text:
Internet has transformed business in Nigeria
In Nigeria, Internet has transformed business a lot; many people now have their businesses on their mobile phones, something which has made people eager to innovate in mobile technology.
Magnus Nmonwu, Regional Director at Sage West Africa said that "a number of developed countries are being powered by small and medium businesses that use digital platforms." He added: These SMEs acquire a number of mobile devices and digital activities so as to power their businesses […] It is our view that small and medium businesses in Nigeria should be given the power and ability to put their services in digital form for users in the market [just like in the developed markets."
Due to relatively inexpensive smartphones
It seems that having the ability to buy cheaper smartphones has made Nigerians more into having online businesses; "we have solutions that we have been able to put in mobile phones for SMEs to utilise. To use these services one requires a smartphone. In the next five years the number of people using smartphones will get to 100 per cent," said Nmonwu. He added that Nigeria now has a golden opportunity to keep investing in the sector; "we need to start design tools and codes that will improve the digital space; we also need to write codes and applications meant for our market. This education of coding should be made available locally so that those who want to enter into this space do not have to travel to countries like India for that knowledge," he explained.
Why Nigeria Is One Of 5 Fastest Growing Countries For Mobile Subscription #Business #Technology #IkejaNigeria https://t.co/tdhHLoASgR
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"Curse These Metal Hands" is featured on Sleep Debt, the debut album from the band Cheer Up, which consists of members from No Thank You, The Superweaks, and Thin Lips. The track captures a controlled, explosive, communally-emotive experience. Releasing the pressure in anthemic harmony, there's a crisp, cathartic swell of feelings/thoughts. It's a reminder that we're all in this together. Sleep Debt is out now via Take This To Heart Records.


