K-SHAP: Policy Clustering Algorithm for Anonymous State-Action Pairs

Learning agent behaviors from observational data has shown to improve our understanding of their decision-making processes, advancing our ability to explain their interactions with the environment and other agents. While multiple learning techniques have been proposed in the literature, there is one particular setting that has not been explored yet: multi agent systems where agent identities remain anonymous. For instance, in financial markets labeled data that identifies market participant strategies is typically proprietary, and only the anonymous state-action pairs that result from the interaction of multiple market participants are publicly available. As a result, sequences of agent actions are not observable, restricting the applicability of existing work. In this paper, we propose a Policy Clustering algorithm, called K-SHAP, that learns to group anonymous state-action pairs according to the agent policies. We frame the problem as an Imitation Learning (IL) task, and we learn a world-policy able to mimic all the agent behaviors upon different environmental states. We leverage the world-policy to explain each anonymous observation through an additive feature attribution method called SHAP (SHapley Additive exPlanations). Finally, by clustering the explanations we show that we are able to identify different agent policies and group observations accordingly. We evaluate our approach on simulated synthetic market data and a real-world financial dataset. We show that our proposal significantly and consistently outperforms the existing methods, identifying different agent strategies.Comment: ICML 202

Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness

Limit order books are a fundamental and widespread market mechanism. This paper investigates the use of conditional generative models for order book simulation. For developing a trading agent, this approach has drawn recent attention as an alternative to traditional backtesting due to its ability to react to the presence of the trading agent. Using a state-of-the-art CGAN (from Coletta et al. (2022)), we explore its dependence upon input features, which highlights both strengths and weaknesses. To do this, we use "adversarial attacks" on the model's features and its mechanism. We then show how these insights can be used to improve the CGAN, both in terms of its realism and robustness. We finish by laying out a roadmap for future work

ATMS: Algorithmic Trading-Guided Market Simulation

The effective construction of an Algorithmic Trading (AT) strategy often relies on market simulators, which remains challenging due to existing methods' inability to adapt to the sequential and dynamic nature of trading activities. This work fills this gap by proposing a metric to quantify market discrepancy. This metric measures the difference between a causal effect from underlying market unique characteristics and it is evaluated through the interaction between the AT agent and the market. Most importantly, we introduce Algorithmic Trading-guided Market Simulation (ATMS) by optimizing our proposed metric. Inspired by SeqGAN, ATMS formulates the simulator as a stochastic policy in reinforcement learning (RL) to account for the sequential nature of trading. Moreover, ATMS utilizes the policy gradient update to bypass differentiating the proposed metric, which involves non-differentiable operations such as order deletion from the market. Through extensive experiments on semi-real market data, we demonstrate the effectiveness of our metric and show that ATMS generates market data with improved similarity to reality compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network (cWGAN) approach. Furthermore, ATMS produces market data with more balanced BUY and SELL volumes, mitigating the bias of the cWGAN baseline approach, where a simple strategy can exploit the BUY/SELL imbalance for profit

Minilaparoscopic cholecystectomy versus conventional laparoscopic cholecystectomy. An endless debate

Background: Our systematic review and meta-analysis examine the impact of minilaparoscopic cholecystectomy (MLC) versus conventional laparoscopic cholecystectomy (CLC). Some authors previously compared these surgical approaches without reaching any clear conclusion, since then, further trials have been performed, but an update was needed. Materials and Methods: PubMed, EMBASE, and the CENTRAL were systematically searched for randomized controlled trials comparing MLC versus CLC up to August 2019. The outcome measures used for comparison were operative time (OT), overall morbidity, intra- and postoperative complications, conversion and reintervention rate, length of hospital stay (LOS), postoperative pain (POP), and cosmetic results. A meta-analysis of relevant studies was performed using RevMan 5.3. Results: Fifteen studies, including 863 patients, were considered eligible to collect data and entered the meta-analysis. A total of 415 patients in the MLC group versus 448 in the CLC group were compared. No statistical difference as for overall morbidity, intra- and postoperative complications, conversion and reintervention rate, LOS, and cosmetic results were retrieved among the groups. CLC results faster and MLC shows to be the least painful. Conclusions: According to the available high-level evidence, both surgical approaches resulted substantially equivalent to perform LC, with some advantages of CLC as for OT and of MLC concerning POP. As a consequence, we can conclude that either procedure is superior or inferior to the other one; actually, we are not able to suggest the adoption of any of the two on a routine basis

Allosteric activation mechanism of bovine chymosin revealed by bias-exchange metadynamics and molecular dynamics simulations

