Miracle-dz/UniFAR-FTUs huggingface dataset:
Learnable Pretext Task for Anomaly Detection

R´esum´e La d´etection d’anomalie profonde est devenue une solution attrayante dans de multiples domaines et a connu r´ecemment de nom-breux d´eveloppements. L’une des pistes les plus prometteuses est l’utilisation de tˆaches pr´etextes. Cependant, celles-ci sont limit´ees par l’absence d’´echantillons anormaux et comportent un biais inductif important. Pour palier ces limitations, nous introduisons le concept de tˆaches pr´etextes apprenables, o`u la tˆache pr´etexte est aussi apprise afin de r´eussir sur les ´echantillons normaux tout en ´echouant sur les anomalies. En appli-quant le concept de tˆache apprenable sur une tˆache de reconnaissance de TPS, notre m´ethode discrimine mieux les anomalies aux cas limite et am´eliore consid´erablement les performances globales. Celle-ci surpasse l’´etat de l’art avec une r´eduction d’erreur relative jusqu’`a 49% sur divers probl`emes de d´etection d’anomalies. Abstract – Deep anomaly detection has become an appealing solution in many fields, and has seen many recent developments. One of the most promising avenues is the use of pretext tasks. However these are limited by the lack of anomalous samples and carries an important inductive bias. To this end, we introduce the concept of learnable pretext tasks, where a pretext task itself is learned to succeed on normal samples while failing on anomalies. By applying the learnable task on a thin plate transform recognition task, our method helps discriminating harder edge-case anomalies and greatly improves anomaly detection. It outperforms state-of-the-art with up to 49% relative error reduction measured with AUROC on various anomaly detection problems.
SVM-based Motion Classification Using Foot-mounted IMU for ZUPT-aided INS

Zero-velocity-UPdaTe (ZUPT)-aided Inertial Navi-gation Systems (INS) is a promising self-contained positioning solution for localizing firefighters and first responders while navigating in extreme environments. This paper reports on a Support Vector Machine (SVM) algorithm for classifying 19 different pedestrian activities based on a foot-mounted Inertial Measurement Unit (IMU). Such classifications are necessary to define correctly the velocity threshold in the ZUPT algorithm. The trained SVM had a classification accuracy of 75.23%. The SVM was demonstrated to enhance the ZUPT-aided INS by adjusting the threshold used in the Stance Hypothesis Optimal dEtection (SHOE) detector and variances of zero-velocity measurements for each classified locomotion. In a pedestrian indoor navigation experiment of traveling 87.8m with a combination of walking, fast walking, jogging, running, sprinting, walking backward, jogging backward, and sidestepping, the ZUPT-aided INS using the SVM-enhanced SHOE detector had a displacement error of 2.4m, outperforming navigation accuracy of a standalone INS by 134.71 ×, and a fixed threshold ZUPT-aided INS by 1.43×.
Nonparametric Estimation of Choice Models

Firms rely on demand predictions to make critical operational decisions. For example, firms need to know how customers respond to price changes in order to optimize the prices it charges. Traditionally, operational decision models relied on what is known as the “independent” demand model. As its name implies, an independent demand model assumes that the demand observed for a product is independent of the availability or characteristics, such as price, of other products. That is, the model ignores any cross-product cannibalization effects. Ignoring crossproduct effects is hard to justify when products are close substitutes of each other; for example, products belonging to the same product category (e.g., different brands of toothpaste), different fare classes of an airline itinerary, different transportation modes (e.g., car, bus, train, etc.) are all close substitutes of each other. In such cases, ignoring the cross-product effects lead to biased demand estimates, especially when product prices and availability change over time. To deal with such cross-product effects, choice-based demand models have gained in popularity over the last couple of decades. In the most general form, a choice model specifies the probability that a customer purchases a product from a given subset, or offer set, of products. If there are N products, then the model specifies choice probabilities for each of the 2 subsets. Because the model is intractable in such a general form, existing literature
CUNI Systems for the WMT 22 Czech-Ukrainian Translation Task

We present Charles University submissions to the WMT 22 GeneralTranslation Shared Task on Czech-Ukrainian and Ukrainian-Czech machine translation. We present two constrained submissions based on block back-translation and tagged back-translation and experiment with rule-basedromanization of Ukrainian. Our results show that the romanization onlyhas a minor effect on the translation quality.Further, we describe Charles Translator,a system that was developed in March 2022 as a response to the migrationfrom Ukraine to the Czech Republic. Compared to our constrained systems,it did not use the romanization and used some proprietary data sources.
HEALTHCARE FOR RESETTLED REFUGEES: BARRIERS, RECOMMENDATIONS, AND TWO THEORIES FOR APPLICATION

