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This paper presents the system description of our entry for the COLING 2025 RegNLP RIRAG (Regulatory Information Retrieval and Answer Generation) challenge, focusing on leveraging advanced information retrieval and answer generation techniques in regulatory domains. We experimented with a combination of embedding models, including Stella, BGE, CDE, and Mpnet, and leveraged fine-tuning and reranking for retrieving relevant documents in top ranks. We utilized a novel approach, LeSeR, which achieved competitive results with a recall@10 of 0.8201 and map@10 of 0.6655 for retrievals. This work highlights the transformative potential of natural language processing techniques in regulatory applications, offering insights into their capabilities for implementing a retrieval augmented generation system while identifying areas for future improvement in robustness and domain adaptation.
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https://arxiv.org/abs/2412.06009v1
|
This paper presents a detailed system description of our entry for the CHiPSAL 2025 shared task, focusing on language detection, hate speech identification, and target detection in Devanagari script languages. We experimented with a combination of large language models and their ensembles, including MuRIL, IndicBERT, and Gemma-2, and leveraged unique techniques like focal loss to address challenges in the natural understanding of Devanagari languages, such as multilingual processing and class imbalance. Our approach achieved competitive results across all tasks: F1 of 0.9980, 0.7652, and 0.6804 for Sub-tasks A, B, and C respectively. This work provides insights into the effectiveness of transformer models in tasks with domain-specific and linguistic challenges, as well as areas for potential improvement in future iterations.
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https://arxiv.org/abs/2411.06850v1
|
Rotations of the electric vector position angle (EVPA) in blazars are often
close to an integral multiple of 180$^\circ$. There are multiple examples of
this in the literature, and our analysis here, of the optical polarization data
from the RoboPol monitoring program, strengthens the evidence by showing that
$n\pi$ rotations occur more frequently than expected by chance. We explain this
with a model consisting of two polarized emission components: a "jet" that is
constant in time, and a "burst" that is variable. The EVPA of the combination
is $\rm EVPA_{jet}$ at both the beginning and the end of the burst, so the net
rotation across the burst must be $n\pi$. Examples are analyzed on the Stokes
plane, where the winding number for the Stokes vector of the combination gives
the value of $n$. The main conclusion is that the EVPA rotation can be much
larger than the physical rotation of the emission region around the axis of the
jet, but this requires the EVPAs of the jet and the burst to be nearly
orthogonal. A shock-in-jet calculation by Zhang et al. can provide a physical
model for our toy model, and in addition automatically gives the needed
orthogonality. The model is illustrated with data on OJ287 published by
Myserlis et al., and we suggest that the large rapid EVPA rotation seen there
might be a phase effect and not representative of a physical rotation.
|
http://arxiv.org/abs/2003.07090v1
|
We have fabricated a 180$^\circ$-twisted bilayer ReSe$_2$ by stacking two centrosymmetric monolayer ReSe$_2$ flakes in opposite directions, which is expected to lose spatial inversion symmetry. By the second harmonic generation and angle-resolved photoemission spectroscopy, we successfully observed spatial inversion symmetry breaking and emergent band dispersions. The band calculation shows the finite lifting of spin degeneracy (~50 meV) distinct from natural monolayer and bilayer ReSe$_2$. Our results demonstrate that the spin-momentum locked state, which leads to spintronic functions and Berry-curvature-related phenomena, can be realized even with the stacking of centrosymmetric monolayers.
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https://arxiv.org/abs/2309.15403v1
|
The introduction of blobs through EIP-4844 has significantly reduced the Data Availability (DA) costs for rollups on Ethereum. However, due to the fixed size of blobs at 128 KB, rollups with low data throughput face a dilemma: they either use blobs inefficiently or decrease the frequency of DA submissions. Blob sharing, where multiple rollups share a single blob, has been proposed as a solution to this problem. This paper examines the effectiveness of blob sharing based on real-world data collected approximately six months after the implementation of EIP-4844. By simulating cost changes using a simple blob sharing format, we demonstrate that blob sharing can substantially improve the costs and DA service quality for small rollups, effectively resolving their dilemma. Notably, we observed cost reductions in USD exceeding 85% for most of the rollups when they cooperate, attributable to the smoothing effect of the blob base fee achieved through blob sharing.
|
https://arxiv.org/abs/2410.04111v2
|
There is growing evidence that domain walls in ferroics can possess emergent
properties that are absent in bulk materials. For example, 180 domain walls in
the ferroelectric-antiferromagnetic BiFeO3 are particularly interesting because
they have been predicted to possess a range of intriguing behaviors; including
electronic conduction and enhanced magnetization. To date, however, ordered
arrays of such domain structures have not been reported. Here, we report the
observation of 180 stripe nanodomains in (110)-oriented BiFeO3 thin films grown
on orthorhombic GdScO3 (010)O substrates, and their impact on exchange coupling
to metallic ferromagnets. Nanoscale ferroelectric 180 stripe domains with {112
} domain walls were observed in films < 32 nm thick to compensate for large
depolarization fields. With increasing film thickness, we observe a domain
structure crossover from the depolarization field-driven 180 stripe nanodomains
to 71 domains determined by the elastic energy. Interestingly, these 180 domain
walls (which are typically cylindrical or meandering in nature due to a lack of
strong anisotropy associated with the energy of such walls) are found to be
highly-ordered. Additional studies of Co0.9Fe0.1/BiFeO3 heterostructures reveal
exchange bias and exchange enhancement in heterostructures based-on BiFeO3 with
180 domain walls and an absence of exchange bias in heterostructures based-on
BiFeO3 with 71 domain walls; suggesting that the 180 domain walls could be the
possible source for pinned uncompensated spins that give rise to exchange bias.
This is further confirmed by X-ray circular magnetic dichroism studies, which
demonstrate that films with predominantly 180 domain walls have larger
magnetization than those with primarily 71 domain walls. Our results could be
useful to extract the structure of domain walls and to explore domain wall
functionalities in BiFeO3.
|
http://arxiv.org/abs/1508.07376v1
|
Presenting context images to a viewer's peripheral vision is one of the most effective techniques to enhance immersive visual experiences. However, most images only present a narrow view, since the field-of-view (FoV) of standard cameras is small. To overcome this limitation, we propose a deep learning approach that learns to predict a 180{\deg} panoramic image from a narrow-view image. Specifically, we design a foveated framework that applies different strategies on near-periphery and mid-periphery regions. Two networks are trained separately, and then are employed jointly to sequentially perform narrow-to-90{\deg} generation and 90{\deg}-to-180{\deg} generation. The generated outputs are then fused with their aligned inputs to produce expanded equirectangular images for viewing. Our experimental results show that single-view-to-panoramic image generation using deep learning is both feasible and promising.
|
https://arxiv.org/abs/2001.04568v1
|
We have investigated optically-excited magnetoelastic waves by phase-resolved
spin-wave tomography (PSWaT). PSWaT reconstructs dispersion relation of spin
waves together with their phase information by using time-resolved
magneto-optical imaging for spin-wave propagation followed by an analysis based
on the convolution theorem and a complex Fourier transform. In PSWaT spectra
for a Bi-doped garnet film, we found a 180 degree phase shift of magnetoelastic
waves at around the crossing of the dispersion relations of spin and elastic
waves. The result is explained by a coupling between spin waves and elastic
waves through magnetoelastic interaction. We also propose an efficient way for
phase manipulation of magnetoelastic waves by rotating the orientation of
magnetization less than 10 degree.
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http://arxiv.org/abs/1803.07697v1
|
The formation of relativistic jets in active galactic nuclei (AGN) is related
to accretion on to their central supermassive black holes, and magnetic fields
are believed to play a central role in launching, collimating and accelerating
the jet streams from very compact regions out to kiloparsec or megaparsec
scales. In the presence of helical or toroidal magnetic fields threading the
AGN jets and their immediate vicinity, gradients in the observed Faraday
rotation measures are expected due to the systematic change in the
line-of-sight component of the magnetic field across the jet. We have analysed
total intensity, linear polarization, fractional polarization and Faraday
rotation maps based on Very Long Baseline Array data obtained at four
wavelengths in the 18-22 cm range for six AGN (OJ 287, 3C 279, PKS 1510-089, 3C
345, BL Lac and 3C 454.3). These observations typically probe projected
distances out to tens of parsecs from the observed core, and are well suited
for Faraday rotation studies due to the relatively long wavelengths used and
the similarity of the structures measured at the different wavelengths. We have
identified statistically significant, monotonic, transverse Faraday rotation
gradients across the jets of four of these six sources, as well as a tentative
transverse Faraday rotation gradient across the jet of OJ 287, providing
evidence for the presence of toroidal magnetic fields, which may be one
component of helical magnetic fields associated with these AGN jets.
|
http://arxiv.org/abs/1702.06659v1
|
We present the results of an ALMA survey to identify 183 GHz H$_2$O maser emission from AGN already known to host 22 GHz megamaser systems. Out of 20 sources observed, we detect significant 183 GHz maser emission from 13; this survey thus increases the number of AGN known to host (sub)millimeter megamasers by a factor of 5. We find that the 183 GHz emission is systematically fainter than the 22 GHz emission from the same targets, with typical flux densities being roughly an order of magnitude lower at 183 GHz than at 22 GHz. However, the isotropic luminosities of the detected 183 GHz sources are comparable to their 22 GHz values. For two of our sources -- ESO 269-G012 and the Circinus galaxy -- we detect rich 183 GHz spectral structure containing multiple line complexes. The 183 GHz spectrum of ESO 269-G012 exhibits the triple-peaked structure characteristic of an edge-on AGN disk system. The Circinus galaxy contains the strongest 183 GHz emission detected in our sample, peaking at a flux density of nearly 5 Jy. The high signal-to-noise ratios achieved by these strong lines enable a coarse mapping of the 183 GHz maser system, in which the masers appear to be distributed similarly to those seen in VLBI maps of the 22 GHz system in the same galaxy and may be tracing the circumnuclear accretion disk at larger orbital radii than are occupied by the 22 GHz masers. This newly identified population of AGN disk megamasers presents a motivation for developing VLBI capabilities at 183 GHz.
|
https://arxiv.org/abs/2302.02572v1
|
Gas-filled hollow core fibers allow the generation of single-cycle pulses at megahertz repetition rates. When coupled with difference frequency generation, they can be an ideal driver for the generation of carrier-envelope phase stable, octave-spanning pulses in the short-wavelength infrared. In this work, we investigate the dependence of the polarization state in gas-filled hollow-core fibers on the subsequent difference frequency generation stage. We show that by adjusting the input polarization state of light in geometrically symmetric systems, such as hollow-core fibers, one can achieve precise control over the polarization state of the output pulses. Importantly, this manipulation preserves the temporal characteristics of the ultrashort pulses generated, especially when operating near the single-cycle regime. We leverage this property to boost the down-conversion efficiency of these pulses in a type I difference frequency generation stage. Our technique overcomes the bandwidth and dispersion constraints of the previous methods that rely on broadband waveplates or adjustment of crystal axes relative to the laboratory frame. This advancement is crucial for experiments demanding pure polarization states in the eigenmodes of the laboratory frame.
|
https://arxiv.org/abs/2404.14153v1
|
Quantum random number generators (QRNGs) can produce true random numbers. Yet, the two most important QRNG parameters highly desired for practical applications, i.e., speed and size, have to be compromised during implementations. Here, we present the fastest and miniaturized QRNG with a record real-time output rate as high as 18.8 Gbps by combining a photonic integrated chip and the technology of optimized randomness extraction. We assemble the photonic integrated circuit designed for vacuum state QRNG implementation, InGaAs homodyne detector and high-bandwidth transimpedance amplifier into a single chip using hybrid packaging, which exhibits the excellent characteristics of integration and high-frequency response. With a sample rate of 2.5 GSa/s in a 10-bit analog-to-digital converter and subsequent paralleled postprocessing in a field programmable gate array, the QRNG outputs ultrafast random bitstreams via a fiber optic transceiver, whose real-time speed is validated in a personal computer.
