How unfair is private learning ?

As machine learning algorithms are deployed on sensitive data in critical decision making processes, it is becoming increasingly important that they are also private and fair. In this paper, we show that, when the data has a long-tailed structure, it is not possible to build accurate learning algorithms that are both private and results in higher accuracy on minority subpopulations. We further show that relaxing overall accuracy can lead to good fairness even with strict privacy requirements. To corroborate our theoretical results in practice, we provide an extensive set of experimental results using a variety of synthetic, vision (CIFAR10 and CelebA), and tabular (Law School) datasets and learning algorithms.Comment: Accepted as an Oral paper in UAI '2022, Major update on 23 Dec, 202

Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations

Large language models (LLMs) can generate fluent natural language texts when given relevant documents as background context. This ability has attracted considerable interest in developing industry applications of LLMs. However, LLMs are prone to generate hallucinations that are not supported by the provided sources. In this paper, we propose a hierarchical framework to detect and mitigate such ungrounded hallucination. Our framework uses Chain of Natural Language Inference (CoNLI) for hallucination detection and hallucination reduction via post-editing. Our approach achieves state-of-the-art performance on hallucination detection and enhances text quality through rewrite, using LLMs without any fine-tuning or domain-specific prompt engineering. We show that this simple plug-and-play framework can serve as an effective choice for hallucination detection and reduction, achieving competitive performance across various contexts.Comment: The source code is available at https://github.com/microsoft/CoNLI_hallucinatio

Dermal Sensory Regenerative Peripheral Nerve Interface for Reestablishing Sensory Nerve Feedback in Peripheral Afferents in the Rat

Background: Without meaningful, intuitive sensory feedback, even the most advanced myoelectric devices require significant cognitive demand to control. The dermal sensory regenerative peripheral nerve interface (DS-RPNI) is a biological interface designed to establish high-fidelity sensory feedback from prosthetic limbs. Methods: DS-RPNIs were constructed in rats by securing fascicles of residual sensory peripheral nerves into autologous dermal grafts, with the objectives of confirming regeneration of sensory afferents within DS-RPNIs and establishing the reliability of afferent neural response generation with either mechanical or electrical stimulation. Results: Two months after implantation, DS-RPNIs were healthy and displayed well-vascularized dermis with organized axonal collaterals throughout and no evidence of neuroma. Electrophysiologic signals were recorded proximal from DS-RPNI's sural nerve in response to both mechanical and electrical stimuli and compared with (1) full-thickness skin, (2) deepithelialized skin, and (3) transected sural nerves without DS-RPNI. Mechanical indentation of DS-RPNIs evoked compound sensory nerve action potentials (CSNAPs) that were like those evoked during indentation of full-thickness skin. CSNAP firing rates and waveform amplitudes increased in a graded fashion with increased mechanical indentation. Electrical stimuli delivered to DS-RPNIs reliably elicited CSNAPs at low current thresholds, and CSNAPs gradually increased in amplitude with increasing stimulation current. Conclusions: These findings suggest that afferent nerve fibers successfully reinnervate DS-RPNIs, and that graded stimuli applied to DS-RPNIs produce proximal sensory afferent responses similar to those evoked from normal skin. This confirmation of graded afferent signal transduction through DS-RPNI neural interfaces validate DS-RPNI's potential role of facilitating sensation in human-machine interfacing. Clinical Relevance Statement: The DS-RPNI is a novel biotic-abiotic neural interface that allows for transduction of sensory stimuli into neural signals. It is expected to advance the restoration of natural sensation and development of sensorimotor control in prosthetics.</p

Selection response and estimation of the genetic parameters for multidimensional measured breast meat yield related traits in a long-term breeding Pekin duck line

Objective This study was conducted to estimate the genetic parameters and breeding values of breast meat related traits of Pekin ducks. Selection response was also determined by using ultrasound breast muscle thickness (BMT) measurements in combination with bosom breadth (BB) and keel length (KL) values. Methods The traits analyzed were breast meat weight (BMW), body weight (BW), breast meat percentage (BMP) and the three parameters of breast meat (BB, KL, and BMT). These measurements were derived from studying 15,781 Pekin ducks selected from 10 generations based on breast meat weight. Genetic parameters and breeding value were estimated for the analysis of the breeding process. Results Estimated heritability of BMW and BMP were moderate (0.23 and 0.16, respectively), and heritability of BW was high (0.48). Other traits such as BB, KL, and BMT indicated moderate heritability ranging between 0.11 and 0.28. Significant phenotypic correlations of BMW with BW and BMP were discovered (p<0.05), and genetic correlations of BMW with BW and BMP were positive and high (0.83 and 0.66, respectively). It was noted that BMW had positive correlations with all the other traits. Generational average estimated breeding values of all traits increased substantially over the course of selection, which demonstrated that the ducks responded efficiently to increased breast meat yield after 10 generations of breeding. Conclusion The results indicated that duck BMW had the potential to be increased through genetic selection with positive effects on BW and BMP. The ultrasound BMT, in combination with the measurement of BB and KL, is shown to be essential and effective in the process of high breast meat yield duck breeding

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Development of innovative biosensors for the determination of melamine in milk

