Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning the Latent “ Look ” : Unsupervised Discovery of a Style-Coherent Embedding from Fashion Images ( Supplementary material )

In our main paper in Section 4.1, we show top images for the five discovered topics on the HipsterWars dataset [1] using our method; here, we show the corresponding top images for the StyleNet CNN [3] and attribute clusters. Figure 1a are results for CNN clusters: Goth, Bohemian, and Preppy styles are succesfully discovered; however, there are 2 variants of Goth, one for pants and one for skirt, thus the baseline sacrifices discovering Pinup style. Figure 1b shows pattern material shape collar article color
Off-Policy Evaluation in Embedded Spaces

Off-policy evaluation methods are important in recommendation systems and search engines, whereby data collected under an old logging policy is used to predict the performance of a new target policy. However, in practice most systems are not observed to recommend most of the possible actions, which is an issue since existing methods require that the probability of the target policy recommending an item can only be non-zero when the probability of the logging policy is non-zero (known as absolute continuity). To circumvent this issue, we explore the use of action embeddings. By representing contexts and actions in an embedding space, we are able to share information to extrapolate behaviors for actions and contexts previously unseen.
Paradoxes of probabilistic programming And how to condition on events of measure zero with infinitesimal probabilities

Probabilistic programming languages allow programmers to write down conditional probability distributions that represent statistical and machine learning models as programs that use observe statements. These programs are run by accumulating likelihood at each observe statement, and using the likelihood to steer random choices and weigh results with inference algorithms such as importance sampling or MCMC. We argue that naive likelihood accumulation does not give desirable semantics and leads to paradoxes when an observe statement is used to condition on a measure-zero event, particularly when the observe statement is executed conditionally on random data. We show that the paradoxes disappear if we explicitly model measure-zero events as a limit of positive measure events, and that we can execute these type of probabilistic programs by accumulating infinitesimal probabilities rather than probability densities. Our extension improves probabilistic programming languages as an executable notation for probability distributions by making it more well-behaved and more expressive , by allowing the programmer to be explicit about which limit is intended when conditioning on an event of measure zero.
AN EVALUATION OF METHODS FOR STANDARDIZING CATCH RATES OF HIGHLY MIGRATORY BY-CATCH SPECIES

To accurately estimate the relative abundance of highly migratory by-catch species from fishery-dependent data, catch-per-unit-effort (CPUE) must be standardized to account for factors that may have impacted CPUE but are not related to changes in abundance. Two commonly applied methods for standardizing CPUE include generalized linear models (GLMs), and habitat-based standardizations (e.g., statHBS). Here, we evaluate the accuracy of these methods in the presence of variable vertical catchability and changes in target fishing depth. Bycatch landings were simulated to follow the general effort dynamics of the Japanese longline fishery in the Atlantic Ocean and a range of assumptions regarding population trajectories and catchability by habitat. Estimated hook depth was calculated without error in this simulation and for most scenarios, the statHBS model provided the most accurate estimates of relative abundance. However, in practice, error in estimates of hook depth will impact the performance of statHBS. Thus, future work evaluating the impact of hook depth uncertainty on the accuracy of the statHBS model, and alternative approaches to considering habitat in GLMs is warranted.
Object Selection under Team Context

Context-aware database has drawn increasing attention from both industry and academia recently by taking users' current situation and environment into consideration. However, most of the literature focus on individual context, overlooking the team users. In this paper, we investigate how to integrate team context into database query process to help the users' get top-ranked database tuples and make the team more competitive. We introduce naive and optimized query algorithm to select the suitable records and show that they output the same results while the latter is more computational efficient. Extensive empirical studies are conducted to evaluate the query approaches and demonstrate their effectiveness and efficiency.
Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems

Although AI has significant potential to transform society, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and guidelines for responsible AI have been issued recently. However, these principles are high-level and difficult to put into practice. In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis. Responsible AI issues go beyond data and algorithms and are often at the system-level crosscutting many system components and the entire software engineering lifecycle. Based on the result of a systematic literature review, this paper identifies one missing element as the system-level guidance - how to design the architecture of responsible AI systems. We present a summary of design patterns that can be embedded into the AI systems as product features to contribute to responsible-AI-by-design.
Structure from Action: Learning Interactions for 3D Articulated Object Structure Discovery