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Risk of breast implant associated anaplastic large cell lymphoma (BIA-ALCL) in a cohort of 3546 women prospectively followed long term after reconstruction with textured breast implants.
Published on Jan 20, 2020in Journal of Plastic Reconstructive and Aesthetic Surgery2.74
· DOI :10.1016/J.BJPS.2019.11.064
Peter G. Cordeiro56
(MSK: Memorial Sloan Kettering Cancer Center), Paola Ghione15
(MSK: Memorial Sloan Kettering Cancer Center)+ 5 AuthorsSteven M. Horwitz67
(MSK: Memorial Sloan Kettering Cancer Center)
ABSTRACT Background : The risk of BIA-ALCL for patients with textured breast implants has been estimated between 1/2832 and 1/30,000 women. Existing studies estimating the numbers exposed and at risk, may have under reported cases, and/or lacked comprehensive follow-up. Our objective is to determine the risk of BIA-ALCL in a defined cohort of patients reconstructed with macro-textured breast implants and consistently followed long-term. Methods : A prospective cohort study was conducted in patients who underwent breast reconstruction by a single surgeon at Memorial Sloan Kettering Cancer Center (MSKCC) from December 1992 to December 2017. Major events related to implants were prospectively recorded. We identified cases of BIA-ALCL by cross-checking clinical, pathology and external records data. Patients were followed until lymphoma occurrence or last follow-up. The primary outcomes were incidence rate per person-years and cumulative incidence. Results : From 1992 to 2017, 3546 patients underwent 6023 breast reconstructions, mainly after breast cancer removal, or contralateral prophylactic mastectomy, using macro-textured surface expanders and implants. All reconstructions were performed by a single surgeon (PGC). Median follow-up was 8.1 years (range, 3 months – 30.9 years). Ten women, 1/354, developed ALCL after a median exposure of 11.5 years (range, 7.4-15.8 years). Overall risk of BIA-ALCL in our cohort was is 0.311 cases per 1000 person-years (95% CI 0.118 to 0.503). Discussion : This study, the first to evaluate the risk of macro-textured breast implants from a prospective database with long term follow-up, demonstrates that the incidence rate of BIA-ALCL may be higher than previously reported. These results can help inform implant choice for women undergoing breast reconstruction.
References (18)Citations (45)
Breast Implant-Associated Anaplastic Large Cell Lymphoma in Australia and New Zealand: High-Surface-Area Textured Implants Are Associated with Increased Risk.
20174.76Plastic and Reconstructive Surgery
15 Authors (Anna Loch-Wilkinson, ..., Anand K. Deva)
Whole exome sequencing reveals activating JAK1 and STAT3 mutations in breast implant-associated anaplastic large cell lymphoma anaplastic large cell lymphoma
20169.94Haematologica
12 Authors (Piers Blombery, ..., David Westerman)
The 2016 revision of the World Health Organization classification of lymphoid neoplasms
201623.63Blood
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The Functional Influence of Breast Implant Outer Shell Morphology on Bacterial Attachment and Growth
Aug 1, 2019·Plastic and Reconstructive Surgery4.76
#1Phoebe Jones (UTSW: University of Texas Southwestern Medical Center)H-Index: 1
#2Maria MempinH-Index: 2
Last. Anand K. DevaH-Index: 33
BACKGROUND: The introduction of texture to the outer shell of breast implants was aimed at increasing tissue incorporation and reducing capsular contracture. It has also been shown that textured surfaces promote a higher growth of bacteria and are linked to the development of breast implant-associated anaplastic large cell lymphoma. METHODS: The authors aimed to measure the surface area and surface roughness of 11 available implants. In addition, the authors aimed to subject these implant shells... more
Current Risk Estimate of Breast Implant-Associated Anaplastic Large Cell Lymphoma in Textured Breast Implants.
Mar 1, 2019·Plastic and Reconstructive Surgery4.76
#1David J. CollettH-Index: 2
#2Hinne A. Rakhorst (Medisch Spectrum Twente)H-Index: 17
Last. Anand K. Deva (UBC: University of British Columbia)H-Index: 33
Background:With breast implant–associated anaplastic large cell lymphoma (BIA-ALCL) now accepted as a unique (iatrogenic) subtype of ALCL directly associated with textured breast implants, we are now at a point where a sound epidemiologic profile and risk estimate are required. The aim of this artic
How I treat breast implant-associated anaplastic large cell lymphoma
Nov 1, 2018·Blood23.63
#1Neha Mehta-Shah (WashU: Washington University in St. Louis)H-Index: 14
#2Mark W. Clemens (University of Texas MD Anderson Cancer Center)H-Index: 33
Last. Steven M. Horwitz (MSK: Memorial Sloan Kettering Cancer Center)H-Index: 67
Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a recently described form of T-cell non-Hodgkin lymphoma now formally recognized by the World Health Organization classification of lymphoid neoplasms. The disease most often presents with a delayed seroma around the breast implant, almost exclusively one with a textured surface, and manifests with breast pain, swelling or asymmetry, capsular contracture, but can also present with a breast mass, and lymph node involvement. Th... more
Breast implants and the risk of anaplastic large-cell lymphoma in the breast
Mar 1, 2018·JAMA Oncology31.78
#1Mintsje de Boer (Maastricht University Medical Centre)H-Index: 6
#2Flora E. van Leeuwen (NKI-AVL: Netherlands Cancer Institute)H-Index: 84
Last. Daphne de Jong (VU: VU University Amsterdam)H-Index: 29
IMPORTANCE Breast implants are among the most commonly used medical devices. Since 2008, the number of women with breast implants diagnosed with anaplastic large-cell lymphoma in the breast (breast-ALCL) has increased, and several reports have suggested an association between breast implants and risk of breast-ALCL. However, relative and absolute risks of breast-ALCL in women with implants are still unknown, precluding evidence-based counseling about implants. OBJECTIVE To determine relative and... more
Oct 1, 2017·Plastic and Reconstructive Surgery4.76
#1Anna Loch-Wilkinson (American Society of Plastic Surgeons)H-Index: 2
#2Ken Beath (Macquarie University)H-Index: 12
Last. Anand K. Deva (Macquarie University)H-Index: 33
Background:The association between breast implants and breast implant–associated anaplastic large cell lymphoma (ALCL) has been confirmed. Implant-related risk has been difficult to estimate to date due to incomplete datasets.Methods:All cases in Australia and New Zealand were identified and analyze
Discussion: Breast Implant-Associated Anaplastic Large Cell Lymphoma in Australia and New Zealand: High-Surface-Area Textured Implants Are Associated with Increased Risk.
#1Mark W. ClemensH-Index: 33
U.S. Epidemiology of Breast Implant-Associated Anaplastic Large Cell Lymphoma.
May 1, 2017·Plastic and Reconstructive Surgery4.76
#1Erin L. Doren (University of Texas MD Anderson Cancer Center)H-Index: 10
#2Roberto N. MirandaH-Index: 57
Last. Mark W. ClemensH-Index: 33
Background:Breast implant–associated anaplastic large cell lymphoma (ALCL) is a distinctive type of T-cell lymphoma that arises around breast implants. Although rare, all cases with adequate history have involved a textured breast implant. The objective of this study was to determine the U.S. incide
Global Adverse Event Reports of Breast Implant-Associated ALCL: An International Review of 40 Government Authority Databases
#1Dhivya Srinivasa (UM: University of Michigan)H-Index: 6
Last. Mark W. Clemens (University of Texas MD Anderson Cancer Center)H-Index: 33
Background: Tracking world cases of breast implant–associated anaplastic large cell lymphoma (ALCL) is currently limited to patient registries at a few academic centers, dependent upon patient referral and case reports in the literature. The purpose of this study was to review and compare federal database adverse event reports of breast implant–associated ALCL encompassing the major breast implant markets worldwide. Methods: Federal implantable device regulatory bodies were contacted and databas... more
Understanding rare adverse sequelae of breast implants: anaplastic large-cell lymphoma, late seromas, and double capsules.
Apr 1, 2017·Gland surgery2.95
#2Maurizio B. NavaH-Index: 23
Last. Roberto N. Miranda (University of Texas MD Anderson Cancer Center)H-Index: 57
Breast implant-associated anaplastic large cell lymphoma (BI-ALCL) is a distinct type of T-cell lymphoma arising around breast implants. The United States FDA recently updated the 2011 safety communication, warning that women with breast implants may have a very low risk of developing ALCL adjacent to a breast implant. According to the World Health Organization, BI-LCL is not a breast cancer or cancer of the breast tissue; it is a lymphoma, a cancer of immune cells. BI-ALCL is highly curable in ... more
NCCN Consensus Guidelines for the Diagnosis and Management of Breast Implant-Associated Anaplastic Large Cell Lymphoma.
Mar 1, 2017·Aesthetic Surgery Journal4.28
#2Steven M. Horwitz (MSK: Memorial Sloan Kettering Cancer Center)H-Index: 67
: Published case series demonstrate a lack of treatment standardization for breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) with a wide variety of therapeutic strategies being employed at all stages of disease. The National Comprehensive Cancer Network (NCCN) annually publishes Clinical Practice Guidelines for Non-Hodgkin Lymphomas. For the first time, BIA-ALCL management will be included which signifies an important and needed guideline addition. The new BIA-ALCL guideline w... more
Cited By45
Breast Implant-Associated Anaplastic Large Cell Lymphoma (BIA-ALCL): Are You Covered?
Apr 15, 2021·Aesthetic Surgery Journal4.28
#1Michael Ha (UMB: University of Maryland, Baltimore)
#2Ledibabari M. Ngaage (UMB: University of Maryland, Baltimore)H-Index: 12
Last. Yvonne M. Rasko (UMB: University of Maryland, Baltimore)H-Index: 13
BACKGROUND Breast Implant-Associated Anaplastic Large Cell Lymphoma (BIA-ALCL) is a locally aggressive T-cell lymphoma that can develop following breast implantation. In 2017, and updated in 2019, the National Comprehensive Cancer Network (NCCN) recommended total capsulectomy with implant removal as definitive therapy. OBJECTIVES The authors evaluated the American insurance coverage for the management of BIA-ALCL and compared it to the NCCN recommendations. METHODS The authors conducted a cross-... more
New Data on the Epidemiology of Breast Implant-Associated Anaplastic Large Cell Lymphoma.
Oct 1, 2021·The Journal of Breast Health
#1Paul Ionescu
#2Florence VibertH-Index: 1
Last. Carole MathelinH-Index: 21
Objective This study aimed to illustrate the epidemiological situation of breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) by focusing on the changes published after 2019 and particularly the new approaches of cosmetic and reconstructive breast surgery. Materials and Methods Article search was performed from January 2019 to date using the PubMed database. Fourteen articles were included in the qualitative evaluation of international data. Moreover, the latest reports regarding... more
Anaplastic large cell lymphoma: a call for disease awareness.
Sep 11, 2021·Hematology, Transfusion and Cell Therapy
#1Lara de Holanda Jucá SilveiraH-Index: 2
#2Vanessa Silva de Oliveira
Last. Ronald Feitosa PinheiroH-Index: 9
What Do Clinicians and Organizations Owe Patients With Recalled Implanted Devices or Materials
#1Michele A. Manahan (Johns Hopkins University)H-Index: 14
Placement of prosthetic breast implants for augmentation or reconstruction is common. Two specific safety concerns are considered in this article: breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) and complexes of symptoms known as breast implant illness. In response to a case involving a patient with concerns about BIA-ALCL
Breast Implant-Associated Anaplastic Large Cell Lymphoma (BIA-ALCL): Review of Epidemiology and Prevalence Assessment in Europe.
Aug 13, 2021·Aesthetic Surgery Journal4.28
#1Fabio Santanelli di Pompeo (Sapienza University of Rome)H-Index: 13
#2Michail SorotosH-Index: 11
Last. K BozikovH-Index: 1
BACKGROUND Breast Implant Associated-Anaplastic Large Cell Lymphoma (BIA-ALCL) epidemiological studies focus on incidence and risk estimates. OBJECTIVES The aim of this manuscript is to perform a thorough review of scientific literature, and provide an accurate estimate of BIA-ALCL prevalence in Europe. METHODS For the review, we searched PubMed, Web of Science, SCOPUS, and Google Scholar databases to identify publications regarding BIA-ALCL epidemiology. Research was conducted between November ... more
Commentary On: Implant-Associated Anaplastic Large Cell Lymphoma (BIA-ALCL): Review of Epidemiology and Prevalence Assessment in Europe.
#1Donald R. Mackay (Penn State Milton S. Hershey Medical Center)H-Index: 19
Why Breast-Conserving Therapy Should be Considered Treatment of Choice in Early-Stage Breast Cancer Patients.
Jun 26, 2021·Annals of Surgical Oncology5.34
#1Casimir A.E. Kouwenberg (EUR: Erasmus University Rotterdam)H-Index: 5
#2Leonieke W. Kranenburg (EUR: Erasmus University Rotterdam)H-Index: 13
Last. Marc A.M. Mureau (EUR: Erasmus University Rotterdam)H-Index: 33
Breast Implant-Associated Anaplastic Large Cell Lymphoma With Invasive Chest Wall Masses.
Jun 23, 2021·Annals of Plastic Surgery1.54
#5Raymond IsakovH-Index: 1
Background null Breast implant-associated anaplastic large-cell lymphoma (BIA-ALCL) might occur in any patient with a history of textured breast devices, and often presents as a spectrum of disease: from a simple late-onset seroma, to complex advanced locally aggressive disease with chest wall involvement. The combination of medical therapy and surgical implant removal with complete capsulectomy is believed to be curative. null Objectives null In patients with advanced cases, however, and althou... more
Breast implant-associated anaplastic large cell lymphoma: clinical follow-up and analysis of sequential pathologic specimens of untreated patients shows persistent or progressive disease
Jun 21, 2021·Modern Pathology7.84
#1Mark G. Evans (Brigham and Women's Hospital)H-Index: 3
#2L. Jeffrey Medeiros (University of Texas MD Anderson Cancer Center)H-Index: 106
Last. Swaminathan P. Iyer (University of Texas MD Anderson Cancer Center)H-Index: 18
Breast implant-associated anaplastic large cell lymphoma (ALCL) is a distinctive type of T-cell lymphoma that arises around textured-surface breast implants. In a subset of patients, this disease can involve surrounding tissues, spread to regional lymph nodes, and rarely metastasize to distant sites. The aim of this study was to assess sequential pathologic specimens from patients with breast implant-associated ALCL to better understand the natural history of early-stage disease. To achieve this... more
Lack of Evidence on Complication Profile of Breast Implant Capsulectomy: A Call to Action for Plastic Surgeons.
Jun 9, 2021·Plastic and Reconstructive Surgery4.76
#1Jad Abi-Rafeh (McGill University)H-Index: 4
#2Tyler Safran (McGill University)H-Index: 7
Last. Joshua Vorstenbosch (McGill University)H-Index: 10

