It Ain't That Bad: Understanding the Mysterious Performance Drop in OOD Generalization for Generative Transformer Models

Generative Transformer-based models have achieved remarkable proficiency on solving diverse problems. However, their generalization ability is not fully understood and not always satisfying. Researchers take basic mathematical tasks like n-digit addition or multiplication as important perspectives for investigating their generalization behaviors. Curiously, it is observed that when training on n-digit operations (e.g., additions) in which both input operands are n-digit in length, models generalize successfully on unseen n-digit inputs (in-distribution (ID) generalization), but fail miserably and mysteriously on longer, unseen cases (out-of-distribution (OOD) generalization). Studies try to bridge this gap with workarounds such as modifying position embedding, fine-tuning, and priming with more extensive or instructive data. However, without addressing the essential mechanism, there is hardly any guarantee regarding the robustness of these solutions. We bring this unexplained performance drop into attention and ask whether it is purely from random errors. Here we turn to the mechanistic line of research which has notable successes in model interpretability. We discover that the strong ID generalization stems from structured representations, while behind the unsatisfying OOD performance, the models still exhibit clear learned algebraic structures. Specifically, these models map unseen OOD inputs to outputs with equivalence relations in the ID domain. These highlight the potential of the models to carry useful information for improved generalization

Impacts of Multiple Environmental Changes on Long- Term Nitrogen Loading From the Chesapeake Bay Watershed

Excessive nitrogen can enter estuarine and coastal areas from land, disturbing coastal ecosystems and causing serious environmental problems. The Chesapeake Bay is one of the regions that have experienced hypoxia and harmful algal blooms in recent decades. This study estimated nitrogen export from the Chesapeake Bay watershed (CBW) to the estuary from 1900 to 2015 by applying a state-of-the-art numerical model. Nitrogen loading from the CBW continually increased from the 1900s to the 1990s and has declined since then. The key contributors to nitrogen export have shifted from atmospheric nitrogen deposition (before the 1960s) to synthetic nitrogen fertilizer (after the 1980s). Antipollution policies and implementation measures have played critical roles in the decrease of nitrogen export since the 1980s, and further reduction in riverine nitrogen export will likely require regulation on the application of nitrogen fertilizer

Speed Ripple Reduction of Direct-Drive PMSM Servo System at Low-Speed Operation Using Virtual Cogging Torque Control Method

This paper presents a virtual cogging torque (VCT) control method to reduce the speed ripple of direct-drive permanent magnet synchronous machine (DD-PMSM) servo system under low-speed conditions. Compared with other factors, at low speeds, the cogging torque is the main factor that deteriorates the drive performance, even induces speed oscillations. Especially in this paper, due to volume limitation, the cogging torque is designed larger than normal one in order to remove the need of brake. Based on the model of PMSM, the cause and effect of the cogging torque are analyzed. Inspired by the characteristic of cogging torque, the VCT control method is proposed and investigated to significantly reduce the speed ripple at low speeds. The main idea of this proposed control method is to produce a proper virtual cogging torque and continuously move the corresponding virtual stable equilibrium point to drive the rotor smoothly. In addition to the principle of this control method, its analysis and implementation are studied as well. Simulation and experimental results from the prototype demonstrate that the proposed control method is correct and valid, and it is simple and effective to smooth the speed at low-speed operations

Protective Effect of Anthocyanin on Neurovascular Unit in Cerebral Ischemia/Reperfusion Injury in Rats

Treating cerebral ischemia continues to be a clinical challenge. Studies have shown that the neurovascular unit (NVU), as the central structural basis, plays a key role in cerebral ischemia. Here, we report that anthocyanin, a safe and natural antioxidant, could inhibit apoptosis and inflammation to protect NVU in rats impaired by middle cerebral artery occlusion/reperfusion (MCAO/R). Administration of anthocyanin significantly reduced infarct volume and neurological scores in MCAO/R rats. Anthocyanin could also markedly ameliorate cerebral edema and reduce the concentration of Evans blue (EB) by inhibiting MMP-9. Moreover, anthocyanin alleviated apoptotic injury resulting from MCAO/R through the regulation of Bcl-2 family proteins. The levels of inflammation-related molecules including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6), which were over-expressed with MCAO/R, were decreased by anthocyanin. In addition, Nuclear factor-kappa B (NF-κB) and the NLRP3 inflammasome pathway might be involved in the anti-inflammatory effect of anthocyanin. In conclusion, anthocyanin could protect the NVU through multiple pathways, and play a protective role in cerebral ischemia/reperfusion injury

