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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:1466
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: How many more requests can the proposed method handle effectively
compared to the RMLSA-OFC method before performance declines, based on the data
in Figure 6?
sentences:
- is a great challenge to synchronize the pulse trains, and this will induce extra
interpulse jitter. Perfect repetition rate multiplication can be realized by the
temporal Talbot or self-imaging effect through propagating a periodic temporal
signal in a dispersive medium under the first-order dispersion conditions.25 Further,
repetition rate demultiplication could also be realized by introducing a suitable
periodic temporal phase modulation to the original signal and carefully controlling
the amount of dispersion.26 However, both of these two schemes mentioned above
require extra optical systems out of the laser cavities to modify the repetition
rates of the pulse sources. They increase the system complexity and make these
systems loose their attractiveness for portable and robust device operation. An
intralaser cavity method is by harmonic mode-locking (HML), where the pulse energy
is quantized by the peak-power-limiting effect. Generally, much higher pump power
is needed for passive HML lasers to boost its repetition rates to tens of GHz.27,28
Both the intracavity noise fluctuation and the chance of the pulse drop in-out
increase along with the increase of the harmonic order.,9,29 This prevents boosting
the laser repetition rate beyond 100 GHz. Although pulse sources at repetition
rates beyond $100\ \mathrm{GHz}$ can be realized by employing active HML schemes,3
a stable radio frequency source is needed and it restricts the dimension and cost
for integration applications of the active HML lasers.
- "$$\nY=\\frac{\\gamma^{2}L^{2}}{\\theta}X^{3}-\\frac{2\\delta_{1}\\gamma L}{\\\
theta}X^{2}+\\frac{(\\delta_{1}^{2}+\\alpha^{2})}{\\theta}X.\n$$ \nwhere $Y=\\\
left|\\psi_{\\mathrm{in}}\\right|^{2}$ is the pump power, $X=\\left|\\psi\\right|^{2}$\
\ is the intracavity power, ${\\alpha}=({\\alpha}_{0}L+\\theta)/2$ is the total\
\ loss per roundtrip. The turning points of the function $Y(X)$ can be calculated\
\ by setting the first derivative $d Y/d X$ to zero as \n$$\n3\\gamma^{2}L^{2}X^{2}-4\\\
delta_{1}\\gamma L X+(\\delta_{1}^{2}+\\alpha^{2})=0.\n$$ \nThe function $X(Y)$\
\ given by Eq. (6) has a bistable region when Eq. (7) has two different real roots\
\ of $X.$ which requires $\\delta_{1}^{2}>3\\alpha^{2}$ . The two real roots corresponding\
\ to the two turning points of the bistable curve are given by \n$$\nX_{1,2}=\\\
frac{2\\delta_{1}\\pm\\sqrt{\\delta_{1}^{2}-3\\alpha^{2}}}{3\\gamma L}.\n$$"
- "To compare the proposed method with widely used state-of-the-art allocation methods,\
\ three approaches were considered: the RMLSA-OFC method [19], the First Fit (FF)\
\ algorithm, and the Random Wavelength Assignment (RWA) algorithm [20,21]. The\
\ method in [19] employs a heuristic algorithm for allocation using optical frequency\
\ combs (OFCs). In contrast, the FF method sequentially assigns resources by selecting\
\ the first available carrier that meets the bandwidth requirement, while the\
\ RWA method allocates wavelengths in a random manner, ensuring that each selected\
\ wavelength satisfies the transmission requirements. \nFigure 6 demonstrates\
\ that the method proposed in [19] can effectively allocate up to 110 requests\
\ with a low BBR, reaching a maximum value of 1.5. This indicates a lower performance\
\ compared to our method (see Figure 5). The ellipses in Figure 6 highlight regions\
\ with the highest BBR values, marking critical performance areas. A dashed line\
\ indicates the threshold at approximately 110 requests, beyond which the allocation\
\ method's performance declines as the number of requests increases. This visual\
\ representation emphasizes the system's limitations under higher demand. In contrast,\
\ our approach maintains effective allocation up to 170 requests, showing a clear\
\ difference of 60 clients. \nWhen comparing the results of our RMLSA-ILP-OFC\
\ approach to those of the FF method (see Figure 7), a marked increase in the\
\ Blocking-to-Bandwidth Ratio (BBR) is observed, with rejections exceeding 1 in\
\ several instances (marked in yellow and light blue). The BBR values, ranging\
\ from 1.5 to 3.0 (highlighted with red ellipses), indicate critical points that\
\ significantly affect Quality of Service (QoS). Additionally, the allocation\
\ method begins to fail at approximately 105 requests (red dotted line), leading\
\ to a sharp rise in BBR. These findings underscore the limitations of the FF\
\ method, which struggles to manage bandwidth efficiently under high-demand scenarios,\
\ highlighting the need for more robust solutions. Our approach demonstrates its\
\ superiority by maintaining resource allocations effectively up to 170 requests."