|
https://arxiv.org/abs/2105.13518v1
|
Unlike the $\mathcal{R}^4$ and $\nabla^4\mathcal{R}^4$ couplings, whose
coefficients are Langlands-Eisenstein series of the U-duality group, the
coefficient $\mathcal{E}_{(0,1)}^{(d)}$ of the $\nabla^6\mathcal{R}^4$
interaction in the low-energy effective action of type II strings compactified
on a torus $T^d$ belongs to a more general class of automorphic functions,
which satisfy Poisson rather than Laplace-type equations. In earlier work, it
was proposed that the exact coefficient is given by a two-loop integral in
exceptional field theory, with the full spectrum of mutually 1/2-BPS states
running in the loops, up to the addition of a particular Langlands-Eisenstein
series. Here we compute the weak coupling and large radius expansions of these
automorphic functions for any $d$. We find perfect agreement with perturbative
string theory up to genus three, along with non-perturbative corrections which
have the expected form for 1/8-BPS instantons and bound states of 1/2-BPS
instantons and anti-instantons. The additional Langlands-Eisenstein series
arises from a subtle cancellation between the two-loop amplitude with 1/4-BPS
states running in the loops, and the three-loop amplitude with mutually 1/2-BPS
states in the loops. For $d=4$, the result is shown to coincide with an
alternative proposal in terms of a covariantised genus-two string amplitude,
due to interesting identities between the Kawazumi-Zhang invariant of genus-two
curves and its tropical limit, and between double lattice sums for the particle
and string multiplets, which may be of independent mathematical interest.
|
http://arxiv.org/abs/2001.05562v3
|
Purpose: Several brain complications of SARS-CoV-2 infection have been reported. It has been moreover speculated that this neurotropism could potentially cause a delayed outbreak of neuropsychiatric and neurodegenerative diseases of neuroinflammatory origin. A propagation mechanism has been proposed across the cribriform plate of the ethmoid bone, from the nose to the olfactory epithelium, and possibly afterward to other limbic structures, and deeper parts of the brain including the brainstem. Methods: Review of clinical examination, and whole-brain voxel-based analysis of $^{18}$F-FDG PET metabolism in comparison with healthy subjects (p voxel<0.001, p-cluster<0.05, uncorrected), of two patients with confirmed diagnosis of SARS-CoV-2 explored at the post-viral stage of the disease. Results: Hypometabolism of the olfactory/rectus gyrus was found on the two patients, especially one with 4-week prolonged anosmia. Additional hypometabolisms were found within amygdala, hippocampus, parahippocampus, cingulate cortex, pre-/post-central gyrus, thalamus/hypothalamus, cerebellum, pons, and medulla in the other patient who complained of delayed onset of a painful syndrome. Conclusion: These preliminary findings reinforce the hypotheses of SARS-CoV-2 neurotropism through the olfactory bulb and the possible extension of this impairment to other brain structures. $^{18}$F-FDG PET hypometabolism could constitute a cerebral quantitative biomarker of this involvement. Post-viral cohort studies are required to specify the exact relationship between such hypometabolisms and the possible persistent disorders, especially involving cognitive or emotion disturbances, residual respiratory symptoms, or painful complaints.
|
https://arxiv.org/abs/2502.09077v1
|
We assessed the predictive value of new radiomic features characterizing the lesion dissemination in baseline 18F-FDG PET and tested whether combining them with baseline metabolic tumour volume (MTV) could improve prediction of progression free survival (PFS) and overall survival (OS) in diffuse large B cell lymphoma (DLBCL) patients.
|
https://arxiv.org/abs/2012.14179v1
|
Dosimetry of salivary glands (SGs) is usually implemented using simplified calculation approaches and approximated geometries. Our aims were to compare different dosimetry methods to calculate SGs absorbed doses (ADs) following 18F-PSMA-1007 injection, and to assess the AD variation across patients and single SG components. Five patients with prostate cancer recurrence underwent PET/CT acquisitions of the head and neck, 0.5, 2 and 4 hours after 18F-PSMA-1007 injection. Parotid and submandibular glands were segmented on CT to derive SGs volumes and masses, while PETs were used to derive Time-Integrated Activity Coefficients. Average ADs to single SG components or total SG (tSG) were calculated with the following methods: i) direct Monte Carlo (MC) simulation with GATE/GEANT4; ii) spherical model (SM) of OLINDA/EXM 2.1, adopting either patient-specific or standard ICRP89 organ masses (SMstd); iii) ellipsoidal model (EM); iv) MIRD approach with organ S-factors from OLINDA/EXM 2.1 and OpenDose collaboration, with or without contribution from cross irradiation originating outside the SGs. The maximum percent AD difference across SG components ({\delta}max) and across patients ({\Delta}max) were calculated. Compared to MC, ADs to single SG components were significantly underestimated by all methods (average relative differences between -14.5% and -30.4%). Using MC, SM and EM, {\delta}max were never below 25% (up to 113%). {\delta}max up to 702% were obtained with SMstd. Concerning tSG, results within 10% of the MC were obtained only if cross irradiation from the remainder of the body or from the remainder of the head was accounted for. The {\Delta}max ranged between 58% and 78% across patients. Specific masses of single SG components should always be considered given their large intra- and inter- patient variability.
|
https://arxiv.org/abs/2210.01616v1
|
This work reports an acoustic solidly mounted resonator (SMR) at 18.64 GHz, among the highest operating frequencies reported. The device is built in scandium aluminum nitride (ScAlN) on top of silicon dioxide (SiO2) and tantalum pentoxide (Ta2O5) Bragg reflectors on silicon (Si) wafer. The stack is analyzed with X-ray reflectivity (XRR) and high-resolution X-ray diffraction (HRXRD). The resonator shows a coupling coefficient (k2) of 2.0%, high series quality factor (Qs) of 156, shunt quality factor (Qp) of 142, and maximum Bode quality factor (Qmax) of 210. The third-order harmonics at 59.64 GHz is also observed with k2 around 0.6% and Q around 40. Upon further development, the reported acoustic resonator platform can enable various front-end signal-processing functions, e.g., filters and oscillators, at future frequency range 3 (FR3) bands.
|
https://arxiv.org/abs/2407.02741v2
|
Commit messages contain diverse and valuable types of knowledge in all aspects of software maintenance and evolution. Links are an example of such knowledge. Previous work on "9.6 million links in source code comments" showed that links are prone to decay, become outdated, and lack bidirectional traceability. We conducted a large-scale study of 18,201,165 links from commits in 23,110 GitHub repositories to investigate whether they suffer the same fate. Results show that referencing external resources is prevalent and that the most frequent domains other than github.com are the external domains of Stack Overflow and Google Code. Similarly, links serve as source code context to commit messages, with inaccessible links being frequent. Although repeatedly referencing links is rare (4%), 14% of links that are prone to evolve become unavailable over time; e.g., tutorials or articles and software homepages become unavailable over time. Furthermore, we find that 70% of the distinct links suffer from decay; the domains that occur the most frequently are related to Subversion repositories. We summarize that links in commits share the same fate as links in code, opening up avenues for future work.
|
https://arxiv.org/abs/2305.16591v1
|
The $^{18}$O$/^{17}$O abundance ratio is, in principle, a powerful tool to estimate the relative contributions of massive stars and low- to intermediate-mass stars to the chemical enrichment of galaxies. We present $^{18}$O$/^{17}$O ratios derived from simultaneous observations of C$^{18}$O and C$^{17}$O 1-0 toward fifty-one massive star forming regions with the Institut de Radioastronomie Millim\'etrique (IRAM) 30 meter telescope. Simultaneous observations of HC$^{18}$O$^{+}$ 1-0 and HC$^{17}$O$^{+}$ 1-0 with the Yebes 40m telescope toward five sources from this sample were also done to test the consistency of $^{18}$O$/^{17}$O ratios derived from different isotopic pairs. From our improved measurements, resulting in smaller errors than previous work in the literature, we obtain a clear trend of increasing $^{18}$O$/^{17}$O ratio with increasing galactocentric distance (D$_{GC}$), which provides a significant constraint on Galactic chemical evolution (GCE) models. Current GCE models have to be improved in order to explain the observed C$^{18}$O/C$^{17}$O 1-0 gradient.
|
https://arxiv.org/abs/2304.01610v1
|
A central theme in quantum information science is to coherently control an
increasing number of quantum particles as well as their internal and external
degrees of freedom (DoFs), meanwhile maintaining a high level of coherence. The
ability to create and verify multiparticle entanglement with individual control
and measurement of each qubit serves as an important benchmark for quantum
technologies. To this end, genuine multipartite entanglement have been reported
up to 14 trapped ions, 10 photons, and 10 superconducting qubits. Here, we
experimentally demonstrate an 18-qubit Greenberger-Horne-Zeilinger (GHZ)
entanglement by simultaneous exploiting three different DoFs of six photons,
including their paths, polarization, and orbital angular momentum (OAM). We
develop high-stability interferometers for reversible quantum logic operations
between the photon's different DoFs with precision and efficiencies close to
unity, enabling simultaneous readout of 262,144 outcome combinations of the
18-qubit state. A state fidelity of 0.708(16) is measured, confirming the
genuine entanglement of all the 18 qubits.
|
http://arxiv.org/abs/1801.04043v1
|
We report on an optical frequency comb with 14nm (~1.8 THz) spectral
bandwidth at -3 dB level that is generated using a passively mode-locked
quantum-well (QW) laser in photonic integrated circuits (PICs) fabricated
through an InP generic photonic integration technology platform. This 21.5-GHz
colliding-pulse mode-locked laser cavity is defined by on-chip reflectors
incorporating intracavity phase modulators followed by an extra-cavity SOA as
booster amplifier. A 1.8-THz-wide optical comb spectrum is presented with
ultrafast pulse that is 0.35-ps-wide. The radio frequency beat note has a 3-dB
linewidth of 450 kHz and 35-dB SNR.
|
http://arxiv.org/abs/1709.07954v1
|
Comet Shoemaker-Levy 9 impacted Jupiter in July 1994, leaving its
stratosphere with several new species, among them water vapor (H2O). With the
aid of a photochemical model H2O can be used as a dynamical tracer in the
jovian stratosphere. In this paper, we aim at constraining vertical eddy
diffusion (Kzz) at the levels where H2O resides. We monitored the H2O
disk-averaged emission at 556.936 GHz with the Odin space telescope between
2002 and 2019, covering nearly two decades. We analyzed the data with a
combination of 1D photochemical and radiative transfer models to constrain
vertical eddy diffusion in the stratosphere of Jupiter. The Odin observations
show us that the emission of H2O has an almost linear decrease of about 40%
between 2002 and 2019.We can only reproduce our time series if we increase the
magnitude of Kzz in the pressure range where H2O diffuses downward from 2002 to
2019, i.e. from ~0.2 mbar to ~5 mbar. However, this modified Kzz is
incompatible with hydrocarbon observations. We find that, even if allowance is
made for the initially large abundances of H2O and CO at the impact latitudes,
the photochemical conversion of H2O to CO2 is not sufficient to explain the
progressive decline of the H2O line emission, suggestive of additional loss
mechanisms. The Kzz we derived from the Odin observations of H2O can only be
viewed as an upper limit in the ~0.2 mbar to ~5 mbar pressure range. The
incompatibility between the interpretations made from H2O and hydrocarbon
observations probably results from 1D modeling limitations. Meridional
variability of H2O, most probably at auroral latitudes, would need to be
assessed and compared with that of hydrocarbons to quantify the role of auroral
chemistry in the temporal evolution of the H2O abundance since the SL9 impacts.
Modeling the temporal evolution of SL9 species with a 2D model would be the
next natural step.
|
http://arxiv.org/abs/2007.05415v1
|
The conceptual bases of Fermi's $\beta$-ray theory (at its 90th anniversary) are examined, highlighting the innovative drive and inspirational role for the progress that followed just afterwards. Moreover, the three different ideas of the neutrino born from the proposals of Pauli 1930, again Fermi 1933 and Majorana 1937 papers are discussed, emphasising the interest of the latter for current expectations.
|
https://arxiv.org/abs/2409.17824v1
|
The year 1953 is pivotal for computational physics: the first application of the Monte-Carlo method is published and calculations of the so-called Fermi-Pasta-Ulam-Tsingou experiment are started. It is the beginning of the massive use in the physical sciences of numerical methods implemented on electronic computers and a decisive step in the development of modern nonlinear dynamics. This will lead to an unpredictable development during the following 70 years. We briefly review the unfolding of these events and present some recent results that show how the issues raised are still relevant today
|
https://arxiv.org/abs/2311.09769v1
|
The 1974 discovery, by Russell A. Hulse and Joseph H. Taylor, of the first
binary pulsar PSR B1913+16, opened up new possibilities for the study of
relativistic gravity. PSR B1913+16, as well as several other binary pulsars,
provided {\it direct} observational proofs that gravity propagates at the
velocity of light and has a quadrupolar structure. Binary pulsars also provided
accurate tests of the strong-field regime of relativistic gravity. General
Relativity has passed all the binary pulsar tests with flying colors. The
discovery of binary pulsars had also very important consequences for
astrophysics: accurate measurement of neutron star masses, improved
understanding of the possible evolution scenarios for the co-evolution of
binary stars, proof of the existence of binary neutron stars emitting
gravitational waves for hundreds of millions of years, before coalescing in
catastrophic events radiating intense gravitational-wave signals, and probably
leading also to important emissions of electromagnetic radiation and neutrinos.
