Reinforcement Learning with Intrinsic Affinity for Personalized Asset Management

The common purpose of applying reinforcement learning (RL) to asset management is the maximization of profit. The extrinsic reward function used to learn an optimal strategy typically does not take into account any other preferences or constraints. We have developed a regularization method that ensures that strategies have global intrinsic affinities, i.e., different personalities may have preferences for certain assets which may change over time. We capitalize on these intrinsic policy affinities to make our RL model inherently interpretable. We demonstrate how RL agents can be trained to orchestrate such individual policies for particular personality profiles and still achieve high returns

Renosterveld Conservation in South Africa: A Case Study for Handling Uncertainty in Knowledge-Based Neural Networks for Environmental Management

This work presents an artificial intelligence method for the development of decision support systems for environmental management and demonstrates its strengths using an example from the domain of biodiversity and conservation biology. The approach takes into account local expert knowledge together with collected field data about plant habitats in order to identify areas which show potential for conserving thriving areas of Renosterveld vegetation and areas that are best suited for agriculture. The available data is limited and cannot be adequately explained by expert knowledge alone. The paradigm combines expert knowledge about the local conditions with the collected ground truth in a knowledge-based neural network. The integration of symbolic knowledge with artificial neural networks is becoming an. increasingly popular paradigm for solving real-world applications. The paradigm provides means for using prior knowledge to determine the network architecture, to program a subset of weights to induce a learning bias which guides network training, and to extract knowledge from trained networks; it thus provides a methodology for dealing with uncertainty in the prior knowledge. The role of neural networks then becomes that of knowledge refinement. The open question on how to determine the strength of the inductive bias of programmed weights is addressed by presenting a heuristic which takes the network architecture and training algorithm, the prior knowledge, and the training data into consideration

Stable Encoding of Large Finite-State Automata in Recurrent Neural Networks with Sigmoid Discriminants

We propose an algorithm for encoding deterministic finite-state automata (DFAs) in second-order recurrent neural networks with sigmoidal discriminant function and we prove that the languages accepted by the constructed network and the DFA are identical. The desired finite-state network dynamics is achieved by programming a small subset of all weights. A worst case analysis reveals a relationship between the weight strength and the maximum allowed network size which guarantees finite-state behavior of the constructed network. We illustrate the method by encoding random DFAs with 10, 100, and 1,000 states. While the theory predicts that the weight strength scales with the DFA size, we find the weight strength to be almost constant for all the experiments. These results can be explained by noting that the generated DFAs represent average cases. We empirically demonstrate the existence of extreme DFAs for which the weight strength scales with DFA size. (Also cross-referenced as UMIACS-TR-94-101

Constructing Deterministic Finite-State Automata in Recurrent Neural Networks

Recurrent neural networks that are {\it trained} to behave like deterministic finite-state automata (DFAs) can show deteriorating performance when tested on long strings. This deteriorating performance can be attributed to the instability of the internal representation of the learned DFA states. The use of a sigmoidal discriminant function together with the recurrent structure contribute to this instability. We prove that a simple algorithm can {\it construct} second-order recurrent neural networks with a sparse interconnection topology and sigmoidal discriminant function such that the internal DFA state representations are stable, i.e. the constructed network correctly classifies strings of {\it arbitrary length}. The algorithm is based on encoding strengths of weights directly into the neural network. We derive a relationship between the weight strength and the number of DFA states for robust string classification. For a DFA with $n$ states and $m$ input alphabet symbols, the constructive algorithm generates a ``programmed" neural network with $O(n)$ neurons and $O(mn)$ weights. We compare our algorithm to other methods proposed in the literature. Revised in February 1996 (Also cross-referenced as UMIACS-TR-95-50

LONG HORIZON ANOMALY PREDICTION IN MULTIVARIATE TIME SERIES WITH CAUSAL AUTOENCODERS

publishedVersio

How to read a next-generation sequencing report-what oncologists need to know.

Next-generation sequencing (NGS) of tumor cell-derived DNA/RNA to screen for targetable genomic alterations is now widely available and has become part of routine practice in oncology. NGS testing strategies depend on cancer type, disease stage and the impact of results on treatment selection. The European Society for Medical Oncology (ESMO) has recently published recommendations for the use of NGS in patients with advanced cancer. We complement the ESMO recommendations with a practical review of how oncologists should read and interpret NGS reports. A concise and straightforward NGS report contains details of the tumor sample, the technology used and highlights not only the most important and potentially actionable results, but also other pathogenic alterations detected. Variants of unknown significance should also be listed. Interpretation of NGS reports should be a joint effort between molecular pathologists, tumor biologists and clinicians. Rather than relying and acting on the information provided by the NGS report, oncologists need to obtain a basic level of understanding to read and interpret NGS results. Comprehensive annotated databases are available for clinicians to review the information detailed in the NGS report. Molecular tumor boards do not only stimulate debate and exchange, but may also help to interpret challenging reports and to ensure continuing medical education

Feasibility and acceptance of electronic monitoring of symptoms and syndromes using a handheld computer in patients with advanced cancer in daily oncology practice