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By O. Aschnu. Saint Andrews Presbyterian College.
The septal cartilage forms the anterior portion of the nasal septum generic detrol 4mg visa, and the paired lateral carti- lages and alar cartilages form the framework around the nostrils discount detrol 1mg without a prescription. The nasal vestibule is the anterior expanded portion of the nasal fossa (fig buy 2mg detrol visa. There are several openings into the nasal cavity, including the openings of the various paranasal sinuses, those of the nasolacrimal ducts that drain from the eyes, and those of the audi- tory tubes that drain from the tympanic cavities. Respiratory System © The McGraw−Hill Anatomy, Sixth Edition Body Companies, 2001 606606 Unit 6 Maintenance of the Body Simple squamous epithelium (lining pulmonary alveoli) FIGURE 17. The roof of the nasal cavity is formed anteriorly by the frontal bone and paired nasal bones, medially by the cribriform plate of the ethmoid bone, and posteriorly by the sphenoid bone (see figs. In the trachea and bronchi, there are The anterior openings of the nasal cavity are lined with stratified squa- about 300 cilia per cell. The cilia move mucus-dust particles toward mous epithelium, whereas the conchae are lined with pseudostratified the pharynx, where they can either be swallowed or expectorated. Mucus-secreting gob- let cells are present in great abundance throughout both regions. Dust, pollen, smoke, and other fine particles are trapped • The nasal epithelium covering the conchae serves to warm, along the moist mucous membrane lining the nasal cavity. The nasal epithelium is highly vascular and covers an extensive surface • The olfactory epithelium in the upper medial portion of area. This is important for warming the air but unfortu- the nasal cavity is concerned with the sense of smell. Respiratory System © The McGraw−Hill Anatomy, Sixth Edition Body Companies, 2001 Chapter 17 Respiratory System 607 FIGURE 17. There are several drainage openings into the nasal cavity (see Pharynx fig. An excessive secretion of tears causes the nose to run as the tears drain into the nasal cavity. The supporting walls of the pharynx are com- accessory connections, it is no wonder that infections can spread so posed of skeletal muscle, and the lumen is lined with a mucous easily from one chamber to another throughout the facial area. To avoid causing damage or spreading infections to other areas, one membrane. Within the pharynx are several paired lymphoid or- must be careful not to blow the nose too forcefully. Commonly referred to as the "throat" or "gul- let," the pharynx has both respiratory and digestive functions. Paranasal Sinuses The pharynx is divided on the basis of location and function into three regions (see fig. Paired air spaces in certain bones of the skull are called paranasal • The nasopharynx serves only as a passageway for air, be- sinuses. These sinuses are named according to the bones in cause it is located above the point of food entry into the which they are found; thus, there are the maxillary, frontal, body (the mouth). It is the uppermost portion of the phar- sphenoidal, and ethmoidal sinuses (fig. Each sinus com- ynx, positioned directly behind the nasal cavity and above municates via drainage ducts within the nasal cavity on its own the soft palate. These sinuses are responsible ditory (eustachian) tubes connect the nasopharynx with for some sound resonance, but most important, they function to the tympanic cavities. The pharyngeal tonsils, or ade- decrease the weight of the skull while providing structural noids, are situated in the posterior wall of the nasal cavity.
Those primarily responsible for transmitter release belong to the N (a1B) order detrol 2 mg otc, P/Q (a1A) and R classes (a1E) detrol 2mg cheap. So far order detrol 4 mg otc, no pharmacological agents capable of uniquely modifying Ca2‡ channels involved in transmitter release have been described (other than polypeptide toxins). These, and other (L-type, T-type), Ca2‡ channels are also variably present in neurons somata and/or dendrites, where they contribute to the regulation of neural activity in other ways (see below). REGULATION OFCa2‡ CHANNELS BY NEUROTRANSMITTERS N and P/Q channels are susceptible to inhibition by many neurotransmitters and extra- cellular mediators that act on receptors coupling to Pertussis toxin-sensitive G-proteins (primarily Go) Ð for example, noradrenaline (via a2 receptors), acetylcholine (via M2 and M4 muscarinic receptors), GABA (via GABA-B receptors), opioid peptides (via m=d receptors) and adenosine (via A2 receptors) (see Fig. Inhibition results from the release of the bg subunits of the trimeric (abg) G-protein following its activation by the receptor. The bg subunit then binds to the Ca2‡ channel in such a way as to shift its voltage sensitivity to more positive potentials, so that the channels do not open as readily during a rapid membrane depolarisation. One interpretation of this is that the binding of the bg subunits is itself voltage- dependent. This is thought to provide the principal mechanism responsible for presynaptic inhibition, whereby neurotransmitters inhibit their own release (autoinhibition) during high-frequency synaptic transmission. This process can be replicated by applying exogenous transmitters or their analogues (see Fig. Records show intra- axonal recordings from (a) a regenerating sciatic nerve axon following nerve crush; (b) a normal sciatic nerve axon; and (c) a demyelinated ventral root axon after treatment with lysopho- sphatidylcholine. Note that 4-AP prolongs the action potential in (a) and (c) but not in (b). Thus, current through 4-AP-sensitive K‡ channels contributes to action potential repolarisation in premyelinated or demyelinated mammalian axons, whereas in normal myelinated axons repolarisation is entirely due to Na‡ channel inactivation. Ion Channel Organization of the Myelinated Fiber, p 48±54 (1990) with permission from Elsevier Science 40 NEUROTRANSMITTERS, DRUGS AND BRAIN FUNCTION Figure 2. Currents were evoked by two successive 10 ms steps from 770 mV to 0 mV, separated by a prepulse to ‡90 mV. Note that the transient inhibition produced by NA (mediated by the G-protein Go) and the tonic inhibition produced by the G-protein b1g2 subunits were temporarily reversed by the ‡90 mV depolarisation. Note that pretreatment with Pertussis toxin (PTX), which prevents coupling of the adrenoceptor to Go, abolished inhibition. Reproduced with permission) CONTROL OFNEURONAL ACTIVITY 41 and suppressed by blocking the presynaptic receptors with antagonist drugs, which thereby selectively enhance the release of individual transmitters. ION CHANNELS AFFECTING THE PATTERN AND FREQUENCY OF ACTION POTENTIAL DISCHARGES The opening of Na‡ ion channels for the initiation of neuronal depolarisation and action potential generation, as described above, can be induced by excitatory neuro- transmitters acting on receptors that are directly linked to cation channels. These include glutamate AMPA receptors (Chapters 3 and 10) and ACh nicotinic receptors (Chapter 6). The inhibitory neurotransmitter GABA has an opposing effect through receptors (GABAA) that are directly linked to the opening of chloride channels, inducing an influx of Cl7 ions and subsequent hyperpolarisation (Chapters 1 and 11). There are, however, a number of other ion channels, generally for K‡ or Ca2‡, that have a more subtle controlling effect on neuronal activity. Their opening may be initiated by (or dependent on) preceding changes in membrane potential and ion flux, but they can be affected indirectly by various neurotransmitters, e. The role of these channels in controlling the overall activity of neurons is clearly important and needs to be considered. However, most nerve cells possess other K‡ channels which are opened during nerve cell discharges but which stay open much longer. These do not contribute much to the repolarisation of individual action potentials but instead affect the excitability of the neuron over periods of hundreds of milliseconds or even seconds. Two principal types of channel having this effect have been identified and their properties are summarised in Table 2. This means that they are activated by the Ca2‡ influx through voltage-gated Ca2‡ channels when these are opened during a somatic or dendritic action potential, or during trains of action potentials.
Her blood you request an angiogram of her abdominal pressure is markedly elevated today generic detrol 2mg on line, and in arteries generic 4 mg detrol mastercard. How can the renal artery stenosis stethoscope buy 1 mg detrol fast delivery, which corresponds to her heart- (narrowing) seen in the adjacent beat. Lab work shows very low serum potas- angiogram cause high blood pressure? Discuss the impact that this condition low potassium, so you start her on potassium may have on the opposite renal artery supplementation. Circulatory System © The McGraw−Hill Anatomy, Sixth Edition Body Companies, 2001 Chapter 16 Circulatory System 599 Chapter Summary Functions and Major Components of the (c) The heart contains right and left (b) Veins have venous valves that direct Circulatory System (pp. The circulatory system transports oxygen and bicuspid valves, respectively); a compressed by the skeletal muscle and nutritive molecules to the tissue cells pulmonary semilunar valve; and an pumps. Capillaries are composed of endothelial from tissue cells; it also carries hormones 2. They are the basic functional and other regulatory molecules to their are the pulmonary and the systemic; in units of the circulatory system. Principal Arteries of the Body protect the body from infection, and (a) The pulmonary circulation includes (pp. The components of the circulatory system right ventricle through the lungs, and the brachiocephalic trunk, the left are the heart, blood vessels, and blood, from there to the left atrium. The brachiocephalic system, and the lymphatic vessels and other arteries, capillaries, and veins trunk divides into the right common lymphoid tissue and organs of the in the body. These vessels carry blood carotid artery and the right subclavian lymphatic system. Blood, a highly specialized connective (c) The myocardium of the heart is external carotid arteries and the vertebral tissue, consists of formed elements served by right and left coronary arteries. Erythrocytes are disc-shaped cells that coronary sinus collects and empties the paired vertebral arteries, which lack nuclei but contain hemoglobin. Contraction of the atria and ventricles is surrounding the pituitary gland. Leukocytes have nuclei and are classified the atria and then enter the 3. The upper extremity is served by the as granular (eosinophils, basophils, and atrioventricular (AV) node. Leukocytes defend the conducted by the atrioventricular the axillary artery and then the body against infections by bundle and conduction myofibers brachial artery as it enters the arm. During contraction of the ventricles, the the radial and ulnar arteries, which cytoplasmic fragments that assist in the intraventricular pressure rises and causes supply blood to the forearm and formation of clots to prevent blood loss. Erythrocytes are formed through a process the pulmonary and aortic valves close 4. The abdominal portion of the aorta has called erythropoiesis; leukocytes are because the pressure is greater in the the following branches: the inferior formed through leukopoiesis. Closing of the AV valves causes the first renal, suprarenal, testicular (or ovarian), sac, liver, and spleen. In the adult, red sound (lub); closing of the pulmonary and and inferior mesenteric arteries. Heart murmurs are commonly internal and external iliac arteries, which caused by abnormal valves or by septal supply branches to the pelvis and lower Heart (pp. The wall of the heart consists of the conduction is called an electrocardiogram Principal Veins of the Body epicardium, myocardium, and (ECG or EKG). Blood from the head and neck is drained (a) The right atrium receives blood from Blood Vessels (pp. Arteries and veins have a tunica externa, blood from the brain is drained by the and the right ventricle pumps blood tunica media, and tunica interna. Arteries ventricle pumps blood into the transport blood away from the heart. Circulatory System © The McGraw−Hill Anatomy, Sixth Edition Body Companies, 2001 600 Unit 6 Maintenance of the Body 3.


