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Most widely used pre-trained language models operate on sequences of tokens corresponding to word or subword units. By comparison, models that operate directly on raw text (bytes or characters) have many benefits: They can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Because byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.

Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the ( ), a generative model of documents that marries traditional topic models with word embeddings. More specifically, the models each word with a categorical distribution whose natural parameter is the inner product between the word’s embedding and an embedding of its assigned topic. To fit the , we develop an efficient amortized variational inference algorithm. The discovers interpretable topics even with large vocabularies that include rare words and stop words. It outperforms existing document models, such as latent Dirichlet allocation, in terms of both topic quality and predictive performance.

Though highly motivated to slow the climate crisis, governments may struggle to impose costly polices on entrenched interest groups, resulting in a greater need for negative emissions. Here, we model wartime-like crash deployment of direct air capture (DAC) as a policy response to the climate crisis, calculating funding, net CO 2 removal, and climate impacts. An emergency DAC program, with investment of 1.2–1.9% of global GDP annually, removes 2.2–2.3 GtCO 2 yr –1 in 2050, 13–20 GtCO 2 yr –1 in 2075, and 570–840 GtCO 2 cumulatively over 2025–2100. Compared to a future in which policy efforts to control emissions follow current trends (SSP2-4.5), DAC substantially hastens the onset of net-zero CO 2 emissions (to 2085–2095) and peak warming (to 2090–2095); yet warming still reaches 2.4–2.5 °C in 2100. Such massive CO 2 removals hinge on near-term investment to boost the future capacity for upscaling. DAC is most cost-effective when using electricity sources already available today: hydropower and natural gas with renewables; fully renewable systems are more expensive because their low load factors do not allow efficient amortization of capital-intensive DAC plants. Governments may struggle to impose costly polices on vital industries, resulting in a greater need for negative emissions. Here, the authors model a direct air capture crash deployment program, finding it can remove 2.3 GtCO 2 yr –1 in 2050, 13–20 GtCO 2 yr –1 in 2075, and 570–840 GtCO 2 cumulative over 2025–2100.

We propose in this paper a method for verifying the non-existence of anatocism in a periodic amortization with n periodic installments, by the replication of its cash flows. The cash flows are obtained by recursively constructing an appropriate sequence of n consecutive single period loans, at the periodic interest rate i , each one with repayment of principal and interest at the end of the single period. Since each elementary transaction is concluded within one time unit, there is no possibility of interest accruing on interest and hence anatocism is ruled out. Therefore, this characteristic must be acknowledged to be valid also for the loan amortization schedule with n periodic installments whose cash flows are perfectly replicated by the unique loan sequence obtained.

In the dial-a-ride problem, user-specified transport requests from origin to destination points have to be served by a fleet of homogeneous vehicles. The problem variant we consider aims at finding a set of minimum-cost routes satisfying constraints on vehicle capacity, time windows, maximum route duration, and maximum user ride times. We propose an adaptive large neighborhood search (ALNS) for its solution. The key novelty of the approach is an exact amortized constant-time algorithm for evaluating the feasibility of request insertions in the repair steps of the ALNS. In addition, we use two optional improvement techniques: a local-search-based intraroute improvement of routes of promising solutions using the Balas–Simonetti neighborhood and the solution of a set covering model over a subset of all routes generated during the search. With these techniques, the proposed algorithm outperforms the state-of-the-art methods in terms of solution quality. New best solutions are found for several benchmark instances. The online appendix is available at https://doi.org/10.1287/trsc.2018.0837 .

This article addresses the amortization rate equivalent or composed on own ways of the structural system. This situation is specific to structural systems consisting of several mechanically coupled components. In this context, there is analyzed the case of dynamic insulation of a road bridge structure with different groups of elastomeric anti-seismic devices. On this basis, there are treated the real cases with parametric values experimentally obtained and with a pre-compressed 40-m reinforced concrete beam configuration conducted for the first time in Romania.

Gene therapies offer potentially life-changing benefits for patients, but their unprecedented high prices exacerbate challenges for reimbursement. Payers must confront high budgetary impacts, as a large up-front payment for each patient makes it difficult to predict and absorb costs. Payers also face considerable clinical uncertainty, as evidence for efficacy and durability is limited at approval. Alternative payment models may address these reimbursement problems and ensure equitable patient access. We developed a taxonomy of possible payment mechanisms for gene therapies, including installments, risk pools, reinsurance, pricevolume agreements, expenditure caps, subscriptions, outcomes-based payments and rebates, warranties, population outcomes-based agreements, and coverage with evidence development. We illustrate how these payment models take three main approaches: amortization, which mitigates initial budget impact by spreading payments over time; risk spreading, which makes budgets more predictable by pooling costs with other payers or capping costs based on expected volume; and performance-based payment, which addresses clinical uncertainty by tying prices to patient- or population-level outcomes. We discuss each payment model, its advantages and challenges, and considerations for US payers.

Prior to SFAS 142, goodwill was subject to periodic amortization and a recoverability-based impairment test. SFAS 142 eliminates periodic amortization and imposes a fair-value-based impairment test. We examine the impact of this standard on the accounting for and valuation of goodwill. Our results indicate that the new standard has resulted in relatively inflated goodwill balances and untimely impairments. We also find that investors do not appear to fully anticipate the untimely nature of post-SFAS 142 goodwill impairments. Overall, our results suggest that, in practice, some managers have exploited the discretion afforded by SFAS 142 to delay goodwill impairments, thus temporarily inflating earnings and stock prices.

It is known that a firefighter's uniform with all equipment will significantly increase the load on the lower limbs during landing. The higher landing height will also increase ground reaction forces. However, it is unknown what effect the firefighters' experience (job seniority) will have on the load. Moreover, age-related changes in neuromuscular control, balance, and muscular strength may alter the way external loads are managed, potentially increasing susceptibility to overload. The present study aimed to evaluate the overload of the musculoskeletal system resulting from changing external conditions. The main study involved 171 firefighters divided into three age categories: up to 25 years (In=83), up to 44 years (IIn=38) and over 44 years (IIIn=50). The subjects performed three landing on to the force platforms from two heights (0.5 m; 1.0 m) and in two types of clothes: sport and fire protection. For the evaluation of lower limb overloads, the value of generated ground reaction force relative to body weight (GRF/BW) of the participant and time of force affecting the locomotor system (amortization time) were evaluated (t > 1 BW). Increasing the landing height from 0.5 m to 1 m resulted in a significant (p < 0.001) increase in ground reaction forces. Furthermore, younger participants generated significantly greater forces (p < 0.001). Performing the landing in protective clothing also resulted in increased load (p < 0.001). Furthermore, older firefighters had a longer (p < 0.01) cushioning time at both landing heights and in both clothing. These results suggest that younger or less experienced firefighters may employ suboptimal landing strategies, increasing their injury risk.