Dielectric microscopy with submillimeter resolution

In analogy with optical near-field scanning methods, we use tapered dielectric waveguides as probes for a millimeter wave vector network analyzer. By scanning thin samples between two such probes we are able to map the spatially varying dielectric properties of materials with sub-wavelength resolution; using a 150 GHz probe in transmision mode we see spatial resolution of around 500 microns. We have applied this method to a variety of highly heterogeneous materials. Here we show dielectric maps of granite and oil shale

*-CFQ: Analyzing the Scalability of Machine Learning on a Compositional Task

We present *-CFQ ("star-CFQ"): a suite of large-scale datasets of varying scope based on the CFQ semantic parsing benchmark, designed for principled investigation of the scalability of machine learning systems in a realistic compositional task setting. Using this suite, we conduct a series of experiments investigating the ability of Transformers to benefit from increased training size under conditions of fixed computational cost. We show that compositional generalization remains a challenge at all training sizes, and we show that increasing the scope of natural language leads to consistently higher error rates, which are only partially offset by increased training data. We further show that while additional training data from a related domain improves the accuracy in data-starved situations, this improvement is limited and diminishes as the distance from the related domain to the target domain increases.Comment: Accepted, AAAI-2

Large Language Models Can Be Easily Distracted by Irrelevant Context

Large language models have achieved impressive performance on various natural language processing tasks. However, so far they have been evaluated primarily on benchmarks where all information in the input context is relevant for solving the task. In this work, we investigate the distractibility of large language models, i.e., how the model problem-solving accuracy can be influenced by irrelevant context. In particular, we introduce Grade-School Math with Irrelevant Context (GSM-IC), an arithmetic reasoning dataset with irrelevant information in the problem description. We use this benchmark to measure the distractibility of cutting-edge prompting techniques for large language models, and find that the model performance is dramatically decreased when irrelevant information is included. We also identify several approaches for mitigating this deficiency, such as decoding with self-consistency and adding to the prompt an instruction that tells the language model to ignore the irrelevant information

Compositional Semantic Parsing with Large Language Models

Humans can reason compositionally when presented with new tasks. Previous research shows that appropriate prompting techniques enable large language models (LLMs) to solve artificial compositional generalization tasks such as SCAN. In this work, we identify additional challenges in more realistic semantic parsing tasks with larger vocabulary and refine these prompting techniques to address them. Our best method is based on least-to-most prompting: it decomposes the problem using prompting-based syntactic parsing, then uses this decomposition to select appropriate exemplars and to sequentially generate the semantic parse. This method allows us to set a new state of the art for CFQ while requiring only 1% of the training data used by traditional approaches. Due to the general nature of our approach, we expect similar efforts will lead to new results in other tasks and domains, especially for knowledge-intensive applications.Comment: Fixed metadata. No other change

Large Language Models Encode Clinical Knowledge

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications

Fruit gardens enhance mammal diversity and biomass in a Southeast Asian rainforest

Protected areas are frequently inhabited by people and conservation must be integrated with traditional management systems. Cultivation of fruit gardens is a low-impact agroforestry technique which alters the structure and composition of forest stands and has the potential to thereby influence animal communities. This is of particular interest in the rainforests of Southeast Asia, where limited fruit availability between intermittent mast fruiting events results in low mammal densities. We assessed how agroforestry practises of an indigenous community affect terrestrial mammal abundance, diversity and assemblage composition within Krau Wildlife Reserve, Pahang, Malaysia. We used baited camera traps to compare mammal abundance and diversity between seven fruit gardens and eight control sites. Fruit gardens contained similar species richness and abundance levels but higher diversity and almost threefold higher mammal biomass. Fruit gardens contained five times as many fruit-producing trees and a positive correlation was found between the number of fruit trees and total mammal biomass. Mammal community composition differed between the two habitats, with fruit gardens attracting nine species of conservation concern. These results suggest that traditional agroforestry systems may provide additional resources for mammals and therefore their net effects should be considered in conservation policy

Resolving fluorescent species by their brightness and diffusion using correlated photon-counting histograms

