Perifiton kao delimična zamena komercijalne hrane u organskom gajenju tilapije u Izraelu

Cena hrane čini jednu od najvećih stavki u tekućim troškovima proizvodnje u akvakulturi. Usled potrebe za korišćenjem samo organskih sastojaka, cena koncentrovane hrane za uzgoj organske ribe je izuzetno visoka. Tokom petogodišnjeg perioda rađeni su eksperimenti kako bi se ispitala mogućnost upotrebe različitih supstrata za indukciju rasta perifitona koji bi služio kao prirodna hrana za tilapiju različite veličine, od mlađi do naprednih uzrasnih stadijuma. Kao supstrat, procenjivan je različit poljoprivredni otpad - plastične cevi, najloni i mreže. Različiti supstrati dali su različite prinose perifitona u zavisnosti od njihove površine (glatka ili hrapava) i boje. Rezultati rasta pokazali su da je ušteda hrane od 40% u naprednim fazama rasta dovela do svega 10% redukcije stope rasta tilapije u odnosu na kontrolna jezera, dok je u mladičnjaku moguće smanjiti količinu koncentrovane hrane do 50% bez ograničenja rasta riba. Ovo smanjenje količine hrane od 30-40% dovelo je do poboljšanja koeficijenta konverzije hrane (FCR) od barem 30% u jezerima sa perifitonom (45% u mladičnjacima). Zaključak: upotreba supstrata hrapavih površina za indukciju rasta perifitona može pomoći u recikliranju otpadnih materijala i značajno redukovati troškove hrane u organskoj akvakulturi

Counterfactual Optimism: Rate Optimal Regret for Stochastic Contextual MDPs

We present the UC$^3$RL algorithm for regret minimization in Stochastic Contextual MDPs (CMDPs). The algorithm operates under the minimal assumptions of realizable function class, and access to offline least squares and log loss regression oracles. Our algorithm is efficient (assuming efficient offline regression oracles) and enjoys an $\widetilde{O}(H^3 \sqrt{T |S| |A|(\log (|\mathcal{F}|/\delta) + \log (|\mathcal{P}|/ \delta) )})$ regret guarantee, with $T$ being the number of episodes, $S$ the state space, $A$ the action space, $H$ the horizon, and $\mathcal{P}$ and $\mathcal{F}$ are finite function classes, used to approximate the context-dependent dynamics and rewards, respectively. To the best of our knowledge, our algorithm is the first efficient and rate-optimal regret minimization algorithm for CMDPs, which operates under the general offline function approximation setting

Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation

We present the OMG-CMDP! algorithm for regret minimization in adversarial Contextual MDPs. The algorithm operates under the minimal assumptions of realizable function class and access to online least squares and log loss regression oracles. Our algorithm is efficient (assuming efficient online regression oracles), simple and robust to approximation errors. It enjoys an $\widetilde{O}(H^{2.5} \sqrt{ T|S||A| ( \mathcal{R}(\mathcal{O}) + H \log(\delta^{-1}) )})$ regret guarantee, with $T$ being the number of episodes, $S$ the state space, $A$ the action space, $H$ the horizon and $\mathcal{R}(\mathcal{O}) = \mathcal{R}(\mathcal{O}_{\mathrm{sq}}^\mathcal{F}) + \mathcal{R}(\mathcal{O}_{\mathrm{log}}^\mathcal{P})$ is the sum of the regression oracles' regret, used to approximate the context-dependent rewards and dynamics, respectively. To the best of our knowledge, our algorithm is the first efficient rate optimal regret minimization algorithm for adversarial CMDPs that operates under the minimal standard assumption of online function approximation

A Computational Approach to Packet Classification

Multi-field packet classification is a crucial component in modern software-defined data center networks. To achieve high throughput and low latency, state-of-the-art algorithms strive to fit the rule lookup data structures into on-die caches; however, they do not scale well with the number of rules. We present a novel approach, NuevoMatch, which improves the memory scaling of existing methods. A new data structure, Range Query Recursive Model Index (RQ-RMI), is the key component that enables NuevoMatch to replace most of the accesses to main memory with model inference computations. We describe an efficient training algorithm that guarantees the correctness of the RQ-RMI-based classification. The use of RQ-RMI allows the rules to be compressed into model weights that fit into the hardware cache. Further, it takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector instructions, achieving a rate of tens of nanoseconds per lookup. Our evaluation using 500K multi-field rules from the standard ClassBench benchmark shows a geometric mean compression factor of 4.9x, 8x, and 82x, and average performance improvement of 2.4x, 2.6x, and 1.6x in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all state-of-the-art algorithms.Comment: To appear in SIGCOMM 202

Latent SHAP: Toward Practical Human-Interpretable Explanations

Model agnostic feature attribution algorithms (such as SHAP and LIME) are ubiquitous techniques for explaining the decisions of complex classification models, such as deep neural networks. However, since complex classification models produce superior performance when trained on low-level (or encoded) features, in many cases, the explanations generated by these algorithms are neither interpretable nor usable by humans. Methods proposed in recent studies that support the generation of human-interpretable explanations are impractical, because they require a fully invertible transformation function that maps the model's input features to the human-interpretable features. In this work, we introduce Latent SHAP, a black-box feature attribution framework that provides human-interpretable explanations, without the requirement for a fully invertible transformation function. We demonstrate Latent SHAP's effectiveness using (1) a controlled experiment where invertible transformation functions are available, which enables robust quantitative evaluation of our method, and (2) celebrity attractiveness classification (using the CelebA dataset) where invertible transformation functions are not available, which enables thorough qualitative evaluation of our method