Refugees in the United States present a unique set of circumstances for their medical providers. This paper will overview the complex process for admission to the United States and the unresolved trauma that many refugees experience. It will discuss the barriers to achieving adequate healthcare experiences for refugees: language, health literacy, transportation, gender issues, provider cultural competency, and communication about trauma. These barriers confound each other and create a multidimensional challenge for refugee patients in the U.S. For this reason, this paper also explores current recommendations for providers. Finally, I will argue that employing the ideas in “The Danger of a Single Story ​ ” ​ and ​ Narrative Medicine ​ will have positive impacts on the way refugee patients experience U.S. healthcare.
Task and Motion Planning for Sequential Assembly - Towards Multi-View Multi-Marker Object Pose Estimation

—In this work, we present a new approach to automate the process of construction using self-interlocking building blocks speciﬁcally SL-Blocks. Utilizing this block type, complex structures are automatically constructed with an industrial robot arm. The challenges inherent to the task, the need for high precision tracking, and occlusion robustness for said tracking are needed to accomplish the joining of parts with tolerances at the millimeter level. They are tackled by building on recent methods for ﬁducial marker based tracking. They are modiﬁed with the additional assumption of known relative transformations between markers on the blocks. Our proof of concept setup nearing completion is described alongside our vision for improvements in future work.
Inefficient Cosmic-Ray Diffusion around Vela X: Constraints from H.E.S.S. Observations of Very High-energy Electrons

Vela X is a nearby pulsar wind nebula (PWN) powered by a ∼104 year old pulsar. Modeling of the spectral energy distribution of the Vela X PWN has shown that accelerated electrons have largely escaped from the confinement, which is likely due to the disruption of the initially confined PWN by the supernova remnant reverse shock. The escaped electrons propagate to the Earth and contribute to the measured local cosmic-ray (CR) electron spectrum. We find that the escaped CR electrons from Vela X would hugely exceed the measured flux by High Energy Stereoscopic System (HESS) at ∼10 TeV if the standard diffusion coefficient for the interstellar medium (ISM) is used. We propose that the diffusion may be highly inefficient around Vela X and find that a spatially dependent diffusion can lead to CR flux that is consistent with the HESS measurement. Using a two-zone model for the diffusion around Vela X, we find that the diffusion coefficient in the inner region of a few tens of parsecs should be ≲1028 cm2 s−1 for ∼10 TeV CR electrons, which is about two orders of magnitude lower than the standard value for the ISM. Such inefficient diffusion around PWN resembles the case of the Geminga and Monogem PWNe, suggesting that inefficient diffusion may be common in the vicinity of PWNe that span a wide range of ages.
NBER WORKING PAPER SERIES IMPACT INVESTING IN DISADVANTAGED URBAN AREAS

We examine whether impact investing is more effective in fostering business venture success and social impact when investments are directed toward ventures located in disadvantaged urban areas compared to similar investments directed toward ventures located outside these areas. We explore this question in the context of loans made to business ventures in French "banlieues" vs. "non-banlieues. " We find that loans issued to banlieue ventures yield greater improvements in financial performance, as well as greater social impact in terms of the creation of local employment opportunities, quality jobs, and gender-equitable jobs. These results suggest that impact investors are able to contract with ventures of greater unrealized potential in banlieues, as banlieue ventures tend to be discriminated on the traditional loan market. This is corroborated in a controlled lab experiment in which participants--working professionals who are asked to act as loan officers--are randomly assigned to identical business ventures that only differ in their geographic location. We find that participants are indeed less likely to grant loans to banlieue ventures compared to non-banlieue ventures, despite the ventures being identical. Romain Boulongne IESE Business School rboulongne@iese.edu Rodolphe Durand HEC Paris durand@hec.fr Caroline Flammer School of International and Public Affairs Columbia University 420 West 118th Street, Office 1429 New York, NY 10026 and NBER caroline.flammer@columbia.edu
Interaction-Grounded Learning for Recommender Systems

Recommender systems have long grappled with optimizing user satisfaction using only implicit user feedback. Many approaches in the literature rely on complicated feedback modeling and costly user studies. We propose online recommender systems as a candidate for the recently introduced Interaction Grounded Learning (IGL) paradigm. In IGL, a learner attempts to optimize a latent reward in an environment by observing feedback with no grounding. We introduce a novel personalized variant of IGL for recommender systems that can leverage explicit and implicit user feedback to maximize user satisfaction, with no feedback signal modeling and minimal assumptions. With our empirical evaluations that include simulations as well as experiments on real product data, we demonstrate the effectiveness of IGL for recommender systems.
OPTIMIZATION-BASED APPROACH TO THE DETECTION OF NON-INFLUENTIAL INPUTS