Fluorescence fluctuation spectroscopy (FFS) refers to techniques that analyze fluctuations in the fluorescence emitted by fluorophores diffusing in a small volume and can be used to distinguish between populations of molecules that exhibit differences in brightness or diffusion. For example, fluorescence correlation spectroscopy (FCS) resolves species through their diffusion by analyzing correlations in the fluorescence over time; photon counting histograms (PCH) and related methods based on moment analysis resolve species through their brightness by analyzing fluctuations in the photon counts. Here we introduce correlated photon counting histograms (cPCH), which uses both types of information to simultaneously resolve fluorescent species by their brightness and diffusion. We define the cPCH distribution by the probability to detect both a particular number of photons at the current time and another number at a later time. FCS and moment analysis are special cases of the moments of the cPCH distribution, and PCH is obtained by summing over the photon counts in either channel. cPCH is inherently a dual channel technique, and the expressions we develop apply to the dual colour case. Using simulations, we demonstrate that two species differing in both their diffusion and brightness can be better resolved with cPCH than with either FCS or PCH. Further, we show that cPCH can be extended both to longer dwell times to improve the signal-to-noise and to the analysis of images. By better exploiting the information available in fluorescence fluctuation spectroscopy, cPCH will be an enabling methodology for quantitative biology

Character Strengths Among Youth

Four hundred and fiftynine students from 20 different high school classrooms in Michigan participated in focus group discussions about the character strengths included in the Values in Action Classification. Students were interested in the subject of good character and able to discuss with candor and sophistication instances of each strength. They were especially drawn to the positive traits of leadership, practical intelligence, wisdom, social intelligence, love of learning, spirituality, and the capacity to love and be loved. Students believed that strengths were largely acquired rather than innate and that these strengths developed through ongoing life experience as opposed to formal instruction. They cited an almost complete lack of contemporary role models exemplifying different strengths of character. Implications of these findings for the quantitative assessment of positive traits were discussed, as were implications for designing character education programs for adolescents. We suggest that peers can be an especially important force in encouraging the development and display of good character among youth.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45293/1/10964_2004_Article_379439.pd

Resolving fluorescent species by their brightness and diffusion properties using correlated photon counting histograms

