Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing for Contestation: Insights from Administrative Law

Algorithmic decision-making systems are increasingly being deployed to make, or to support humans to make, decisions that impact people’s lives in significant ways. Yet, decision subjects, those affected by algorithmic decisions, can be limited in their ability to contest these decisions. For example, the Education Value-Added Assessment System (EVAAS), a statistical method used to predict academic growth, was used by the Houston Independent School District to evaluate teachers’ performance and, in a number of cases, to terminate teachers’ contracts. Twelve teachers and the Houston Federation of Teachers successfully argued in court that the teachers’ constitutional right to due process was violated because they were unable to contest, or ‘meaningfully challenge’, the termination of their contracts because there was a ‘lack of sufficient information’ — the private company that designed EVAAS would not release the source codes or methodology used as they were proprietary trade secrets [1]. Even for decision subjects who are able to understand why a decision has been made and are provided with means to contest that decision, contestation systems can be seen as severely lacking [12]. Sarah Myers West studied content moderation across a number of social media platforms, most of which offered a way for users to contest a decision to remove their content from the platform [12]. Myers West reported user dissatisfaction with the contestation systems for a number of reasons, including a lack of clear instruction about how to lodge an appeal, no reply being received, no resolution being reached after a challenge has been lodged, and a lack of access to human intervention. These examples demonstrate that being able to challenge algorithmic decisions is important to decision subjects, yet numerous factors can limit a person’s ability to contest such decisions. We propose that administrative law systems, which were created to ensure that governments are kept accountable for their actions and decision making in relation to individuals [5, 6], can provide guidance on how to design contestation systems for algorithmic decision-making. There are similarities between government decision-making and algorithmic decision-making that suggest there is value in considering how the administrative law system enables contestation. For example, in both cases decision making can be said to occur ‘behind closed doors’, which limits
Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator

Unsupervised image-to-image translation is used to transform images from a source domain to generate images in a target domain without using source-target image pairs. Promising results have been obtained for this problem in an adversarial setting using two independent GANs and attention mechanisms. We propose a new method that uses a single shared discriminator between the two GANs, which improves the overall efficacy. We assess the qualitative and quantitative results on image transfiguration, a cross-domain translation task, in a setting where the target domain shares similar semantics to the source domain. Our results indicate that even without adding attention mechanisms, our method performs at par with attention-based methods and generates images of comparable quality.
Learning from Shader Program Traces

Deep learning for image processing typically treats input imagery as pixels in some color space. This paper proposes instead to learn from program traces of procedural fragment shaders – programs that generate images. At each pixel, we collect the intermediate values computed at program execution, and these data form the input to the learned model. We investigate this learning task for a variety of applications: our model can learn to predict a low‐noise output image from shader programs that exhibit sampling noise; this model can also learn from a simplified shader program that approximates the reference solution with less computation, as well as learn the output of postprocessing filters like defocus blur and edge‐aware sharpening. Finally we show that the idea of learning from program traces can even be applied to non‐imagery simulations of flocks of boids. Our experiments on a variety of shaders show quantitatively and qualitatively that models learned from program traces outperform baseline models learned from RGB color augmented with hand‐picked shader‐specific featues like normals, depth, and diffuse and specular color. We also conduct a series of analyses that show certain features within the trace are more important, and even learning from a small subset of the trace outperforms the baselines.
Feature Engineering for Scalable Application-Level Post-Silicon Debugging

We present systematic and efficient solutions for both observability enhancement and root-cause diagnosis of post-silicon System-on-Chips (SoCs) validation with diverse usage scenarios. We model specification of interacting flows in typical applications for message selection. Our method for message selection optimizes flow specification coverage and trace buffer utilization. We define the diagnosis problem as identifying buggy traces as outliers and bug-free traces as inliers/normal behaviors, for which we use unsupervised learning algorithms for outlier detection. Instead of direct application of machine learning algorithms over trace data using the signals as raw features, we use feature engineering to transform raw features into more sophisticated features using domain specific operations. The engineered features are highly relevant to the diagnosis task and are generic to be applied across any hardware designs. We present debugging and root cause analysis of subtle post-silicon bugs in industry-scale OpenSPARC T2 SoC. We achieve a trace buffer utilization of 98.96\% with a flow specification coverage of 94.3\% (average). Our diagnosis method was able to diagnose up to 66.7\% more bugs and took up to 847$\times$ less diagnosis time as compared to the manual debugging with a diagnosis precision of 0.769.
Establishing and Maintaining a Reliable Optical Wireless Communication in Underwater Environment