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School Crossing Patrols (SCPs) provide a vital service at sites across Fife by helping pedestrians on their journey to and from school.
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I ve worked for 2 phone companies Ameritech and Qwest, did about everything for Qwest got laid off now for a cable company (cant say go in trouble for posting it in a help forum) and I trying to get my company Wired Communications launched (wait a real PITA) which is going to be a full serivce low voltage (Data, Phone, video, CCTV, security systems, and computer support company for small business) and new home wiring installations.
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Edit: With benefits of course!


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The facility also serves as a water playground. In addition to this, it's a recreation center. It has various athletic fields where you can run to keep your muscles and lungs in excellent condition as part of the physical fitness goals you wish to meet. The water playground is great for the little ones, with water squirting from every direction, animals to climb on, and water cannons and slides to enjoy. An adult Annual Pass Holder must directly accompany children under the age of eight. Open spring break week through Thanksgiving weekend.
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Posted on 30th Nov
Mansfield is set for 197 new homes after Gleeson achieved approval for its plans at Crown Farm Way.
It is Gleeson's second development in the town.
Allen Marshall, operations director for Gleeson in the Midlands, said: "We are thrilled to have achieved planning consent for this exciting development.
"We believe it will add real value to the area and provide local people on low incomes the opportunity to achieve their dreams of home ownership and the wealth of benefits that it brings.
"By utilising a shared ownership scheme, a monthly mortgage for a two-bedroom home is anticipated to be cheaper than local rent figures. Plus, if using the government backed Help to Buy scheme, many of the homes will only require a 5% deposit. Located less than a 10 minute drive from the town centre, the development is also supported by superb amenities, including good schools, a medical centre and local stores."
Gleeson will be making a contribution towards off-site education to Mansfield District Council, as well as allocating a certain number of homes to the council's affordable housing initiative. The housebuilder will also be making ecological enhancements to the development, as well as improvements to road junctions in the vicinity in accordance with the town's regeneration masterplan.
The new development bring financial contributions to the area, combined with a host of employment opportunities for local people, sub-contractors and young workers looking to start an apprenticeship in construction.
To find out more about the areas in which Gleeson build and information on the work Gleeson does in the community, visit www.gleesonhomes.co.uk
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Spain's newly crowned king Felipe VI arrived in Rabat on Monday afternoon for the start of his first trip to Morocco since becoming a king. Morocco is his third destination after having visited the Vatican and Portugal. This official visit confirms the excellent bilateral relations between the two countries.
The Spanish monarch and his wife Letizia were welcomed at the airport by King Mohamed VI and his wife Lalla Salma before heading to the royal palace in Rabat for a welcoming ceremony where they shared an Iftar meal with the Moroccan Royal family.
Morocco's Princess Lalla Salma, Spain's King Felipe VI , Morocco's King Mohammed VI and Spain's Queen Letizia attend a dinner at the royal palace in Rabat.
The Spanish royal couple was accompanied by a large delegation including ministers and entrepreneurs. Many meetings will be held between the two sovereigns and the delegations of the two countries. A first formal meeting between the two monarchs took place in the afternoon where both sides expressed their will to continue cooperating on various fields.
Morocco's Princess Lalla Hasna, Morocco's Princess Lalla Salma, Princess Lalla Meryem, wife of Morocco's King Mohammed VI, , Spain's King Felipe VI, King Mohammed VI of Morocco, Spain's Queen Letizia, Morocco's Crown Prince Moulay Rachid and Morocco's Princess Lalla Asmaa pose at the royal palace in Rabat.
Spain and Morocco have been enjoying really good bilateral relations in the past few years. Spain is considered Morocco's top economic partner along with France, and Madrid and Rabat have signed in June a new cooperation agreement worth 150 million euros for a period of two years. They have also worked closely in matters of high importance to both sides such as fighting terrorism, limiting illegal immigration and fighting drug trafficking.
Felipe 6 is set to meet with Morocco Prime Minister Abdelilah Benkirane. They will then inaugurate a tourism training center with the Moroccan Monarch while Queen Letizia will be visiting a cancer research center with Princess Lalla Salma.


text:
\section{Introduction}

Calibration requires that the probability a model assigns to a prediction (i.e., {\em confidence}) equals to the correctness measure of the prediction (i.e., {\em accuracy}).
Calibrated models are important in user-facing applications such as natural language processing~\cite{Nguyen:2015:EMNLP} and speech recognition~\cite{Yu:2011:TASLP}, in which one needs to assess the confidence of a prediction.
For example, in computer-assisted translation, a calibrated machine translation model is able to tell a user when the model's predictions are likely to be incorrect, which is helpful for the user to correct errors.