- source_sentence: What was the length of the Bi-EDF used as the gain medium in the
experiment?
sentences:
- Figure 1 shows the experimental setup that is used in order to measure the relative
phases between individual microcomb modes. A microcomb is generated in a microrod-resonator
[7, 8] using an amplified extemal cavity diode laser that is coupled into a microresonator
via a tapered optical fiber (launched power ${\approx}100~\mathrm{mW}$ , resonator
quality factor $Q\approx1.8\times10^{8}$ , resonator mode spacing. $25.6\mathrm{GHz}$
). The generated comb is amplified and sent through. a liquid crystal array based
"waveshaper', that allows control of the amplitude and phase of the individual
comb modes. In order to retrieve the relative phases of the comb modes, we first
flatten the power in the comb modes. Then we use a computer controlled feedback
loop in order to optimize the phases of the comb modes until we generate the shortest
possible pulse (\*zero phases'), which is measured with an optical intensity autocorrelator.
The original comb mode phases directly follow from the phases that had to be applied
to generate the shortest pulse multiplied by (-1). Note that the measurement is
not sensitive to linear increasing phases with frequency, which would just shift
the pulse in time without changing the pulse shape. Thus we subtract any linear
component from the measured phases. We also subtract the dispersion of the setup
itself, which is measured by sending a reference pulse through the setup and determining
the phase mask that is required to compensate for the setup dispersion. Since
the microcomb enters the experimental setup in the middle of a tapered optical
fiber, the setup dispersion is measured by sending the reference pulse into the
setup before and after the tapered fiber and taking the average value.
- "Fig. 1 explains the schematic diagram of the experimental setup. As a very short\
\ gain medium, we employed a Bi-EDF with a length of $1.5\\mathsf{m}$ . The Bi-EDF\
\ was provided by Asahi-Glass Co., LTD [11]- [14], [18]-[22]. The fiber length\
\ was optimally determined so as to maximize the wavelength tuning range. The\
\ refractive indexes of the core and the cladding at 1550 nm were 2.03 and 2.02,\
\ respectively. The core and cladding diameters were 5.1 $\\mu\\mathsf{m}$ and\
\ $124\\mu\\mathsf{m}$ , respectively. The erbium concentration was 3250 ppm.\
\ The peak absorption levels around $980~\\mathsf{n m}$ $1480\\mathsf{n m}$ ,\
\ and $1530\\mathsf{n m}$ were 90 dB/m, 130 dB/m, and $210~\\mathsf{d B/m}$ ,\
\ respectively. The group-velocity dispersion (GVD) at 1550 nm was - 130 ps/nm/km.\
\ In order to achieve better mode field diameter matching, both ends of the Bi-EDF\
\ were first fusion spliced to high numerical aperture (NA) $\\mathsf{S i O}_{2}$\
\ fibers (HI980, Corning) before being spliced to conventional $\\mathsf{S i O}_{2}$\
\ fibers (SMF28, Corning). Thus, the fiber-tofiber loss at 1310 nm was 2.3 dB\
\ [11], [13]. The Bi-EDF was bidirectionally pumped with two high power laser\
\ diodes (LDs). A 974-nm LD with an output power of + 26 dBm and a 976-nm LD with\
\ an output power of + 27 dBm were used for the forward pumping and the backward\
\ pumping, respectively. These pump lights were coupled in the fiber cavity through\
\ wavelength-division multiplexed (wDM) fiber couplers. Two optical isolators\
\ ensured the unidirectional ring laser oscillation. \nSinewave signal from a\
\ low noise microwave oscillator was amplified by a RF amplifier and then led\
\ to a $\\mathsf{L i N b O}_{3}$ Mach-Zehnder intensity modulator 1 (LNMOD 1).\
\ The LNMOD 1 had an electrical bandwidth of $13.2G H z$ , an insertion loss of\
\ 3.7 dB, and an extinction ratio of 30.3 dB. The LNMOD 1 driven by the RF synthesizer\
\ achieved active mode Iocking at 10 GHz. A polarization controller 1 (PC 1) was\
\ used to align the polarization state to that of the LNMOD 1. Instead of adjusting\
\ the cavity length by a variable optical delay, the modulation frequency was\
\ adjusted in the range form 10.000563 GHz to 10.002038 GHz so that optimal mode\
\ locking was maintained."
