52 research outputs found
Integrating Sensing, Communication, and Power Transfer: Multiuser Beamforming Design
In the sixth-generation (6G) networks, massive low-power devices are expected
to sense environment and deliver tremendous data. To enhance the radio resource
efficiency, the integrated sensing and communication (ISAC) technique exploits
the sensing and communication functionalities of signals, while the
simultaneous wireless information and power transfer (SWIPT) techniques
utilizes the same signals as the carriers for both information and power
delivery. The further combination of ISAC and SWIPT leads to the advanced
technology namely integrated sensing, communication, and power transfer
(ISCPT). In this paper, a multi-user multiple-input multiple-output (MIMO)
ISCPT system is considered, where a base station equipped with multiple
antennas transmits messages to multiple information receivers (IRs), transfers
power to multiple energy receivers (ERs), and senses a target simultaneously.
The sensing target can be regarded as a point or an extended surface. When the
locations of IRs and ERs are separated, the MIMO beamforming designs are
optimized to improve the sensing performance while meeting the communication
and power transfer requirements. The resultant non-convex optimization problems
are solved based on a series of techniques including Schur complement
transformation and rank reduction. Moreover, when the IRs and ERs are
co-located, the power splitting factors are jointly optimized together with the
beamformers to balance the performance of communication and power transfer. To
better understand the performance of ISCPT, the target positioning problem is
further investigated. Simulations are conducted to verify the effectiveness of
our proposed designs, which also reveal a performance tradeoff among sensing,
communication, and power transfer.Comment: This paper has been submitted to IEEE for possible publicatio
Constructing and proving the ground state of a generalized Ising model by the cluster tree optimization algorithm
Generalized Ising models, also known as cluster expansions, are an important
tool in many areas of condensed-matter physics and materials science, as they
are often used in the study of lattice thermodynamics, solid-solid phase
transitions, magnetic and thermal properties of solids, and fluid mechanics.
However, the problem of finding the global ground state of generalized Ising
model has remained unresolved, with only a limited number of results for simple
systems known. We propose a method to efficiently find the periodic ground
state of a generalized Ising model of arbitrary complexity by a new algorithm
which we term cluster tree optimization. Importantly, we are able to show that
even in the case of an aperiodic ground state, our algorithm produces a
sequence of states with energy converging to the true ground state energy, with
a provable bound on error. Compared to the current state-of-the-art polytope
method, this algorithm eliminates the necessity of introducing an exponential
number of variables to counter frustration, and thus significantly improves
tractability. We believe that the cluster tree algorithm offers an intuitive
and efficient approach to finding and proving ground states of generalized
Ising Hamiltonians of arbitrary complexity, which will help validate
assumptions regarding local vs. global optimality in lattice models, as well as
offer insights into the low-energy behavior of highly frustrated systems
Construction of ground-state preserving sparse lattice models for predictive materials simulations
This work was supported primarily by the US Department of Energy (DOE) under Contract No. DE-FG02-96ER45571.First-principles based cluster expansion models are the dominant approach in ab initio thermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states. However, despite recent advances, the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation, since this property is not guaranteed by default. In this paper, we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data. The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters. The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes, i.e., Li2x Fe2(1-x)O2 and Li2x Ti2(1-x)O2, for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging. We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction, but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement. This method provides a general tool for building robust, compressed and constrained physical models with predictive power.Publisher PDFPeer reviewe
Angle-resolved photoemission spectroscopy with an tunable magnetic field
Angle-resolved photoemission spectroscopy (ARPES) is a powerful tool for
probing the momentum-resolved single-particle spectral function of materials.
