Are AlphaZero-like Agents Robust to Adversarial Perturbations?

The success of AlphaZero (AZ) has demonstrated that neural-network-based Go AIs can surpass human performance by a large margin. Given that the state space of Go is extremely large and a human player can play the game from any legal state, we ask whether adversarial states exist for Go AIs that may lead them to play surprisingly wrong actions. In this paper, we first extend the concept of adversarial examples to the game of Go: we generate perturbed states that are ``semantically'' equivalent to the original state by adding meaningless moves to the game, and an adversarial state is a perturbed state leading to an undoubtedly inferior action that is obvious even for Go beginners. However, searching the adversarial state is challenging due to the large, discrete, and non-differentiable search space. To tackle this challenge, we develop the first adversarial attack on Go AIs that can efficiently search for adversarial states by strategically reducing the search space. This method can also be extended to other board games such as NoGo. Experimentally, we show that the actions taken by both Policy-Value neural network (PV-NN) and Monte Carlo tree search (MCTS) can be misled by adding one or two meaningless stones; for example, on 58\% of the AlphaGo Zero self-play games, our method can make the widely used KataGo agent with 50 simulations of MCTS plays a losing action by adding two meaningless stones. We additionally evaluated the adversarial examples found by our algorithm with amateur human Go players and 90\% of examples indeed lead the Go agent to play an obviously inferior action. Our code is available at \url{https://PaperCode.cc/GoAttack}.Comment: Accepted by Neurips 202

Study on the Micro-structures of Long Fiber through Runner and Cavity in Injection Molding for Reinforced Thermoplastics (FRT)

[[abstract]]Lightweight technology has been applied into many industries especially for automotive to enhance the fuel efficiency. One of most famous methods is applied fiber-reinforced thermoplastics (FRT) technology, it includes short and long fiber-reinforced thermoplastics (FRT) to support lightweight technology. However, the enhancement mechanism by the microstructures of the fibers in FRT is still too complicated to understand. In this study, we designed a benchmark to study the fiber microstructures based on ASTM D638 with dog-bond system. First, we have tried to study how the geometry of cavity influences the fiber orientation during the injection processes. Furthermore, we have paid the attention on the variation of the fiber length distribution as the injection molding processing. Results show that the geometry of cavity has significant effect on the fiber orientation during the injection processes. Since the system has contraction and expansion structure, the orientation tensor component a11 corresponding to the flow direction, will be enhanced and then decreased along the cavity. Moreover, the fiber lengths have dramatically sharp distribution on skin layer when melt goes through the gate into the cavity. It will allow almost 90% lengths are broken through the skin layer. Meanwhile, using numerical visualization from runner to cavity through core layer, there is about 30% length broken during the journey in runner section. Finally, some fiber orientation results are compared with some literature’s. Results showed that our numerical predictions are matched with that of literature quite well in the trend.[[notice]]補正完

Bispecific antibodies revolutionizing breast cancer treatment: a comprehensive overview

Breast cancer (BCa) is known as a complex and prevalent disease requiring the development of novel anticancer therapeutic approaches. Bispecific antibodies (BsAbs) have emerged as a favorable strategy for BCa treatment due to their unique ability to target two different antigens simultaneously. By targeting tumor-associated antigens (TAAs) on cancer cells, engaging immune effector cells, or blocking critical signaling pathways, BsAbs offer enhanced tumor specificity and immune system involvement, improving anti-cancer activity. Preclinical and clinical studies have demonstrated the potential of BsAbs in BCa. For example, BsAbs targeting human epidermal growth factor receptor 2 (HER2) have shown the ability to redirect immune cells to HER2-positive BCa cells, resulting in effective tumor cell killing. Moreover, targeting the PD-1/PD-L1 pathway by BsAbs has demonstrated promising outcomes in overcoming immunosuppression and enhancing immune-mediated tumor clearance. Combining BsAbs with existing therapeutic approaches, such as chemotherapy, targeted therapies, or immune checkpoint inhibitors (ICIs), has also revealed synergistic effects in preclinical models and early clinical trials, emphasizing the usefulness and potential of BsAbs in BCa treatment. This review summarizes the latest evidence about BsAbs in treating BCa and the challenges and opportunities of their use in BCa

Prediction of epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients on computed tomography (CT) images using 3-dimensional (3D) convolutional neural network.