- "To evaluate the performance of our OPLL system using COTS ICs, residual phase\
\ noise of the OPLL was measured from $10~\\mathrm{Hz}$ to $1\\mathrm{GHz}$ using\
\ the setup shown in Fig. 5. The locked beat note at $2.9\\:\\mathrm{GHz}$ produced\
\ between SG-DBR and comb was connected to the ESA and the single-sideband (SSB)\
\ phase-noise spectral density (PNSD) was then measured. The signal power level\
\ of this measurement was $42~\\mathrm{dBm}$ Figure 11 shows the residual OPLL\
\ phase noise at offsets from $10~\\mathrm{Hz}$ to $1~\\mathrm{GHz}$ . For the\
\ comparison, PNSD of the background, RF synthesizer at $2.9~\\mathrm{GHz}$ ,\
\ and comb source (through the RF beat note generated between comb lines) are\
\ superimposed in Fig. 11. The output signal power levels were kept the same during\
\ the measurement in order to obtain consistency. \nThe phase noise variance\
\ from $1~\\mathrm{\\kHz}$ to $10~\\mathrm{{\\GHz}}$ is calculated to be $0.08\\\
mathrm{\\Delta}\\mathrm{\\}\\mathrm{rad}^{2}$ corresponding to $14^{\\circ}$ standard\
\ deviation from the locking point. This result is better than the one reported\
\ in [39]. As can be seen in Fig. 11, low frequency noise with a value less than\
\ 80 $\\mathrm{{dBc/Hz}}$ at an offset above $200~\\mathrm{Hz}$ for PNSD was achieved.,\
\ whereas the same value at an offset above $10\\mathrm{kHz}$ was achieved in\
\ [29, 42]. Lu et al. also reported better than $80~\\mathrm{dBc/Hz}$ at offsets\
\ above $5\\mathrm{kHz}$ which is again worse than the performance reported here\
\ [35]. However, the phase variance of our results is comparable with [29, 42]\
\ which could be attributed to the pedestal after $1\\mathrm{kHz}$ which may be\
\ caused by a fiber path length mismatch between the comb and OPLL laser paths\
\ (see Fig. 5). Thus, after $1\\ \\mathrm{kHz}$ some additional noise from the\
\ slave laser is observed and contributes to the overall phase variance. Matched\
\ path length will be used in the future work."
- source_sentence: Which fields are accounted for in the system of nonlinearly coupled
Lugiato-Lefever equations when considering two driving fields?
sentences:
- "The system under study is schematically presented in Fig. 1. A Kerr-nonlinear\
\ whispering-gallery mode resonator with an ultra-high $Q$ factor is pumped by\
\ a resonant continuous-wave laser around 1500 nm where the overall dispersion\
\ of the resonator is anomalous. The spectrum of the output signal, which above\
\ a certain threshold is a Kerr comb, can be monitored using an optical spectrum\
\ analyzer. $^{22,23}$ The resonances can be monitored as well using an oscilloscope\
\ when the laser frequency is scanned. \nAt the theoretical level, the dynamics\
\ of the intracavity laser field is generally investigated with the following\
\ dimensionless Lugiato-Lefever equation \n$$\n\\frac{\\partial\\psi}{\\partial\\\
tau}=-(1+i\\alpha)\\psi+i|\\psi|^{2}\\psi-i\\frac{\\beta}{2}\\frac{\\partial^{2}\\\
psi}{\\partial\\theta^{2}}+F,\n$$ \nwhere $\\begin{array}{r}{\\psi(\\theta,\\\
tau)=\\sum_{l}\\psi_{l}(\\tau)e^{i l\\theta}}\\end{array}$ is the intra-cavity\
\ field, $\\psi_{l}$ is the field in the mode of reduced azimuthal order $\\it{l}$\
\ $\\tau$ is the dimensionless time, $\\theta\\in[-\\pi,\\pi]$ is the azimuthal\
\ angle of the disk-resonator, $\\alpha$ stands for laser detuning with regards\
\ to the resonance of the pumped mode, $\\beta$ stands for dispersion, and $F$\
\ is the pump field. Depending \non the parameters, various kinds of combs can\
\ be excited, as comprehensively reviewed in the literature. $^{9-13}$ In particular,\
\ all stationary Kerr combs are symmetrical with regards to the pump and the output\
\ signal . can be explicitly written as \n$$\n\\begin{array}{r c l}{{\\psi_{\\\
mathrm{out}}}}&{{=}}&{{\\displaystyle\\sum_{l}\\psi_{\\mathrm{out},l}e^{i l\\\
theta},}}\\\\ {{}}&{{}}&{{}}\\\\ {{}}&{{=}}&{{\\displaystyle\\psi_{\\mathrm{out,0}}\\\
left[1+\\sum_{l>1}2m_{l}e^{i\\xi_{l}}\\cos\\left(l\\Omega_{\\mathrm{FSR}}t+\\\