Historically, magnetic fields have been carefully avoided as
they are detrimental to the control of photoelectron trajectory during the
photoelectron detection process. However, magnetic field is an important
experimental knob for both probing and tuning symmetry-breaking phases and
electronic topology in quantum materials. In this paper, we introduce an easily
implementable method for realizing an tunable magnetic field
at the sample position in an ARPES experiment and analyze magnetic field
induced artifacts in ARPES data. Specifically, we identified and quantified
three distinct extrinsic effects of a magnetic field: Fermi surface rotation,
momentum shrinking, and momentum broadening. We examined these effects in three
prototypical quantum materials, i.e., a topological insulator (BiSe),
an iron-based superconductor (LiFeAs), and a cuprate superconductor
(BiSrCaCuO), and demonstrate the feasibility of ARPES
measurements in the presence of a controllable magnetic field. Our studies lay
the foundation for the future development of the technique and interpretation
of ARPES measurements of field-tunable quantum phases.Comment: 23 pages, 6 figure
An LL-norm compressive sensing paradigm for the construction of sparse predictive lattice models using mixed integer quadratic programming
First-principles based lattice models allow the modeling of ab initio
thermodynamics of crystalline mixtures for applications such as the
construction of phase diagrams and the identification of ground state atomic
orderings. The recent development of compressive sensing approaches for the
construction of lattice models has further enabled the systematic construction
of sparse physical models without the need for human intuition other than
requiring the compactness of effective cluster interactions. However,
conventional compressive sensing based on L1-norm regularization is strictly
only applicable to certain classes of optimization problems and is otherwise
not guaranteed to generate optimally sparse and transferable results, so that
the method can only be applied to some materials science applications. In this
paper, we illustrate a more robust L0L1-norm compressive-sensing method that
removes the limitations of conventional compressive sensing and generally
results in sparser lattice models that are at least as predictive as those
obtained from L1-norm compressive sensing. Apart from the theory, a practical
implementation based on state-of-the-art mixed-integer quadratic programming
(MIQP) is proposed. The robustness of our methodology is illustrated for four
different transition-metal oxides with relevance as battery cathode materials:
Li2xTi2(1-x)O2, Li2xNi2yO2, MgxCr2O4, and NaxCrO2. This method provides a
practical and robust approach for the construction of sparser and more
predictive lattice models, improving on the compressive sensing paradigm and
making it applicable to a much broader range of applications.Comment: 25 pages, 3 figure
Finding and proving the exact ground state of a generalized Ising model by convex optimization and MAX-SAT
This paper was supported primarily by the US Department of Energy (DOE) under Contract No. DE-FG02-96ER45571. In addition, some of the test cases for ground states were supported by the Office of Naval Research under contract N00014-14-1-0444.Lattice models, also known as generalized Ising models or cluster expansions, are widely used in many areas of science and are routinely applied to the study of alloy thermodynamics, solid-solid phase transitions, magnetic and thermal properties of solids, fluid mechanics, and others. However, the problem of finding and proving the global ground state of a lattice model, which is essential for all of the aforementioned applications, has remained unresolved for relatively complex practical systems, with only a limited number of results for highly simplified systems known. In this paper, we present a practical and general algorithm that provides a provable periodically constrained ground state of a complex lattice model up to a given unit cell size and in many cases is able to prove global optimality over all other choices of unit cell. We transform the infinite-discrete-optimization problem into a pair of combinatorial optimization (MAX-SAT) and nonsmooth convex optimization (MAX-MIN) problems, which provide upper and lower bounds on the ground state energy, respectively. By systematically converging these bounds to each other, we may find and prove the exact ground state of realistic Hamiltonians whose exact solutions are difficult, if not impossible, to obtain via traditional methods. Considering that currently such practical Hamiltonians are solved using simulated annealing and genetic algorithms that are often unable to find the true global energy minimum and inherently cannot prove the optimality of their result, our paper opens the door to resolving longstanding uncertainties in lattice models of physical phenomena. An implementation of the algorithm is available at https://github.com/dkitch/maxsat-isingPublisher PDFPeer reviewe
MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision
Previous works concerning single-view hand-held object reconstruction
typically utilize supervision from 3D ground truth models, which are hard to
collect in real world. In contrast, abundant videos depicting hand-object
interactions can be accessed easily with low cost, although they only give
partial object observations with complex occlusion. In this paper, we present
MOHO to reconstruct hand-held object from a single image with multi-view
supervision from hand-object videos, tackling two predominant challenges
including object's self-occlusion and hand-induced occlusion. MOHO inputs
semantic features indicating visible object parts and geometric embeddings
provided by hand articulations as partial-to-full cues to resist object's
self-occlusion, so as to recover full shape of the object. Meanwhile, a novel
2D-3D hand-occlusion-aware training scheme following the synthetic-to-real
paradigm is proposed to release hand-induced occlusion. In the synthetic
pre-training stage, 2D-3D hand-object correlations are constructed by
supervising MOHO with rendered images to complete the hand-concealed regions of
the object in both 2D and 3D space. Subsequently, MOHO is finetuned in real
world by the mask-weighted volume rendering supervision adopting hand-object
correlations obtained during pre-training. Extensive experiments on HO3D and
DexYCB datasets demonstrate that 2D-supervised MOHO gains superior results
against 3D-supervised methods by a large margin. Codes and key assets will be
released soon
OR-051 Exploration of Potential Integrated Biomarkers for Sports Monitoring Based on Metabolic Profiling
Objective Metabolomic analysis is extensively applied to identify sensitive and specific biomarkers capable of reflecting pathological processes and physical responses or adaptations. Exercise training leads to profound metabolic changes, manifested as detectable alterations of metabolite levels and significant perturbations of metabolic pathways in sera, urine, and rarely, in saliva. Several metabolites have been exploited as biomarkers for generally evaluating physical states in almost all sports. However, alterations of metabolic profile caused by specific sports would be heterogeneous. Thus, developments of new techniques are eagerly required to identify characteristic metabolites as unique biomarkers for specifically accessing training stimulus and sports performances. In the present work, we conducted both metabolic profiling and a binary logistic regression model (BRM) of biological fluids derived from rowing ergometer test with the following aims: 1) to examine changes of metabolite profiles and identify characteristic metabolites in the samples of sera, urine, and saliva; 2) to screen out potential integrated biomarkers for sports-specific monitoring.