After discovering the potential detriment of melamine to humans, the society calls for novel techniques to accomplish accurate, rapid, high-throughput, and on-line or in-field detection of melamine in foods, as required by the food industry and government laboratories. The aim of this study was to investigate different innovative biosensors combining antibodies or molecularly imprinted polymers (MIPs) with surface enhanced Raman spectroscopy (SERS) to determine melamine in a representative dairy product (i.e. milk). A “two-step” antibody-SERS biosensor was developed to detect melamine in whole milk. The anti-melamine antibody, produced by immunizing New Zealand white rabbits with melamine hapten-ovalbumin immunogen, was used to extract melamine from whole milk exclusively. After releasing melamine from the antibody, the eluents were deposited onto silver dendrite SERS-active substrate for SERS spectral collection. The limit of detection (LOD) calculated by the principal component analysis (PCA) model was lower than 0.79×10-³ mmol/L. The overall analysis was completed in 20 min. The MIP for the “two-step” MIPs-SERS biosensor was synthesized by bulk polymerization of melamine, methacrylic acid, ethylene glycol dimethacrylate and 2,2’-azobisisobutyronitrile. After confirming the specific affinity of the MIP towards melamine by adsorption capacity tests, MIP was used as sorbent for solid phase extraction (SPE) to extract melamine from whole milk. SERS spectra were collected by depositing the eluents from MISPE onto silver dendrite. The LOD and limit of quantification (LOQ) calculated by the linear regression model correlating relative intensity of melamine SERS band at 703 cm-¹ and melamine concentration in whole milk were 0.012 mmol L-¹ and 0.039 mmol L-¹, respectively, and the full analysis was accomplished in 18 min. “One-step” MIPs-SERS biosensor incorporated silver nanoparticles (AgNPs) into MIPs synthesized by bulk polymerization. Adsorption capacity tests verified the specific affinity of MIPs-AgNPs to melamine, and PCA model resulted in the LOD between 0.01 and 0.017 mmol L-¹ melamine in skim milk. The time required to detect melamine in skim milk was 25 min. The low LOD and LOQ, as well as rapid detection confirm the potential of applying these three types of biosensors for accurate and high-throughput detection of melamine in dairy products.Land and Food Systems, Faculty ofGraduat

Development of innovative techniques for food authentication - the last barrier to prevent food fraud

Food fraud was estimated to cost the global food industry $10-15 billion per year. Various traceability and risk assessment systems have been developed to deter food fraud. With rapid globalization and complex supply chain, effective product tracing and tracking and accurate vulnerability assessment have been inevitably hindered. Serving as the last barrier to ensure food authenticity, reliable techniques to identify fraudulent foods are indispensable. Traditional techniques (e.g. liquid chromatography-based assays) are usually time-consuming, labor- intensive, lack the sensitivity and/or specificity, and/or complicated. Therefore, the overall objective of my Ph.D. thesis project was to validate the feasibility of spectroscopic techniques [i.e. Raman, mid-infrared, and nuclear magnetic resonance (NMR) spectroscopies] and an advanced DNA amplification method [i.e. loop-mediated isothermal amplification (LAMP)] to detect food fraud. During my studies, an optimized protocol was developed to authenticate ground beef meat and identify and quantify the offal adulterants using mid-infrared spectroscopy coupled with chemometric models, providing a limit of detection (LOD) <10% w/w of offal in ground beef meat. With simple/no sample pretreatment, solution NMR spectroscopy and solid-state NMR spectroscopy were confirmed to detect 6.7 and 128.6 mg/kg Sudan I in paprika powder in <30 min, respectively. A confocal micro-Raman spectrometer and a portable Raman spectrometer were applied to identify 11 species of raw finfish purchased from seafood markets in Vancouver. The method developed using the portable Raman spectrometer exhibited promising results with a 100% accuracy in differentiating Salmonidae and non-Salmonidae, 88% accuracy in identifying four species of salmon, and 91% accuracy in separating the seven species of non-Salmonidae fish. A LAMP-based assay was developed to authenticate pure pomegranate juice from juice adulterated by apple and/or grape juice. With a novel paper-based DNA extraction device and a simple LAMP result visualization method, the overall sample-to-result analysis was completed in ~1 h with a LOD of 10~100 ng of DNA. Methods and devices developed in my studies may be used to authenticate many other food commodities and have the potential to be adopted by governmental laboratories, food industries and even consumers to rapidly authenticate food products, and thus better ensure the integrity of foods.Land and Food Systems, Faculty ofGraduat

The impact of market imperfections on stock prices : an empirical analysis of AH price premium in China

With the development of financial market, many Chinese companies have taken the opportunity to go public by listing on various stock exchanges. Besides mainland China, many companies have chosen Hong Kong Stock Exchange to benefit from its international recognition and its tie with mainland China. Contrary to what has been observed in other countries with similar dual-class structures, H shares that are open for off-board investors in Hong Kong trade at a discount relative to A shares that are restricted to domestic investors in mainland China. Such price difference, known as ”Puzzles in the Chinese Stock Market”, exists persistently at both cross-sectional and time-series horizon. We argue that price difference between A shares and H shares could be a result of market imperfections in China. Structural market imperfections, such as asymmetric information, speculative motive, liquidity preference, market sentiment and differential demand, have explanatory power in the existence of AH price premium. We utilize Ordinary Least Squared (OLS) estimation and Generalized Method of Moment (GMM) estimation to study all existing hypotheses thoroughly. Moreover, we argue that capital control in China plays a unique and significant role in interpreting AH price premium. By using panel data, our empirical results reinforce most of existing hypotheses and provide strong quantitative support for proposed hypotheses of capital control. We find, for example, a company with higher oversea revenue ratio tends to have lower AH price premium.Bachelor of Arts in Economic