This paper proposes the trajectory tracking problem between an autonomous underwater vehicle (AUV) and a mobile surface ship, both equipped with optical communication transceivers. The challenging issue is to maintain stable connectivity between the two autonomous vehicles within an optical communication range. We define a directed optical line-of-sight (LoS) link between the two vehicle systems. The transmitter is mounted on the AUV, while the surface ship is equipped with an optical receiver. However, this optical communication channel needs to preserve a stable transmitter-receiver position to reinforce service quality, which typically includes a bit rate and bit error rates. A cone-shaped beam region of the optical receiver is approximated based on the channel model; then, a minimum bit rate is ensured if the AUV transmitter remains inside of this region. Additionally, we design two control algorithms for the transmitter to drive the AUV to the angle of the maximum achievable data rate and maintain it in the cone-shaped beam region and under an uncertain oceanic environment. Lyapunov function-based analysis that ensures asymptotic stability of the resulting closed-loop tracking error is used to design the proposed Non-linear Proportional and Derivative (NLPD) controller. Numerical simulations are performed using MATLAB/Simulink to show the controllers’ ability to achieve favorable tracking in the presence of the solar background noise within competitive times. Finally, results demonstrate the proposed NLPD controller improves the tracking error performance more than 70% under nominal conditions and 35% with model uncertainties and disturbances compared to the original Proportional and Derivative (PD) strategy.
Pairwise Weights for Temporal Credit Assignment

How much credit (or blame) should an action taken in a state get for a future reward? This is the fundamental temporal credit assignment problem in Reinforcement Learning (RL). One of the earliest and still most widely used heuristics is to assign this credit based on a scalar coefﬁcient λ (treated as a hyperparameter) raised to the power of the time interval between the state-action and the reward. In this empirical paper, we explore heuristics based on more general pairwise weight-ings that are functions of the state in which the action was taken, the state at the time of the reward, as well as the time interval between the two. Of course it isn’t clear what these pairwise weight functions should be, and because they are too complex to be treated as hyperparameters we develop a metagradient procedure for learning these weight functions during the usual RL training of a policy. Our empirical work shows that it is often possible to learn these pairwise weight functions during learning of the policy to achieve better performance than competing approaches.
A Numerical-Analytical Method for Constructing Periodic Solutions of the Lorenz System

This article describes a method for constructing approximations to periodic solutions of dynamic Lorenz system with classical values of the system parameters. The author obtained a system of nonlinear algebraic equations in general form concerning of the cyclic frequency, constant terms and amplitudes of harmonics that make up harmonic approximations to the desired solutions. The initial approximation for the Newton method is selected, which converges to a solution describing a periodic solution diﬀerent from the equilibrium posi-tion. The results of a computational experiment are presented. The results are veriﬁed using high-precision calculations.
Hallmarks of Human-Machine Collaboration: A framework for assessment in the DARPA Communicating with Computers Program

There is a growing desire to create computer systems that can communicate effectively to collaborate with humans on complex, open-ended activities. Assessing these systems presents significant challenges. We describe a framework for evaluating systems engaged in open-ended complex scenarios where evaluators do not have the luxury of comparing performance to a single right answer. This framework has been used to evaluate human-machine creative collaborations across story and music generation, interactive block building, and exploration of molecular mechanisms in cancer. These activities are fundamentally different from the more constrained tasks performed by most contemporary personal assistants as they are generally open-ended, with no single correct solution, and often no obvious completion criteria. We identified the Key Properties that must be exhibited by successful systems. From there we identified"Hallmarks"of success -- capabilities and features that evaluators can observe that would be indicative of progress toward achieving a Key Property. In addition to being a framework for assessment, the Key Properties and Hallmarks are intended to serve as goals in guiding research direction.
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge and may severely deteriorate the generalization performance. In this paper, we investigate and identify the limitation of several decentralized optimization algorithms for different degrees of data heterogeneity. We propose a novel momentum-based method to mitigate this decentralized training difficulty. We show in extensive empirical experiments on various CV/NLP datasets (CIFAR-10, ImageNet, and AG News) and several network topologies (Ring and Social Network) that our method is much more robust to the heterogeneity of clients' data than other existing methods, by a significant improvement in test performance ($1\% \! -\! 20\%$). Our code is publicly available.
Robust Bandit Learning with Imperfect Context

