79 research outputs found

    A Muffin-Theorem Generator

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    Consider the following FUN problem. Given m,s you want to divide m muffins among s students so that everyone gets m/(s) muffins; however, you want to maximize the minimum piece so that nobody gets crumbs. Let f(m,s) be the size of the smallest piece in an optimal procedure. We study the case where ceil(2m/s)=3 because (1) many of our hardest open problems were of this form until we found this method, (2) we have used the technique to generate muffin-theorems, and (3) we conjecture this can be used to solve the general case. We give (1) an algorithm to find an upper bound for f(m,s) when ceil(2m/s)(and some ways to speed up that algorithm if certain conjectures are true), (2) an algorithm that uses the information from (1) to try to find a lower bound on f(m,s) (a procedure) which matches the upper bound, (3) an algorithm that uses the information from (1) to generate muffin-theorems, and (4) an algorithm that we think works well in practice to find f(m,s) for any m,s

    Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation

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    The recently proposed massively multilingual neural machine translation (NMT) system has been shown to be capable of translating over 100 languages to and from English within a single model. Its improved translation performance on low resource languages hints at potential cross-lingual transfer capability for downstream tasks. In this paper, we evaluate the cross-lingual effectiveness of representations from the encoder of a massively multilingual NMT model on 5 downstream classification and sequence labeling tasks covering a diverse set of over 50 languages. We compare against a strong baseline, multilingual BERT (mBERT), in different cross-lingual transfer learning scenarios and show gains in zero-shot transfer in 4 out of these 5 tasks

    Anomalous wind circulation over Taipei, Taiwan during the northern winter seasons of 2004 and 2005- A case study

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    This research reports, for the first time, an anomalous wind circulation over Taipei (Latitude 25.030N, Longitude 121.510E), Taiwan during the northern hemisphere winter season (December, January, and February) of years 2004 and 2005. The anomalous wind circulation of meridional winds, which showed southward directions during the winter seasons of 2004 and 2005 instead of northward winds, is noticed from one kilometer altitude range (lower troposphere) and that trend continued till around 20 km altitude range (lower stratosphere). To ascertain whether such a disturbed nature of wind pattern existed over nearby locations to Taipei, we have analyzed radiosonde-measured meridional and zonal winds over four nearby stations station to Taipei including, Roig, Xiamen, Minami and Fuzhou. Surprisingly, no anomalous wind behavior is seen except over Taipei during the northern winter seasons of 2004 and 2005. On the other hand, the European Centre for Medium-Range Weather Forecasts (ECMWF) model-predicted winds do not show any anomalous wind patterns over Taipei and other nearby stations, possibly due to the large averaging of internal variabilities of reanalysis databases. The plausible physical mechanisms of these disturbed meridional wind patterns are not understood at this juncture, but it is believed that local winds and atmospheric pollutants might have created an amicable environment as to provide such a disturbed meridional wind pattern over Taipei, Taiwan in the winter season of 2004 and 2005

    Inhibition of FGF receptor blocks adaptive resistance to RET inhibition in CCDC6-RET-rearranged thyroid cancer.

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    Genetic alterations in RET lead to activation of ERK and AKT signaling and are associated with hereditary and sporadic thyroid cancer and lung cancer. Highly selective RET inhibitors have recently entered clinical use after demonstrating efficacy in treating patients with diverse tumor types harboring RET gene rearrangements or activating mutations. In order to understand resistance mechanisms arising after treatment with RET inhibitors, we performed a comprehensive molecular and genomic analysis of a patient with RET-rearranged thyroid cancer. Using a combination of drug screening and proteomic and biochemical profiling, we identified an adaptive resistance to RET inhibitors that reactivates ERK signaling within hours of drug exposure. We found that activation of FGFR signaling is a mechanism of adaptive resistance to RET inhibitors that activates ERK signaling. Combined inhibition of FGFR and RET prevented the development of adaptive resistance to RET inhibitors, reduced cell viability, and decreased tumor growth in cellular and animal models of CCDC6-RET-rearranged thyroid cancer

    Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

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    BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

    Balancing Fairness and Profit in Rideshare using Deep Learning

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    Rideshare services such as Uber and Lyft have become much more popular over the last few years. Determining which riders are allocated to which drivers is a challenging problem, complicated by the number of combinations of riders and drivers. Current solutions typically optimize for gross mean volume or profit, while ignoring fairness in driver pay and rider wait-time. We address these by taking a long-term view, using Neural Networks to simulate value functions, and use Markov Decision Processes to maximize fairness

    Data Driven Methods to Balance Fairness and Profit in Ride-Pooling

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    Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers. However, these platforms can induce inequality either through an unequal income distribution or disparate treatment of riders. We investigate two methods to reduce forms of inequality in ride-pooling platforms: (1) incorporating fairness constraints into the objective function and (2) redistributing income to drivers to reduce income fluctuation and inequality. To evaluate our solutions, we use the New York City taxi data set. For the first method, we find that optimizing for driver-side fairness outperforms state-of-the-art models on the number of riders serviced, both in the worst-off neighborhood and overall, showing that optimizing for fairness can assist profitability in certain circumstances. For the second method, we explore income redistribution as a way to combat income inequality by having drivers keep an r-fraction of their income, and contributing the rest to a redistribution pool. For certain values of r, most drivers earn near their Shapley value, while still incentivizing drivers to maximize value, thereby avoiding the free-rider problem while reducing income variability
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