BACKGROUND: Noninvasively detecting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients before targeted therapy remains a challenge. This study aimed to develop a 3-dimensional (3D) convolutional neural network (CNN)-based deep learning model to predict EGFR mutation status using computed tomography (CT) images. METHODS: We retrospectively collected 660 patients from 2 large medical centers. The patients were divided into training (n=528) and external test (n=132) sets according to hospital source. The CNN model was trained in a supervised end-to-end manner, and its performance was evaluated using an external test set. To compare the performance of the CNN model, we constructed 1 clinical and 3 radiomics models. Furthermore, we constructed a comprehensive model combining the highest-performing radiomics and CNN models. The receiver operating characteristic (ROC) curves were used as primary measures of performance for each model. Delong test was used to compare performance differences between different models. RESULTS: Compared with the clinical [training set, area under the curve (AUC) =69.6%, 95% confidence interval (CI), 0.661-0.732; test set, AUC =68.4%, 95% CI, 0.609-0.752] and the highest-performing radiomics models (training set, AUC =84.3%, 95% CI, 0.812-0.873; test set, AUC =72.4%, 95% CI, 0.653-0.794) models, the CNN model (training set, AUC =94.3%, 95% CI, 0.920-0.961; test set, AUC =94.7%, 95% CI, 0.894-0.978) had significantly better predictive performance for predicting EGFR mutation status. In addition, compared with the comprehensive model (training set, AUC =95.7%, 95% CI, 0.942-0.971; test set, AUC =87.4%, 95% CI, 0.820-0.924), the CNN model had better stability. CONCLUSIONS: The CNN model has excellent performance in non-invasively predicting EGFR mutation status in patients with lung adenocarcinoma and is expected to become an auxiliary tool for clinicians

Individualized Intervention Guided by BCR-ABL Transcript Levels after HLA-Identical Sibling Donor Transplantation Improves HSCT Outcomes for Patients with Chronic Myeloid Leukemia

The aim of this study was to determine the effect of individualized intervention guided by BCR-ABL transcript levels after hematopoietic stem cell transplantation (HSCT) on relapse and leukemia-free survival (LFS) of patients with chronic myelogenous leukemia (CML). Eighty-four consecutive patients who received HLA-identical sibling HSCT were enrolled. The patients were conditioned with a modified busulfan and cyclophosphamide regimen, and received stem cells from a HLA-identical sibling donor. Patients were identified as high risk of relapse based on serial monitoring of post-HSCT BCR-ABL transcript levels, and patients in the high-risk group were given individualized intervention. Interventions included immunosuppressant withdrawal, modified donor lymphocyte infusion, and imatinib mesylate. Engraftment was successful in all patients. Twenty-eight patients were categorized as high risk because of higher post-HSCT BCR-ABL transcript levels and received intervention. The 56 low-risk patients received no intervention. Twenty-five high-risk patients achieved complete molecular remission at a median of 49 days (range: 18-232 days) after intervention. Two high-risk patients and 1 low-risk patient ultimately relapsed, the 4-year relapse rate was 3.9% ± 4.4%. Overall 4-year survival was 89.0% ± 7% and the 4-year LFS was 89.2% ± 6.8%. All surviving patients remains in complete molecular remission after a median of 1481 (1040-1794) days follow-up. Individualized intervention based on the post-HSCT BCR-ABL transcript level can decrease relapse and increase LFS of patients with CML after HLA-identical sibling HSCT

