, A. Shaw, X. Sun, M. Endres, A universal quantum processor for entanglement enhanced optical tweezer clocks, in preparation. [3] P. Scholl*, A. Shaw*, R. Finkelstein*, R. Tsai, J. Choi, M. Endres, Erasure cooling, control, and hyper-entanglement of motion in optical tweezers, arXiv:2311.15580 (2023). [4] J. Choi, A. Shaw, I. Madjarov, X. Xie, R. Finkelstein, J. Covey, J. Cotler, D. Mark, H.Y. Huang, A. Kale, H. Pichler, F. Brandão, S. Choi, and M. Endres, Preparing random states and benchmarking with many-body quantum chaos, Nature 617 (2023) [5] P. Scholl, A. Shaw, R. Tsai, R. Finkelstein, J. Choi, M. Endres, Erasure conversion in a high-fidelity Rydberg quantum simulator, Nature 622 (2023). [6] A. Shaw, Z. Chen, J. Choi, D.K. Mark, P. Scholl, R. Finkelstein, A. Elben, S. Choi, M. Endres, Benchmarking highly entangled states on a 60-atom analog quantum simulator, arXiv:2308.07914 (2023).
    Lecture
  • Date:04ThursdayJanuary 2024

    Advancing MRI: Sequences and Applications

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    Time
    09:00 - 10:00
    Location
    Max and Lillian Candiotty Building
    LecturerDr. Edna Furman-Haran
    MRI Unit
    Organizer
    Department of Life Sciences Core Facilities
    Contact
    Lecture
  • Date:04ThursdayJanuary 2024

    Special Guest Seminar with Prof. Yegor Bazykin

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    Time
    10:00 - 11:00
    Title
    “Towards understanding and forecasting evolution of pathogenic viruses”
    Location
    Arthur and Rochelle Belfer Building for Biomedical Research
    LecturerProf. Yegor Bazykin
    Organizer
    Department of Molecular Genetics
    Contact
    Lecture
  • Date:04ThursdayJanuary 2024

    Vision and AI

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    Time
    12:15 - 13:15
    Title
    Generative Models for Abstractions
    Location
    Jacob Ziskind Building
    LecturerYael Vinker
    Tel Aviv University
    Organizer
    Department of Computer Science and Applied Mathematics
    Contact
    AbstractShow full text abstract about Humans often express, organize, and develop their thoughts v...»
    Humans often express, organize, and develop their thoughts visually, employing abstract representations like sketches, scribbles, and symbols. These abstract representations range from simple drawings made by kids to structured charts and diagrams made by researchers. Creating such abstract representations comes naturally to humans
    Lecture
  • Date:04ThursdayJanuary 2024

    Olfactory information processing: timing, sequences, geometry  and relevance

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    Time
    12:30 - 13:30
    Location
    Gerhard M.J. Schmidt Lecture Hall
    LecturerProf. Dmitry Rinberg
    Dept of Neuroscience and Physiology Neuroscience Institute NYU
    Organizer
    Department of Brain Sciences
    Contact
    Lecture
  • Date:04ThursdayJanuary 2024

    Geometric Functional Analysis and Probability Seminar

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    Time
    13:30 - 14:30
    Title
    Two applications of entropy in combinatorics
    Location
    Jacob Ziskind Building
    LecturerElad Tzalic
    WIS
    Organizer
    Department of Mathematics
    Contact
    AbstractShow full text abstract about In the talk I will present two applications of entropy in co...»
    In the talk I will present two applications of entropy in combinatorics and give tight bounds for the following problems:

    1. Let $G$ be a graph of maximum degree $d$. For two vertices $x,y$ how many shortest paths from $x$ to $y$ paths can there be in $G$? 

    2. A subset $S subseteq Z^d$ tiles the integer lattice $Z^d$ if one can write $Z^d$ as a union of disjoint translations of $S$. How many subsets of $[n]^d$ tile $Z^d$?

    Based on joint works with Itai Benjamini and Gady Kozma.
    Lecture

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