We introduce Structure from Action (SfA), a framework to discover 3D part geometry and joint parameters of unseen articulated objects via a sequence of inferred interactions. Our key insight is that 3D interaction and perception should be considered in conjunction to construct 3D articulated CAD models, especially for categories not seen during training. By selecting informative interactions, Sf A discovers parts and reveals occluded surfaces, like the inside of a closed drawer. By aggregating visual observations in 3D, Sf A accurately segments multiple parts, reconstructs part geometry, and infers all joint parameters in a canonical coordinate frame. Our experiments demonstrate that a Sf A model trained in simulation can generalize to many unseen object categories with diverse structures and to real-world objects. Empirically, Sf A outperforms a pipeline of state-of-the-art components by 25.4 3D IoU percentage points on unseen categories, while matching already performant joint estimation baselines.11For code, data, and videos, see sfa.cs. columbia.edu/
Generalized Multiple Access Channels with Confidential Messages

A discrete memoryless generalized multiple access channel (GMAC) with confidential messages is studied, where two users attempt to transmit common information to a destination and each user also has private (confidential) information intended for the destination. The two users are allowed to receive channel outputs, and hence may obtain the confidential information sent by each other from channel outputs they receive. However, each user views the other user as a wire-tapper, and wishes to keep its confidential information as secret as possible from the other user. The level of secrecy of the confidential information is measured by the equivocation rate, i.e., the entropy rate of the confidential information conditioned on channel outputs at the wire-tapper. The performance measure of interest for the GMAC with confidential messages is the rate-equivocation tuple that includes the common rate, two private rates and two equivocation rates as components. The set that includes all these achievable rate-equivocation tuples is referred to as the capacity-equivocation region. The GMAC with one confidential message set is first studied, where only one user (user 1) has private (confidential) information for the destination. Inner and outer bounds on the capacity-equivocation region are derived, and the capacity-equivocation are established for some classes of channels including the Gaussian GMAC. Furthermore, the secrecy capacity region is established, which is the set of all achievable rates with user 2 being perfectly ignorant of confidential messages of user 1. For the GMAC with two confidential message sets, where both users have confidential messages for the destination, an inner bound on the capacity-equivocation region is obtained.
Robust hierarchical image representation using non-negative matrix factorization with sparse code shrinkage preprocessing

When analyzing patterns, our goals are (i) to find structure in the presence of noise, (ii) to decompose the observed structure into sub-components, and (iii) to use the components for pattern completion. Here, a novel loop architecture is introduced to perform these tasks in an unsupervised manner. The architecture combines sparse code shrinkage with non-negative matrix factorization and blends their favorable properties: Sparse code shrinkage aims to remove Gaussian noise in a robust fashion; Non-negative matrix factorization extracts sub-structures from the noise filtered inputs. The loop architecture performs robust pattern completion when organized into a two-layered hierarchy. We demonstrate the power of the proposed architecture on the so-called ‘bar-problem’ and on the Feret facial database.
Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video

Fig. 1. This figure shows three thin structured objects reconstructed using our method, together with selected frames of the input videos. Using an RGB video as input, our method performs curve-based camera pose estimation and reconstructs complex 3D thin structures in better quality than other existing methods. See the comparisons with the other methods on the bucket model and the hanger model in Section 5.5.
Control-NeRF: Editable Feature Volumes for Scene Rendering and Manipulation

where vs = S(Vs,p) is the 64-dimensional sampled feature vector from the volume Vs for scene s at point p ∈ R, and S represents the trilinear sampling operation. The density values sampled from the network can thus be used to determine the probability of a ray terminating at the sampled point along the ray. In practice, following the example of [6], we use a discretized approximation of this integral, using a 2-stage process in which we optimize a coarse network Ĉc(r) that samples 64 points from evenly spaced bins along the ray length, followed by sampling these points plus another 64 points from our fine network Ĉf (r) using the coarse network opacity results to sample from more relevant portions of the scene volume (see Sec. 5.2 of [6]. For our experiments, the networks are trained using 1024 rays per batch sampled from the LLFF [5] multi-view image datasets, scaled to a resolution of 504 × 378. 1 The
Big Data Mining and Analytics Big Data Mining and Analytics

: Due to the widespread availability of implicit feedback (e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback, implicit feedback cannot directly reﬂect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative ﬁltering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems, some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative ﬁltering. However, there is as yet no research combining collaborative ﬁltering and content-based recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders (DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative ﬁltering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods.
MODERN DEVLETİN DÖNÜŞÜMÜNDE BİR ARA DÖNEM: SOSYAL REFAH DEVLETİ

