Adaptive-Aggressive Traders Don't Dominate

For more than a decade Vytelingum's Adaptive-Aggressive (AA) algorithm has been recognized as the best-performing automated auction-market trading-agent strategy currently known in the AI/Agents literature; in this paper, we demonstrate that it is in fact routinely outperformed by another algorithm when exhaustively tested across a sufficiently wide range of market scenarios. The novel step taken here is to use large-scale compute facilities to brute-force exhaustively evaluate AA in a variety of market environments based on those used for testing it in the original publications. Our results show that even in these simple environments AA is consistently out-performed by IBM's GDX algorithm, first published in 2002. We summarize here results from more than one million market simulation experiments, orders of magnitude more testing than was reported in the original publications that first introduced AA. A 2019 ICAART paper by Cliff claimed that AA's failings were revealed by testing it in more realistic experiments, with conditions closer to those found in real financial markets, but here we demonstrate that even in the simple experiment conditions that were used in the original AA papers, exhaustive testing shows AA to be outperformed by GDX. We close this paper with a discussion of the methodological implications of our work: any results from previous papers where any one trading algorithm is claimed to be superior to others on the basis of only a few thousand trials are probably best treated with some suspicion now. The rise of cloud computing means that the compute-power necessary to subject trading algorithms to millions of trials over a wide range of conditions is readily available at reasonable cost: we should make use of this; exhaustive testing such as is shown here should be the norm in future evaluations and comparisons of new trading algorithms.Comment: To be published as a chapter in "Agents and Artificial Intelligence" edited by Jaap van den Herik, Ana Paula Rocha, and Luc Steels; forthcoming 2019/2020. 24 Pages, 1 Figure, 7 Table

Tamoxifen for prevention of breast cancer: extended long-term follow-up of the IBIS-I breast cancer prevention trial

© Cuzick et al. Open Access article distributed under the terms of CC BY.http://dx.doi.org/10.1016/S1470-2045(14)71171-

Biologic markers of risk in nipple aspirate fluid are associated with residual cancer and tumour size

We previously demonstrated that nipple aspirate fluid (NAF) can be obtained from virtually all non-Asian women between the ages of 30 and 72. The focus of this report is to (1) determine the association of candidate markers of breast cancer risk in NAF obtained from fresh mastectomy specimens with residual breast carcinoma, and (2) evaluate the association of the markers with breast tumour progression. Nipple aspiration was performed on 97 specimens. Cytology, DNA index (including % hypertetraploid cells), cell cycle parameters (S phase fraction, % cells in G2/M), prostate-specific antigen (PSA), epidermal growth factor (EGF), testosterone, carcinoembryonic antigen (CEA) and prostaglandin D synthase (PGDS) were evaluated in NAF for their association with (1) residual ductal carcinoma in situ (DCIS) or invasive cancer, and (2) pathologic tumour size. NAF was obtained from 99% (96/97) of specimens. Atypical and malignant NAF cytology were significantly associated with residual DCIS or invasive cancer (P = 0.001) and with larger tumours (P = 0.004). One hundred per cent and 88% of subjects with malignant and atypical NAF cytology, respectively, had residual carcinoma. The percentage of cells in G2/M and DNA index were associated both with risk of residual carcinoma (P = 0.01 for each) and larger tumour size (DNA index, P = 0.03; G2/M, P = 0.05), although neither biomarker improved the ability of NAF cytology, to predict residual breast cancer. Higher DNA index was associated with atypical cytology (P = 0.0001). In summary, atypical and malignant NAF cytology are associated with larger tumour size, and are highly predictive of residual carcinoma after needle or excisional biopsy of the breast. © 1999 Cancer Research Campaig

Deficiency in trefoil factor 1 (TFF1) increases tumorigenicity of human breast cancer cells and mammary tumor development in TFF1-knockout mice

Although trefoil factor 1 (TFF1; previously named pS2) is abnormally expressed in about 50% of human breast tumors, its physiopathological role in this disease has been poorly studied. Moreover, controversial data have been reported. TFF1 function in the mammary gland therefore needs to be clarified. In this study, using retroviral vectors, we performed TFF1 gain- or loss-of-function experiments in four human mammary epithelial cell lines: normal immortalized TFF1-negative MCF10A, malignant TFF1-negative MDA-MB-231 and malignant TFF1-positive MCF7 and ZR75.1. The expression of TFF1 stimulated the migration and invasion in the four cell lines. Forced TFF1 expression in MCF10A, MDA-MB-231 and MCF7 cells did not modify anchorage-dependent or -independent cell proliferation. By contrast, TFF1 knockdown in MCF7 enhanced soft-agar colony formation. This increased oncogenic potential of MCF7 cells in the absence of TFF1 was confirmed in vivo in nude mice. Moreover, chemically induced tumorigenesis in TFF1-deficient (TFF1-KO) mice led to higher tumor incidence in the mammary gland and larger tumor size compared with wild-type mice. Similarly, tumor development was increased in the TFF1-KO ovary and lung. Collectively, our results clearly show that TFF1 does not exhibit oncogenic properties, but rather reduces tumor development. This beneficial function of TFF1 is in agreement with many clinical studies reporting a better outcome for patients with TFF1-positive breast primary tumors