Alternative Ingredient Recommendation: A Co-occurrence and Ingredient Category Importance Based Approach

As many people will refer to a recipe when cooking, there are several recipe-sharing websites that include lots of recipes and make recipes easier to access than before. However, there is often the case that we could not get all the ingredients listed on the recipe. Prior research on alternative ingredient substitution has built a recommendation system considering the suitability of a recommended ingredient with the remained ingredients. In this paper, in addition to suitability, we also take the diversity of the ingredient categories and the novelty of new combination of ingredients into account. Besides, we combine suitability with novelty as an index, to see whether our method could help find out a new combination of ingredients that is possibly to be a new dish. Our evaluation results show that our proposed method attains a comparable or even better performance on each perspective

Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits

Modifying the reward-biased maximum likelihood method originally proposed in the adaptive control literature, we propose novel learning algorithms to handle the explore-exploit trade-off in linear bandits problems as well as generalized linear bandits problems. We develop novel index policies that we prove achieve order-optimality, and show that they achieve empirical performance competitive with the state-of-the-art benchmark methods in extensive experiments. The new policies achieve this with low computation time per pull for linear bandits, and thereby resulting in both favorable regret as well as computational efficiency

Simulation of Riding a Full Suspension Bicycle for Analyzing Comfort and Pedaling Force

AbstractRecently, there is an increasing interest on bicycle riding for recreation or fitness purpose. Bicycles are also accepted as urban transportation due to the consciousness of environmental protection. For a more comfortable riding experience, many bicycles are equipped with a suspension system including a front suspension fork and/or rear suspension. However, when a suspension system is added to a bicycle, it makes riding a little heavier since suspension dissipates some pedalling energy. This paper discusses front and rear suspensions corresponding to rider comfort and pedalling effort when riding on a rough road and smooth road. A human body computer model LifeMOD® is employed to model the cyclist. Dynamic analysis software ADAMS® is employed to analyze human body vibration and leg muscle forces of bicycle riding. Human body acceleration vs. vibration frequencies are used as the comfort criteria. The results show that a suspension system may effectively reduce high frequency vibration of the human body when riding on a rough road. Pedalling forces are mostly contributed by the biceps femoris and semitendinosus. The suspension system would increase the pedaling forces of femoris and semitendinosus. Other leg muscles have a minor effect on pedaling forces. Results obtained from this research are useful for the design of bicycle suspension systems with better comfort and less loss of pedalling efficiency

Lepton Flavor Violating Muon Decays in a Model of Electroweak-Scale Right-Handed Neutrinos

The small neutrino mass observed in neutrino oscillations is nicely explained by the seesaw mechanism. Rich phenomenology is generally expected if the heavy neutrinos are not much heavier than the electroweak scale. A model with this feature built in has been suggested recently by Hung. The model keeps the standard gauge group but introduces chirality-flipped partners for the fermions. In particular, a right-handed neutrino forms a weak doublet with a charged heavy lepton, and is thus active. We analyze the lepton flavor structure in gauge interactions. The mixing matrices in charged currents (CC) are generally non-unitary, and their deviation from unitarity induces flavor changing neutral currents (FCNC). We calculate the branching ratios for the rare decays \mu\to e\gamma and \mu\to ee\bar e due to the gauge interactions. Although the former is generally smaller than the latter by three orders of magnitude, parameter regions exist in which \mu\to e\gamma is reachable in the next generation of experiments even if the current stringent bound on \mu\to ee\bar e is taken into account. If light neutrinos dominate for \mu\to e\gamma, the latter cannot set a meaningful bound on unitarity violation in the mixing matrix of light leptons due to significant cancelation between CC and FCNC contributions. Instead, the role is taken over by the decay \mu\to ee\bar e.Comment: 11 pages, 2 figures. v2: added 2 refs and improved a comment on previous work; no other changes. v3: proofread version for PLB; added a few clarifying sentences in paragraph before eq (17) plus minor editting change

Parametrically tunable soliton-induced resonant radiation by three-wave mixing

We show that a temporal soliton can induce resonant radiation by three-wave mixing nonlinearities. This constitutes a new class of resonant radiation whose spectral positions are parametrically tunable. The experimental verification is done in a periodically poled lithium niobate crystal, where a femtosecond near-IR soliton is excited and resonant radiation waves are observed exactly at the calculated soliton phase-matching wavelengths via the sum-and difference-frequency generation nonlinearities. This extends the supercontinuum bandwidth well into the mid IR to span 550-5000 nm, and the mid-IR edge is parametrically tunable over 1000 nm by changing the three-wave mixing phase-matching condition. The results are important for the bright and broadband supercontinuum generation and for the frequency comb generation in quadratic nonlinear microresonators

On Connected Target Coverage for Wireless Heterogeneous Sensor Networks with Multiple Sensing Units

The paper considers the connected target coverage (CTC) problem in wireless heterogeneous sensor networks (WHSNs) with multiple sensing units, termed MU-CTC problem. MU-CTC problem can be reduced to a connected set cover problem and further formulated as an integer linear programming (ILP) problem. However, the ILP problem is an NP-complete problem. Therefore, two distributed heuristic schemes, REFS (remaining energy first scheme) and EEFS (energy efficiency first scheme), are proposed. In REFS, each sensor considers its remaining energy and its neighbors’ decisions to enable its sensing units and communication unit such that all targets can be covered for the required attributes and the sensed data can be delivered to the sink. The advantages of REFS are its simplicity and reduced communication overhead. However, to utilize sensors’ energy efficiently, EEFS is proposed. A sensor in EEFS considers its contribution to the coverage and the connectivity to make a better decision. To our best knowledge, this paper is the first to consider target coverage and connectivity jointly for WHSNs with multiple sensing units. Simulation results show that REFS and EEFS can both prolong the network lifetime effectively. EEFS outperforms REFS in network lifetime, but REFS is simpler