frac{1}{2}\\Delta\\phi_{l}\\right)\\right],}}\\end{array}\n$$ \nwhere $\\psi_{\\\
mathrm{out},l}$ is the modal output field in the mode $l$ $m_{l}=|\\psi_{\\mathrm{out,}\\\
pm l}|/|\\psi_{\\mathrm{out,0}}|$ is the sidemode-to-pump ratio, and $\\Omega_{\\\
mathrm{{FSR}}}$ is the free-spectral range of the resonator. The parameter $\\\
xi_{l}$ is a constant that only depends on the parameters of the system, while\
\ the phase shift $\\Delta\\phi_{l}$ depends on the initial conditions. \nThe\
\ microwave signal at the output of the photodetector is proportional to \n$$\n\
|\\psi_{\\mathrm{out}}|^{2}=\\frac{1}{2}\\mathcal{M}_{0}+\\sum_{n=1}^{+\\infty}\\\
left[\\frac{1}{2}\\mathcal{M}_{n}\\exp(i n\\Omega_{\\mathrm{FSR}}t)+\\mathrm{c.c.}\\\
right],\n$$ \nwhere \n$$"
- The Talbot effect has been described for the first time in 1836 as a peculiar
phenomenon observed in the near field of an optical grating. Summing over contributions
of the individual rulings to the total field in the Fresnel approximation, a term
of the form $\exp(-i k l^{2}a^{2}/2z)$ appears with the wave number $k$ , the
rulings numbered by $l$ and spaced by $a$ and the distance from the grating z.
Summing over $\mathbf{\xi}_{l}$ generally yields a rather chaotic intensity distribution.
Talbot noted though that all these terms reduce to $\exp(-i2\pi l^{2})=1$ at a
distance of $z=k a^{2}/4\pi$ . The remaining terms add up to the intensity at
$z=0$ provided that this intensity is periodic with $a$ [1]. The same phenomenon
can be observed in the time domain with a periodic pulse train signal that is
subject to group velocity dispersion $\phi^{\prime\prime}$ that provides the quadratic
phase evolution. The pulses first spread out in time, an then reassemble after
propagation the distance $t_{r}^{2}/2\pi|\phi^{\prime\prime}|,$ where $t_{r}$
is the pulse repetition time [2, 3]. The same behaviour should be observable with
a single pulse that is on a repetitive path in an optical cavity. In contrast
to a free space pulse train, higher order dispersion is required as we will show
below.
- "The addition of other driving fields can be introduced in a single Lugiato-Lefever\
\ equation (LLE) accounting for their phase and azimuthal offsets30. Although\
\ this multipump LLE approach captures the KIS phenomenon', it can be challenging\
\ to study the different colours individually as they are all included in the\
\ single cavity field. Instead, one can obtain a system of nonlinearly coupled\
\ LLEs accounting for the DKS field $a_{\\mathrm{dks}}$ and phase-offset second\
\ colour $a_{\\mathrm{sec.}}$ both driven by their respective pump $P_{\\mathrm{main}}$\
\ and $P_{\\mathrm{aux}}$ . Such formalism enables the re-normalization of their\
\ respective phase and allows study of each colour independently (see Methods\
\ for the derivation): \n$$\n\\begin{array}{r l}&{\\frac{\\partial a_{\\mathrm{dks}}}{\\\
partial t}=\\left(-\\frac{\\kappa}{2}+\\mathrm{i}\\delta\\omega_{\\mathrm{main}}\\\
right)a_{\\mathrm{dks}}+\\mathrm{i}\\Sigma_{\\mu}D_{\\mathrm{int}}(\\mu)A_{\\\
mathrm{dks}}\\mathrm{e}^{\\mathrm{i}\\mu\\theta}}\\\\ &{\\qquad-\\mathrm{i}\\\
gamma L(|a_{\\mathrm{dks}}|^{2}+2|a_{\\mathrm{sec}}|^{2})a_{\\mathrm{dks}}}\\\\\
\ &{\\qquad+\\mathrm{i}\\sqrt{\\kappa_{\\mathrm{ext}}P_{\\mathrm{main}}}}\\end{array}\n\
$$ \n$$\n\\begin{array}{r l}&{\\frac{\\partial a_{\\mathrm{sec}}}{\\partial t}=\\\
left(-\\frac{\\kappa}{2}+\\mathrm{i}\\delta\\omega_{\\mathrm{aux}}-\\mathrm{i}D_{\\\
mathrm{int}}(\\mu_{\\mathrm{aux}})\\right)a_{\\mathrm{sec}}}\\\\ &{\\qquad+\\\
mathrm{i}\\Sigma_{\\mu}D_{\\mathrm{int}}(\\mu)A_{\\mathrm{sec}}\\mathrm{e}^{\\\
mathrm{i}\\mu\\theta}}\\\\ &{\\qquad-\\mathrm{i}\\gamma L(2|a_{\\mathrm{dixs}}|^{2}+|a_{\\\
mathrm{sec}}|^{2})a_{\\mathrm{sec}}}\\\\ &{\\qquad+\\mathrm{i}\\sqrt{K_{\\mathrm{ext}}P_{\\\
mathrm{aux}}}\\mathrm{e}^{\\mathrm{i}\\mu_{\\mathrm{aux}}\\theta}}\\end{array}\n\
$$"
- source_sentence: What effect does switching on the second pump have on the pulses
of the Kerr frequency comb?