The study of calibration on classification tasks has a long history, from  statistical machine learning~\cite{Platt:1999:ALMC,Niculescu:2005:ICML} to  deep learning~\cite{Guo:2017:ICML}. However, calibration on structured generation tasks such as neural machine translation (NMT) has not been well studied.
Recently,~\newcite{Muller:2019:arXiv} and ~\newcite{Kumar:2019:ICLR-Workshop} studied the calibration of NMT in the training setting, and found that NMT trained with label smoothing~\cite{Szegedy:2016:CVPR} is well-calibrated.
We believe that this setting would cover up a central problem of NMT, {\em the exposure bias}~\cite{Ranzato:2015:arXiv} -- the training-inference discrepancy caused by teacher forcing in the training of auto-regressive models.

\begin{figure}[t]
    \centering
    \subfloat[Training]{
    \includegraphics[height=0.236\textwidth]{figures/ende_train_bar.pdf}}
    \hspace{0.0\textwidth}
    \subfloat[Inference]{
    \includegraphics[height=0.236\textwidth]{figures/ende_inf_bar.pdf}} \hspace{0.02\textwidth}
\caption{Reliability diagrams in training and inference for the WMT14 En-De task. ``Gap'' denotes the difference between confidence and accuracy. Smaller gaps denotes better calibrated outputs.
We find that the average gaps between confidence and accuracy are much larger in inference than in training (i.e., 15.83 $>$ 1.39).
}
\label{fig:ende-diag}
\end{figure}

In response to this problem, this work focuses on the calibration of NMT in inference, which can better reflect the generative capacity of NMT models. To this end, we use translation error rate (TER)~\cite{Snover:2006:MT} to automatically annotate the correctness of generated tokens, which makes it feasible to evaluate calibration in inference.
Experimental results on several datasets across language pairs show that even trained with label smoothing, NMT models still suffer from miscalibration errors in inference. Figure~\ref{fig:ende-diag} shows an example.
While modern neural networks on classification tasks have been found to be miscalibrated in the direction of {\em over-estimation} (i.e., confidence $>$ accuracy)~\cite{Guo:2017:ICML}, NMT models are also {\em under-estimated} (i.e., confidence $<$ accuracy) on low-confidence predictions.
In addition, we found that miscalibrated predictions correlate well with the translation errors in inference.
Specifically, the over-estimated predictions correlate more with over-translation and mis-translation errors, while the under-estimated predictions correlate more with under-translation errors.
This demonstrates the necessity of studying inference calibration for NMT.

By investigating the linguistic properties of miscalibrated tokens in NMT outputs, we have several interesting findings:
\begin{itemize}
    \item {\em Frequency}: Low-frequency tokens generally suffer from under-estimation. Moreover, low-frequency tokens contribute more to over-estimation than high-frequency tokens, especially on large-scale data.
    \item {\em Position}: Over-estimation does not have a bias on the position of generated tokens, while under-estimation occurs more in the left part of a generated sentence than in the right part.
    \item {\em Fertility}: Predicted tokens that align to more than one source token (``fertility$\ge$2'') suffer more from under-estimation, while tokens with fertility $<1$ suffer from over-estimation.
    \item {\em Syntactic Roles}: Content tokens are more likely to suffer from miscalibration than content-free tokens. Specifically, verbs are more likely to suffer from over-estimation than under-estimation.
    \item {{\em Word Granularity}}: sub-words suffer more from both over-estimation and under-estimation, while full words are less likely to be miscalibrated.
\end{itemize}

Inspired by the finding that miscalibration on classification tasks is closely related to lack of regularization and increased model size~\cite{Guo:2017:ICML}, we revisit these techniques on the NMT (i.e., structured generation) task:
\begin{itemize}
    \item {\em Regularization Techniques}: We investigate label smoothing and dropout~\cite{Hinton:2012:arXiv}, which directly affect the confidence estimation. Both label smoothing and dropout improve the inference calibration by alleviating the over-estimation. Label smoothing is the key for well-calibration, which is essential for maintaining translation performance for inference in large search space. Inspired by this finding, we propose a novel {\em graduated label smoothing} approach, in which the smoothing penalty for high-confidence predictions is higher than that for low-confidence predictions. The graduated label smoothing can improve translation performance by alleviating inference miscalibration.
    \item {\em Model Size}: Increasing model size consistently improves translation performance at the cost of negatively affecting inference calibration. The problem can be alleviated by increasing the capacity of encoder only, which  maintains the inference calibration and obtains a further improvement of translation performance in large search space.
\end{itemize}

To summarize, the main contributions of our work are listed as follows:
\begin{itemize}
    \item We demonstrate the necessity of studying inference calibration for NMT, which can serve as useful indicators of translation errors.
    \item We reveal certain linguistic properties of miscalibrated predictions in NMT, which provides potentially useful information for the design of training procedures.
    \item We revisit recent advances in architectures and regularization techniques, and provide variants that can boost translation performance by improving inference calibration.
\end{itemize}

\begin{figure*}[ht]
    \centering
    \includegraphics[width=0.8\textwidth]{figures/TER_example.pdf}
\caption{An example of TER labels. ``C'': correct, ``S'': substitution, corresponding to mis-translation, ``I'': insertion, corresponding to over-translation, ``D'': deletion, corresponding to under-translation. Dash line denotes mapping the label ``D'' from the ground-truth sequence to the generated sequence.}
\label{fig:ter_exp}
\end{figure*}

\section{Related Work}

\paragraph{Calibration on Classification}
Calibration on classification tasks has been studied for a long history in the statistics literature, including Platt scaling~\cite{Platt:1999:ALMC}, isotonic regression~\cite{Niculescu:2005:ICML} and many other methods for non-binary classification~\cite{Zadrozny:2002:KDD,Menon:2012:ICML,Zhong:2013:IJCAI}.
For modern deep neural networks, \citet{Guo:2017:ICML} demonstrated that recent advances in training and model architecture have strong effects on the calibration. \citet{Szegedy:2016:CVPR} propose the label smoothing technique which can effectively reduce the calibration error. \citet{Ding:2019:arXiv} extend label smoothing to adaptive label regularization.

\paragraph{Calibration on Structured Prediction} Different from classification tasks, most natural language processing (NLP) tasks deal with complex structures~\cite{Kuleshov:2015:NeurIPS}.  \citet{Nguyen:2015:EMNLP} verified the finding of \citet{Niculescu:2005:ICML} in NLP tasks on log-linear structured models. For NMT, some works directed their attention to the uncertainty in prediction~\cite{Ott:2018:ICML,Wang:2019:EMNLP}, \citet{Kumar:2019:ICLR-Workshop} studied the calibration of several NMT models and found that the end of a sentence is severely miscalibrated. \citet{Muller:2019:arXiv} investigated the effect of label smoothing, finding that NMT models are well-calibrated in training. Different from previous works, we are interested in the calibration of NMT models in inference, given that the training and inference are discrepant for standard NMT models~\cite{Vaswani:2017:NeurIPS}.

\section{Definitions of Calibration}

\subsection{Neural Machine Translation}

\paragraph{Training}
In machine translation task, an NMT model $F$: $\textbf{x} \rightarrow \textbf{y}$ maximizes the probability of a target sequence $\textbf{y} = \{y_1,...,y_T\}$ given a source sentence $\textbf{x} = \{x_1,...,x_S\}$:
\begin{equation}
    P(\textbf{y}|\textbf{x};\bm{\theta}) = \displaystyle \prod_{t=1}^{T} P(y_t|\textbf{y}_{<t},\textbf{x};\bm{\theta}),
    \label{eqn:mle}
\end{equation}
where $\bm{\theta}$ is a set of model parameters and $\textbf{y}_{<t}$ is a partial translation. At each time step, the model generates an output token of the highest probability based on the source sentence $\textbf{x}$ and the partial translation $\textbf{y}_{<t}$.
The training objective is to minimize the negative log-likelihood loss on the training corpus.