This article reviews the history of the discovery of the first binary pulsar,
and describes both its immediate impact, and its longer-term effect on
theoretical and experimental studies of relativistic gravity.
|
http://arxiv.org/abs/1411.3930v2
|
The Nancay Decameter Array (NDA) routinely observes low frequency (10-100
MHz) radio emissions of Jupiter and the Sun since 4 decades. The NDA
observations, acquired with a variety of receivers with increasing
performances, were the basis for numerous studies of jovian and solar radio
emissions and now form a unique long-term database spanning >3 solar cycles and
jovian revolutions. In addition, the NDA historically brought a fruitful
support to space-based radio observatories of the heliosphere, to
multi-wavelength analyses of solar activity and contributes to the development
of space weather services. After having summarized the NDA characteristics,
this article presents latest instrumental and database developments, some
recent scientific results and perspectives for the next decade.
|
http://arxiv.org/abs/1709.03821v3
|
We present a new method for the measurements of photonuclear reaction flux-weighted average cross sections and isomeric ratios using a laser-driven bremsstrahlung $\gamma$-ray source. An ultra-bright ultra-fast 60$\,\thicksim\,$250 MeV bremsstrahlung $\gamma$-ray source was established using the 200 TW laser facility in the Compact Laser Plasma Accelerator Laboratory, Peking University, which could cover the energy range from knocking out neutrons to producing pions. Stable quasi-monoenergetic electron beams were generated via laser wakefield acceleration with a charge of 300$\,\thicksim\,$600 pC per shot. The averaged $\gamma$-ray intensities ($\geqslant$8 MeV) were higher than 10$^{8}$ per shot and the instantaneous intensities can reach above 10$^{19}$ s$^{-1}$ with a duration time about 6.7 ps. $^{65}$Cu($\gamma,\,n$)$^{64}$Cu and $^{27}$Al($\gamma,\,x$)$^{24}$Na reactions were used as $\gamma$-ray flux monitors in the experiments. The flux-weighted average cross sections and isomeric ratios of $^{197}$Au($\gamma,\,xn;\,x\,=\,1\thicksim9$) reactions were analyzed through activation measurements. The results showed good agreement with previous works and proved this method to be accurate. The $^{197}$Au($\gamma,\,xn;\,x\,=\,7\thicksim\,9$) reaction cross sections were first achieved with the highest threshold energy of 71.410 MeV. Theoretical cross sections of TALYS 1.9 were calculated to compare with experiment results. This method offered a unique way of gaining insight into photonuclear reaction research, especially for short-lived isomers which extremely lack experimental data.
|
https://arxiv.org/abs/2209.13947v2
|
We present 197 planet candidates discovered using data from the first year of
the NASA K2 mission (Campaigns 0-4), along with the results of an intensive
program of photometric analyses, stellar spectroscopy, high-resolution imaging,
and statistical validation. We distill these candidates into sets of 104
validated planets (57 in multi-planet systems), 30 false positives, and 63
remaining candidates. Our validated systems span a range of properties, with
median values of R_P = 2.3 R_E, P=8.6 d, Tef = 5300 K, and Kp=12.7 mag. Stellar
spectroscopy provides precise stellar and planetary parameters for most of
these systems. We show that K2 has increased by 30% the number of small planets
known to orbit moderately bright stars (1-4 R_E, Kp=9-13 mag). Of particular
interest are 37 planets smaller than 2 R_E, 15 orbiting stars brighter than
Kp=11.5, five receiving Earth-like irradiation levels, and several multi-planet
systems -- including four planets orbiting the M dwarf K2-72 near mean-motion
resonances. By quantifying the likelihood that each candidate is a planet we
demonstrate that our candidate sample has an overall false positive rate of
15-30%, with rates substantially lower for small candidates (< 2 R_E) and
larger for candidates with radii > 8 R_E and/or with P < 3 d. Extrapolation of
the current planetary yield suggests that K2 will discover between 500-1000
planets in its planned four-year mission -- assuming sufficient follow-up
resources are available. Efficient observing and analysis, together with an
organized and coherent follow-up strategy, is essential to maximize the
efficacy of planet-validation efforts for K2, TESS, and future large-scale
surveys.
|
http://arxiv.org/abs/1607.05263v3
|
Des enregistrements de voix se trouvent de plus en plus souvent au c{\oe}ur d{'}affaires judiciaires importantes, notamment de par l{'}essor de la t{\'e}l{\'e}phonie mobile. La justice demande {\`a} ce que des expertises en identification de voix soient r{\'e}alis{\'e}es alors que dans le m{\^e}me temps, la pertinence scientifique de telles expertises est fortement mise en cause par les scientifiques. Ainsi, d{\`e}s 1990, les chercheurs en communication parl{\'e}e r{\'e}unis dans le GFCP, devenu depuis AFCP, ont vot{\'e} une motion affirmant que « l{'}identification d{'}un individu par sa voix est {\`a} l{'}heure actuelle un probl{\`e}me {\`a} sa connaissance non r{\'e}solu ». Cette motion est toujours en vigueur, apr{\`e}s avoir {\'e}t{\'e} r{\'e}affirm{\'e}e en 1997 et renforc{\'e}e par une p{\'e}tition en 2002. Malgr{\'e} cela, des expertises judiciaires en identification de voix sont r{\'e}alis{\'e}es en France chaque ann{\'e}e. Cet article revient sur les actions men{\'e}es par le GFCP et l{'}AFCP depuis la motion initiale jusqu{'}aux actions contemporaines. Il se propose d{'}{\'e}valuer les r{\'e}percussions de ces actions, tant au niveau de la Justice qu{'}au niveau acad{\'e}mique.
|
https://aclanthology.org/2020.jeptalnrecital-eternal.5
|
Understanding the nature of the luminous 1991T-like supernovae is of great importance to supernova cosmology as they are likely to have been more common in the early universe. In this paper we explore the observational properties of 1991T-like supernovae to study their relationship to other luminous, slow-declining Type~Ia supernovae (SNe Ia). From the spectroscopic and photometric criteria defined in Phillips et al. (1992), we identify 17 1991T-like supernovae from the literature. Combining these objects with ten 1991T-like supernovae from the Carnegie Supernova Project-II, the spectra, light curves, and colors of these events, along with their host galaxy properties, are examined in detail. We conclude that 1991T-like supernovae are closely related in essentially all of their UV, optical, and near-infrared properties -- as well as their host galaxy parameters -- to the slow-declining subset of Branch core-normal supernovae and to the intermediate 1999aa-like events, forming a continuum of luminous SNe Ia. The overriding difference between these three subgroups appears to be the extent to which $^{56}$Ni mixes into the ejecta, producing the pre-maximum spectra dominated by Fe III absorption, the broader UV light curves, and the higher luminosities that characterize the 1991T-like events. Nevertheless, the association of 1991T-like SNe with the rare Type Ia CSM supernovae would seem to run counter to this hypothesis, in which case 1991T-like events may form a separate subclass of SNe Ia, possibly arising from single-degenerate progenitor systems.
|
https://arxiv.org/abs/2405.15027v1
|
Type Ia supernovae remain poorly understood despite decades of investigation. Massive computationally intensive hydrodynamic simulations have been developed and run to model an ever-growing number of proposed progenitor channels. Further complicating the matter, a large number of sub-types of Type Ia supernovae have been identified in recent decades. Due to the massive computational load required, inference of the internal structure of Type Ia supernovae ejecta directly from observations using simulations has previously been computationally intractable. However, deep-learning emulators for radiation transport simulations have alleviated such barriers. We perform abundance tomography on 40 Type Ia supernovae from optical spectra using the radiative transfer code TARDIS accelerated by the probabilistic DALEK deep-learning emulator. We apply a parametric model of potential ejecta structures to comparatively investigate abundance distributions and internal ionization fractions of intermediate-mass elements between normal and 1991T-like Type Ia supernovae. Our inference shows that 1991T-like Type Ia supernovae are under-abundant in the typical intermediate mass elements that heavily contribute to the spectral line formation seen in normal Type Ia supernovae at early times. Additionally, we find that the intermediate-mass elements present in 1991T-like Type Ia supernovae are highly ionized compared to those in the normal Type Ia population. Finally, we conclude that the transition between normal and 1991T-like Type Ia supernovae appears to be continuous observationally and that the observed differences come out of a combination of both abundance and ionization fractions in these supernovae populations.
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https://arxiv.org/abs/2306.08137v1
|
We describe 19B in terms of a 17B-n-n three-body system, where the two-body subsystems 17B-n and n-n are unbound (virtual) states close to the unitary limit. The energy of 19B ground state is well reproduced and two low-lying resonances are predicted. Their eventual link with the Efimov physics is discussed. This model can be extended to describe the recently discovered resonant states in 20,21B.
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https://arxiv.org/abs/2012.13342v1
|
The marginal likelihood of a model is a key quantity for assessing the
evidence provided by the data in support of a model. The marginal likelihood is
the normalizing constant for the posterior density, obtained by integrating the
product of the likelihood and the prior with respect to model parameters. Thus,
the computational burden of computing the marginal likelihood scales with the
dimension of the parameter space. In phylogenetics, where we work with tree
topologies that are high-dimensional models, standard approaches to computing
marginal likelihoods are very slow. Here we study methods to quickly compute
the marginal likelihood of a single fixed tree topology. We benchmark the speed
and accuracy of 19 different methods to compute the marginal likelihood of
phylogenetic topologies on a suite of real datasets. These methods include
several new ones that we develop explicitly to solve this problem, as well as
existing algorithms that we apply to phylogenetic models for the first time.
Altogether, our results show that the accuracy of these methods varies widely,
and that accuracy does not necessarily correlate with computational burden. Our
newly developed methods are orders of magnitude faster than standard
approaches, and in some cases, their accuracy rivals the best established
estimators.
|
http://arxiv.org/abs/1811.11804v1
|
We report results of magnetization and $^{19}$F NMR measurements in the normal state of as-grown LaO$_{0.5}$F$_{0.5}$BiS$_2$. The magnetization is dominated by a temperature-independent diamagnetic component and a field- and temperature-dependent paramagnetic contribution $M_\mu(H,T)$ from a $\sim$1000~ppm concentration of local moments, an order of magnitude higher than can be accounted for by measured rare-earth impurity concentrations. $M_\mu(H,T)$ can be fit by the Brillouin function $B_J(x)$ or, perhaps more realistically, a two-level $\tanh(x)$ model for magnetic Bi $6p$ ions in defect crystal fields. Both fits require a phenomenological Curie-Weiss argument $x = \mu_\mathrm{eff}H/(T + T_W)$, $T_W \approx 1.7$ K. There is no evidence for magnetic order down to 2 K, and the origin of $T_W$ is not clear. $^{19}$F frequency shifts, linewidths, and spin-lattice relaxation rates are consistent with purely dipolar $^{19}$F/defect-spin interactions. The defect-spin correlation time $\tau_c(T)$ obtained from $^{19}$F spin-lattice relaxation rates obeys the Korringa relation $\tau_cT = \text{const.}$, indicating the relaxation is dominated by conduction-band fluctuations.
|
https://arxiv.org/abs/2408.06284v2
|
The $^{19}$F$(p,\gamma)$$^{20}$Ne reaction is the only process to break out of the CNO cycle at temperature below 0.1 GK and may serve as the origin of calcium in first generation of stars after the Big Bang. In the recent measurement, the Jinping Underground Nuclear Experiment (JUNA) obtained the rate of $^{19}$F$(p,\gamma)$$^{20}$Ne reaction, significantly larger than the previously recommended values. In this work, we perform the theoretical studies of the $^{19}$F$(p,\gamma)$$^{20}$Ne reaction using the Gamow shell model in the coupled-channel representation (GSM-CC). At temperature around 0.1 GK, the predicted rate by GSM-CC is close to the rate found by JUNA. Thus, based on GSM-CC, the break-out reaction $^{19}$F$(p,\gamma)$$^{20}$Ne from the CNO-cycle might win over its competing reaction $^{19}$F$(p,\alpha)$$^{16}$O, and produce enough calcium in the metal poor stars.
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https://arxiv.org/abs/2411.17243v1
|
As particle accelerators increase their collision rates, and deep learning solutions prove their viability, there is a growing need for lightweight and fast neural network architectures for low-latency tasks such as triggering. We examine the potential of one recent Lorentz- and permutation-symmetric architecture, PELICAN, and present its instances with as few as 19 trainable parameters that outperform generic architectures with tens of thousands of parameters when compared on the binary classification task of top quark jet tagging.
|
https://arxiv.org/abs/2310.16121v3
|
19th century real analysis received a major impetus from Cauchy's work.