In fluorescence fluctuation spectroscopy (FFS), fluctuations in the fluorescence signal emitted by molecules diffusing in a sample of interest are analyzed to extract information about the properties of the molecules themselves, such as their concentration, molecular brightness, or diffusion coefficients. Most techniques, however, either analyze correlations over time to extract information about the diffusion properties of the sample, or analyze fluctuations in the amplitude to extract information about the molecular brightness of the different species in the sample, but cannot do both. We first extend dual color photon counting histograms theory, a brightness analysis method that monitors the brightness of different species in two spectral channels, so that the observation volumes in each channel can be of different sizes, which is necessary if different excitation wavelengths are used for each channel. We then extend the theory so that the two channels can be shifted in time from one another, so that both correlation information and amplitude information can be extracted simultaneously. This new method, which we call correlated photon counting histograms (cPCH), is sensitive to both the brightness and diffusion properties of the sample of interest. We also derive expressions for the factorial cumulants of cPCH, which provide a convenient and efficient means of summarizing the information in the distributions. We show that fluorescence correlation spectroscopy (FCS) curves can be generated using the first joint moment of the cPCH distribution, while a regular photon counting histogram (PCH) can be generated at any time shift by summing over the photon counts in one channel. We show, using simulated data, that cPCH can resolve two different species with less uncertainty than either FCS or PCH if the two species differ in both their brightness and diffusion properties. If spectral information can be used as well, dual color cPCH can resolve two different species with less uncertainty than single channel cPCH. We develop a novel fitting algorithm that takes advantage of the analytical solutions to the factorial cumulant equations to sample the parameters that are consistent with the data, by resampling the measured factorial cumulants using the measured variances of each factorial cumulant. We show, using simulated data, that the parameter set that results in the minimum fit energy is often a poor estimate of the actual parameters used to create the data. We develop extensions to the theory that take into consideration triplet states, longer binning times, detector dead-times, and detector afterpulsing. Next, we extend cPCH so that it can be applied to images. Spatial cPCH allows both immobile and mobile species in a series of images to be resolved, using their brightness and diffusion properties, and can be used to generate spatiotemporal information about the different species in the sample. Finally, we develop models to take into account the effect that the photomultiplication process in analog photomultiplier tubes can have on the signal statistics, making fluorescence fluctuation spectroscopy techniques such as spatial cPCH available to a wider range of researchers.En spectroscopie de fluctuation de fluorescence (FFS), les fluctuations du signal de fluorescence émis par des molécules diffusantes dans un échantillon d'intérêt sont analysés pour extraire des informations sur les propriétés des molécules elles-mêmes, comme leur concentration, la luminosité moléculaire, ou des coefficients de diffusion. Cependant, la plupart des techniques analyse des corrélations dans le temps pour extraire des informations sur les propriétés de diffusion de l'échantillon, ou bien analyse les fluctuations de l'amplitude pour extraire des informations sur la luminosité moléculaire des différentes espèces dans l'échantillon, mais ne peut pas faire les deux. Nous étendons d'abord la théorie de histogramme de comptage de photons bicolores, une méthode d'analyse de la luminosité qui surveille la luminosité de différentes espèces dans deux canaux spectraux, de sorte que les volumes d'observation dans chaque canal peut être de tailles différentes, ce qui est nécessaire si des longueurs d'onde d'excitation différentes sont utilisées pour chaque canal. Ensuite nous améliorons la théorie de telle sorte que les deux canaux peuvent être décalées dans le temps les uns des autres, de sorte que les détails de corrélation et d'amplitude peuvent être extraits simultanément. Cette nouvelle méthode, que nous appelons histogramme de comptage de photons corrélés (cPCH), est sensible à la luminosité et des propriétés de diffusion de l'échantillon d'intérêt. Nous dérivons également des expressions pour les cumulants factoriels de cPCH, qui fournissent un moyen pratique et efficace de résumer l'information contenue dans les distributions. Nous montrons que les courbes de la spectroscopie de corrélation de fluorescence (FCS) peuvent être générées en utilisant le premier moment conjoint de la distribution cPCH, tandis qu'un histogramme de comptage de photons (PCH) régulier peut être généré à tout décalage dans le temps par sommation sur les comptages de photons dans un canal. Nous montrons, en utilisant des données simulées, que le cPCH peut résoudre deux espèces différentes avec moins d'incertitude que soit FCS ou PCH si les deux espèces sont distiguées à la fois par leur luminosité et leur propriétés de diffusion. Si l'information spectrale peut être utilisée aussi bien, cPCH bicolore peut résoudre deux espèces différentes avec moins d'incertitude que cPCH seul canal. Nous développons un nouvel algorithme de montage qui utilise des solutions analytiques aux équations cumulants factoriels pour échantillonner les paramètres qui sont compatibles avec les données, par rééchantillonnage des cumulants factoriels mesurées en utilisant les variances mesurées de chaque cumulant factoriel. Nous montrons, en utilisant des données simulées, que le jeu de paramètres qui se traduit par l'énergie d'ajustement minimale est souvent une mauvaise estimation des paramètres réels utilisés pour créer les données. Nous développons des extensions de la théorie qui considères les états triplets, les intervalles de classe plus longues, le temps morts du détecteur, et les post impulsions (afterpulsing) du détecteur. Ensuite, nous étendons cPCH afin qu'il puisse être appliqué aux images. cPCH spatiale permet de résoudre des espèces à la fois immobiles et mobiles dans une série d'images, à l'aide de leur luminosité et de leurs propriétés de diffusion, et peut être utilisé pour générer des informations spatio-temporelle sur les différentes espèces dans l'échantillon. Enfin, nous développons des modèles pour tenir compte de l'effet que le processus de photomultiplication dans les tubes photomultiplicateurs analogues peut avoir sur les statistiques du signal, ce qui rend les techniques de spectroscopie de fluctuation de fluorescence comme cPCH spatiale disponible à un éventail de chercheurs plus large