\paragraph{Inference}
NMT models are trained on the ground-truth data distribution ({\em teaching forcing}), while in inference the models generate target tokens based on previous model predictions, which can be erroneous.
The training-inference discrepancy caused by teacher forcing in maximum likelihood estimation training (Equation~\ref{eqn:mle}) is often referred to as {\em exposure bias}~\cite{Ranzato:2015:arXiv}.
In this work, we aim to investigate the calibration of NMT in inference, which we believe can better reflect the generation capacity of NMT models.

\begin{figure*}[t]
    \centering
    \subfloat[En-Jp]{
    \includegraphics[height=0.236\textwidth]{figures/enjp_train_bar.pdf}
    \includegraphics[height=0.236\textwidth]{figures/enjp_inf_bar.pdf}
    }
    \hspace{0.02\textwidth}
    \subfloat[Zh-En]{
    \includegraphics[height=0.236\textwidth]{figures/zhen_train_bar.pdf}
    \includegraphics[height=0.236\textwidth]{figures/zhen_inf_bar.pdf}
    }

\iffalse
    \centering
    \subfloat[En-Jp]{
    \includegraphics[width=0.27\textwidth]{figures/enjp_train_bar.pdf}} \hspace{0.02\textwidth}
    \subfloat[En-De]{
    \includegraphics[width=0.27\textwidth]{figures/ende_train_bar.pdf}} \hspace{0.02\textwidth}
    \subfloat[Zh-En]{
    \includegraphics[width=0.27\textwidth]{figures/zhen_train_bar.pdf}}
    \\
    \subfloat[En-Jp]{
    \includegraphics[width=0.27\textwidth]{figures/enjp_inf_bar.pdf}} \hspace{0.02\textwidth}
    \subfloat[En-De]{
    \includegraphics[width=0.27\textwidth]{figures/ende_inf_bar.pdf}} \hspace{0.02\textwidth}
    \subfloat[Zh-En]{
    \includegraphics[width=0.27\textwidth]{figures/zhen_inf_bar.pdf}}
\fi
\caption{Reliability diagrams on (a) En-Jp and (b) Zh-En datasets. Left: training, right: inference.}
\label{fig:acc_conf_bar}
\end{figure*}

\subsection{Calibration of NMT}
\label{sec:cali-nmt}

Calibration requires that the probability a model assigns to a prediction (i.e., {\em confidence}) equals to the true correctness measure of the prediction (i.e., {\em accuracy}).
Modern neural networks have been found to be miscalibrated in the direction of over-estimation~\cite{Guo:2017:ICML}. In this study, we revisit the calibration problem in NMT. If an NMT model is well-calibrated, the gap between the confidence of the generated tokens and the accuracy of them will be small.~\footnote{For example, given 100 predictions, each with confidence 0.7. If the accuracy is also 0.7 (i.e., 70 of the 100 tokens are correct), then the NMT model is well calibrated.}

\paragraph{Expected Calibration Error (ECE)}
ECE is a commonly-used metric to evaluate the miscalibration, which measures the difference in expectation between confidence and accuracy~\cite{Naeini:2015:AAAI}. Specifically, ECE partitions predictions into $M$ bins $\{B_1, \dots, B_M\}$ according to their confidence and takes a weighted average of the bin's accuracy/confidence difference:
\begin{equation}
\label{eq:ece}
    ECE = \sum_{m=1}^M \frac{|B_m|}{N} \Big| acc(B_m) - conf({B_m}) \Big|,
\end{equation}
where $N$ is the number of prediction samples and $|B_m|$ is the number of samples in the $m$-th bin.

\paragraph{ECE in Training and Inference}
In the case of considering just the topmost token in structured prediction tasks (e.g., machine translation), the prediction is $\hat{y} = \arg\max_{y \in \mathcal{V}} P(y)$ with $P(\hat{y})$ as {\em confidence}. The {\em accuracy} $C(\hat{y}) \in \{1, 0\}$ denotes whether the prediction $\hat{y}$ is correct.

In training, the correctness of the prediction $\hat{y}$ is calculated as whether $\hat{y}$ matches the ground-truth token $y_n$: $C(\hat{y}) \in \{1, 0\}$.
However,  in inference it is not straightforward to measure the accuracy of $\hat{y}$, since it requires to build an alignment between the generated tokens and the ground-truth tokens.

To this end, we turn to the metric of Translation Error Rate (TER)~\cite{Snover:2006:MT}, which measures the number of edits required to change a model output into the ground-truth sequence. Specifically, it assigns a label $l \in \{$C, S, I$\}$ to each generated token. Figure \ref{fig:ter_exp} shows an example of TER labels of each generated token with respect to the reference.
As a side product, TER annotations provide the information of translation errors.
While TER only labels the mis-translation (``S'') and over-translation (``I'') errors, we describe a simple heuristic method to annotate the under-translation error by mapping the label ``D'' from the ground-truth sequence to the generated sequence.

\section{Miscalibration in NMT}

\paragraph{Data and Setup}
We carried out experiments on three different language pairs, including WAT17 English-Japanese (En-Jp), WMT14 English-German (En-De), and WMT17 Chinese-English (Zh-En). The training datasets consist of 1.9M, 4.5M, and 20.6M sentence pairs respectively.
We employed Byte pair encoding (BPE) \cite{Sennrich:2016:ACL} with 32K merge operations for all the three language pairs.
We used BLEU~\cite{Papineni:2001:ACL} to evaluate the NMT models.
We used the TER toolkit~\cite{Snover:2006:MT} to label whether the tokens in NMT outputs are correctly translated. Normalization was not used, and the maximum shift distance was set to 50.

The NMT model that we used in our experiments is Transformer~\cite{Vaswani:2017:NeurIPS}. We used base model as default, which consists of a 6-layer encoder and a 6-layer decoder and the hidden size is 512. The model parameters are optimized by Adam~\cite{Kingma:2015:CoRR}, with $\beta_{1}=0.9$, $\beta_{2}=0.98$ and $\epsilon=10^{-9}$. We used the same warm-up strategy for learning rate as \citet{Vaswani:2017:NeurIPS} with $\rm warmup\_steps=4,000$.

\subsection{Observing Miscalibration}

Reliability diagrams are a visual representation of model calibration, which plot accuracy as a function of confidence~\cite{Niculescu:2005:ICML}.
Specifically, it partitions the output tokens into several bins according to their prediction confidence, and calculate the average confidence and accuracy of each bin.
Figure~\ref{fig:ende-diag} shows the reliability diagrams of both training and inference on En-De and Figure~\ref{fig:acc_conf_bar} shows those on En-Jp and Zh-En. Results are reported on the validation sets.

{\em NMT still suffers from miscalibration.}
The difference between training and inference ECEs is that when estimating training ECE, NMT models are fed with ground-truth prefixes~\cite{Kumar:2019:ICLR-Workshop,Muller:2019:arXiv}, while for inference ECE, NMT models are fed with previous model predictions.
As seen, the training ECE is very small, indicating that NMT models are well-calibrated in training. This is consistent with the findings of \citet{Kumar:2019:ICLR-Workshop,Muller:2019:arXiv}.
However, the inference ECE is much higher, suggesting that NMT models still suffer from miscalibration in inference.

{\em NMT models are miscalibrated in directions of both over- and under-estimation.}
Modern neural networks have been found to be miscalibrated on classification tasks in the direction of over-estimation~\cite{Guo:2017:ICML}. In contrast, NMT models also suffer from under-estimation problems.
The under-estimation problem is more serious on En-Jp than on Zh-En, which we attribute to the smaller size of the training data of the En-Jp task.

\iffalse
is a visual representation of the calibration of NMT models. We partition the output tokens into several bins according to their prediction confidence. Then we calculate the average confidence and accuracy of each bin. We plot accuracy as a function of confidence, and calculate $\mathrm{gap} = |\mathrm{acc} - \mathrm{conf}|$ for each bin. Small gaps indicates well calibrated outputs.

