Tunable operation of a gain-switched diode laser by nonresonant self-injection seeding

In this letter, we report tunable operation of a gain-switched diode laser by nonresonant self-injection seeding from an uncoated glass slide used as an external cavity reflector. A spectral linewidth reduction from 11 to 0.05 nm has been achieved for picosecond pulses with little effect on other laser characteristics. Good agreement with numerical simulations based on a compound-cavity laser model is also reported

Nonresonant self-injection seeding of a gain-switched diode laser

We demonstrate step-tunable single-mode operation of a gain-switched diode laser by nonresonant self-injection seeding from an uncoated glass slide used as an external cavity reflector. A spectral bandwidth reduction from 11 mn to 0.05 nm and wavelength tunability has been achieved for picosecond (near-transform-limited) pulses with little effect on other laser characteristics. Good agreement with numerical simulations based on a compound-cavity laser model is also reported

Ultrafast electroabsorption dynamics in an InAs quantum dot saturable absorber at 1.3 mu m

The authors report a direct measurement of the absorption dynamics in an InAs p-i-n ridge waveguide quantum dot modulator. The carrier escape mechanisms are investigated via subpicosecond pump-probe measurements at room temperature, under reverse bias conditions. The optical pulses employed are degenerate in wavelength with the quantum dot ground state transition at 1.28 mu m. The absorption change recovers with characteristic times ranging from 62 ps (0 V) to similar to 700 fs (-10 V), showing a decrease of nearly two orders of magnitude. The authors show that at low applied fields, this recovery is attributed to thermionic emission while for higher applied fields, tunneling becomes the dominant mechanism. (c) 2006 American Institute of Physics.</p

An Emulator for Fine-Tuning Large Language Models using Small Language Models

Widely used language models (LMs) are typically built by scaling up a two-stage training pipeline: a pre-training stage that uses a very large, diverse dataset of text and a fine-tuning (sometimes, 'alignment') stage that uses targeted examples or other specifications of desired behaviors. While it has been hypothesized that knowledge and skills come from pre-training, and fine-tuning mostly filters this knowledge and skillset, this intuition has not been extensively tested. To aid in doing so, we introduce a novel technique for decoupling the knowledge and skills gained in these two stages, enabling a direct answer to the question, "What would happen if we combined the knowledge learned by a large model during pre-training with the knowledge learned by a small model during fine-tuning (or vice versa)?" Using an RL-based framework derived from recent developments in learning from human preferences, we introduce emulated fine-tuning (EFT), a principled and practical method for sampling from a distribution that approximates (or 'emulates') the result of pre-training and fine-tuning at different scales. Our experiments with EFT show that scaling up fine-tuning tends to improve helpfulness, while scaling up pre-training tends to improve factuality. Beyond decoupling scale, we show that EFT enables test-time adjustment of competing behavioral traits like helpfulness and harmlessness without additional training. Finally, a special case of emulated fine-tuning, which we call LM up-scaling, avoids resource-intensive fine-tuning of large pre-trained models by ensembling them with small fine-tuned models, essentially emulating the result of fine-tuning the large pre-trained model. Up-scaling consistently improves helpfulness and factuality of instruction-following models in the Llama, Llama-2, and Falcon families, without additional hyperparameters or training

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF). However, RLHF is a complex and often unstable procedure, first fitting a reward model that reflects the human preferences, and then fine-tuning the large unsupervised LM using reinforcement learning to maximize this estimated reward without drifting too far from the original model. In this paper, we leverage a mapping between reward functions and optimal policies to show that this constrained reward maximization problem can be optimized exactly with a single stage of policy training, essentially solving a classification problem on the human preference data. The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant and computationally lightweight, eliminating the need for fitting a reward model, sampling from the LM during fine-tuning, or performing significant hyperparameter tuning. Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning with DPO exceeds RLHF's ability to control sentiment of generations and improves response quality in summarization and single-turn dialogue while being substantially simpler to implement and train