sentences:
- The first time-domain characterization of Kerr combs was performed through spectral
line-by-line shaping of frequency combs generated in normal dispersion $\mathrm{Si}_{3}\mathrm{N}_{4}$
microrings [31]. The experimental scheme is shown in Figure 10. A pulse shaper
based on a spatial light modulator [8o] is used to tailor the amplitude and phase
of each comb line after the microring. The waveform after shaping is diagnosed
by measuring the second-order intensity autocorrelation [60]. In the line-by-line
shaping procedure, the comb lines are selected one by one to pass through the
pulse shaper. The phase shift applied to each comb line is optimized to maximize
the second-harmonic strength at zero time delay. In this way, if the comb lines
from the microring maintain a time-invariant relative phase profile (i.e., high
coherence), their phases will be compensated to a uniform state after the pulse
shaper, forming transform-limited pulses in the time domain. In contrast, transform-limited
pulses cannot be obtained for lowcoherence combs. A similar scheme was also employed
in [32] to characterize the time-domain behavior of frequency combs from a fused
quartz microresonator.
- As we discussed in Section 3, the train of optical pulses corresponding to the
mode-locked Kerr frequency comb can be synchronized with the beatnote between
the Dw, generated because of the specific frequency-dependent GVD of the resonator,
and the pump light. The DW and the frequency comb are generated simultaneously,
so it is hard to study their synchronization mechanism. In this section, we consider
a different situation and introduce a second Cw pump harmonic to a resonator characterized
by an ideal quadratic anomalous GVD. We provide numerical simulation results which
support the idea rendered in Figure 1, i.e., locking the comb supported by the
main pump (Pump 1) to the second pump (Pump 2) through tuning the frequency of
Pump 2. Pump 2 resembles a DW, however it can be added after the Kerr frequency
comb is formed. We see that the Kerr comb generated initially corresponds to a
train of dissipative solitons having arbitrary relative positions. Switching the
second pump on results in shifting the pulses to the positions defined by the
beatnote between Pumps 1 and 2, and in the synchronization of the frequency comb
with the beatnote signal of the pump waves. In this case, the phase locking of
the microcomb to the second pump can be explained by the dynamic interaction of
the pulse with the Cw background modulation since the time scale of the modification
of the frequency comb can be longer compared to that of establishing steady state
for the second pump in the resonator. The locking also results in modified repetition
rate of the frequency comb, similar to the cases studied in the previous section.
- 'Abstract: Microresonator-based optical frequency combs have been greatly developed
in the last decade and have shown great potential for many applications. A dual-comb
scheme is usually required for lidar ranging, spectroscopy, spectrometer and microwave
photonic channelizer. However, dual-comb generation with microresonators would
require doubled hardware resources and more complex feedback control. Here we
propose a novel scheme for dual-comb generation with a single laser diode self-injection
locked to a single microresonator. The output of the laser diode is split and
pumps the microresonator in clockwise and counter-clockwise directions. The scheme
is investigated intensely through numerical simulations based on a set of coupled
Lugiato-Lefever equations. Turnkey counter-propagating single soliton generation
and repetition rate tuning are demonstrated.'