A standard assumption in contextual multi-arm bandit is that the true context is perfectly known before arm selection. Nonetheless, in many practical applications (e.g., cloud resource management), prior to arm selection, the context information can only be acquired by prediction subject to errors or adversarial modification. In this paper, we study a novel contextual bandit setting in which only imperfect context is available for arm selection while the true context is revealed at the end of each round. We propose two robust arm selection algorithms: MaxMinUCB (Maximize Minimum UCB) which maximizes the worst-case reward, and MinWD (Minimize Worst-case Degradation) which minimizes the worst-case regret. Importantly, we analyze the robustness of MaxMinUCB and MinWD by deriving both regret and reward bounds compared to an oracle that knows the true context. Our results show that as time goes on, MaxMinUCB and MinWD both perform as asymptotically well as their optimal counterparts that know the reward function. Finally, we apply MaxMinUCB and MinWD to online edge datacenter selection, and run synthetic simulations to validate our theoretical analysis.
Consensus Based Multi-Layer Perceptrons for Edge Computing

In recent years, storing large volumes of data on distributed devices has become commonplace. Applications involving sensors, for example, capture data in different modalities including image, video, audio, GPS and others. Novel algorithms are required to learn from this rich distributed data. In this paper, we present consensus based multi-layer perceptrons for resource-constrained devices. Assuming nodes (devices) in the distributed system are arranged in a graph and contain vertically partitioned data, the goal is to learn a global function that minimizes the loss. Each node learns a feed-forward multi-layer perceptron and obtains a loss on data stored locally. It then gossips with a neighbor, chosen uniformly at random, and exchanges information about the loss. The updated loss is used to run a back propagation algorithm and adjust weights appropriately. This method enables nodes to learn the global function without exchange of data in the network. Empirical results reveal that the consensus algorithm converges to the centralized model and has performance comparable to centralized multi-layer perceptrons and tree-based algorithms including random forests and gradient boosted decision trees.
Black-Box Model Risk in Finance

Machine learning models are increasingly used in a wide variety of financial settings. The difficulty of understanding the inner workings of these systems, combined with their wide applicability, has the potential to lead to significant new risks for users; these risks need to be understood and quantified. In this sub-chapter, we will focus on a well studied application of machine learning techniques, to pricing and hedging of financial options. Our aim will be to highlight the various sources of risk that the introduction of machine learning emphasises or de-emphasises, and the possible risk mitigation and management strategies that are available. <br>
On Permutation Invariant Training For Speech Source Separation

We study permutation invariant training (PIT), which targets at the permutation ambiguity problem for speaker independent source separation models. We extend two state-of-the-art PIT strategies. First, we look at the two-stage speaker separation and tracking algorithm based on frame level PIT (tPIT) and clustering, which was originally proposed for the STFT domain, and we adapt it to work with waveforms and over a learned latent space. Further, we propose an efficient clustering loss scalable to waveform models. Second, we extend a recently proposed auxiliary speaker-ID loss with a deep feature loss based on "problem agnostic speech features", to reduce the local permutation errors made by the utterance level PIT (uPIT). Our results show that the proposed extensions help reducing permutation ambiguity. However, we also note that the studied STFT-based models are more effective at reducing permutation errors than waveform-based models, a perspective overlooked in recent studies.
Vertical structure of conventionally neutral atmospheric boundary layers

Significance The presented model describes the vertical structure of conventionally neutral atmospheric boundary layers. Due to the complicated interplay between buoyancy, shear, and Coriolis effects, analytical descriptions have been limited to the mean wind speed. We introduce an analytical approach based on the Ekman equations and the basis function of the universal potential temperature flux profile that allows one to describe the wind and turbulent shear stress profiles and hence capture features like the wind veer profile. The analytical profiles are validated against high-fidelity large-eddy simulations and atmospheric measurements. Our findings contribute to the scientific community’s fundamental understanding of atmospheric turbulence with direct relevance for weather forecasting, climate modeling, and wind energy applications.
Dynamic Properties of Temperature-Sensitive Poly(N-isopropylacrylamide) Gel Cross-Linked through Siloxane Linkage