Estimating ammonia emissions from cropland in China based on the establishment of agro-region-specific models

ACKNOWLEDGMENTS This work was financially supported by Natural Science Foundation of China under a grant numbers 41877546 and U1612441, and a BBSRC-Newton Funded project (BB/N013484/1). This work also contributes to the activities of Top-notch Academic Programs Project of Jiangsu Higher Education Institution of China (PPZY2015A061), and Program for Student Innovation through Research and Training (1913A22).Peer reviewedPostprin

Relationship between serum iPTH and peritonitis episodes in patients undergoing continuous ambulatory peritoneal dialysis

BackgroundPeritonitis is considered as one of the most serious complications that cause hospitalization in patients undergoing continuous ambulatory peritoneal dialysis (CAPD). There is limited evidence on the impact of the parathyroid hormone (PTH) on the first peritoneal dialysis (PD)-associated peritonitis episode. We aimed to investigate the influence of serum intact parathyroid hormone (iPTH) on peritonitis in patients undergoing PD.MethodsThis was a retrospective cohort study. Patients undergoing initial CAPD from a single center in China were enrolled. The baseline characteristics and clinical information were recorded. The primary outcome of interest was the occurrence of the first PD-associated peritonitis episode. Five Cox proportional hazard models were constructed in each group set. In group set 1, all participants were divided into three subgroups by tertiles of the serum concentration of iPTH; in group set 2, all participants were divided into three subgroups based on the serum concentration of iPTH with 150 pg/ml interval (<150, 150–300, and >300 pg/ml). Hazard ratios and 95% confidence intervals (CIs) were calculated for each model. The multivariate linear regression analysis elimination procedure assessed the association between the clinical characteristics at baseline and the iPTH levels. Restricted cubic spline models were constructed, and stratified analyses were also conducted.ResultsA total of 582 patients undergoing initial PD (40% women; mean age, 45.1 ± 11.5 years) from a single center in China were recruited. The median follow-up duration was 25.3 months. Multivariate Cox regression analysis showed that, in the fully adjusted model, a higher serum iPTH level (tertile 3, iPTH >300 pg/ml) was significantly associated with a higher risk of PD-associated peritonitis at 3 years [tertile 3: hazard ratio (HR) = 1.53, 95%CI = 1.03–2.55, p = 0.03; iPTH > 300 pg/ml: HR = 1.57, 95%CI = 1.08–2.27, p = 0.02]. The hazard ratio for every 100 pg/ml increase in serum iPTH level was 1.12 (95%CI = 1.05–1.20, p < 0.01) in the total cohort when treating iPTH as a continuous variable.ConclusionsAn elevated iPTH level was significantly associated with an increased risk of peritonitis in patients undergoing CAPD

ICESsuHN105, a Novel Multiple Antibiotic Resistant ICE in Streptococcus suis Serotype 5 Strain HN105

