The multi-stage dynamic stochastic decision process with unknown distribution of the random utilities

We consider a decision maker who performs a stochastic decision process over a multiple number of stages, where the choice alternatives are characterized by random utilities with unknown probability distribution. The decisions are nested each other, i.e. the decision taken at each stage is affected by the subsequent stage decisions. The problem consists in maximizing the total expected utility of the overall multi-stage stochastic dynamic decision process. By means of some results of the extreme values theory, the probability distribution of the total maximum utility is derived and its expected value is found. This value is proportional to the logarithm of the accessibility of the decision maker to the overall set of alternatives in the different stages at the start of the decision process. It is also shown that the choice probability to select alternatives becomes a Nested Multinomial Logit model

The multi-stage dynamic stochastic decision process with unknown distribution of the random utilities

We consider a decision maker who performs a stochastic decision process over a multiple number of stages, where the choice alternatives are characterized by random utilities with unknown probability distribution. The decisions are nested each other, i.e. the decision taken at each stage is affected by the subsequent stage decisions. The problem consists in maximizing the total expected utility of the overall multi-stage stochastic dynamic decision process. By means of some results of the extreme values theory, the probability distribution of the total maximum utility is derived and its expected value is found. This value is proportional to the logarithm of the accessibility of the decision maker to the overall set of alternatives in the different stages at the start of the decision process. It is also shown that the choice probability to select alternatives becomes a Nested Multinomial Logit model

Smart Steaming: A New Flexible Paradigm for Synchromodal Logistics

Slow steaming, i.e., the possibility to ship vessels at a significantly slower speed than their nominal one, has been widely studied and implemented to improve the sustainability of long-haul supply chains. However, to create an efficient symbiosis with the paradigm of synchromodality, an evolution of slow steaming called smart steaming is introduced. Smart steaming is about defining a medium speed execution of shipping movements and the real-time adjustment (acceleration and deceleration) of traveling speeds to pursue the entire logistic system’s overall efficiency and sustainability. For instance, congestion in handling facilities (intermodal hubs, ports, and rail stations) is often caused by the common wish to arrive as soon as possible. Therefore, smart steaming would help avoid bottlenecks, allowing better synchronization and decreasing waiting time at ports or handling facilities. This work aims to discuss the strict relationships between smart steaming and synchromodality and show the potential impact of moving from slow steaming to smart steaming in terms of sustainability and efficiency. Moreover, we will propose an analysis considering the pros, cons, opportunities, and risks of managing operations under this new policy

Social Bias Probing: Fairness Benchmarking for Language Models

Large language models have been shown to encode a variety of social biases, which carries the risk of downstream harms. While the impact of these biases has been recognized, prior methods for bias evaluation have been limited to binary association tests on small datasets, offering a constrained view of the nature of societal biases within language models. In this paper, we propose an original framework for probing language models for societal biases. We collect a probing dataset to analyze language models' general associations, as well as along the axes of societal categories, identities, and stereotypes. To this end, we leverage a novel perplexity-based fairness score. We curate a large-scale benchmarking dataset addressing drawbacks and limitations of existing fairness collections, expanding to a variety of different identities and stereotypes. When comparing our methodology with prior work, we demonstrate that biases within language models are more nuanced than previously acknowledged. In agreement with recent findings, we find that larger model variants exhibit a higher degree of bias. Moreover, we expose how identities expressing different religions lead to the most pronounced disparate treatments across all models

A Generalized Bin Packing Problem for parcel delivery in last-mile logistics

Abstract In this paper, we present a new problem arising at a tactical level of setting a last-mile parcel delivery service in a city by considering different Transportation Companies (TC), which differ in cost and service quality. The courier must decide which TCs to select for the service in order to minimize the total cost and maximize the total service quality. We show that the problem can be modeled as a new packing problem, the Generalized Bin Packing Problem with bin-dependent item profits (GBPPI), where the items are the parcels to deliver and the bins are the TCs. The aim of the GBPPI is to select the appropriate fleet from TCs and determine the optimal assignment of parcels to vehicles such that the overall net cost is minimized. This cost takes into account both transportation costs and service quality. We provide a Mixed Integer Programming formulation of the problem, which is the starting point for the development of efficient heuristics that can address the GBPPI for instances involving up to 1000 items. Extensive computational tests show the accuracy of the proposed methods. Finally, we present a last-mile logistics case study of an international courier which addresses this problem

Performance evaluation of wavelet-based HD video coding

This paper is intended as a complement of document n3954 and presents some preliminary evaluation of coding efficiency of a scalable wavelet-based encoder. Two HD video sequences have been encoded according to test conditions derived from those used to evaluate the coding efficiency of JSVM and VidWav[4]. Assuming that exploiting temporal redundancy in video coding improves compression efficiency, the aim of this work is to further investigate advantage and disadvantage of applying a motion compensated temporal filtering, in term of compression gain, with respect to pure intra coding

The stochastic multi-path traveling salesman problem with dependent random travel costs.