Both the training ECE and the inference ECE are computed using the predictions of NMT models on the development. The difference is that when estimating training ECE, NMT models are fed with ground-truth prefixes~\cite{Kumar:2019:ICLR-Workshop,Muller:2019:arXiv} (i.e., next-token generation), while for inference ECE, NMT models are fed with machine translation prefixes. We find that the training ECE is very small, indicating that NMT models are well-calibrated in the scenario of next token prediction, confirming the findings of \citet{Kumar:2019:ICLR-Workshop,Muller:2019:arXiv}. However, the inference ECE is much higher, suggesting that NMT models suffer from miscalibration errors in inference. We also realize that NMT models are negatively affected by not only over-estimation but also under-estimation, different from classification neural network models~\cite{Guo:2017:ICML}.
\fi

\subsection{Correlation with Translation Errors}

We investigated the calibration error of tokens with different TER labels. As the development set is small, to make the results more convincing, we sampled 100K sentences from the training set as a held-out set and retrained the NMT model on the remained training set excluding the held-out set. All results in this section is reported by the retrained model.
We firstly compute the gap between the confidence and the accuracy of each token in each confidence bin on the held-out set. Tokens in bins whose gaps are less than a threshold are labeled as well-calibrated, otherwise they are labeled as miscalibrated. We use the inference ECE estimated on the development set as the threshold for each language pair respectively. Miscalibrated tokens can be divided into two categories: over-estimation and under-estimation.

\begin{table}[t]
  \centering
  \begin{tabular}{c|c||c|c}
    \multicolumn{2}{c||}{\bf Translation}    &   \bf Well-Cali.   &   \bf Mis-Cali. \\
    \hline
    \hline
    \multirow{4}{*}{\rotatebox[origin=c]{90}{\bf Correct}}
        & \em En-Jp & \bf 0.53  &   0.47 \\
        & \em En-De & \bf 0.57  &   0.43 \\
        & \em Zh-En & \bf 0.60  &   0.40 \\
        \cdashline{2-4}
        & All      & \bf 0.57  &   0.43 \\
    \hline
    \multirow{4}{*}{\rotatebox[origin=c]{90}{\bf Error}}
        & \em En-Jp & 0.46  &  \bf 0.54 \\
        & \em En-De & 0.43  &  \bf 0.57 \\
        & \em Zh-En & 0.36  &  \bf 0.63 \\
        \cdashline{2-4}
        & All      & 0.42  &  \bf 0.58 \\
  \end{tabular}
  \caption{Cosine similarity between the calibration and the translation errors on the held-out data.
  }
  \label{tab:correlation-mistaken}
\end{table}

As shown in Table~\ref{tab:correlation-mistaken}, correct translations (i.e., ``C'') have higher correlations to well-calibrated predictions and erroneous translations (i.e., ``S'', ``I'', and ``D'') correlate more to miscalibrated predictions. This finding is more obvious when NMT models are trained on larger data (e.g., Zh-En).

Table~\ref{tab:correlation-error} lists the correlation between different translation errors and different kinds of miscalibration. We find that over-estimated predictions are closely correlated with over-translation and mis-translation errors, while the under-estimated predictions correlate well with under-translation errors. This finding demonstrates the necessity of studying inference calibration for NMT.

\begin{table}[t]
  \centering
  \begin{tabular}{c|c||cc}
    \multicolumn{2}{c||}{\bf Type}   &   \bf Under-Est.    &   \bf Over-Est.\\
    \hline
    \hline
    \multirow{4}{*}{\rotatebox[origin=c]{90}{\bf Under-Tra.}}
        & En-Jp & \bf 0.35  &   0.22 \\
        & En-De & \bf 0.28  &   0.24 \\
        & Zh-En & 0.31  &   0.31 \\
        \cdashline{2-4}
        & All & \bf 0.32 & 0.26 \\
    \hline
    \multirow{4}{*}{\rotatebox[origin=c]{90}{\bf Over-Tra.}}
        & En-Jp & 0.28  &   \bf 0.32 \\
        & En-De & 0.20  &   \bf 0.36 \\
        & Zh-En & 0.29  &   \bf 0.35 \\
        \cdashline{2-4}
        & All & 0.26 & \bf 0.34 \\
    \hline
    \multirow{4}{*}{\rotatebox[origin=c]{90}{\bf Mis-Tra.}}
        & En-Jp & 0.24  &  \bf 0.36 \\
        & En-De & 0.17  &  \bf 0.42 \\
        & Zh-En & 0.24  &  \bf 0.40 \\
        \cdashline{2-4}
        & All & 0.21 & \bf 0.39 \\
  \end{tabular}
  \caption{Cosine similarity between the miscalibration errors (under-estimation and over-estimation) and the translation errors (under-translation, mis-translation, and over-translation) on the held-out data.
  }
  \label{tab:correlation-error}
\end{table}

\iffalse
\begin{table*}[t]
  \centering
  \begin{tabular}{c|c|cc||c|c|cc}
    \multicolumn{2}{c|}{\bf English$\Rightarrow *$}   &   \bf Under-Est.    &   \bf Over-Est.   &   \multicolumn{2}{c|}{\bf $* \Rightarrow$English}   &   \bf Under-Est.    &   \bf Over-Est.\\
    \hline
    \hline
    \multirow{3}{*}{\rotatebox[origin=c]{90}{\bf En$\Rightarrow$Jp}}
        & Under-Tran.   &   \bf 0.35    &   0.22            &   \multirow{3}{*}{\rotatebox[origin=c]{90}{\bf Jp$\Rightarrow$En}}  & Under-Tran.   & 0.27 & 0.24 \\
        & Mis-Tran.     &   0.24    &   \bf 0.36    &   & Mis-Tran.   &  0.19 & \bf 0.38\\
        & Over-Tran.    &   0.28    &   0.32    &   & Over-Tran.   & 0.20 & \bf 0.35 \\
    \hline
    \multirow{3}{*}{\rotatebox[origin=c]{90}{\bf En$\Rightarrow$De}}
        & Under-Tran.   &   0.28    &   0.24        &  \multirow{3}{*}{\rotatebox[origin=c]{90}{\bf De$\Rightarrow$En}}    & Under-Tran.   &  \bf 0.35 & 0.24 \\
        & Mis-Tran.     &   0.17    &   \bf 0.42    &   & Mis-Tran.   & 0.21 & \bf 0.39 \\
        & Over-Tran.    &   0.20    &   \bf 0.36    &   & Over-Tran.   & 0.29 & 0.31 \\
        \cdashline{2-4} \cdashline{6-8}
    \hline
    \multirow{3}{*}{\rotatebox[origin=c]{90}{\bf En$\Rightarrow$Zh}}
        & Under-Tran.   &  &    &   \multirow{3}{*}{\rotatebox[origin=c]{90}{\bf Zh$\Rightarrow$En}}  & Under-Tran.   &  0.31 &   0.31 \\
        & Mis-Tran.    &   &  && Mis-Tran.     &  0.24     &   \bf 0.40    \\
        & Over-Tran.   &   &  && Over-Tran.    &   0.29    &    0.35    \\
  \end{tabular}
  \caption{Cosine similarity between the miscalibration errors (under-estimation and over estimation) and the translation errors (under-translation, mis-translation, and over-translation) on {\em the held-out data}. Numbers are bold when the difference is larger than $0.1$.}
  \label{tab:correlation-error}
\end{table*}
\fi

\section{Linguistic Properties of Miscalibration}
\label{sec:lingual}

In this section, we investigate the linguistic properties of miscalibrated tokens in NMT outputs.
We explore the following five types of properties: frequency, position, fertility, syntactic roles, and word granularity.

Frequency is generally related to miscalibration; position, fertility, and word granularity are three factors associated with structured prediction; syntactic roles or linguistic roles may vary across language pairs. The results in this section are reported on the held-out set by the retrained model.

\paragraph{Relative Change} We use the relative change of the proportion of a certain category of tokens to quantify to what extent they suffer from the under/over-estimation. For instance, in the Zh-En task, high-frequency tokens account for 87.6\% on the whole held-out set, and among over-estimated tokens, high-frequency tokens account for 77.3\%, thus for over-estimation the relative change of high-frequency tokens is (77.3-87.6)/87.6=-11.76\% in Zh-En. Accordingly, the value of the red rectangle of Zh-En is -11.76\% in Figure~\ref{fig:freq-over}.

Positive relative change denotes that a certain type of linguistic property accounts more in miscalibrated predictions than in all the predictions, suggesting this type of linguistic property suffers from the miscalibration problem. Similarly, negative relative change suggests that a certainty type of linguistic property is less likely to be impaired by the miscalibration problem.