Cauchy mentions variable quantities, limits, and infinitesimals, but the
meaning he attached to these terms is not identical to their modern meaning.
Some Cauchy historians work in a conceptual scheme dominated by an assumption
of a teleological nature of the evolution of real analysis toward a preordained
outcome. Thus, Gilain and Siegmund-Schultze assume that references to limite in
Cauchy's work necessarily imply that Cauchy was working with an Archi-medean
continuum, whereas infinitesimals were merely a convenient figure of speech,
for which Cauchy had in mind a complete justification in terms of Archimedean
limits. However, there is another formalisation of Cauchy's procedures
exploiting his limite, more consistent with Cauchy's ubiquitous use of
infinitesimals, in terms of the standard part principle of modern infinitesimal
analysis.
We challenge a misconception according to which Cauchy was allegedly forced
to teach infinitesimals at the Ecole Polytechnique. We show that the debate
there concerned mainly the issue of rigor, a separate one from infinitesimals.
A critique of Cauchy's approach by his contemporary de Prony sheds light on the
meaning of rigor to Cauchy and his contemporaries. An attentive reading of
Cauchy's work challenges received views on Cauchy's role in the history of
analysis, and indicates that he was a pioneer of infinitesimal techniques as
much as a harbinger of the Epsilontik.
|
http://arxiv.org/abs/1907.07451v1
|
Let $R$ be a commutative ring with non-zero identity and $M$ be a unitary $R$-module. The goal of this paper is to extend the concept of 1-absorbing primary ideals to 1-absorbing primary submodules. A proper submodule $N$ of $M$ is said to be a 1-absorbing primary submodule if whenever non-unit elements $a,b\in R$ and $m\in M$ with $abm\in N$, then either $ab\in(N:_{R}M)$ or $m\in M-rad(N).$ Various properties and chacterizations of this class of submodules are considered. Moreover, 1-absorbing primary avoidance theorem is proved.
|
https://arxiv.org/abs/2102.12148v1
|
Membrane distillation (MD) stands at the forefront of desalination technology, harnessing the power of phase change to separate water vapor from saline using minimal energy resources efficiently. In response to this challenge, membranes with tuned pores morphology and surface chemistry with biomimetic 3D pine-like structures with improved affinity to water (desalination) and/or hazardous VOC (VOC removal) were developed and studied systematically. By implementing VIPS-PVDF membranes and a green modifier of 1-adamantanamine for the first time, membranes with a revolutionary network architecture were generated. The modifier was introduced either physically to the polymeric matrix or chemically through covalent attachment onto the surface and inside the porous structure. As a result, membranes that defy wetting under extreme hydrostatic pressures (>11.5 bar) were produced while preserving unparalleled vapor transport efficiency. The 1-adamantanamine promotes transport and enhances the affinity to the VOC, ensuring excellent membrane performance at different applications of the MD process. Transport was enhanced more than 3.6 times and separation factor beta changed from 3.48 to 15.22 for MTBE removal and from 2.0 to 3.46 for EtOH removal when comparing pristine PVDF with membrane chemically modified with 1-adamantanamine (PVDF_Ch02). The process separation index during the MTBE removal changed from 20 kg m-2 h-1 (PVDF) to 297 kg m-2 h-1 (PVDF_Ch02). All materials were highly stable and durable during the MD applications. This innovative approach not only revolutionizes desalination but also holds immense promise for diverse applications beyond, particularly in the realm of wastewater treatment. A study of the icing process on a cold plate with new membranes provided deeper insight into the icing mechanism and the role of membrane LEP in it.
|
https://arxiv.org/abs/2503.15930v1
|
We consider the nonstationary circuit QED setup in which a 3-level artificial
atom in the $\Delta$-configuration interacts with a single-mode cavity field of
natural frequency $\omega $. It is demonstrated that when some atomic energy
level(s) undergoes a weak harmonic modulation, photons can be generated from
vacuum via effective 1- and 3-photon transitions, while the atom remains
approximately in the ground state. These phenomena occur in the dispersive
regime when the modulation frequency is accurately tuned near $\omega $ and
$3\omega $, respectively, and the generated field states exhibit strikingly
different statistics from the squeezed vacuum state attained in standard cavity
dynamical Casimir effect.
|
http://arxiv.org/abs/1805.04887v2
|
We present the first wide area (2.5 x 2.5 deg^2) LOFAR High Band Antenna image at a resolution of 1.2'' x 2'' with a median noise of approximately 80 microJy per beam. It was made from an 8-hour International LOFAR Telescope (ILT) observation of the ELAIS-N1 field at frequencies ranging from 120 to 168 MHz with the most up-to-date ILT imaging methods. This intermediate resolution falls between the highest possible resolution (0.3'') achievable by using all International LOFAR Telescope (ILT) baselines and the standard 6-arcsecond resolution in the LoTSS (LOFAR Two-meter Sky Survey) image products utilising the LOFAR Dutch baselines only. This is the first demonstration of the feasibility of imaging using the ILT at a resolution of around 1'', which provides unique information on source morphology at scales that fall below the surface brightness limits at higher resolutions. The total calibration and imaging computational time is approximately 52,000 core hours, nearly 5 times more than required to produce a 6'' resolution image. We also present a radio source catalogue containing 2263 sources detected over the 2.5 x 2.5 deg^2 image of the ELAIS-N1 field, with a peak intensity threshold of 5.5 sigma. The catalogue has been cross-matched with the LoTSS deep ELAIS-N1 field radio catalogue, and its flux density and positional accuracy have been investigated and corrected accordingly. We find that approximately 80% of sources which we expect to be detectable based on their peak brightness in the LoTSS 6'' resolution image are detected in this image, which is approximately a factor of two higher than for 0.3'' resolution imaging in the Lockman Hole, implying there is a wealth of information on these intermediate scales.
|
https://arxiv.org/abs/2309.16560v3
|
This document demonstrates our groups approach to the CL-SciSumm shared task 2020. There are three tasks in CL-SciSumm 2020. In Task 1a, we apply a Siamese neural network to identify the spans of text in the reference paper best reflecting a citation. In Task 1b, we use a SVM to classify the facet of a citation.
|
https://aclanthology.org/2020.sdp-1.31
|
A graph is 1-planar if it has a drawing where each edge is crossed at most
once. A drawing is RAC (Right Angle Crossing) if the edges cross only at right
angles. The relationships between 1-planar graphs and RAC drawings have been
partially studied in the literature. It is known that there are both 1-planar
graphs that are not straight-line RAC drawable and graphs that have a
straight-line RAC drawing but that are not 1-planar. Also, straight-line RAC
drawings always exist for IC-planar graphs, a subclass of 1-planar graphs. One
of the main questions still open is whether every 1-planar graph has a RAC
drawing with at most one bend per edge. We positively answer this question.
|
http://arxiv.org/abs/1608.08418v1
|
It is proved that every series-parallel digraph whose maximum vertex-degree
is $\Delta$ admits an upward planar drawing with at most one bend per edge such
that each edge segment has one of $\Delta$ distinct slopes. This is shown to be
worst-case optimal in terms of the number of slopes. Furthermore, our
construction gives rise to drawings with optimal angular resolution
$\frac{\pi}{\Delta}$. A variant of the proof technique is used to show that
(non-directed) reduced series-parallel graphs and flat series-parallel graphs
have a (non-upward) one-bend planar drawing with $\lceil\frac{\Delta}{2}\rceil$
distinct slopes if biconnected, and with $\lceil\frac{\Delta}{2}\rceil+1$
distinct slopes if connected.
|
http://arxiv.org/abs/1608.08425v1
|
Scalable training of large models (like BERT and GPT-3) requires careful optimization rooted in model design, architecture, and system capabilities. From a system standpoint, communication has become a major bottleneck, especially on commodity systems with standard TCP interconnects that offer limited network bandwidth. Communication compression is an important technique to reduce training time on such systems. One of the most effective methods is error-compensated compression, which offers robust convergence speed even under 1-bit compression. However, state-of-the-art error compensation techniques only work with basic optimizers like SGD and momentum SGD, which are linearly dependent on the gradients. They do not work with non-linear gradient-based optimizers like Adam, which offer state-of-the-art convergence efficiency and accuracy for models like BERT. In this paper, we propose 1-bit Adam that reduces the communication volume by up to $5\times$, offers much better scalability, and provides the same convergence speed as uncompressed Adam. Our key finding is that Adam's variance (non-linear term) becomes stable (after a warmup phase) and can be used as a fixed precondition for the rest of the training (compression phase). Experiments on up to 256 GPUs show that 1-bit Adam enables up to $3.3\times$ higher throughput for BERT-Large pre-training and up to $2.9\times$ higher throughput for SQuAD fine-tuning. In addition, we provide theoretical analysis for our proposed work.
|
https://arxiv.org/abs/2102.02888v2
|
Recent advances in 1-bit Large Language Models (LLMs), such as BitNet and BitNet b1.58, present a promising approach to enhancing the efficiency of LLMs in terms of speed and energy consumption. These developments also enable local LLM deployment across a broad range of devices. In this work, we introduce bitnet.cpp, a tailored software stack designed to unlock the full potential of 1-bit LLMs. Specifically, we develop a set of kernels to support fast and lossless inference of ternary BitNet b1.58 LLMs on CPUs. Extensive experiments demonstrate that bitnet.cpp achieves significant speedups, ranging from 2.37x to 6.17x on x86 CPUs and from 1.37x to 5.07x on ARM CPUs, across various model sizes. The code is available at https://github.com/microsoft/BitNet.
|
https://arxiv.org/abs/2410.16144v2
|
For distributed learning among collaborative users, this paper develops and analyzes a communication-efficient scheme for federated learning (FL) over the air, which incorporates 1-bit compressive sensing (CS) into analog aggregation transmissions. To facilitate design parameter optimization, we theoretically analyze the efficacy of the proposed scheme by deriving a closed-form expression for the expected convergence rate of the FL over the air. Our theoretical results reveal the tradeoff between convergence performance and communication efficiency as a result of the aggregation errors caused by sparsification, dimension reduction, quantization, signal reconstruction and noise. Then, we formulate 1-bit CS based FL over the air as a joint optimization problem to mitigate the impact of these aggregation errors through joint optimal design of worker scheduling and power scaling policy. An enumeration-based method is proposed to solve this non-convex problem, which is optimal but becomes computationally infeasible as the number of devices increases. For scalable computing, we resort to the alternating direction method of multipliers (ADMM) technique to develop an efficient implementation that is suitable for large-scale networks. Simulation results show that our proposed 1-bit CS based FL over the air achieves comparable performance to the ideal case where conventional FL without compression and quantification is applied over error-free aggregation, at much reduced communication overhead and transmission latency.
|
https://arxiv.org/abs/2103.16055v1
|
Recently, the 1-bit compressive sensing (1-bit CS) has been studied in the
field of sparse signal recovery. Since the amplitude information of sparse
signals in 1-bit CS is not available, it is often the support or the sign of a
signal that can be exactly recovered with a decoding method. In this paper, we
first show that a necessary assumption (that has been overlooked in the
literature) should be made for some existing theories and discussions for 1-bit
CS. Without such an assumption, the found solution by some existing decoding
algorithms might be inconsistent with 1-bit measurements. This motivates us to
pursue a new direction to develop uniform and nonuniform recovery theories for
1-bit CS with a new decoding method which always generates a solution
consistent with 1-bit measurements. We focus on an extreme case of 1-bit CS, in
which the measurements capture only the sign of the product of a sensing matrix
and a signal. We show that the 1-bit CS model can be reformulated equivalently
as an $\ell_0$-minimization problem with linear constraints. This reformulation
naturally leads to a new linear-program-based decoding method, referred to as
the 1-bit basis pursuit, which is remarkably different from existing
formulations. It turns out that the uniqueness condition for the solution of
the 1-bit basis pursuit yields the so-called restricted range space property
(RRSP) of the transposed sensing matrix. This concept provides a basis to
develop sign recovery conditions for sparse signals through 1-bit measurements.
