LAMBADA: Backward Chaining for Automated Reasoning in Natural Language

Remarkable progress has been made on automated reasoning with knowledge specified as unstructured, natural text, by using the power of large language models (LMs) coupled with methods such as Chain-of-Thought prompting and Selection-Inference. These techniques search for proofs in the forward direction from axioms to the conclusion, which suffers from a combinatorial explosion of the search space, and thus high failure rates for problems requiring longer chains of reasoning. The classical automated reasoning literature has shown that reasoning in the backward direction (i.e. from the intended conclusion to the set of axioms that support it) is significantly more efficient at proof-finding problems. We import this intuition into the LM setting and develop a Backward Chaining algorithm, which we call LAMBADA, that decomposes reasoning into four sub-modules, each of which can be simply implemented by few-shot prompted LM inference. We show that LAMBADA achieves massive accuracy boosts over state-of-the-art forward reasoning methods on two challenging logical reasoning datasets, particularly when deep and accurate proof chains are required.Comment: 16 page

Astrocytes Do Not Forfeit Their Neuroprotective Roles After Surviving Intense Oxidative Stress

In order to fulfill their evolutionary role as support cells, astrocytes have to tolerate intense oxidative stress under conditions of brain injury and disease. It is well known that astrocytes exposed to mild oxidative stress are preconditioned against subsequent stress exposure in dual hit models. However, it is unclear whether severe oxidative stress leads to stress tolerance, stress exacerbation, or no change in stress resistance in astrocytes. Furthermore, it is not known whether reactive astrocytes surviving intense oxidative stress can still support nearby neurons. The data in this Brief Report suggest that primary cortical astrocytes surviving high concentrations of the oxidative toxicant paraquat are completely resistant against subsequent oxidative challenges of the same intensity. Inhibitors of multiple endogenous defenses (e.g., glutathione, heme oxygenase 1, ERK1/2, Akt) failed to abolish or even reduce their stress resistance. Stress-reactive cortical astrocytes surviving intense oxidative stress still managed to protect primary cortical neurons against subsequent oxidative injuries in neuron/astrocyte co-cultures, even at concentrations of paraquat that otherwise led to more than 80% neuron loss. Although our previous work demonstrated a lack of stress tolerance in primary neurons exposed to dual paraquat hits, here we show that intensely stressed primary neurons can resist a second hit of hydrogen peroxide. These collective findings suggest that stress-reactive astroglia are not necessarily neurotoxic, and that severe oxidative stress does not invariably lead to stress exacerbation in either glia or neurons. Therefore, interference with the natural functions of stress-reactive astrocytes might have the unintended consequence of accelerating neurodegeneration

Streaming End-to-end Speech Recognition For Mobile Devices

End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories

Tc-99m-tamoxifen: A novel diagnostic imaging agent for estrogen receptor-expressing breast cancer patients

PURPOSEThe aim of the study was to radiolabel, characterize, and perform in vitro and in vivo assessment of Technetium-99m (Tc-99m) tamoxifen for screening ER expressing lesions in breast cancer patients.METHODSIn this study, tamoxifen has been radiolabeled with Tc-99m via Tc-99m-tricarbonyl core. The characterization and quality control tests of Tc-99m-tamoxifen were performed. In vitro recep- tor binding and blocking studies were performed in both positive control (MCF-7) and negative control cell lines (MDA-MB-231). Normal biodistribution studies were performed in female Wistar albino rats. The pilot clinical studies were performed in 4 ER-expressing breast cancer patients. Of the 4 patients, 1 was on tamoxifen therapy. All 4 patients had also undergone Fluorine-18 fluorodeoxyglucose (F-18-FDG) positron emission tomography/computed tomography.RESULTSTamoxifen was radiolabeled with Tc-99m via Tc-99m-tricarbonyl core with more than 95% radio- chemical yield. Mass spectra showed a peak corresponding to the molecular weight of Tc-99m- tricarbonyl and Tc-99m-tamoxifen. The site of binding of Tc-99m-tricarbonyl with tamoxifen was determined by proton nuclear magnetic resonance. The Tc-99m-tamoxifen showed 30% binding with MCF-7 and only 1%-2% receptor binding with MDA-MB-231 cell lines. Also, the percentage of receptor binding was drastically reduced (up to 72%) when ER was saturated with 50 times the excess molar ratio of unlabeled tamoxifen. In a pilot patient study, Tc-99m-tamoxifen uptake was observed in primary and metastatic lesions. However, no uptake was observed in a patient who was on tamoxifen therapy. The uptake of F-18-FDG was noted in all the patients.CONCLUSIONTamoxifen was radiolabeled with an in-house-synthesized Tc-99m-tricarbonyl core. The radio- labeled complex has been characterized and evaluated for receptor specificity in in vitro and in vivo studies. Also, this is the first clinical study using Tc-99m-tamoxifen for imaging ER. More patients need to be evaluated to further explore the role of Tc-99m-tamoxifen in ER-expressing lesions

A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm

In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%