- source_sentence: What is required to access soliton states in terms of the pump
laser frequency tuning direction?
sentences:
- "To gain further insights into the CW and CCW fields, we define modified detuning\
\ as the frequency difference between the pump laser and the XPM shifted resonance,\
\ given by \n$$\n\\Delta\\omega_{\\mathrm{mod,CW}}=\\Delta\\omega-(2-f_{R})P_{\\\
mathrm{CCW}}\n$$ \n$$\n\\Delta\\omega_{\\mathrm{mod,CCW}}=\\Delta\\omega-(2-f_{R})P_{\\\
mathrm{CW}}\n$$ \nwhere $P_{\\mathrm{CCW}}={\\overline{{|B|^{2}}}}$ and $P_{\\\
mathrm{CW}}={\\overline{{|A|^{2}}}}$ are the average power in the corresponding\
\ directions. By substituting the modified detunings into Eqs. (4) and (5), the\
\ equations become a form similar to the unidirectionally driven Lugiato-Lefever\
\ equation [31]. \nFor a general model with unidirectional pump, the soliton\
\ peak power is mainly determined by the cold cavity detuning [31,32]. A larger\
\ detuning corresponds to a higher soliton peak power. As illustrated in Fig.\
\ 4, if the CW direction has a larger pump power than the CCW direction, the CW\
\ intracavity power will be higher, i.e., $P_{\\mathrm{CW}}{>}P_{\\mathrm{CCW}}$\
\ . Thus the modified detuning $\\Delta\\omega_{\\mathrm{mod,CW}}{>}\\Delta\\\
omega_{\\mathrm{mod,CCW}}$ . As a consequence, the soliton peak power in the CW\
\ direction becomes larger than that in the CCW direction. Therefore, by tuning\
\ the MZI to change the pump splitting ratio, different modified detunings are\
\ introduced in the CW and CCW directions through the XPM effect, leading to different\
\ soliton peak power in the two directions. The evolution of the average intracavity\
\ power and soliton peak power shown in Figs. 3(i) and 3(j) is consistent with\
\ the above analysis. To further validate the conclusion, we run simulations with\
\ different pump spliting ratios and calculate the modified detunings. Figure\
\ 5(a) illustrates the relationship between the pump splitting ratio and the modified\
\ detuning. Figure 5(b) illustrates the relationship between the soliton peak\
\ power and the modified detuning. Due to symmetry, the curves are degenerated\
\ in the CW and CCW directions. \nIt has been known that the soliton group velocity\
\ and repetition rate can be changed due to Raman induced soliton self-frequency\
\ shift [17]. Therefore, the different soliton peak power in the CW and CCW directions\
\ will cause different Raman self-frequency shifts and different soliton repetition\
\ rates. Theoretically, the normalized repetition rate difference is related to\
\ the detuning by [17] \n$$"
- microcomb into a THz wave. The optical power of the auxiliary light is monitored
by a third photodiode (not shown in Fig. 1(a)). The frequency of the auxiliary
laser is tuned into its resonance from the blue detuned side and fixed on the
blue side of the resonance. Note that, the auxiliary laser is free-running without
feedback control on the laser frequency or the power during the soliton generation.
By optimizing the laser detuning and optical power of the auxiliary laser, soliton
states can be accessed by slowly tuning the pump laser frequency into a soliton
regime from the blue detuned side. A detailed description of the soliton generation
process can be found in the Ref. [21].
- "$$ \nwhere the overdot indicates the time derivative. Note that higher-order\
\ dispersion at arbitrary order can be accounted for by replacing $\\zeta_{2}l^{2}/2$\
\ by $\\scriptstyle\\sum_{n=2}^{n_{\\mathrm{max}}}\\zeta_{n}l^{n}/n!$ . which\
\ is obtained from Eq. (1). Without loss of generality, we can arbitrarily consider\
\ the phase of the external pump field as a reference and set it to zero, so that\
\ this field becomes real valued and can be written as \n$$\n\\mathcal{A}_{\\\
mathrm{in}}\\equiv A_{\\mathrm{in}}=\\sqrt{\\frac{P}{\\hbar\\omega_{\\mathrm{L}}}}.\n\
$$ \nIt is important to recall the normalization in the semiclassical Eqs. (4)\
\ is such that $\\vert\\mathcal{A}_{l}\\vert^{2}$ is a number of photons (cavity\
\ fields), while $|A_{\\mathrm{in}}|^{2}$ is a number of photons per second (propagating\
\ fields). This normalization is physically the most appropriate at the time to\
\ perform the canonical quantization."