Gain-managed nonlinear amplification of ultra-long mode-locked fiber laser

In this study, we explored the gain-managed nonlinear (GMN) amplification of ultra-low repetition rate pulses in the range of less than 1 MHz. By seeding the developed 1040 nm ultralong fiber modelocked laser to the GMN amplifier, we achieved high gain and boosted the nonlinear pulse propagation effects. We demonstrated that GMN amplification of low repetition rate pulses provided amplification exceeding 32 dB and spectral broadening up to 91 nm at relatively low pump power levels. Achieving broadband 57 fs pulses with energy exceeding 55 nJ suggests that ultralong fiber lasers paired with GMN amplifiers can be effectively utilized as powerful tools for generating femtosecond broadband pulses at ultra-low repetition rates, with controllable spectral characteristics

Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback

A trustworthy real-world prediction system should be well-calibrated; that is, its confidence in an answer is indicative of the likelihood that the answer is correct, enabling deferral to a more expensive expert in cases of low-confidence predictions. While recent studies have shown that unsupervised pre-training produces large language models (LMs) that are remarkably well-calibrated, the most widely-used LMs in practice are fine-tuned with reinforcement learning with human feedback (RLHF-LMs) after the initial unsupervised pre-training stage, and results are mixed as to whether these models preserve the well-calibratedness of their ancestors. In this paper, we conduct a broad evaluation of computationally feasible methods for extracting confidence scores from LLMs fine-tuned with RLHF. We find that with the right prompting strategy, RLHF-LMs verbalize probabilities that are much better calibrated than the model's conditional probabilities, enabling fairly well-calibrated predictions. Through a combination of prompting strategy and temperature scaling, we find that we can reduce the expected calibration error of RLHF-LMs by over 50%

Tunable single- and dual-wavelength SHG from diode-pumped PPKTP waveguides

A compact, all-room-temperature, widely tunable, continuous wave laser source in the green spectral region (502.1–544.2 nm) with a maximum output power of 14.7 mW is demonstrated. This was made possible by utilizing second-harmonic generation (SHG) in a periodically poled potassium titanyl phosphate (PPKTP) crystal waveguide pumped by a quantum-well external-cavity fiber-coupled diode laser and exploiting the multimode-matching approach in nonlinear crystal waveguides. The dual-wavelength SHG in the wavelength region between 505.4 and 537.7 nm (with a wavelength difference ranging from 1.8 to 32.3 nm) and sum-frequency generation in a PPKTP waveguide is also demonstrated

Tourism Firms’ Vulnerability to Risk: The Role of Organizational Slack in Performance and Failure

This study explores the influence of political risk on firms in the tourism industry. It addresses a research gap regarding the impact of political risk on firm-level performance and failure and uncovers the role of organizational slack in this relationship. Firm-level political risk is estimated from 2002 to 2019 financial data for firms across six tourism sectors in a developed economy, the United States. Such risk is found to be significantly associated with firm performance and business failure. From the perspectives of the resource-based view and the threat-rigidity hypothesis, the results support the moderating effects of absorbed and unabsorbed slack on links between risk, performance, and business failure. Given that the COVID-19 pandemic has highlighted the tourism industry’s vulnerability, this study will be of interest to tourism firms seeking to improve business sustainability and resilience

Disorder induced collapse of the electron phonon coupling in MgB2_{2} observed by Raman Spectroscopy

The Raman spectrum of the superconductor MgB$_{2}$ has been measured as a function of the Tc of the film. A striking correlation is observed between the $T_{c}$ onset and the frequency of the $E_{2g}$ mode. Analysis of the data with the McMillan formula provides clear experimental evidence for the collapse of the electron phonon coupling at the temperature predicted for the convergence of two superconducting gaps into one observable gap. This gives indirect evidence of the convergence of the two gaps and direct evidence of a transition to an isotropic state at 19 K. The value of the electron phonon coupling constant is found to be 1.22 for films with T$_{c}$ 39K and 0.80 for films with T$_{c}\leq$19K.Comment: 5 pages, 4 figure