Streptococcussuis serotype 5, an emerging zoonosis bacterial pathogen, has been isolated from infections in both pigs and humans. In this study, we sequenced the first complete genome of a virulent, multidrug-resistant SS5 strain HN105. The strain HN105 displayed enhanced pathogenicity in zebrafish and BABL/c mouse infection models. Comparative genome analysis identified a novel 80K integrative conjugative element (ICE), ICESsuHN105, as required for the multidrug resistance phenotype. Six corresponding antibiotic resistance genes in this ICE were identified, namely tet (O), tet (M), erm (two copies), aph, and spc. Phylogenetic analysis classified the element as a homolog of the ICESa2603 family, containing the typical family backbone and insertion DNA. DNA hybrids mediated by natural transformation between HN105 and ZY05719 verified the antibiotic resistant genes of ICESsuHN105 that could be transferred successfully, while they were dispersedly inserted with a single gene in different genomic locations of ZY05719(HN105) transformants. To further identify the horizontal transfer of ICESsuHN105 as a whole mobile genetic element, a circular intermediate form of ICESsuHN105 was detected by PCR. However, the effective conjugation using serotype 2 S. suis as recipients was not observed in current assays in vitro. Further studies confirmed the presence of the complete lantibiotic locus encoded in ICESsuHN105 that effectively inhibits the growth of other streptococci. In summary, this study demonstrated the presence of antibiotic resistance genes in ICE that are able to transfer between different clinical isolates and adapt to a broader range of Streptococcus serotype or species

Riverine Carbon Cycling Over The Past Century in the Mid‐Atlantic Region of the United States

The lateral transport and degassing of carbon in riverine ecosystems is difficult to quantify on large spatial and long temporal scales due to the relatively poor representation of carbon processes in many models. Here, we coupled a scale‐adaptive hydrological model with the Dynamic Land Ecosystem Model to simulate key riverine carbon processes across the Chesapeake and Delaware Bay Watersheds from 1900 to 2015. Our results suggest that throughout this time period riverine CO2 degassing and lateral dissolved inorganic carbon fluxes to the coastal ocean contribute nearly equally to the total riverine carbon outputs (mean ± standard deviation: 886 ± 177 Gg C∙yr−1 and 883 ± 268 Gg C∙yr−1, respectively). Following in order of decreasing importance are the lateral dissolved organic carbon flux to the coastal ocean (293 ± 81 Gg C∙yr−1), carbon burial (118 ± 32 Gg C∙yr−1), and lateral particulate organic carbon flux (105 ± 35 Gg C∙yr−1). In the early 2000s, carbon export to the coastal ocean from both the Chesapeake and Delaware Bay watersheds was only 15%–20% higher than it was in the early 1900s (decade), but it showed a twofold increase in standard deviation. Climate variability (changes in temperature and precipitation) explains most (225 Gg C∙yr−1) of the increase since 1900, followed by changes in atmospheric CO2 (82 Gg C∙yr−1), atmospheric nitrogen deposition (44 Gg C∙yr−1), and applications of nitrogen fertilizer and manure (27 Gg C∙yr−1); in contrast, land conversion has resulted in a 188 Gg C∙yr−1 decrease in carbon export

Impacts of Multiple Environmental Changes on Long‐Term Nitrogen Loading From the Chesapeake Bay Watershed

Excessive nutrient inputs from land, particularly nitrogen (N), have been found to increase the occurrence of hypoxia and harmful algal blooms in coastal ecosystems. To identify the main contributors of increased N loading and evaluate the efficacy of water pollution control policies, it is essential to quantify and attribute the long‐term changes in riverine N export. Here, we use a state‐of‐the‐art terrestrial–aquatic interface model to examine how multiple environmental factors may have affected N export from the Chesapeake Bay watershed since 1900. These factors include changes in climate, carbon dioxide, land use, and N inputs (i.e., atmospheric N deposition, animal manure, synthetic N fertilizer use, and wastewater discharge). Our results estimated that ammonium (NH4+) and nitrate (NO3−) export increased substantially (66% for NH4+ and 123% for NO3−) from the 1900s to the 1990s and then declined (32% for NH4+ and 14% for NO3−) since 2000. The temporal trend of dissolved organic nitrogen (DON) export paralleled that of dissolved inorganic N, while particulate organic nitrogen export was relatively constant during 1900–2015. Precipitation was the primary driver of interannual variability in N export to the Bay. Wastewater discharge explained most of the long‐term change in riverine NH4+ and DON fluxes from 1900 to 2015. The changes in atmospheric deposition, wastewater, and synthetic fertilizer were responsible for the trend of riverine NO3−. In light of our model‐based attribution analysis, terrestrial non‐point source nutrient management will play an important role in achieving water quality goals