Purpose: We investigated the feasibility and acceptance of electronic monitoring of symptoms and syndromes in oncological outpatient clinics using a PALM (handheld computer). Methods: The assessment of a combination of symptoms and clinical benefit parameters grouped in four pairs was tested in a pilot phase in advanced cancer patients. Based on these experiences, the software E-MOSAIC was developed, consisting of patient-reported symptoms and nutritional intake and objective assessments (weight, weight loss, performance status and medication for pain, fatigue, and cachexia). E-MOSAIC was then tested in four Swiss oncology centers. In order to compare the methods, patients completed the E-MOSAIC as a paper and a PALM version. Preferences of version and completion times were collected. Assessments were compared using Wilcoxon signed-rank tests , and the test-retest reliability was evaluated. Results: The pilot phase was completed by 22 patients. Most patients and physicians perceived the assessment as useful. Sixty-two patients participated in the feasibility study. Twelve patients reported problems (understanding, optical, tactile), and five patients could not complete the assessment. The median time to complete the PALM-based assessment was 3min. Forty-nine percent of patients preferred the PALM, 23% preferred a paper version, and 28% of patients had no preference. Paper vs. PALM revealed no significant differences in symptoms, but in nutritional intake (p = 0.013). Test-retest (1h, n = 20) reliability was satisfactory (r = 073-98). Conclusion: Electronic symptom and clinical benefit monitoring is feasible in oncology outpatient clinics and perceived as useful by patients, oncology nurses, and oncologists. E-MOSAIC is tested in a prospective randomized trial

61MO Biomarker analysis of men with enzalutamide (enza)-resistant metastatic castration-resistant prostate cancer (mCRPC) treated with pembrolizumab (pembro) + enza in KEYNOTE-199

Background: In KEYNOTE-199 (NCT02787005), pembro + enza had durable antitumor activity in enza-refractory mCRPC. We evaluated the association between prespecified biomarkers and clinical outcomes. Methods: Cohorts 4 (C4; RECIST-measurable disease) and 5 (C5; nonmeasurable, bone-predominant disease) enrolled men with chemotherapy-naive mCRPC, irrespective of PD-L1 status, that progressed after initial response to enza. We evaluated TMB by whole exome sequencing (n = 64), PD-L1 combined positive score (CPS) by IHC (n = 124), and 18-gene T-cell–inflamed gene expression profile (TcellinfGEP) by NanoString (n = 51). Outcomes were DCR, PFS, PSA response, PSA progression, OS, and ORR per blinded independent review (C4 only). Significance of continuous biomarkers (CPS, TMB, GEP) was prespecified at 0.05 for 1-sided P values from logistic (ORR, DCR, PSA response) and Cox proportional hazard (PFS, OS, PSA progression) regression adjusted for ECOG PS. Results: In C4, ORR was 10% (5/48) in pts with evaluable TMB data and 12% (10/81) in pts with CPS data. In C4 and C5, 16% (10/64) and 14% (17/124) of pts with TMB and CPS data, respectively, achieved a PSA response. TMB was significantly associated with DCR (P = 0.03) and trended toward an association with PSA response (P = 0.08). TMB (AUROC [95% CI]: 0.68 [0.51-0.86]), but not CPS (0.54 [0.41-0.67]) or TcellinfGEP (0.55 [0.37-0.74]), enriched for PSA response. TMB (P = 0.04), but not CPS (P = 0.57) or TcellinfGEP (P = 0.32), was significantly associated with PSA progression. There was 1 MSI-H pt (per Promega PCR assay); this pt achieved an objective and PSA response and had PFS \u3e6 months. TMB, CPS, and TcellinfGEP were not associated with PFS or OS. There was a low prevalence of TMB ≥175 mut/exome (11%) and TcellinfGEP-high (≥−0.318; 16%). Conclusions: In this biomarker analysis of KEYNOTE-199 C4-C5, PD-L1 CPS and TcellinfGEP were not significantly associated with clinical outcome. Despite the low prevalence of TMB ≥175 mut/exome, TMB was positively associated with outcomes of pembro + enza in pts with mCRPC. The sample sizes for the exploratory analyses were small, and results should be interpreted with caution

Malaria mosquito control using edible fish in western Kenya: preliminary findings of a controlled study

<p>Abstract</p> <p>Background</p> <p>Biological control methods are once again being given much research focus for malaria vector control. This is largely due to the emerging threat of strong resistance to pesticides. Larvivorous fish have been used for over 100 years in mosquito control and many species have proved effective. In the western Kenyan highlands the larvivorous fish <it>Oreochromis niloticus </it>L. (Perciformes: Cichlidae) (formerly <it>Tilapia nilotica</it>) is commonly farmed and eaten but has not been previously tested in the field for malaria mosquito control.</p> <p>Methods</p> <p>This fish was introduced into abandoned fishponds at an altitude of 1,880 m and the effect measured over six months on the numbers of mosquito immatures. For comparison an untreated control pond was used. During this time, all ponds were regularly cleared of emergent vegetation and fish re-stocking was not needed. Significant autocorrelation was removed from the time series data, and t-tests were used to investigate within a pond and within a mosquito type any differences before and after the introduction of <it>O. niloticus</it>. Mulla's formula was also used on the raw data to calculate the percentage reduction of the mosquito larvae.</p> <p>Results</p> <p>After <it>O. niloticus </it>introduction, mosquito densities immediately dropped in the treated ponds but increased in the control pond. This increase was apparently due to climatic factors. Mulla's formula was applied which corrects for that natural tendency to increase. The results showed that after 15 weeks the fish caused a more than 94% reduction in both <it>Anopheles gambiae s.l</it>. and <it>Anopheles funestus </it>(Diptera: Culicidae) in the treated ponds, and more than 75% reduction in culicine mosquitoes. There was a highly significantly reduction in <it>A. gambiae s.l</it>. numbers when compared to pre-treatment levels.</p> <p>Conclusion</p> <p>This study reports the first field trial data on <it>O. niloticus </it>for malaria mosquito control and shows that this species, already a popular food fish in western Kenya, is an apparently sustainable mosquito control tool which also offers a source of protein and income to people in rural areas. There should be no problem with acceptance of this malaria control method since the local communities already farm this fish species.</p