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'What is required to access soliton states in terms of the pump laser frequency tuning direction?',
'microcomb into a THz wave. The optical power of the auxiliary light is monitored by a third photodiode (not shown in Fig. 1(a)). The frequency of the auxiliary laser is tuned into its resonance from the blue detuned side and fixed on the blue side of the resonance. Note that, the auxiliary laser is free-running without feedback control on the laser frequency or the power during the soliton generation. By optimizing the laser detuning and optical power of the auxiliary laser, soliton states can be accessed by slowly tuning the pump laser frequency into a soliton regime from the blue detuned side. A detailed description of the soliton generation process can be found in the Ref. [21].',
'To gain further insights into the CW and CCW fields, we define modified detuning as the frequency difference between the pump laser and the XPM shifted resonance, given by \n$$\n\\Delta\\omega_{\\mathrm{mod,CW}}=\\Delta\\omega-(2-f_{R})P_{\\mathrm{CCW}}\n$$ \n$$\n\\Delta\\omega_{\\mathrm{mod,CCW}}=\\Delta\\omega-(2-f_{R})P_{\\mathrm{CW}}\n$$ \nwhere $P_{\\mathrm{CCW}}={\\overline{{|B|^{2}}}}$ and $P_{\\mathrm{CW}}={\\overline{{|A|^{2}}}}$ are the average power in the corresponding directions. By substituting the modified detunings into Eqs. (4) and (5), the equations become a form similar to the unidirectionally driven Lugiato-Lefever equation [31]. \nFor a general model with unidirectional pump, the soliton peak power is mainly determined by the cold cavity detuning [31,32]. A larger detuning corresponds to a higher soliton peak power. As illustrated in Fig. 4, if the CW direction has a larger pump power than the CCW direction, the CW intracavity power will be higher, i.e., $P_{\\mathrm{CW}}{>}P_{\\mathrm{CCW}}$ . Thus the modified detuning $\\Delta\\omega_{\\mathrm{mod,CW}}{>}\\Delta\\omega_{\\mathrm{mod,CCW}}$ . As a consequence, the soliton peak power in the CW direction becomes larger than that in the CCW direction. Therefore, by tuning the MZI to change the pump splitting ratio, different modified detunings are introduced in the CW and CCW directions through the XPM effect, leading to different soliton peak power in the two directions. The evolution of the average intracavity power and soliton peak power shown in Figs. 3(i) and 3(j) is consistent with the above analysis. To further validate the conclusion, we run simulations with different pump spliting ratios and calculate the modified detunings. Figure 5(a) illustrates the relationship between the pump splitting ratio and the modified detuning. Figure 5(b) illustrates the relationship between the soliton peak power and the modified detuning. Due to symmetry, the curves are degenerated in the CW and CCW directions. \nIt has been known that the soliton group velocity and repetition rate can be changed due to Raman induced soliton self-frequency shift [17]. Therefore, the different soliton peak power in the CW and CCW directions will cause different Raman self-frequency shifts and different soliton repetition rates. Theoretically, the normalized repetition rate difference is related to the detuning by [17] \n$$',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5185, 0.1030],
# [0.5185, 1.0000, 0.2032],
# [0.1030, 0.2032, 1.0000]])
```
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1,466 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 13 tokens</li><li>mean: 27.84 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 90 tokens</li><li>mean: 402.23 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 25 tokens</li><li>mean: 394.46 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How does the behavior of front solutions differ between high and low drive powers in a normal dispersion Kerr resonator without spectral filtering?</code> | <code>Here, we develop some insight into the difference between dark solitons in the LLE and LLE-F as well as into the formation of chirped-pulse solitons unique to the LLE-F. For fixed detuning, Fig. 7 indicates that the drive power is a suitable parameter for traversing between the different solution types. We therefore examine the variation of steady-state solutions along the dashed line in Fig. 7. For a normal dispersion Kerr resonator without spectral filtering, front solutions (also known as domain walls or switching waves) often move in the reference frame of the driving field [19,54,55,66]. To examine the moving properties of front solutions, we initialize the simulation with a two-front intensity variation in the time domain. The equation is numerically solved with this initial condition and examined as a function of propagation distance until the waveform converges. At large drive powers without a filter [Fig. 8(a) and a from Fig. 7], the front solutions move together and vanish to...</code> | <code>Dissipative solitons are self-localised structures resulting from the double balance of dispersion by nonlinearity and dissipation by a driving force arising in numerous systems. In Kerr-nonlinear optical resonators, temporal solitons permit the formation of light pulses in the cavity and the generation of coherent optical frequency combs. Apart from shape-invariant stationary solitons, these systems can support breathing dissipative solitons exhibiting a periodic oscillatory behaviour. Here, we generate and study single and multiple breathing solitons in coherently driven microresonators. We present a deterministic route to induce soliton breathing, allowing a detailed exploration of the breathing dynamics in two microresonator platforms. We measure the relation between the breathing frequency and two control parameters--pump laser power and effective-detuning--and observe transitions to higher periodicity, irregular oscillations and switching, in agreement with numerical predictions....</code> |