Methods A total of 11 rowers (6 male, 5 female; aged 15±1 years; 4±2 years rowing training) underwent an indoor 6000m rowing ergometer test. Samples of sera, urine and saliva were collected before and immediately after the test. 1D 1H NMR spectra were recorded with a Bruker Avance III 650 MHz NMR spectrometer. NMR spectra were processed and aligned, resonances of metabolites were assigned and confirmed, and metabolite levels were calculated based on NMR integrals. Multivariate statistical analysis was carried out using partial least-squares discrimination analysis (PLS-DA) to distinguish metabolic profiles between the groups. The validated PLS-DA model gave the variable importance in the projection (VIP) for a given metabolite. Moreover, inter-group comparisons of metabolite levels were quantitatively conducted using the paired-sample t-test. Then, we identified characteristic metabolites with VIP>1 in PLS-DA and p<0.05 in t-test. Furthermore, we screened out potential biomarkers based on the characteristic metabolites identified from the three types of biological fluids using the BRM (stepwise).
Results The rowing training induced profound changes of metabolic profiles in serum and saliva samples rather than in urine samples. Totally, 44 metabolites were assigned in which 19, 20, and 19 metabolites were identified from serum, urine and saliva samples, respectively. Seven metabolites were shared by the three types of samples. Moreover, five characteristic metabolites (pyruvate, lactate, succinate, N-acetyl-L-cysteine, and acetone) were identified from the serum samples. The elevated levels of pyruvate, lactate and succinate suggested that, the rowing training evidently promoted both oxidative phosphorylation and glycolysis pathways. Furthermore, three characteristic metabolites (tyrosine, formate, and methanol) were identified from the saliva samples. Given that tyrosine is the precursor of dopamine, the increased level of salivary tyrosine in all rowers experiencing the test, suggesting that salivary tyrosine could be explored as a potential indicator closely related to nervous fatigue in the test. On the other hand, PLS-DA did not show observable distinction of metabolic profiles between the urine samples before and immediately after the test. Moreover, 20 urinary metabolites did not display detectable altered levels. We then established the BRM with the identified characteristic metabolites, from which we selected one optimal regression model based on serum pyruvate and salivary tyrosine (adjusted R square was 0.935, P<0.001), indicating that the two selected metabolites would efficiently reflect the metabolic alterations in the test.
Conclusions As far as the 6000m rowing ergometer test is concerned, serum samples could be a preferred resource for assessing the changes of energy metabolism in the test, while urine samples might have a relatively lower sensitivity to exercise-induced metabolic responses. Even though metabolite levels in saliva samples are generally lower than those in serum and urine samples, some salivary metabolites potentially have higher sensitivities to exercise-induced metabolic responses. Thus, the integration of multiple biomarkers identified from different type of species could potentially provide more sensitive and specific manners to monitor physical states in sports and exercise. This work may be of benefit to the exploration of integrated biomarkers for sports-specific monitoring
Three-Dimensional Flat Bands and Dirac Cones in a Pyrochlore Superconductor
Emergent phases often appear when the electronic kinetic energy is comparable
to the Coulomb interactions. One approach to seek material systems as hosts of
such emergent phases is to realize localization of electronic wavefunctions due
to the geometric frustration inherent in the crystal structure, resulting in
flat electronic bands. Recently, such efforts have found a wide range of exotic
phases in the two-dimensional kagome lattice, including magnetic order,
time-reversal symmetry breaking charge order, nematicity, and
superconductivity. However, the interlayer coupling of the kagome layers
disrupts the destructive interference needed to completely quench the kinetic
energy. Here we experimentally demonstrate that an interwoven kagome network--a
pyrochlore lattice--can host a three dimensional (3D) localization of electron
wavefunctions. In particular, through a combination of angle-resolved
photoemission spectroscopy, fundamental lattice model and density functional
theory (DFT) calculations, we present the novel electronic structure of a
pyrochlore superconductor, CeRu. We find striking flat bands with
bandwidths smaller than 0.03 eV in all directions--an order of magnitude
smaller than that of kagome systems. We further find 3D gapless Dirac cones
predicted originally by theory in the diamond lattice space group with
nonsymmorphic symmetry. Our work establishes the pyrochlore structure as a
promising lattice platform to realize and tune novel emergent phases
intertwining topology and many-body interactions.Comment: 12 pages, 3 figure
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