Bir kavram ve kurum olarak devletin nitelikleri, kokeni, bicimi, sinirlari vs. tartisilagelmistir. Bu tartismalara devletin tanimi da dahildir ve farkli tarihsel donemlerde farkli devlet tanimlari da bu nedenlerle kabul edilmistir. Devlet bicimleri arasinda 20. yuzyilda en fazla uzerinde tartismalar olan Sosyal refah devleti olmustur. Modern devletin donusumunde onemli bir evre olarak 1945-1975 arasi gelismis Bati toplumlarinda hakim olan refah devleti, kapitalizmin esitsizliklerini azaltmis, sosyal guvenlik sistemini yerlestirmis, vatandaslarina koruyucu bir semsiye gorevi gormustur. Ancak 1970’li yillarla birlikte dunya ekonomisindeki krizler ve siyasal degismeler refah devleti politikalarinin da terk edilmeye baslanmasina neden olmustur.
Bayesian Additive Regression Trees

We develop a Bayesian \sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fltting and inference are accomplished via an iterative Bayesian backfltting MCMC algorithm that generates samples from a posterior. Efiectively, BART is a nonparametric Bayesian regression approach which uses dynamic random basis elements that are dimensionally adaptive. BART is motivated by ensemble methods in general, and boosting algorithms in particular. However, BART is deflned by a statistical model: a prior and a likelihood, while boosting is deflned by an algorithm. This model-based approach enables a full assessment of prediction uncertainty while remaining highly competitive in terms of prediction accuracy. The potential of BART is illustrated on examples where it compares favorably with competing methods including gradient boosting, neural nets and random forests. It is also seen that BART is remarkably efiective at flnding low dimensional structure in high dimensional data.
A Psychophysics Driven Evaluation Framework for Visual Recognition

By providing substantial amounts of data and standardized evaluation protocols, datasets in computer vision have helped fuel advances across all areas of visual recognition. But even in light of breakthrough results on recent benchmarks, it is still fair to ask if our recognition algorithms are doing as well as we think they are. The vision sciences at large make use of a very different evaluation regime known as Visual Psychophysics to study visual perception. Psychophysics is the quantitative examination of the relationships between controlled stimuli and the behavioral responses they elicit in experimental test subjects. Instead of using summary statistics to gauge performance, psychophysics directs us to construct item-response curves made up of individual stimulus responses to find perceptual thresholds, thus allowing one to identify the exact point at which a subject can no longer reliably recognize the stimulus class. In this article, we introduce a comprehensive evaluation framework for visual recognition models that is underpinned by this methodology. Over millions of procedurally rendered 3D scenes and 2D images, we compare the performance of well-known convolutional neural networks. Our results bring into question recent claims of humanlike performance, and provide a path forward for correcting newly surfaced algorithmic deficiencies.
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints Supplementary Material

PESC computes a Gaussian approximation to the NFCPD (main text, Eq. (11)) using Expectation Propagation (EP) (Minka, 2001). EP is a method for approximating a product of factors (often a single prior factor and multiple likelihood factors) with a tractable distribution, for example a Gaussian. EP generates a Gaussian approximation by approximating each individual factor with a Gaussian. The product all these Gaussians results in a single Gaussian distribution that approximates the product of all the exact factors. This is in contrast to the Laplace approximation which fits a single Gaussian distribution to the whole posterior. EP can be intuitively understood as fitting the individual Gaussian approximations by minimizing the Kullback-Leibler (KL) divergences between each exact factor and its corresponding Gaussian approximation. This would correspond to matching first and second moments between exact and approximate factors. However, EP does this moment matching in the context of all the other approximate factors, since we are ultimately interested in having a good approximation in regions where the overall posterior probability is high. Concretely, assume we wish to approximate the distribution
DiffSketching: Sketch Control Image Synthesis with Diffusion Models Supplementary Material

Fine-tuning procedure After pretraining diffusion models, we get the diffusion kernel ε̂θ . We clone the latent noise xT to x̂T (θ), and iterate through Eq. 3 to get x0. The image is sampled through the reverse generation process. And guided by image identity loss Li and sketch perceptual loss Lp, the kernel ε̂θ is updated. We repeat this update process M times until the model converges, shown in Algorithm 1.
The Great African Plume Emerges as a Tectonic Player

GEOPHYSICSA massive upwelling of hot rock beneath southern Africa may be shaping the continent as it cools Earth's core, in the flip side of plate tectonics. At the spring meeting of the American Geophysical Union in Boston and in recent publications, geophysicists report signs that a blob of hot rock several thousand kilometers wide at its base, long known to lurk beneath southern Africa, extends toward the surface, spanning the mantle from the core to the volcanic hotspot of northeastern Africa. Its ascent could be pushing up much of southern Africa, and it could be feeding a dozen or more volcanic hotspots across the continent.
DYNAMIQUE EROSIVE ACTUELLE ET TRANSFERTS FLUVIATILES DANS L'OUEST DU BASSIN DE PARIS. Exemple de bassins versants littoraux en Haute Normandie : le Dun, la Ganzeville et l'Yères.

