Is a Dataframe Just a Table?

Querying data is core to databases and data science. However, the two communities have seemingly different concepts and use cases. As a result, both designers and users of the query languages disagree on whether the core abstractions - dataframes (data science) and tables (databases) - and the operations are the same. To investigate the difference from a PL-HCI perspective, we identify the basic affordances provided by tables and dataframes and how programming experiences over tables and dataframes differ. We show that the data structures nudge programmers to query and store their data in different ways. We hope the case study could clarify confusions, dispel misinformation, increase cross-pollination between the two communities, and identify open PL-HCI questions

Symmetry of Hydrogen Bonds in Two Enols in Solution.

The enols of 4-cyano-2,2,6,6-tetramethyl-3,5-heptanedione and of nitromalonamide were prepared as statistical mixtures of 18O n ( n = 0, 1, 2) isotopologues. The symmetries of their hydrogen bonds were probed by isotopic perturbation of their 13CO NMR signals. The former mixture shows a total of four signals, due to both intrinsic and perturbation isotope shifts. Therefore, that enol is a mixture of tautomers with an asymmetric hydrogen bond. In contrast, the mixture of isotopologues of nitromalonamide enol shows only two signals, due to an intrinsic isotope shift. Therefore, this is the first case, to be compared with the FHF- anion, of a neutral species with a single symmetric structure in solution and with a centered hydrogen

Online Learning with Gaussian Payoffs and Side Observations

We consider a sequential learning problem with Gaussian payoffs and side information: after selecting an action $i$, the learner receives information about the payoff of every action $j$ in the form of Gaussian observations whose mean is the same as the mean payoff, but the variance depends on the pair $(i,j)$ (and may be infinite). The setup allows a more refined information transfer from one action to another than previous partial monitoring setups, including the recently introduced graph-structured feedback case. For the first time in the literature, we provide non-asymptotic problem-dependent lower bounds on the regret of any algorithm, which recover existing asymptotic problem-dependent lower bounds and finite-time minimax lower bounds available in the literature. We also provide algorithms that achieve the problem-dependent lower bound (up to some universal constant factor) or the minimax lower bounds (up to logarithmic factors)

Comparative Assessment of Human Exposure to Antibiotic-Resistant \u3ci\u3eSalmonella\u3c/i\u3e due to the Consumption of Various Food Products in the United States

Antibiotic resistance (AR) was increasingly recognized as a global and national problem. Prevention efforts are hampered by a lack of complete understanding of how transmission pathways contribute to human AR exposure. Many reports have indicated the presence of antibiotic-resistant bacteria in foods at retail, suggesting that food consumption, animal-derived foods in particular, can represent a significant source of AR exposure among consumers. The presence of Salmonella, including antibiotic-resistant Salmonella, has been frequently reported in terrestrial animal-derived foods such as meat, poultry, and dairy products, as well as in aquaculture products. Identification of the significant food sources that harbor relatively substantial antibiotic-resistant Salmonella is the key for the design and implementation of effective and target AR mitigation strategies. Thus, a systematic evaluation of the relative contribution of different food sources to human antibiotic-resistant Salmonella was imperative. This thesis aimed to gather qualitative and quantitative information about the contamination of antibiotic-resistant non-typhoidal Salmonella in various retail foods in the U.S. and identify knowledge gaps using systematic review (SR) and meta-analysis (MA) approaches. The data on resistant Salmonella concentration in foods has not been found. Resistant Salmonella prevalence in regulated commodities (beef, chicken, turkey, pork) and other food categories were conducted for major antibiotic classes. Generally, poultry, pork, and turkey had a higher prevalence of resistant Salmonella than beef, while vegetables and imported foods (mainly spices in documented studies) had a lower prevalence. For antibiotics classes, tetracycline resistance was the most prevalent across major commodities. There is a moderate level of resistance to beta-lactam antibiotics, but the significance in clinical practice indicates a potential threats to public health. Another objective was to develop a stochastic comparative exposure assessment model to estimate the relative contribution of various animal-derived food groups to overall foodborne exposure to cephem-resistant Salmonella. The model consists of four modules: retail, transport, storage, and preparation. Generally, the results showed that ground beef and chicken parts accounted for the largest proportion of total exposure to cephem-resistant Salmonella compared to pork cuts and ground turkey. The contamination level in products at retail and cooking temperature were the top influencing factors of the foodborne exposure for all food products evaluated in the present study. Foodborne illness source attribution is the foundation of a risk-based food safety system. The present project provides a risk-based estimation of the degree to which different food categories are responsible for resistant Salmonella infections. With these estimates, target prevention measures can be designed and implemented to effectively mitigate the AR threat to public health attributable to the food consumption. Advisor: Bing Wan