We prove that if the sign of a sparse signal can be exactly recovered from
1-bit measurements with 1-bit basis pursuit, then the sensing matrix must admit
a certain RRSP, and that if the sensing matrix admits a slightly enhanced RRSP,
then the sign of a $k$-sparse signal can be exactly recovered with 1-bit basis
pursuit.
|
http://arxiv.org/abs/1412.5514v2
|
1-bit compressive sensing aims to recover sparse signals from quantized 1-bit measurements. Designing efficient approaches that could handle noisy 1-bit measurements is important in a variety of applications. In this paper we use the approximate message passing (AMP) to achieve this goal due to its high computational efficiency and state-of-the-art performance. In AMP the signal of interest is assumed to follow some prior distribution, and its posterior distribution can be computed and used to recover the signal. In practice, the parameters of the prior distributions are often unknown and need to be estimated. Previous works tried to find the parameters that maximize either the measurement likelihood via expectation maximization, which becomes increasingly difficult to solve in cases of complicated probability models. Here we propose to treat the parameters as unknown variables and compute their posteriors via AMP as well, so that the parameters and the signal can be recovered jointly. Compared to previous methods, the proposed approach leads to a simple and elegant parameter estimation scheme, allowing us to directly work with 1-bit quantization noise model. Experimental results show that the proposed approach generally perform much better than the other state-of-the-art methods in the zero-noise and moderate-noise regimes, and outperforms them in most of the cases in the high-noise regime.
|
https://arxiv.org/abs/2007.07679v2
|
Stealthy electronically reconfigurable transmitarray antennas are essential components in wireless communication and radar detection systems. Therefore, this paper proposes a 1 bit electronically reconfigurable transmitarray antenna with out-of-band scatter suppression. The transmitarray consists of two layers, the absorptive frequency selective transmission (AFST) layer and reconfigurable transmitarray (RTA) layer, separated by air. Specifically, the AFST layer achieves out-of-band scattering suppression and in-band transmission performance by utilizing the first three resonant modes of a bent metallic strip with a centrally loaded resistor. Additionally, the RTA layer adopts a receiver-transmitter structure with an active receiving dipole and a passive orthogonal transmitting dipole. The 1 bit phase shift is achieved by alternating two pin diodes integrated on the active dipole to reverse its current direction. To evaluate the proposed design, a 256-element transmitarray prototype is designed, fabricated and measured. For scattering, the 10-dB radar cross section reduction is realized within 4~5.2 GHz and 10.9~11.4 GHz, respectively. For radiation, the measured gain is 19.9 dBi at 7.5 GHz, corresponding to an aperture efficiency of 12.1%. and the beam scanning covers 60{\deg} with gain loss of 5 dB in both two principal planes.
|
https://arxiv.org/abs/2306.10580v1
|
Fully quantized training (FQT) accelerates the training of deep neural networks by quantizing the activations, weights, and gradients into lower precision. To explore the ultimate limit of FQT (the lowest achievable precision), we make a first attempt to 1-bit FQT. We provide a theoretical analysis of FQT based on Adam and SGD, revealing that the gradient variance influences the convergence of FQT. Building on these theoretical results, we introduce an Activation Gradient Pruning (AGP) strategy. The strategy leverages the heterogeneity of gradients by pruning less informative gradients and enhancing the numerical precision of remaining gradients to mitigate gradient variance. Additionally, we propose Sample Channel joint Quantization (SCQ), which utilizes different quantization strategies in the computation of weight gradients and activation gradients to ensure that the method is friendly to low-bitwidth hardware. Finally, we present a framework to deploy our algorithm. For fine-tuning VGGNet-16 and ResNet-18 on multiple datasets, our algorithm achieves an average accuracy improvement of approximately 6%, compared to per-sample quantization. Moreover, our training speedup can reach a maximum of 5.13x compared to full precision training.
|
https://arxiv.org/abs/2408.14267v1
|
To train large models (like BERT and GPT-3) on hundreds of GPUs, communication has become a major bottleneck, especially on commodity systems with limited-bandwidth TCP network. On one side large batch-size optimization such as LAMB algorithm was proposed to reduce the frequency of communication. On the other side, communication compression algorithms such as 1-bit Adam help to reduce the volume of each communication. However, we find that simply using one of the techniques is not sufficient to solve the communication challenge, especially under low network bandwidth. Motivated by this we aim to combine the power of large-batch optimization and communication compression, but we find that existing compression strategies cannot be directly applied to LAMB due to its unique adaptive layerwise learning rates. To this end, we design a new communication-efficient algorithm, 1-bit LAMB, which introduces a novel way to support adaptive layerwise learning rates under compression. In addition, we introduce a new system implementation for compressed communication using the NCCL backend of PyTorch distributed, which improves both usability and performance. For BERT-Large pre-training task with batch sizes from 8K to 64K, our evaluations on up to 256 GPUs demonstrate that 1-bit LAMB with NCCL-based backend is able to achieve up to 4.6x communication volume reduction, up to 2.8x end-to-end time-wise speedup, and the same sample-wise convergence speed (and same fine-tuning task accuracy) compared to uncompressed LAMB.
|
https://arxiv.org/abs/2104.06069v2
|
We present a novel scheme allowing for 2D target localization using highly
quantized 1-bit measurements from a Frequency Modulated Continuous Wave (FMCW)
radar with two receiving antennas. Quantization of radar signals introduces
localization artifacts, we remove this limitation by inserting a dithering on
the unquantized observations. We then adapt the projected back projection
algorithm to estimate both the range and angle of targets from the dithered
quantized radar observations, with provably decaying reconstruction error when
the number of observations increases. Simulations are performed to highlight
the accuracy of the dithered scheme in noiseless conditions when compared to
the non-dithered and full 32-bit resolution under severe bit-rate reduction.
Finally, measurements are performed using a radar sensor to demonstrate the
effectiveness and performances of the proposed quantized dithered scheme in
real conditions.
|
http://arxiv.org/abs/1806.05408v2
|
In this paper, we focus on the multiuser massive multiple-input single-output
(MISO) downlink with low-cost 1-bit digital-to-analog converters (DACs) for PSK
modulation, and propose a low-complexity refinement process that is applicable
to any existing 1-bit precoding approaches based on the constructive
interference (CI) formulation. With the decomposition of the signals along the
detection thresholds, we first formulate a simple symbol-scaling method as the
performance metric. The low-complexity refinement approach is subsequently
introduced, where we aim to improve the introduced symbol-scaling performance
metric by modifying the transmit signal on one antenna at a time. Numerical
results validate the effectiveness of the proposed refinement method on
existing approaches for massive MIMO with 1-bit DACs, and the performance
improvements are most significant for the low-complexity quantized zero-forcing
(ZF) method.
|
http://arxiv.org/abs/1810.12039v1
|
The deployment of large-scale antenna arrays for cellular base stations (BSs), termed as `Massive MIMO', has been a key enabler for meeting the ever-increasing capacity requirement for 5G communication systems and beyond. Despite their promising performance, fully-digital massive MIMO systems require a vast amount of hardware components including radio frequency chains, power amplifiers, digital-to-analog converters (DACs), etc., resulting in a huge increase in terms of the total power consumption and hardware costs for cellular BSs. Towards both spectrally-efficient and energy-efficient massive MIMO deployment, a number of hardware limited architectures have been proposed, including hybrid analog-digital structures, constant-envelope transmission, and use of low-resolution DACs. In this paper, we overview the recent interest in improving the error-rate performance of massive MIMO systems deployed with 1-bit DACs through precoding at the symbol level. This line of research goes beyond traditional interference suppression or cancellation techniques by managing interference on a symbol-by-symbol basis. This provides unique opportunities for interference-aware precoding tailored for practical massive MIMO systems. Firstly, we characterize constructive interference (CI) and elaborate on how CI can benefit the 1-bit signal design by exploiting the traditionally undesired multi-user interference as well as the interference from imperfect hardware components. Subsequently, we overview several solutions for 1-bit signal design to illustrate the gains achievable by exploiting CI. Finally, we identify some challenges and future research directions for 1-bit massive MIMO systems that are yet to be explored.
|
https://arxiv.org/abs/2007.13950v3
|
Due to challenging applications such as collaborative filtering, the matrix
completion problem has been widely studied in the past few years. Different
approaches rely on different structure assumptions on the matrix in hand. Here,
we focus on the completion of a (possibly) low-rank matrix with binary entries,
the so-called 1-bit matrix completion problem. Our approach relies on tools
from machine learning theory: empirical risk minimization and its convex
relaxations. We propose an algorithm to compute a variational approximation of
the pseudo-posterior. Thanks to the convex relaxation, the corresponding
minimization problem is bi-convex, and thus the method behaves well in
practice. We also study the performance of this variational approximation
through PAC-Bayesian learning bounds. On the contrary to previous works that
focused on upper bounds on the estimation error of M with various matrix norms,
we are able to derive from this analysis a PAC bound on the prediction error of
our algorithm.
We focus essentially on convex relaxation through the hinge loss, for which
we present the complete analysis, a complete simulation study and a test on the
MovieLens data set. However, we also discuss a variational approximation to
deal with the logistic loss.
|
http://arxiv.org/abs/1604.04191v1
|
We consider the problem of noisy 1-bit matrix completion under an exact rank
constraint on the true underlying matrix $M^*$. Instead of observing a subset
of the noisy continuous-valued entries of a matrix $M^*$, we observe a subset
of noisy 1-bit (or binary) measurements generated according to a probabilistic
model. We consider constrained maximum likelihood estimation of $M^*$, under a
constraint on the entry-wise infinity-norm of $M^*$ and an exact rank
constraint. This is in contrast to previous work which has used convex
relaxations for the rank. We provide an upper bound on the matrix estimation
error under this model. Compared to the existing results, our bound has faster
convergence rate with matrix dimensions when the fraction of revealed 1-bit
observations is fixed, independent of the matrix dimensions. We also propose an
iterative algorithm for solving our nonconvex optimization with a certificate
of global optimality of the limiting point. This algorithm is based on low rank
factorization of $M^*$. We validate the method on synthetic and real data with
improved performance over existing methods.
|
http://arxiv.org/abs/1502.06689v1
|
Recent advances in large language models have led to specialized models excelling in specific domains, creating a need for efficient model merging techniques. While traditional merging approaches combine parameters into a single static model, they often compromise task-specific performance. However, task-specific routing methods maintain accuracy but introduce substantial storage overhead. We present \texttt{1bit}-Merging, a novel framework that integrates task-specific routing with 1-bit quantized task vectors to balance performance and storage efficiency. Our approach leverages the observation that different task-specific models store knowledge in distinct layers-chat models primarily in attention layers and math/code models in MLP layers-enabling targeted compression strategies. Through extensive experiments with LLaMA2 and Mistral model families across chat, mathematical reasoning, and code generation tasks, we demonstrate that \texttt{1bit}-Merging achieves comparable or superior performance to existing methods while significantly reducing storage requirements. Our framework offers a practical solution for combining specialized models while maintaining their individual strengths and addressing the storage challenges of current approaches.
|
https://arxiv.org/abs/2502.10743v1
|
This paper tackles the problem of single-user multiple-input multiple-output communication with 1-bit digital-to-analog and analog-to-digital converters. With the information-theoretic capacity as benchmark, the complementary strategies of beamforming and equiprobable signaling are contrasted in the regimes of operational interest, and the ensuing spectral efficiencies are characterized. Various canonical channel types are considered, with emphasis on line-of-sight settings under both spherical and planar wavefronts, respectively representative of short and long transmission ranges at mmWave and terahertz frequencies. In all cases, a judicious combination of beamforming and equiprobable signaling is shown to operate within a modest gap from capacity.
|
https://arxiv.org/abs/2109.04390v1
|
Optical Diffraction Neural Networks (DNNs), a subset of Optical Neural Networks (ONNs), show promise in mirroring the prowess of electronic networks. This study introduces the Hybrid Diffraction Neural Network (HDNN), a novel architecture that incorporates matrix multiplication into DNNs, synergizing the benefits of conventional ONNs with those of DNNs to surmount the modulation limitations inherent in optical diffraction neural networks. Utilizing a singular phase modulation layer and an amplitude modulation layer, the trained neural network demonstrated remarkable accuracies of 96.39% and 89% in digit recognition tasks in simulation and experiment, respectively. Additionally, we develop the Binning Design (BD) method, which effectively mitigates the constraints imposed by sampling intervals on diffraction units, substantially streamlining experimental procedures. Furthermore, we propose an on-chip HDNN that not only employs a beam-splitting phase modulation layer for enhanced integration level but also significantly relaxes device fabrication requirements, replacing metasurfaces with relief surfaces designed by 1-bit quantization. Besides, we conceptualized an all-optical HDNN-assisted lesion detection network, achieving detection outcomes that were 100% aligned with simulation predictions. This work not only advances the performance of DNNs but also streamlines the path towards industrial optical neural network production.