As the complexity of control systems grows, the verification of their correct functioning assumes a more important role. In the testing phase, when the system is checked against possible misbehaviors, the verification task consists in computing the input sequence to be injected in the system so as to generate that undesired behavior. When such an inputs sequence is found, the cause of the malfunctioning can be understood and the design of the control system improved accordingly. Nonetheless, it may happen that the detected input sequence is too complicated to interpret, since, for example, it requires to simultaneously set too many inputs. This prompts the need for novel verification methods, that provide "minimal" inputs sequences that satisfy a given specification (i.e., the misbehavior in the considered testing framework). In this work we address this need and develop techniques for the maximization of the number of non-influential inputs, i.e., those inputs that can take an arbitrary value in their range without compromising the satisfaction of the specification. We address this problem for the class of discrete time linear systems and discrete time Piecewise Affine (PWA) systems, in the case of reachability and safety specifications. For linear systems we develop two different techniques. In the first technique we rely on an input parametrization that treats the inputs as set valued signals and formulate a linear optimization problem with the objective of maximizing the range on where each input can vary, with the constraint on the satisfaction of the specification. In the second technique, we rely on a parametrization that allows the influential inputs to depend on the non-influential ones and formulate a Mixed Integer Linear Programming
CS 330 Autumn 2022/2023 Warmup Homework 0 Multitask Training for Recommender Systems Due

In this assignment, we will implement a multi-task movie recommender system based on the classic Matrix Factorization [1] and Neural Collaborative Filtering [2] algorithms. In particular, we will build a model based on the BellKor solution to the Netflix Grand Prize challenge and extend it to predict both likely user-movie interactions and potential scores. In this assignment you will implement a multi-task neural network architecture and explore the effect of parameter sharing and loss weighting on model performance.
Robust Option Learning for Compositional Generalization

Compositional reinforcement learning is a promising approach for training policies to perform complex long-horizon tasks. Typically, a high-level task is decomposed into a sequence of subtasks and a separate policy is trained to perform each subtask. In this paper, we focus on the problem of training subtask policies in a way that they can be used to perform any task; here, a task is given by a sequence of subtasks. We aim to maximize the worst-case performance over all tasks as opposed to the average-case performance. We formulate the problem as a two agent zero-sum game in which the adversary picks the sequence of subtasks. We propose two RL algorithms to solve this game: one is an adaptation of existing multi-agent RL algorithms to our setting and the other is an asynchronous version which enables parallel training of subtask policies. We evaluate our approach on two multi-task environments with continuous states and actions and demonstrate that our algorithms outperform state-of-the-art baselines.
Invernet: An Inversion Attack Framework to Infer Fine-Tuning Datasets through Word Embeddings

Word embedding aims to learn the dense representation of words and has become a regular input preparation in many NLP tasks. Due to the data and computation intensive nature of learning embeddings from scratch, a more af-fordable way is to borrow the pretrained embedding available in public and fine-tune the embedding through a domain specific downstream dataset. A privacy concern can arise if a malicious owner of the pretrained embedding gets access to the fine-tuned embedding and tries to infer the critical information from the downstream datasets. In this study, we pro-pose a novel embedding inversion framework called Invernet that materializes the privacy concern by inferring the context distribution in the downstream dataset, which can lead to key information breach. With extensive experimental studies on two real-world news datasets: Antonio Gulli’s News and New York Times, we validate the feasibility of proposed privacy attack and demonstrate the effectiveness of Invernet on inferring downstream datasets based on multiple word embedding methods.
Comparison of Different Scoring Systems for Prediction of Mortality and ICU Admission in Elderly CAP Population

Background: The incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. Different scoring systems, including The quick Sequential Organ Function Assessment (qSOFA), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), were used widely for predicting mortality and ICU admission of patients with community-acquired pneumonia (CAP). This study aimed to identify the most suitable score system for better hospitalization. Methods: We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University from 1 January 2018 to 1 January 2020. We recorded information of the patients including age, gender, underlying disease, consciousness state, vital signs, physiological and laboratory variables and further calculated the qSOFA, CURB-65, MEWS, and NEWS scores. Receiver operating characteristic (ROC) curves were used to predict the mortality risk and ICU admission. Kaplan–Meier survival curves were used in survival rate. Results: In total, 1044 patients were selected for analysis and divided into two groups, namely survivor groups (902 cases) and non-survivor groups (142 cases). Depending on ICU admission enrolled patients were classified into ICU admission (n = 102) and non-ICU admission (n = 942) groups. Mortality expressed as AUC values were 0.844 (p < 0.001), 0.868 (p < 0.001), 0.927 (p < 0.001) and 0.892 (p < 0.001) for qSOFA, CURB 65, MEWS and NEWS, respectively. There were clear differences in MEWS vs CURB-65 (p < 0.0001), MEWS vs NEWS (p < 0.001), MEWS vs qSOFA (p < 0.0001). For ICU-admission, the AUC values of qSOFA, CURB-65, MEWS and NEWS scores were 0.866 (p < 0.001), 0.854 (p < 0.001), 0.922 (p < 0.001), 0.976 (p < 0.001), respectively. There were significant differences in NEWS vs CURB-65 (p < 0.0001), NEWS vs MEWS (p < 0.001), NEWS vs qSOFA (p < 0.0001). Conclusion: We explored the outcome prediction values of CURB65, qSOFA, MEWS and NEWS for patients aged 65-years and older with community-acquired pneumonia. We found that MEWS showed superiority over the other severity scores in predicting hospital mortality, and NEWS showed superiority over the other scores in predicting ICU admission.
Automatic pre-selection of potential misinformation for later expert evaluation