The aspartic protease, bovine chymosin, catalyses the proteolysis of κ-casein proteins in milk. The bovine chymosin–κ-casein complex is of industrial interest as the enzyme is widely employed in the manufacturing of processed dairy products. The apo form of the enzyme adopts a self-inhibited conformation in which the side chain of Tyr77 occludes the binding site. On the basis of kinetic, mutagenesis and crystallographic data, it has been widely reported that a HPHPH sequence in the P8-P4 residues of the natural substrate κ-casein acts as the allosteric activator, but the mechanism by which this occurs has not previously been elucidated due to the challenges associated with studying this process by experimental methods. Here we have employed two computational techniques, molecular dynamics and bias exchange metadynamics simulations, to study the mechanism of allosteric activation and to compute the free energy surface for the process. The simulations reveal that allosteric activation is initiated by interactions between the HPHPH sequence of κ-casein and a small α-helical region of chymosin (residues 112-116). A small conformational change in the α-helix causes the side chain of Phe114 to vacate a pocket that may then be occupied by the side chain of Tyr77. The free energy surface for the self-inhibited to open transition is significantly altered by the presence of the HPHPH sequence of κ-casein

A2^2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems

To perform advanced surveillance, Unmanned Aerial Vehicles (UAVs) require the execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV networks, the successful transmission of these tasks to the edge is severely challenged due to severe bandwidth constraints. For this reason, we propose a novel A$^2$-UAV framework to optimize the number of correctly executed tasks at the edge. In stark contrast with existing art, we take an application-aware approach and formulate a novel pplication-Aware Task Planning Problem (A$^2$-TPP) that takes into account (i) the relationship between deep neural network (DNN) accuracy and image compression for the classes of interest based on the available dataset, (ii) the target positions, (iii) the current energy/position of the UAVs to optimize routing, data pre-processing and target assignment for each UAV. We demonstrate A$^2$-TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A$^2$-UAV through real-world experiments with a testbed composed by four DJI Mavic Air 2 UAVs. We consider state-of-the-art image classification tasks with four different DNN models (i.e., DenseNet, ResNet152, ResNet50 and MobileNet-V2) and object detection tasks using YoloV4 trained on the ImageNet dataset. Results show that A$^2$-UAV attains on average around 38% more accomplished tasks than the state-of-the-art, with 400% more accomplished tasks when the number of targets increases significantly. To allow full reproducibility, we pledge to share datasets and code with the research community.Comment: Accepted to INFOCOM 202

Solvent Dependency of the UV-Vis Spectrum of Indenoisoquinolines: Role of Keto-Oxygens as Polarity Interaction Probes.

Indenoisoquinolines are the most promising non-campthotecins topoisomerase IB inhibitors. We present an integrated experimental/computational investigation of the UV-Vis spectra of the IQNs parental compound (NSC314622) and two of its derivatives (NSC724998 and NSC725776) currently undergoing Phase I clinical trials. In all the three compounds a similar dependence of the relative absorption intensities at 270 nm and 290 nm on solvent polarity is found. The keto-oxygens in positions 5 and 11 of the molecular scaffold of the molecule are the principal chromophores involved in this dependence. Protic interactions on these sites are also found to give rise to absorptions at wavelengthsolution, due to the stabilization of highly polarized tautomers of the molecule. These results suggest that the keto-oxygens are important polarizable groups that can act as useful interactors with the molecular receptor, providing at the same time an useful fingerprint for the monitoring of the drug binding to topoisomerase IB

Prevalence and clinical outcome of hepatic haemangioma with specific reference to the risk of rupture: a large retrospective cross-sectional study.

BACKGROUND: Prevalence and incidence of hepatic haemangioma are estimated from autopsy series only. Although benign and generally asymptomatic, hepatic haemangioma can cause serious complications. AIMS: The aim of the study was to assess the prevalence of hepatic haemangioma and to attempt to quantify the risk of major complications such as spontaneous rupture. METHODS: We retrospectively analyzed the radiology database of a Regional University Hospital over a 7-year period: the radiological records of 83,181 patients who had an abdominal computed tomography or magnetic resonance scan were reviewed. Diagnoses made at imaging were reviewed and related to clinical course. RESULTS: Hepatic haemangioma was diagnosed in 2071 patients (2.5% prevalence). In 226 patients (10.9%), haemangioma had diameter of 4 cm or more (giant haemangioma). The risk of bleeding was assessed on patients without concomitant malignancies. Spontaneous bleeding occurred in 5/1067 patients (0.47%). All 5 patients had giant haemangioma: 4 had exophytic lesions and presented with haemoperitoneum; 1 with centrally located tumour experienced intrahepatic bleeding. CONCLUSION: Giant haemangiomas have a low but relevant risk of rupture (3.2% in this series), particularly when peripherally located and exophytic. Surgery might be considered in these cases

LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study

The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL models focusing on Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data. To carry out this study, we developed LOBCAST, an open-source framework that incorporates data preprocessing, DL model training, evaluation and profit analysis. Our extensive experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability. Our work serves as a benchmark, illuminating the potential and the limitations of current approaches and providing insight for innovative solutions