|
https://arxiv.org/abs/2404.07443v1
|
Recently we had reported commissioning of a prototype for pulsar observations at low radio frequencies (<100 MHz) using log-periodic dipole antennas (LPDAs) in the Gauribidanur Radio Observatory near Bangalore in India. The aforementioned system (GAuribidanur Pulsar System, GAPS) is currently being augmented to directly digitize the radio frequency signals from the individual antennas in the array. Our initial results using 1-bit raw voltage recording system indicates that such a back-end receiver offers distinct advantages like, (i) simultaneous observations of any set of desired directions in the sky with multiple offline beams and smaller data rate/volume, (ii) archival of the observed data with minimal resources for re-analysis in the future, either in the same or different set of directions in the sky.
|
https://arxiv.org/abs/2404.15031v1
|
In this paper, a proof-of-concept study of a $1$-bit wideband reconfigurable intelligent surface (RIS) comprising planar tightly coupled dipoles (PTCD) is presented. The developed RIS operates at subTHz frequencies and a $3$-dB gain bandwidth of $27.4\%$ with the center frequency at $102$ GHz is shown to be obtainable via full-wave electromagnetic simulations. The binary phase shift offered by each RIS unit element is enabled by changing the polarization of the reflected wave by $180^\circ$. The proposed PTCD-based RIS has a planar configuration with one dielectric layer bonded to a ground plane, and hence, it can be fabricated by using cost-effective printed circuit board (PCB) technology. We analytically calculate the response of the entire designed RIS and showcase that a good agreement between that result and equivalent full-wave simulations is obtained. To efficiently compute the $1$-bit RIS response for different pointing directions, thus, designing a directive beam codebook, we devise a fast approximate beamforming optimization approach, which is compared with time-consuming full-wave simulations. Finally, to prove our concept, we present several passive prototypes with frozen beams for the proposed $1$-bit wideband RIS.
|
https://arxiv.org/abs/2402.08445v1
|
We investigate the singular subspace of an inclusion of tracial von Neumann
algebras. The singular subspace is a canonical N-N subbimodule of L^{2}(M) and
it contains the quasinormalizer introduced by Popa, one-sided quasinormalizer
introduced by Fang-Gao-Smith, and wq-normalizer introduced in Galatan-Popa
(following upon work in Ioana-Peterson-Popa and Popa). We then obtain a weak
notion of regularity (called spectral regularity) by demanding that the
singular subspace of N in M generates M. By abstracting Voiculescu's original
proof of absence of Cartan subalgebras, we show that there can be no diffuse,
hyperfinite subalgebra of L(\FF_{n}) which is spectrally regular. Our
techniques are robust enough to repeat this process by transfinite induction
and rule out chains of spectrally regular inclusions of algebras starting from
a diffuse, hyperfinite algebra and ending in L(\FF_{n}). We use this to prove
some conjectures made by Galatan-Popa in their study of smooth cohomology of
II_{1}-factors. Our results may be regarded as a consistency check for the
possibility of existence of a "good" cohomology theory of II_{1}-factors.
Lastly, we deduce nonisomorphism results for crossed products of q-deformed
free group factors by Bogoliubov actions, as well as for the continuous core of
q-deformed Free Araki-Woods algebras. This extends work of
Houdayer-Shlyakhtenko as well as Shlyakhtenko.
|
http://arxiv.org/abs/1505.06682v5
|
This paper describes our system for the SemEval2022 task of matching dictionary glosses to word embeddings. We focus on the Reverse Dictionary Track of the competition, which maps multilingual glosses to reconstructed vector representations. More specifically, models convert the input of sentences to three types of embeddings: SGNS, Char, and Electra. We propose several experiments for applying neural network cells, general multilingual and multitask structures, and language-agnostic tricks to the task. We also provide comparisons over different types of word embeddings and ablation studies to suggest helpful strategies. Our initial transformer-based model achieves relatively low performance. However, trials on different retokenization methodologies indicate improved performance. Our proposed Elmobased monolingual model achieves the highest outcome, and its multitask, and multilingual varieties show competitive results as well.
|
https://arxiv.org/abs/2206.03702v1
|
In this paper, we present our approach and empirical observations for Cause-Effect Signal Span Detection -- Subtask 2 of Shared task 3~\cite{tan-etal-2022-event} at CASE 2022. The shared task aims to extract the cause, effect, and signal spans from a given causal sentence. We model the task as a reading comprehension (RC) problem and apply a token-level RC-based span prediction paradigm to the task as the baseline. We explore different training objectives to fine-tune the model, as well as data augmentation (DA) tricks based on the language model (LM) for performance improvement. Additionally, we propose an efficient beam-search post-processing strategy to due with the drawbacks of span detection to obtain a further performance gain. Our approach achieves an average $F_1$ score of 54.15 and ranks \textbf{$1^{st}$} in the CASE competition. Our code is available at \url{https://github.com/Gzhang-umich/1CademyTeamOfCASE}.
|
https://arxiv.org/abs/2210.17157v1
|
This paper details our participation in the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) workshop @ EMNLP 2022, where we take part in Subtask 1 of Shared Task 3. We approach the given task of event causality detection by proposing a self-training pipeline that follows a teacher-student classifier method. More specifically, we initially train a teacher model on the true, original task data, and use that teacher model to self-label data to be used in the training of a separate student model for the final task prediction. We test how restricting the number of positive or negative self-labeled examples in the self-training process affects classification performance. Our final results show that using self-training produces a comprehensive performance improvement across all models and self-labeled training sets tested within the task of event causality sequence classification. On top of that, we find that self-training performance did not diminish even when restricting either positive/negative examples used in training. Our code is be publicly available at https://github.com/Gzhang-umich/1CademyTeamOfCASE.
|
https://arxiv.org/abs/2211.02729v1
|
This work introduces a novel family of boundary conditions for AdS$_3$ General Relativity, constructed through a polynomial expansion in negative integer powers of the Brown-Henneaux central charge. The associated dynamics is governed by the Dym hierarchy of integrable equations. It is shown that the infinite set of Dym conserved charges generates an abelian asymptotic symmetry group. Additionally, these boundary conditions encompass black hole solutions, whose thermodynamic properties are examined.
|
https://arxiv.org/abs/2401.12338v2
|
In this paper, we classify 1-cocycles of the Witt algebra with coefficients in the tensor product of two arbitrary tensor density modules. In a special case, we recover a theorem originally established by Ng and Taft in \cite{NT}. Furthermore, by these 1-cocycles, we determine Lie bialgebra structures over certain infinite-dimensional Lie algebras containing the Witt algebra.
|
https://arxiv.org/abs/2406.12565v1
|
We develop a cohomological method to classify amalgams of groups. We
generalize this to simplicial amalgams in any concrete category. We compute the
non-commutative 1-cohomology for several examples of amalgams defined over
small simplices.
|
http://arxiv.org/abs/1509.04679v1
|
We discuss two approaches to a recent question of Loh: must a 3-colored
transitive tournament on $N$ vertices have a 1-color-\emph{avoiding} path of
vertex-length at least $N^{2/3}$? This question generalizes the
Erd\H{o}s--Szekeres theorem on monotone subsequences.
First, we define three canonical transformations on these tournaments called
Color, Record, and Dual. We use these to establish a reduction to special
tournaments with natural geometric and combinatorial properties. In many cases
(including all known tight examples), these tournaments have recursive Gallai
decompositions. Not all relevant tournaments have Gallai decompositions, but
those that do satisfy the desired $N^{2/3}$ bound by recent work of Wagner,
roughly analogous to earlier work of Fox, Grinshpun, and Pach on a similar
\emph{undirected} problem.
Second, we consider the related geometric problem of bounding
\emph{slice-increasing} sets $S\subseteq [n]^3$, which---under an additional
ordering hypothesis on $S$---was shown by Loh to be equivalent to the original
question. In particular, we establish a rigorous connection from a problem of
Szab\'o and Tardos, raise a stronger $L^2$-question on slice-counts, and
mention a surprising overlap with the joints problem.
|
http://arxiv.org/abs/1608.04153v2
|
The present paper contributes to the ongoing programme of quantification of isomorphic Banach space theory focusing on Pe{\l}czy\'nski's classical work on dual Banach spaces containing $L_{1}$ ($=L_{1}[0,1]$) and the Hagler--Stegall characterisation of dual spaces containing complemented copies of $L_{1}$. We prove the following quantitative version of the Hagler--Stegall theorem asserting that for a Banach space $X$ the following statements are equivalent: $\bullet$ $X$ contains almost isometric copies of $(\bigoplus_{n=1}^{\infty} \ell_{\infty}^{n})_{\ell_1}$, $\bullet$ for all $\varepsilon>0$, $X^{*}$ contains a $(1+\varepsilon)$-complemented, $(1+\varepsilon)$-isomorphic copy of $L_{1}$, $\bullet$ for all $\varepsilon>0$, $X^{*}$ contains a $(1+\varepsilon)$-complemented, $(1+\varepsilon)$-isomorphic copy of $C[0,1]^{*}$. Moreover, if $X$ is separable, one may add the following assertion: $\bullet$ for all $\varepsilon>0$, there exists a $(1+\varepsilon)$-quotient map $T\colon X\rightarrow C(\Delta)$ so that $T^{*}[C(\Delta)^{*}]$ is $(1+\varepsilon)$-complemented in $X^{*}$, where $\Delta$ is the Cantor set.
|
https://arxiv.org/abs/2108.03057v2
|
We first unify all notions of partial injectivity appearing in the literature
---(universal) separable injectivity, (universal) $\aleph$-injectivity --- in
the notion of $(\alpha, \beta)$-injectivity ($(\alpha,
\beta)_\lambda$-injectivity if the parameter $\lambda$ has to be specified).
Then, extend the notion of space of universal disposition to space of universal
$(\alpha, \beta)$-disposition. Finally, we characterize the $1$-complemented
subspaces of spaces of universal $(\alpha, \beta)$-disposition as precisely the
spaces $(\alpha, \beta)_1$-injective.
|
http://arxiv.org/abs/1708.03823v1
|
The model of incomplete cooperative games incorporates uncertainty into the classical model of cooperative games by considering a partial characteristic function. Thus the values for some of the coalitions are not known. The main focus of this paper is the class of 1-convex cooperative games under this framework. We are interested in two heavily intertwined questions. First, given an incomplete game, in which ways can we fill in the missing values to obtain a classical 1-convex game? Such complete games are called \emph{1-convex extensions}. For the class of minimal incomplete games (in which precisely the values of singletons and grand coalitions are known), we provide an answer in terms of a description of the set of 1-convex extensions. The description employs extreme points and extreme rays of the set. Second, how to determine in a rational, fair, and efficient way the payoffs of players based only on the known values of coalitions? Based on the description of the set of 1-convex extensions, we introduce generalisations of three solution concepts (values) for complete games, namely the $\tau$-value, the Shapley value and the nucleolus. We consider two variants where we compute the centre of gravity of either extreme games or of a combination of extreme games and extreme rays. We show that all of the generalised values coincide for minimal incomplete games which allows to introduce the \emph{average value}. For this value, we provide three different axiomatisations based on axiomatic characterisations of the $\tau$-value and the Shapley value for classical cooperative games. Finally, we turn our attention to \emph{incomplete games with defined upper vector}, asking the same questions and this time arriving to different conclusions. This provides a benchmark to test our tools and knowledge of the average value.
|
https://arxiv.org/abs/2107.04679v2
|
We prove results about 1-cycles on certain Fano varieties using techniques
that rely on rational curves. Firstly, we show that Fano weighted complete
intersections with index bigger than half their dimension have trivial first
Griffiths group. Secondly, we prove that the first Chow group of most $2$-Fano
weighted complete intersections, and of $2$-Fano conic-connected varieties in
$\mathbb{P}^n$ of high enough index (with $3$ obvious exceptions), are
generated by lines. Furthermore, if the Fano variety of lines is irreducible,
the first Chow group is isomorphic to $\mathbb{Z}$.
|
http://arxiv.org/abs/1711.09987v1
|
In this letter, we highlight the enhanced functionalization of the electronic and optical properties in the hybrid heterojunction of 1D Tellurene with 2D monolayer of Graphene and MoS2 in both lateral and vertical geometries, having potential applications in the field of photonics and energy harvesting. The structural geometries of the lateral and vertical assemblies are optimized with a comparative and systematic analysis of the energetics of the different positional placement of the 1D system with respect to the hexagonal 2D layer. The 1D/2D coupling of the electronic structure in this unique assembly enables the realization of the four different types of heterojunctions, viz. type I, type II, Z-scheme and Schottky type, with the band-alignments being entirely dependent upon the stacking geometry of 1D Tellurene with respect to the 2D monolayer. With the static and time-dependent first-principles calculations, we indicate the potential applications of these hybrid systems in broadband photo detection and absorption, covering the full range of Infra-red to visible (IR-Vis) spectrum and in green energy harvesting with an effective separation and migration of photo-generated charge carriers.
|
https://arxiv.org/abs/2203.09124v2
|
An important problem in terrain analysis is modeling how water flows across a
terrain creating floods by forming channels and filling depressions. In this
paper we study a number of \emph{flow-query} related problems: Given a terrain
$\Sigma$, represented as a triangulated $xy$-monotone surface with $n$
vertices, a rain distribution $R$ which may vary over time, determine how much
water is flowing over a given edge as a function of time. We develop
internal-memory as well as I/O-efficient algorithms for flow queries.