| <code>What are the key advantages of microcombs that make them suitable for portable applications?</code> | <code>Microresonator based optical frequency comb (often termed "microcomb" or "Kerr comb) generation was first demonstrated in 2007 [1]. It quickly attracted people's great interest and evolved to a hot research area. Microcombs are very promising for portable applications because they have many unique advantages including the capability of generating ultra-broad comb spectra (even more than one octave [2,3]), chip-level integration [4,5], and low power consumption. The basic scheme of microcomb generation is shown in Fig. 1(a). The frequency of a pump laser is tuned into the resonance of one high-quality-factor $(\boldsymbol{Q})$ microresonator which is made of Kerr nonlinear material. When the pump power exceeds some threshold, new frequency lines grow due to parametric gain. More lines are generated through cascaded four-wave mixing between the pump and initial lines, forming a broad frequency comb [6]. Intense studies have been performed to investigate microcomb generation. Various mate...</code> | <code>We briefly review the physics of the parametric process in microresonators, discussed in detail in (30, 80). Kerr frequency combs were initially discovered in silica microtoroids, and experiments proved that the parametrically generated (11, 81) sidebands were equidistant to at least one part in $10^{-17}$ as compared with the optical carrier. In these early experiments, the combs repetition rate was in the terahertz range, and a femtosecond-laser frequency comb was used to bridge and verify the equidistant nature of the teeth spacing. It is today understood that such highly coherent combs only exist in certain regimes.</code> |
| <code>What is the formula for determining the number of rolls that appear in the azimuthal direction when the cavity is pumped just above the threshold of modulational instability?</code> | <code>Roll patterns emerge from noise after the breakdown of an unstable flat background through modulational instability, when the resonator is pumped above a certain threshold. This mechanism preferably occurs in the regime of anomalous GVD, but, however, rolls can also be sustained in the normal GVD regime, although under very marginal conditions (typically, very large detuning, see refs. [9, 18, 47]). <br>When the pump is below the threshold, there is only one excited mode in the resonator $\left(l\ =\ 0\right)$ , while all the sidemodes amplitudes $\mathcal{A}_{l}$ with $l\neq0$ are null. From the spatiotemporal standpoint, the intracavity feld is constant (flat background). Under certain conditions, when the pump $F$ is increased beyond a certain threshold value $F_{\mathrm{th}}$ , the flat background solution becomes unstable and breaks down into a roll pattern characterized by a periodic modulation of the intracavity power as a function of the azimuthal angle (see Fig. 6). This phenome...</code> | <code>$$ <br>Note that $\mathcal{N}_{h}=S\mathcal{M}_{h}S^{-1}$ and $\mathcal{I N}_{h}=S\mathcal{I M}_{h}S^{-1}$ , so the spectra of the full linearized operator, $\mathcal{I N}_{h}$ , is equivalent to $\mathcal{I M}_{h}$ . Also, $\sigma(\mathcal{N}_{h})$ is equivalent to $\sigma(\mathcal{M}_{h})$ <br>Since the two problems are equivalent, we note that the form (4.3) of the eigenvalue problem is more suggestive of our approach. For $h=0$ , we have two dimensional $\mathrm{Ker}[\mathcal{M}_{0}]$ , spanned by the vectors is?. $\big(\begin{array}{c}{\varphi_{0}^{\prime}}\\ {0}\end{array}\big)$ and $\big(\begin{array}{c}{0}\\ {\varphi_{0}}\end{array}\big)$ . We need to see what the evolution of the modulational eigenvalue is as $h:0<h<<1$ , i.e., the one corresponding to the eigenvector $\big(\begin{array}{c}{0}\\ {\varphi_{0}}\end{array}\big)$ . This is because, by index counting theory, the instability can only appear in the even subspace of the problem. Also, we can clearly consider $\mathcal{M}_...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `num_train_epochs`: 5
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 2.7174 | 500 | 0.2217 |
### Framework Versions
- Python: 3.9.19
- Sentence Transformers: 5.1.0
- Transformers: 4.51.0
- PyTorch: 2.5.0+cu124
- Accelerate: 0.34.2
- Datasets: 2.19.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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