A gel is a water-insoluble polymeric material though it can absorb a lot of water in its network structure. The poly(N-isopropylacrylamide) (PNIPA) gel exhibits a change in volume in response to external temperature changes and this property is useful in many fields. In this manuscript, a novel PNIPA gel was synthesized by using ethylene triethoxy silane (ETEOS) as the cross-linking agent under acidic conditions. This siloxane incorporated PNIPA gel achieves a dramatically rapid response rate when the external temperature is changed. We suggest that the improvement of the response rate could be due to the macroporous structure of the produced polymer network during the polymerization/cross-linking process. This macroporous structure can provide interconnected water release channels and prevent the formation of a dense, thick skin layer at the shrinking course. On the other hand, water can also diffuse into the polymer network through these water channels during the reswelling process.
Characterization and In Vitro Evaluations of Injectable Calcium Phosphate Cement Doped with Magnesium and Strontium

Injectable calcium phosphate cement is a promising biomaterial for hard tissue repair due to its osteoinductivity, biocompatibility properties, and its use to correct defect areas involving narrow cavities with limited accessibility by the minimally invasive technique. Microwave-synthesized hydroxyapatite (HA) was used for the preparation of cement. In recent years, both magnesium and strontium calcium phosphate cements have exhibited rapid setting, improved mechanical strength, and a good resorption rate. A big step still remains to develop injectable magnesium and strontium phosphate cements with ideal self-setting properties, adequate mechanical strength, and good biocompatibility for clinical applications. In this study, both magnesium and strontium were doped with synthesized semiamorphous and crystalline hydroxyapatite (HA). The powder mixture was mixed with Na2HPO4, NaH2PO4, and a carboxymethyl cellulose (CMC) solution to develop the novel magnesium and strontium calcium phosphate cement. The setting time, physiochemical properties of hardened cement, microstructure, mechanical strength, and injectability of the prepared cement were studied. The toxicity evaluation and cell adhesion, which are necessary to identify the suitability of the material for different applications, were quantified and investigated using fibroblast cells. The setting time of cement was reduced substantially for magnesium- or strontium-doped cement by 2 min. The phase composition of the hardened cement expresses the semiamorphous or crystalline phase of HA with additives. Smooth and complete injection of cement paste was observed in semiamorphous HA-based cement. The intercellular reactive oxygen stress (ROS) of the Sr2+-doped cement sample showed varied degrees of toxicity to cells in terms of different concentrations. The Mg2+-doped cement showed significant attachment of cells after treatment at varying incubation times.
TNRC6C Functions as a Tumor Suppressor and Is Frequently Downregulated in Papillary Thyroid Cancer

Our previous study found that trinucleotide repeat containing adaptor 6C (TNRC6C) may act as a tumor suppressor in papillary thyroid cancer (PTC). In this study, we aimed to confirm the effect of TNRC6C on PTC and investigate the underlying molecular mechanism. The difference of mRNA level of TNRC6C between PTC tissue and noncancerous thyroid tissue and the association of expression level of TNRC6C with clinicopathological features of PTC were analyzed using TCGA data. Immunohistochemical assay was performed to detect the protein expression of TNRC6C in PTC and its adjacent noncancerous tissue. Cell proliferation, migration, invasion, and apoptosis were analyzed after knockdown or overexpression of TNRC6C in BCPAP cells. RNA-sequencing was performed to find the target genes of TNRC6C, and potential targets were validated in BCPAP and TPC1 cells. Our results showed that TNRC6C was downregulated in PTC, and lower expression level of TNRC6C was associated with worse clinicopathological features. Overexpression of TNRC6C significantly inhibited proliferation, migration, and invasion of BCPAP cells and promoted its apoptosis, while knockdown of TNRC6C acted the opposite role. By analyzing RNA-sequencing data and TCGA data, 12 genes (SCD, CRLF1, APCDD1L, CTHRC1, PTPRU, ALDH1A3, VCAN, TNC, ECE1, COL1A1, CAMK2N2, and MMP14) were considered as potential target genes of TNRC6C, and most of them were associated with clinicopathological features of PTC in TCGA. All of them except CAMK2N2 were significantly downregulated after overexpressing TNRC6C. Our study demonstrated that TNRC6C functions as a tumor suppressor in PTC and may serve as a useful therapeutic target and prognostic marker for PTC patients.