This paper contains four main results:
(i) We present an internal-memory algorithm that preprocesses $\Sigma$ into a
linear-size data structure that for a (possibly time varying) rain distribution
$R$ can return the flow-rate functions of all edges of $\Sigma$ in $O(\rho
k+|\phi| \log n)$ time, where $\rho$ is the number of sinks in $\Sigma$, $k$ is
the number of times the rain distribution changes, and $|\phi|$ is the total
complexity of the flow-rate functions that have non-zero values;
(ii) We also present an I/O-efficient algorithm for preprocessing $\Sigma$
into a linear-size data structure so that for a rain distribution $R$, it can
compute the flow-rate function of all edges using $O(\text{Sort}(|\phi|))$ I/Os
and $O(|\phi| \log |\phi|)$ internal computation time.
(iii) $\Sigma$ can be preprocessed into a linear-size data structure so that
for a given rain distribution $R$, the flow-rate function of an edge $(q,r) \in
\Sigma$ under the single-flow direction (SFD) model can be computed more
efficiently.
(iv) We present an algorithm for computing the two-dimensional channel along
which water flows using Manning's equation; a widely used empirical equation
that relates the flow-rate of water in an open channel to the geometry of the
channel along with the height of water in the channel.
|
http://arxiv.org/abs/2009.08014v1
|
Classically, anisotropic surface wave tomography is treated as an optimisation problem where it proceeds through a linearised two-step approach. It involves the construction of 2D group or phase velocity maps for each considered period, followed by the inversion of local dispersion curves inferred from these maps for 1D depth-functions of the elastic parameters. Here, we cast the second step into a fully Bayesian probability framework. Solutions to the inverse problem are thus an ensemble of model parameters (\textit{i.e.} 1D elastic structures) distributed according to a posterior probability density function and their corresponding uncertainty limits. The method is applied to azimuthally-varying synthetic surface wave dispersion curves generated by a 3D-deforming upper mantle. We show that such a procedure captures essential features of the upper mantle structure. The robustness of these features however strongly depend on the wavelength of the wavefield considered and the choice of the model parameterisation. Additional information should therefore be incorporated to regularise the problem such as the imposition of petrological constraints to match the geodynamic predictions.
|
https://arxiv.org/abs/2012.03915v1
|
The atmospheric composition of exoplanets with masses between 2 and 10 M$_\oplus$ is poorly understood. In that regard, the sub-Neptune K2-18b, which is subject to Earth-like stellar irradiation, offers a valuable opportunity for the characterisation of such atmospheres. Previous analyses of its transmission spectrum from the Kepler, Hubble (HST), and Spitzer space telescopes data using both retrieval algorithms and forward-modelling suggest the presence of H$_2$O and an H$_2$--He atmosphere, but have not detected other gases, such as CH$_4$. We present simulations of the atmosphere of K2-18 b using Exo-REM, our self-consistent 1D radiative-equilibrium model, using a large grid of atmospheric parameters to infer constraints on its chemical composition. We show that our simulations favour atmospheric metallicities between 40 and 500 times solar and indicate, in some cases, the formation of H$_2$O-ice clouds, but not liquid H$_2$O clouds. We also confirm the findings of our previous study, which showed that CH$_4$ absorption features nominally dominate the transmission spectrum in the HST spectral range. We compare our results with results from retrieval algorithms and find that the H$_2$O-dominated spectrum interpretation is either due to the omission of CH$_4$ absorptions or a strong overfitting of the data. Finally, we investigated different scenarios that would allow for a CH$_4$-depleted atmosphere. We were able to fit the data to those scenarios, finding, however, that it is very unlikely for K2-18b to have a high internal temperature. A low C/O ratio ($\approx$ 0.01--0.1) allows for H$_2$O to dominate the transmission spectrum and can fit the data but so far, this set-up lacks a physical explanation. Simulations with a C/O ratio $<$ 0.01 are not able to fit the data satisfactorily.
|
https://arxiv.org/abs/2011.10459v1
|
Multi-step stock index forecasting is vital in finance for informed decision-making. Current forecasting methods on this task frequently produce unsatisfactory results due to the inherent data randomness and instability, thereby underscoring the demand for advanced forecasting models. Given the superiority of capsule network (CapsNet) over CNN in various forecasting and classification tasks, this study investigates the potential of integrating a 1D CapsNet with an LSTM network for multi-step stock index forecasting. To this end, a hybrid 1D-CapsNet-LSTM model is introduced, which utilizes a 1D CapsNet to generate high-level capsules from sequential data and a LSTM network to capture temporal dependencies. To maintain stochastic dependencies over different forecasting horizons, a multi-input multi-output (MIMO) strategy is employed. The model's performance is evaluated on real-world stock market indices, including S&P 500, DJIA, IXIC, and NYSE, and compared to baseline models, including LSTM, RNN, and CNN-LSTM, using metrics such as RMSE, MAE, MAPE, and TIC. The proposed 1D-CapsNet-LSTM model consistently outperforms baseline models in two key aspects. It exhibits significant reductions in forecasting errors compared to baseline models. Furthermore, it displays a slower rate of error increase with lengthening forecast horizons, indicating increased robustness for multi-step forecasting tasks.
|
https://arxiv.org/abs/2310.02090v2
|
This paper proposes a 1D residual convolutional neural network (CNN) architecture for music genre classification and compares it with other recent 1D CNN architectures. The 1D CNNs learn a representation and a discriminant directly from the raw audio signal. Several convolutional layers capture the time-frequency characteristics of the audio signal and learn various filters relevant to the music genre recognition task. The proposed approach splits the audio signal into overlapped segments using a sliding window to comply with the fixed-length input constraint of the 1D CNNs. As a result, music genre classification can be carried out on a single audio segment or on the aggregation of the predictions on several audio segments, which improves the final accuracy. The performance of the proposed 1D residual CNN is assessed on a public dataset of 1,000 audio clips. The experimental results have shown that it achieves 80.93% of mean accuracy in classifying music genres and outperforms other 1D CNN architectures.
|
https://arxiv.org/abs/2105.07302v1
|
This paper presents an alternate representation framework to commonly used time-frequency representation for acoustic scene classification (ASC). A raw audio signal is represented using a pre-trained convolutional neural network (CNN) using its various intermediate layers. The study assumes that the representations obtained from the intermediate layers lie in low-dimensions intrinsically. To obtain low-dimensional embeddings, principal component analysis is performed, and the study analyzes that only a few principal components are significant. However, the appropriate number of significant components are not known. To address this, an automatic dictionary learning framework is utilized that approximates the underlying subspace. Further, the low-dimensional embeddings are aggregated in a late-fusion manner in the ensemble framework to incorporate hierarchical information learned at various intermediate layers. The experimental evaluation is performed on publicly available DCASE 2017 and 2018 ASC datasets on a pre-trained 1-D CNN, SoundNet. Empirically, it is observed that deeper layers show more compression ratio than others. At 70% compression ratio across different datasets, the performance is similar to that obtained without performing any dimensionality reduction. The proposed framework outperforms the time-frequency representation based methods.
|
https://arxiv.org/abs/2204.00555v1
|
Intrusion detection system (IDS) plays an essential role in computer networks protecting computing resources and data from outside attacks. Recent IDS faces challenges improving flexibility and efficiency of the IDS for unexpected and unpredictable attacks. Deep neural network (DNN) is considered popularly for complex systems to abstract features and learn as a machine learning technique. In this paper, we propose a deep learning approach for developing the efficient and flexible IDS using one-dimensional Convolutional Neural Network (1D-CNN). Two-dimensional CNN methods have shown remarkable performance in detecting objects of images in computer vision area. Meanwhile, the 1D-CNN can be used for supervised learning on time-series data. We establish a machine learning model based on the 1D-CNN by serializing Transmission Control Protocol/Internet Protocol (TCP/IP) packets in a predetermined time range as an invasion Internet traffic model for the IDS, where normal and abnormal network traffics are categorized and labeled for supervised learning in the 1D-CNN. We evaluated our model on UNSW\_NB15 IDS dataset to show the effectiveness of our method. For comparison study in performance, machine learning-based Random Forest (RF) and Support Vector Machine (SVM) models in addition to the 1D-CNN with various network parameters and architecture are exploited. In each experiment, the models are run up to 200 epochs with a learning rate in 0.0001 on imbalanced and balanced data. 1D-CNN and its variant architectures have outperformed compared to the classical machine learning classifiers. This is mainly due to the reason that CNN has the capability to extract high-level feature representations that represent the abstract form of low-level feature sets of network traffic connections.
|
https://arxiv.org/abs/2003.00476v2
|
Online signature verification plays a pivotal role in security infrastructures. However, conventional online signature verification models pose significant risks to data privacy, especially during training processes. To mitigate these concerns, we propose a novel federated learning framework that leverages 1-D Convolutional Neural Networks (CNN) for online signature verification. Furthermore, our experiments demonstrate the effectiveness of our framework regarding 1-D CNN and federated learning. Particularly, the experiment results highlight that our framework 1) minimizes local computational resources; 2) enhances transfer effects with substantial initialization data; 3) presents remarkable scalability. The centralized 1-D CNN model achieves an Equal Error Rate (EER) of 3.33% and an accuracy of 96.25%. Meanwhile, configurations with 2, 5, and 10 agents yield EERs of 5.42%, 5.83%, and 5.63%, along with accuracies of 95.21%, 94.17%, and 94.06%, respectively.
|
https://arxiv.org/abs/2406.06597v1
|
The demand of the Internet of Things (IoT) has witnessed exponential growth. These progresses are made possible by the technological advancements in artificial intelligence, cloud computing, and edge computing. However, these advancements exhibit multiple challenges, including cyber threats, security and privacy concerns, and the risk of potential financial losses. For this reason, this study developed a computationally inexpensive one-dimensional convolutional neural network (1DCNN) algorithm for cyber-attack classification. The proposed study achieved an accuracy of 99.90% to classify nine cyber-attacks. Multiple other performance metrices have been evaluated to validate the efficacy of the proposed scheme. In addition, comparison has been done with existing state-of-the-art schemes. The findings of the proposed study can significantly contribute to the development of secure intrusion detection for IIoT systems.
|
https://arxiv.org/abs/2409.08529v1
|
Indonesia ranks fourth globally in the number of deaf cases. Individuals with hearing impairments often find communication challenging, necessitating the use of sign language. However, there are limited public services that offer such inclusivity. On the other hand, advancements in artificial intelligence (AI) present promising solutions to overcome communication barriers faced by the deaf. This study aims to explore the application of AI in developing models for a simplified sign language translation app and dictionary, designed for integration into public service facilities, to facilitate communication for individuals with hearing impairments, thereby enhancing inclusivity in public services. The researchers compared the performance of LSTM and 1D CNN + Transformer (1DCNNTrans) models for sign language recognition. Through rigorous testing and validation, it was found that the LSTM model achieved an accuracy of 94.67%, while the 1DCNNTrans model achieved an accuracy of 96.12%. Model performance evaluation indicated that although the LSTM exhibited lower inference latency, it showed weaknesses in classifying classes with similar keypoints. In contrast, the 1DCNNTrans model demonstrated greater stability and higher F1 scores for classes with varying levels of complexity compared to the LSTM model. Both models showed excellent performance, exceeding 90% validation accuracy and demonstrating rapid classification of 50 sign language gestures.
|
https://arxiv.org/abs/2409.01975v1
|
Topological materials confined in one-dimension (1D) can transform computing technologies, such as 1D topological semimetals for nanoscale interconnects and 1D topological superconductors for fault-tolerant quantum computing. As such, understanding crystallization of 1D-confined topological materials is critical. Here, we demonstrate 1D-confined crystallization routes during template-assisted nanowire synthesis where we observe diameter-dependent phase selectivity for topological metal tungsten phosphides. A phase bifurcation occurs to produce tungsten monophosphide and tungsten diphosphide at the cross-over nanowire diameter of ~ 35 nm. Four-dimensional scanning transmission electron microscopy was used to identify the two phases and to map crystallographic orientations of grains at a few nm resolution. The 1D-confined phase selectivity is attributed to the minimization of the total surface energy, which depends on the nanowire diameter and chemical potentials of precursors. Theoretical calculations were carried out to construct the diameter-dependent phase diagram, which agrees with experimental observations. Our find-ings suggest a new crystallization route to stabilize topological materials confined in 1D.