The objective of the stochastic multi-path Traveling Salesman Problem is to determine the expected minimum-cost Hamiltonian tour in a network characterized by the presence of different paths between each pair of nodes, given that a random travel cost with an unknown probability distribution is associated with each of these paths. Previous works have proved that this problem can be deterministically approximated when the path travel costs are independent and identically distributed. Such an approximation has been demonstrated to be of acceptable quality in terms of the estimation of an optimal solution compared to consolidated approaches such as stochastic programming with recourse, completely overcoming the computational burden of solving enormous programs exacerbated by the number of scenarios considered. Nevertheless, the hypothesis regarding the independence among the path travel costs does not hold when considering real settings. It is well known, in fact, that traffic congestion influences travel costs and creates dependence among them. In this paper, we demonstrate that the independence assumption can be relaxed and a deterministic approximation of the stochastic multi-path Traveling Salesman Problem can be derived by assuming just asymptotically independent travel costs. We also demonstrate that this deterministic approximation has strong operational implications because it allows the consideration of realistic traffic models. Computational tests on extensive sets of random and realistic instances indicate the excellent efficiency and accuracy of the deterministic approximation

Bias Discovery within Human Raters: A Case Study of the Jigsaw Dataset

Understanding and quantifying the bias introduced by human annotation of data is a crucial problem for trustworthy supervised learning. Recently, a perspectivist trend has emerged in the NLP community, focusing on the inadequacy of previous aggregation schemes, which suppose the existence of a single ground truth. This assumption is particularly problematic for sensitive tasks involving subjective human judgments, such as toxicity detection. To address these issues, we propose a preliminary approach for bias discovery within human raters by exploring individual ratings for specific sensitive topics annotated in the texts. Our analysis’s object focuses on the Jigsaw dataset, a collection of comments aiming at challenging online toxicity identification

Inhibition of lactic dehydrogenase: a possible strategy to improve the pharmacological treatment of hepatocellular carcinoma

Il lavoro svolto nel corso del mio dottorato ha avuto per oggetto lo studio dell’ inibizione della glicolisi aerobia (il principale processo metabolico utilizzato dalle cellule neoplastiche per produrre energia) ottenuta mediante il blocco dell’enzima lattato deidrogenasi (LDH). La mia attività si è concentrata sulla possibilità di utilizzare questo approccio allo scopo di migliorare l’efficacia della terapia antitumorale, valutandone gli effetti su colture di carcinoma epatocellulare umano Inizialmente, per valutare gli effetti della inibizione della LDH, è stato usato l’acido ossamico ( OXA). Questo composto è l’unico inibitore noto specifico per LDH ; è una molecola non tossica in vivo, ma attiva a concentrazioni troppo elevate per consentirne un uso terapeutico. Un importante risultato ottenuto è stata la dimostrazione che l’ inibizione della LDH ottenuta con OXA non è solo in grado di innescare una risposta di morte nelle cellule trattate, ma, associata alla somministrazione di sorafenib, aumenta fortemente l’efficacia di questo farmaco, determinando un effetto di sinergismo. Questo forte effetto di potenziamento dell’azione del farmaco è stato spiegato con la dimostrazione che il sorafenib ha la capacità di inibire il consumo di ossigeno delle cellule trattate, rendendole più dipendenti dalla glicolisi. Grazie alla collaborazione con il Dipartimento di Scienze Farmaceutiche il nostro gruppo di ricerca è arrivato alla identificazione di un composto (galloflavina) che inibisce la LDH con una efficienza molto maggiore di OXA. I risultati preliminari ottenuti sulle cellule di epatocarcinoma suggeriscono che la galloflavina potrebbe essere un composto promettente nel campo degli inibitori metabolici tumorali e inducono a una sua valutazione più approfondita come potenziale farmaco antineoplastico.The aim of this work was to study the inhibition of aerobic glycolysis (the main metabolic pathway used by cancer cells to produce energy) achieved by blocking the enzyme lactate dehydrogenase (LDH). My activity has focused on the possibility of using this approach in order to improve the efficacy of anticancer therapy, evaluating its effects on cultured human hepatocellular carcinoma. In order to evaluate the effects of LDH inhibition, in a first set of experiments, we used oxamic acid (OXA). This compound is the only known specific inhibitor of LDH; it is a non-toxic molecule in vivo, but it is active at too high concentrations to allow its therapeutic use. An important result was the demonstration that LDH inhibition achieved by OXA not only triggers cell death signals but, when combined with the administration of sorafenib, also greatly increases the efficacy of this drug, leading to a synergistic effect. This strong potentiating effect of the drug action has been explained with the demonstration that sorafenib has the ability to inhibit the oxygen consumption of the treated cells, making them more dependent on glycolysis for ATP generation. In collaboration with the Department of Pharmaceutical Sciences our research group has identified a compound (galloflavin) that inhibits LDH with much higher efficacy than OXA. To our knowledge, inhibition of LDH is the only biochemical effect described for galloflavin. According to the results obtained on hepatocarcinoma cultured cells, galloflavin might be a promising lead candidate in the field of tumor metabolic inhibitors, deserving a more exhaustive evaluation as a potential anticancer agent