La comprehension des transferts sedimentaires depuis les zones " source " jusqu'aux zones de depot des sediments constitue un enjeu majeur pour la lutte contre l'erosion des sols et la qualite des eaux. Portant sur de petits bassins versants cotiers de l'Ouest du bassin parisien, l'interet de ce travail est de presenter une demarche integree de la dynamique erosive a l'echelle du bassin versant, depuis l'analyse de la stabilite structurale des sols jusqu'au suivi haute frequence des transferts de matieres en riviere. L'objectif est d'identifier les facteurs controlant le ruissellement et l'erosion sur ces sites et de proposer une estimation des apports fluviatiles de matieres en suspension (MES) a la Manche orientale. Les resultats obtenus soulignent que malgre des sols limoneux peu structures generant des taux d'erosion a la parcelle importants, les bilans d'erosion enregistres en riviere sont parmi les plus faibles du monde (10,1 t.km-2.an-1 pour le bassin de l'Yeres, 1,3 t.km-2.an-1 pour le bassin du Dun et 6,6 t.km-2.an-1 pour le bassin de la Ganzeville). La comparaison de ces donnees avec celles de 214 bassins versants mondiaux montre que ces faibles bilans sont expliques par la taille des bassins etudies (< 1000 km2), le relief modere et le faible debit specifique. Regionalement, ce dernier facteur est contraint par un fonctionnement hydrologique karstique, qui est a l'origine non seulement du deficit de la balance des ecoulements de surface mais aussi d'un stockage intrakarstique de materiaux fins issus de l'erosion des sols. Les resultats mettent en evidence des echanges nappe-riviere tres importants sur les sites d'etude : la contribution des eaux souterraines au debit de surface est de l'ordre de 90 %. Concernant les apports fluviatiles de MES a la Manche orientale, l'extrapolation des bilans d'erosion obtenus a l'ensemble des fleuves cotiers du littoral haut-normand conduit a une estimation du flux exporte a la mer de 48 500 t.an-1. Issu de l'erosion des bassins versants cotiers, ce flux de MES apparait tres faible tant a l'echelle mondiale que regionale ou le flux de MES de la Seine atteint en moyenne 715 000 t.an-1 a Poses en amont de l'estuaire.
Relevance-Aware Anomalous Users Detection in Social Network

—Anomalous users detection in social network is an imperative task for security problems. Graph Neural Networks (GNNs) have been widely applied to reveal the suspicious nodes or relevance by aggregating the neighborhood information. However, the increasing scale of users’ behaviors and interactions as well as the manifold camouﬂage techniques have largely complicated the discrimination process, which result in the serious loss of detection accuracy. In this paper, we propose an innovate Relevance-aware Anomalous Users Detection model (R AUD ), which extracts and learns both the explicit and implicit relevance from meta data based on heterogeneous information network (H IN ) and GNNs. We employ two distinct exploring layers including a self-attention layer and a similarity calculation layer to fully consider the users’ intimacy across different relationships, and a following fusion layer based on graph convolution network(GCN) to consolidate the similarity and enhance the node embedding. We evaluate our proposed model with real-world datasets and results show our approach can achieve over 90% accuracy for user classiﬁcation, far better than other comparable baselines
Distributed Asynchronous Policy Iteration for Sequential Zero-Sum Games and Minimax Control

We introduce a contractive abstract dynamic programming framework and related policy iteration algorithms, speciﬁcally designed for sequential zero-sum games and minimax problems with a general structure. Aside from greater generality, the advantage of our algorithms over alternatives is that they resolve some long-standing convergence diﬃculties of the “natural” policy iteration algorithm, which have been known since the Pollatschek and Avi-Itzhak method [PoA69] for ﬁnite-state Markov games. Mathematically, this “natural” algorithm is a form of Newton’s method for solving Bellman’s equation, but Newton’s method, con-trary to the case of single-player DP problems, is not globally convergent in the case of a minimax problem, because the Bellman operator may have components that are neither convex nor concave. Our algorithms address this diﬃculty by introducing alternating player choices, and by using a policy-dependent mapping with a uniform sup-norm contraction property, similar to earlier works by Bertsekas and Yu [BeY10], [BeY12], [YuB13]. Moreover, our algorithms allow a convergent and highly parallelizable implementation, which is based on state space partitioning, and distributed asynchronous policy evaluation and policy improvement operations within each set of the partition. Our framework is also suitable for the use of reinforcement learning methods based on aggregation, which may be useful for large-scale problem instances.