|
https://arxiv.org/abs/2309.11314v1
|
We study conformal field theory in $d=1$ space-time dimensions. We derive a dispersion relation for the 4-point correlation function of identical bosons and fermions, in terms of the double discontinuity. This extends the conformal dispersion relation of arXiv:1910.12123, which holds for CFTs in dimensions $d\geq 2$, to the case of $d=1$. The dispersion relation is obtained by combining the Lorentzian inversion formula with the operator product expansion of the 4-point correlator. We perform checks of the dispersion relation using correlators of generalised free fields and derive an integral relation between the kernel of the dispersion relation and that of the Lorentzian inversion formula. Finally, for $1$-$d$ holographic conformal theories, we analytically compute scalar Witten diagrams in $AdS_2$ at tree-level and $1$-loop.
|
https://arxiv.org/abs/2408.09870v2
|
It is well known that a systematic analysis of the pupil size variations, recorded by means of an eye-tracker, is a rich source of information about a subject's arousal and cognitive state. Current methods for pupil analysis are limited to descriptive statistics, struggle in handling the wide inter-subjects variability and must be coupled with a long series of pre-processing signal operations. In this we present a data-driven approach in which 1-D Convolutional Neural Networks are applied directly to the raw pupil size data. To test its effectiveness, we apply our method in a binary classification task with two different groups of subjects: a group of elderly patients with Parkinson disease (PDs), a condition in which pupil abnormalities have been extensively reported, and a group of healthy adults subjects (HCs). Long-range registration (10 minutes) of the pupil size were collected in scotopic conditions (complete darkness, 0 lux). 1-D convolutional neural network models are trained for classification of short-range sequences (10 to 60 seconds of registration). The model provides prediction with high average accuracy on a hold out test set. Dataset and codes are released for reproducibility and benchmarking purposes.
|
https://arxiv.org/abs/2002.02383v2
|
Recently, convolutional neural networks (CNNs) have achieved excellent
performances in many computer vision tasks. Specifically, for hyperspectral
images (HSIs) classification, CNNs often require very complex structure due to
the high dimension of HSIs. The complex structure of CNNs results in
prohibitive training efforts. Moreover, the common situation in HSIs
classification task is the lack of labeled samples, which results in accuracy
deterioration of CNNs. In this work, we develop an easy-to-implement capsule
network to alleviate the aforementioned problems, i.e., 1D-convolution capsule
network (1D-ConvCapsNet). Firstly, 1D-ConvCapsNet separately extracts spatial
and spectral information on spatial and spectral domains, which is more
lightweight than 3D-convolution due to fewer parameters. Secondly,
1D-ConvCapsNet utilizes the capsule-wise constraint window method to reduce
parameter amount and computational complexity of conventional capsule network.
Finally, 1D-ConvCapsNet obtains accurate predictions with respect to input
samples via dynamic routing. The effectiveness of the 1D-ConvCapsNet is
verified by three representative HSI datasets. Experimental results demonstrate
that 1D-ConvCapsNet is superior to state-of-the-art methods in both the
accuracy and training effort.
|
http://arxiv.org/abs/1903.09834v1
|
This paper presents a 1-D convolutional graph neural network for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined recurrent graph convolutional neural networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The achievable accuracy of 99.27%, 98.1%, 98.75%, and 95.6% for fault detection, fault type classification, fault phase identification, and fault location respectively.
|
https://arxiv.org/abs/2211.02930v1
|
Sleep arousals transition the depth of sleep to a more superficial stage. The
occurrence of such events is often considered as a protective mechanism to
alert the body of harmful stimuli. Thus, accurate sleep arousal detection can
lead to an enhanced understanding of the underlying causes and influencing the
assessment of sleep quality. Previous studies and guidelines have suggested
that sleep arousals are linked mainly to abrupt frequency shifts in EEG
signals, but the proposed rules are shown to be insufficient for a
comprehensive characterization of arousals. This study investigates the
application of five recent convolutional neural networks (CNNs) for sleep
arousal detection and performs comparative evaluations to determine the best
model for this task. The investigated state-of-the-art CNN models have
originally been designed for image or speech processing. A detailed set of
evaluations is performed on the benchmark dataset provided by
PhysioNet/Computing in Cardiology Challenge 2018, and the results show that the
best 1D CNN model has achieved an average of 0.31 and 0.84 for the area under
the precision-recall and area under the ROC curves, respectively.
|
http://arxiv.org/abs/1903.01552v1
|
During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.
|
https://arxiv.org/abs/1905.03554v1
|
Machine and deep learning algorithms have increasingly been applied to solve problems in various areas of knowledge. Among these areas, Chemometrics has been benefited from the application of these algorithms in spectral data analysis. Commonly, algorithms such as Support Vector Machines and Partial Least Squares are applied to spectral datasets to perform classification and regression tasks. In this paper, we present a 1D convolutional neural networks (1D-CNN) to evaluate the effectiveness on spectral data obtained from spectroscopy. In most cases, the spectrum signals are noisy and present overlap among classes. Firstly, we perform extensive experiments including 1D-CNN compared to machine learning algorithms and standard algorithms used in Chemometrics on spectral data classification for the most known datasets available in the literature. Next, spectral samples of the SARS-COV2 virus, which causes the COVID-19, have recently been collected via spectroscopy was used as a case study. Experimental results indicate superior performance of 1D-CNN over machine learning algorithms and standard algorithms, obtaining an average accuracy of 96.5%, specificity of 98%, and sensitivity of 94%. The promissing obtained results indicate the feasibility to use 1D-CNN in automated systems to diagnose COVID-19 and other viral diseases in the future.
|
https://arxiv.org/abs/2301.10746v1
|
This paper presents an efficient deep neural network model for diagnosing Parkinson's disease from gait. More specifically, we introduce a hybrid ConvNet-Transformer architecture to accurately diagnose the disease by detecting the severity stage. The proposed architecture exploits the strengths of both Convolutional Neural Networks and Transformers in a single end-to-end model, where the former is able to extract relevant local features from Vertical Ground Reaction Force (VGRF) signal, while the latter allows to capture long-term spatio-temporal dependencies in data. In this manner, our hybrid architecture achieves an improved performance compared to using either models individually. Our experimental results show that our approach is effective for detecting the different stages of Parkinson's disease from gait data, with a final accuracy of 88%, outperforming other state-of-the-art AI methods on the Physionet gait dataset. Moreover, our method can be generalized and adapted for other classification problems to jointly address the feature relevance and spatio-temporal dependency problems in 1D signals. Our source code and pre-trained models are publicly available at https://github.com/SafwenNaimi/1D-Convolutional-transformer-for-Parkinson-disease-diagnosis-from-gait.
|
https://arxiv.org/abs/2311.03177v1
|
We apply the supervariable approach to derive the proper quantum Becchi-Rouet-Stora-Tyutin (BRST) and anti-BRST symmetries for the 1D diffeomorphism invariant model of a free scalar relativistic particle by exploiting the infinitesimal classical reparameterization (i.e. 1D diffeomorphism) symmetry of this theory. We derive the conserved and off-shell nilpotent (anti-)BRST charges and prove their absolute anticommutativity property by using the virtues of Curci-Ferrari (CF)-type restriction of our present theory. We establish the sanctity of the existence of CF-type restriction (i) by considering the (anti-)BRST symmetry transformations of the coupled (but equivalent) Lagrangians, and (ii) by proving the symmetry invariance of the Lagrangians within the framework of supervariable approach. We capture the nilpotency and absolute anticommutativity of the conserved (anti-)BRST charges within the framework of (anti-)chiral supervariable approach (ACSA) to BRST formalism. One of the novel observations of our present endeavor is the derivation of CF-type restriction by using the modified Bonora-Tonin (BT) supervariable approach (while deriving the (anti-)BRST symmetries for the target spacetime and/or momenta variables) and by symmetry considerations of the Lagrangians of the theory. The rest of the (anti-)BRST symmetries, for the other variables, are derived by using the newly proposed ACSA. We also demonstrate the existence of CF-type restriction in the proof of absolute anticommutativity of the (anti-)BRST charges.
|
https://arxiv.org/abs/1912.12909v2
|
This study proposed a novel robotic gripper that can achieve grasping and infinite wrist twisting motions using a single actuator. The gripper is equipped with a differential gear mechanism that allows switching between the grasping and twisting motions according to the magnitude of the tip force applied to the finger. The grasping motion is activated when the tip force is below a set value, and the wrist twisting motion is activated when the tip force exceeds this value. "Twist grasping," a special grasping mode that allows the wrapping of a flexible thin object around the fingers of the gripper, can be achieved by the twisting motion. Twist grasping is effective for handling objects with flexible thin parts, such as laminated packaging pouches, that are difficult to grasp using conventional antipodal grasping. In this study, the gripper design is presented, and twist grasping is analyzed. The gripper performance is experimentally validated.
|
https://arxiv.org/abs/2211.05303v1
|
Although Majorana platforms are promising avenues to realizing topological
quantum computing, they are still susceptible to errors from thermal noise and
other sources. We show that the error rate of Majorana qubits can be
drastically reduced using a 1D repetition code. The success of the code is due
the imbalance between the phase error rate and the flip error rate. We
demonstrate how a repetition code can be naturally constructed from segments of
Majorana nanowires. We find the optimal lifetime may be extended from a
millisecond to over one second.
|
http://arxiv.org/abs/1906.01658v3
|
Graphene is the first truly two-dimensional (2D) material, possessing a cone-like energy spectrum near the Fermi energy and treated as a gapless semiconductor. Its unique properties trigger researchers to find more applications of it, such as high carrier mobility at room temperature, superior thermoconductivity, high modulus and tensile strength, high transparency, and anomalous quantum Hall effect. However, the gapless feature limits the development of graphene nanoelectronics. Making one-dimensional (1D) strips of graphene (i.e., graphene nanoribbons (GNRs)) could be one of the most promising approaches to modulating the electronic and optical properties of graphene. The electronic and optical properties have been theoretically predicted and experimentally verified highly sensitive to the edge and width. The tunable electronic and optical properties further imply the possibilities of GNR application. Recently, the dangling bond problem is under consideration in the GNR system. Various passivation at the ribbon edge might change the physical properties. In this work, some passivation conditions are studied, such as alkalization and hydrogenation.
|
https://arxiv.org/abs/2206.11162v1
|
Materials with flat bands can serve as a promising platform to investigate strongly interacting phenomena. However, experimental realization of ideal flat bands is mostly limited to artificial lattices or moir\'e systems. Here we report a general way to construct one-dimensional (1D) flat bands in phosphorene nanoribbons (PNRs) with pentagonal nature: penta-hexa-PNRs and penta-dodeca-PNRs, wherein the corresponding 1D flat bands are directly verified by using angle-resolved photoemission spectroscopy. We confirm that the observed 1D flat bands originate from the electronic 1D zigzag and Lieb lattices, respectively, as revealed by the combination of bond-resolved scanning tunneling microscopy, scanning tunneling spectroscopy, tight-binding models, and first-principles calculations. Our study demonstrates a general way to construct 1D flat bands in 1D solid materials system, which provides a robust platform to explore strongly interacting phases of matter.
|
https://arxiv.org/abs/2407.08353v2
|
A limited number of 2D and 3D materials under a constant pressure contract in volume upon heating isobarically; this anomalous phenomenon is known as the negative thermal expansion (NTE). In this paper, the NTE anomaly is observed in 1D fluids of classical particles interacting pairwisely with two competing length scales: the hard-core diameter $a$ and the finite range $a'>a$ of a soft repulsive potential component. If $a'\le 2a$, the pair interactions reduce themselves to nearest neighbours which permits a closed-form solution of thermodynamics in the isothermal-isobaric ensemble characterized by temperature $T$ and pressure $p$. We focus on the equation of state (EoS) which relates the average distance between particles (reciprocal density) $l$ to $T$ and $p\ge 0$. The EoS is expressible explicitly in terms of elementary or special functions for specific, already known and new, cases like the square shoulder, the linear and quadratic ramps as well as certain types of logarithmic interaction potentials. The emphasis is put on low-$T$ anomalies of the EoS. Firstly, the equidistant ground state as the function of the pressure can exhibit, at some ``compressibility'' pressures, a jump in chain spacing from $a'$ to $a$. Secondly, the analytical structure of the low-$T$ expansion of $l(T,p)$ depends on ranges of $p$-values. Thirdly, the presence of the NTE anomaly depends very much on the shape of the core-softened potential.
|
https://arxiv.org/abs/2503.11310v1
|
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