Model of a multiverse providing the dark energy of our universe

It is shown that the dark energy presently observed in our universe can be regarded as the energy of a scalar field driving an inflation-like expansion of a multiverse with ours being a subuniverse among other parallel universes. A simple model of this multiverse is elaborated: Assuming closed space geometry, the origin of the multiverse can be explained by quantum tunneling from nothing; subuniverses are supposed to emerge from local fluctuations of separate inflation fields. The standard concept of tunneling from nothing is extended to the effect that in addition to an inflationary scalar field, matter is also generated, and that the tunneling leads to an (unstable) equilibrium state. The cosmological principle is assumed to pertain from the origin of the multiverse until the first subuniverses emerge. With increasing age of the multiverse, its spatial curvature decays exponentially so fast that, due to sharing the same space, the flatness problem of our universe resolves by itself. The dark energy density imprinted by the multiverse on our universe is time-dependent, but such that the ratio $w{=}\varrho/(c^2p)$ of its mass density and pressure (times $c^2$) is time-independent and assumes a value $-1{+}\epsilon$ with arbitrary $\epsilon{>}0$. $\epsilon$ can be chosen so small, that the dark energy model of this paper can be fitted to the current observational data as well as the cosmological constant model.Comment: 32 pages, 4 figure

Nonmalignant Late Effects in Survivors of Partially Matched Donor Hematopoietic Stem Cell Transplantation

AbstractHuman leukocyte antigen (HLA) partially matched related donor (PMRD) hematopoietic stem cell transplantation (HSCT) is an effective option for hematological malignancies. In this study, the nonmalignant late effects of PMRD HSCT were evaluated and compared with HLA-identical sibling donor (ISD) HSCT. Three hundred thirteen patients (ISD, n = 160; PMRD, n = 153) who survived at least 6 months and received regular follow-up examinations after their HSCT were enrolled. The 5-year cumulative incidence (±SE) of at least one late effect and multiple late effects was 47.30% ± .17% versus 58.21% ± .16% (P = .134) and 17.97% ± .10% versus 34.28% ± .15% (P = .001) for PMRD HSCT recipients versus ISD HSCT recipients, respectively. The cumulative incidence of keratoconjunctivitis sicca, periodontitis, ankylosis, myalgia, and nephrotic syndrome was lower among PMRD HSCT recipients compared with ISD HSCT recipients. Severe chronic graft-versus-host disease, multiple pre-HSCT chemotherapy cycles, female donor, and older age were risk factors for at least one late effect. Female donor, older age, and long-term immunosuppressive therapy were associated with multiple late effects. In summary, PMRD HSCT recipients have a lower risk of late effects compared with ISD HSCT recipients, possibly due to differences in protocols for graft-versus-host disease prophylaxis, and long-term follow-up after transplantation is recommended

Abnormalities of intrinsic brain activity in essential tremor: A meta-analysis of resting-state functional imaging

Neuroimaging studies using a variety of techniques have demonstrated abnormal patterns of spontaneous brain activity in patients with essential tremor (ET). However, the findings are variable and inconsistent, hindering understanding of underlying neuropathology. We conducted a meta‐analysis of whole‐brain resting‐state functional neuroimaging studies in ET compared to healthy controls (HC), using anisotropic effect‐size seed‐based d mapping, to identify the most consistent brain activity alterations and their relation to clinical features. After systematic literature search, we included 13 studies reporting 14 comparisons, describing 286 ET patients and 254 HC. Subgroup analyses were conducted considering medication status, head tremor status, and methodological factors. Brain activity in ET is altered not only in the cerebellum and cerebral motor cortex, but also in nonmotor cortical regions including prefrontal cortex and insula. Most of the results remained unchanged in subgroup analyses of patients with head tremor, medication‐naive patients, studies with statistical threshold correction, and the large subgroup of studies using functional magnetic resonance imaging. These findings not only show consistent and robust abnormalities in specific brain regions but also provide new information on the biology of patient heterogeneity, and thus help to elucidate the pathophysiology of ET