Slovak University of Technology in Bratislava Faculty of Electrical Engineering and Information Technology Study Programme: Applied Informatics Author: Bc. Jakub Fedorko Master’s thesis: Automatic pre-selection of potential misinformation for later expert evaluation Supervisor: doc. Ing. Jakub Šimko, PhD. Place and year of submission: Bratislava 2022 The rapid spread of false information on social media renders manual fact-checking methods ineffective. Automatic fact-checking systems try to inhibit potential damage caused by false information by detecting check-worthy claims, gathering relevant information, and inferring their veracity. The recent research in the check-worthiness field relies increasingly on word and even sentence embeddings technology to convey meaning, substituting simpler text features such as tf-idf, sentiment and others found in earlier works. Motivated by the above and general scarcity of use of the sentence embeddings, we present our sent-nnmodel utilising BERT-based sentence embeddings as its text representation, which outperforms baseline methods by more than 2.5% and 4.2% in average precision and f1-score on the positive class.
Conformational dynamics of charged polymers interacting with charged nanoparticles.

The transition from globular to elongated states of biopolymers in shear flow occurs at a distinct critical shear rate, $\dot{\gamma }^{*}$. The magnitude of $\dot{\gamma }^{*}$ depends on the internal potential and the polymer length. For example, the critical shear is much larger for von Willebrand Factor (vWF) compared to DNA. Furthermore, it is shown through computational analysis of vWF (model-vWF) that $\dot{\gamma }^{*}\sim N^{1/3}$, where N is the number of dimeric units in the vWF. In this study, we show that in the presence of charged nanoparticles (CNP) and polymer, the critical shear rate scales differently depending on the polymer length and the charge-strength of the CNP. It is shown that CNP alter the conformational dynamics of polymers under shear flow when the polymer beads have an opposite charge. The introduction of CNP shifts the critical shear rate and alters the scaling. Furthermore, it is shown that the critical zeta potential of CNP, $\zeta^{*}$, scales linearly with shear rate, and scales cubically with ratio $\beta$ between final CNP-polymer composite size and original polymer size, that is $\zeta^{*}\sim \dot{\gamma }\beta ^{-3}$.
Digital video microscopy enhanced by deep learning

Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard methods rely on algorithmic approaches; by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python software package, which can be easily personalized and optimized for specific applications.
INHIBITORY CONTROL OF VOLUNTARY ARM MOVEMENTS: A DOORWAY TO FREE WON’T

Throughout the course of our life, we constantly have the impression of being able to act as we want. In other words, we have the strong feeling that we are able to exert volitional control over our own actions and that our conscious decisions initiate them. The conscious experience of initiating an action is at the basis of the notion of self-control and of responsibility. Surprisingly, the experimental evidence seems to show that the subjective experience of free will is something of a perceptual illusion and that unconscious brain processes begin well before we become aware of our intention to act. But if the conscious intention is a subjective corollary of an action being about to be executed, are we in control of ourselves? Are we morally responsible for our actions? The solution to this issue seems to come from our ability to inhibit prepotent actions. In fact, even though the awareness of intention follows the start of an action it still precedes the physical execution of a movement by several hundred milliseconds, allowing a subject enough time to withhold the upcoming action whenever the expected outcome does not seem any more valuable in terms of biological fitness. Therefore it is likely that the basis of our decisions is not "free will" but "free won't". Despite the key role of inhibitory control, the way it is implemented and its neural substrates are still debated and controversial. The main aim of the present project is to shed light on this issue by interpreting the pattern of electrical activity recorded over a wide region of the lateral surface of frontotemporal lobes of pharmacoresistant epileptic patients while performing a task probing their inhibitory control (stop signal task).