\subsection{Frequency}

\begin{figure}[t]
    \centering

    \subfloat[Over-Estimation]{
    \label{fig:freq-over}
    \includegraphics[height=0.211\textwidth]{figures/frequency-over_est.pdf}}
    \hspace{0.02\textwidth}
    \subfloat[Under-Estimation]{
    \label{fig:freq-under}
    \includegraphics[height=0.211\textwidth]{figures/frequency-under_est.pdf}}
\caption{Effect of frequency on miscalibration.}
\label{fig:frequency}
\end{figure}

We divide tokens into three categories based on their frequency, including {\em High}: the most 3,000 frequent tokens; {\em Medium}: the most 3,001-12,000 frequent tokens; {\em Low}: the other tokens.

{\em Low-frequency tokens are miscalibrated in the direction of under-estimation.} As shown in Figure~\ref{fig:frequency},
the relative changes of low- and medium-frequency tokens are much bigger than those of high-frequency tokens. The under-estimation in low- and medium-frequency tokens can be alleviated by increasing the size of training data (Figure~\ref{fig:freq-under}, data size: En-Jp $<$ En-De $<$ Zh-En).

{\em Low-frequency tokens contribute more to over-estimation.} As shown in Figure~\ref{fig:freq-over}, the relative changes of low- and medium-frequency tokens are positive while those of high-frequency tokens are negative, regarding over-estimation.

{\em High-frequency tokens are less likely to be miscalibrated.} We find the relative changes of high frequency tokens are negative across the three language pairs. The imbalance in token frequency plays an important role in the calibration of NMT.

\subsection{Position}

\iffalse
\begin{table}[h]
  \centering
  \begin{tabular}{c|rrr}
    {\bf Position}  & \bf En-Jp    &   \bf En-De   &   \bf Zh-En \\
    \hline
    Left & 34.4\% & 34.5\% & 34.6\%\\
    Middle & 33.3\% & 33.3\% & 33.3\%\\
    Right & 32.2\% & 32.1\% & 32.0\%\\
  \end{tabular}
  \caption{Distribution of position.}
  \label{tab:position-distribution}
\end{table}
\fi

In structured prediction, different positions may behave differently regarding miscalibration. Thus we divide all the tokens equally into three categories: {\em Left}: tokens on the left third; {\em Middle}: tokens on the middle third; {\em Right}: tokens on the right third.
Figure~\ref{fig:position} depicts the relative changes of these three positions. Since Japanese is a head-final language~\cite{Wu:2018:EMNLP}, we also include the results of Japanese-English (``Jp-En'') for comparison.

\begin{figure}[t]
    \centering
    \subfloat[Over-Estimation]{
    \label{fig:position-over}
    \includegraphics[height=0.211\textwidth]{figures/position-over_est.pdf}}
    \hspace{0.02\textwidth}
    \subfloat[Under-Estimation]{
    \label{fig:position-under}
    \includegraphics[height=0.211\textwidth]{figures/position-under_est.pdf}}
\caption{Effect of relative position on miscalibration.}
\label{fig:position}
\end{figure}

{\em Over-estimation does not have a bias on position.} And this holds for both left-branching and right-branching languages. Increasing the size of training data is less likely to affect the over-estimation in different positions.

{\em Under-estimation occurs more in the left part.} This phenomenon is more obvious in left-branching languages (e.g., Japanese) than in right-branching languages (e.g., English and German), confirming that characteristics of a language play an important role in machine translation~\cite{Wu:2018:EMNLP}.

\subsection{Fertility}

\begin{figure}[h]
    \centering
    \subfloat[Over-Estimation]{
    \includegraphics[height=0.209\textwidth]{figures/fertility-over_est.pdf}}
    \hspace{0.02\textwidth}
    \subfloat[Under-Estimation]{
    \includegraphics[height=0.209\textwidth]{figures/fertility-under_est.pdf}}
\caption{Effect of fertility on miscalibration.}
\label{fig:fertility}
\end{figure}

\begin{figure*}[h]
    \centering
    \subfloat[Over-Estimation]{
    \includegraphics[height=0.211\textwidth]{figures/pos-over_est.pdf}}
    \hspace{0.05\textwidth}
    \subfloat[Under-Estimation]{
    \includegraphics[height=0.211\textwidth]{figures/pos-under_est.pdf}}
\caption{Effect of POS tags on miscalibration.}
\label{fig:pos-tags}
\end{figure*}

Fertility indicates how many source tokens a target token is aligned to, which is highly related to  inference in NMT.
We use \verb|Fast Align|~\cite{Dyer:2013:NAACL} to extract bilingual alignment.
We distinguish between four categories regarding fertility: ``{\bf $\ge2$}'': target tokens that are aligned to more than one source tokens; ``{\bf $1$}'': target tokens that are aligned to a single source token; ``{\bf $(0, 1)$}'': target tokens that are aligned to a single source token along with other target tokens; ``{\bf $0$}'': target tokens that are not aligned to any source token.
Figure~\ref{fig:fertility} plots the results.

{\em Tokens aligning to less than one source token suffer from over-estimation.} The extent grows with the data size. In addition, these tokens (``(0, 1)'') are less likely to suffer from under-estimation.

{\em Tokens aligning to more than one source token suffer more from under-estimation.} The relative change of "fertility$>=$2" is much larger than that of the other types of fertility. Meanwhile, the null-aligned target tokens ("fertility$=$0") also suffer from under-estimation problem instead of over-estimation problem on the large-scale Zh-En data.

\subsection{Syntactic Roles}

In this experiment, we investigate the syntactic roles of miscalibrated tokens.~\footnote{If a token is a sub-word segmented by BPE, the token shares the syntactic role of the full word that it belongs to.}
Words in English and German sentences are labeled by \verb|Stanford POS tagger|\footnote{https://nlp.stanford.edu/software/tagger.shtml},
and Japanese sentences are labeled by \verb|Kytea|\footnote{http://www.phontron.com/kytea/}.
We distinguish between the following POS tags: noun, verb, adjective, preposition, determiner, punctuation, and the others.
Noun, verb, and adjective belong to content tokens. Preposition, determiner, punctuation and the others belong to content-free tokens.

{\em Content tokens are more likely to suffer from miscalibration.} From Figure~\ref{fig:pos-tags} we find that the most relative changes of content tokens (i.e., ``Noun'', ``Verb'' and ``Adj'') are positive, while most of the relative changes of the content-free tokens (i.e., ``Prep.'', ``Dete.'', ``Punc.'', ``Others'') are negative. Among content tokens, the verbs (``Verb'') face the over-estimation problem instead of the under-estimation problem. Surprisingly, the adjectives (``Adj'') suffer from under-estimation problem on large data (e.g., En-De and Zh-En).

\begin{figure}[t]
    \centering
    \subfloat[Over-Estimation]{
    \label{fig:bpe-over}
    \includegraphics[height=0.211\textwidth]{figures/bpe-over_est.pdf}}
    \hspace{0.02\textwidth}
    \subfloat[Under-Estimation]{
    \label{fig:bpe-under}
    \includegraphics[height=0.211\textwidth]{figures/bpe-under_est.pdf}}
\caption{Effect of word granularity on miscalibration.}
\label{fig:bpe}
\end{figure}

\subsection{{Word Granularity}}
BPE segmentation is the preliminary step for current NMT systems, which may segment some words into sub-words. To explore the effect of word granularity on the miscalibration of NMT models, we divide the tokens after BPE segmentation into two categories: {\em Sub-Words} that are divided into word fragments by BPE (e.g., with ``@@''), and {\em Full Words} that are not divided by BPE. Figure~\ref{fig:bpe} depicts the results.

{\em Sub-words suffer more from miscalibration, while full words are less likely to be miscalibrated.} The relative changes of sub-words are all positive for both over- and under-estimation, while those of full words are all negative.
~\newcite{Sennrich:2016:ACL} showed that BPE addresses the open-vocabulary translation by encoding rare and unknown words as sequences of sub-word units. Our results confirm their claim: the behaviors of sub-words and full words correlate well with those of low- and high-frequency tokens respectively.

\begin{table*}[t]
  \centering
  \begin{tabular}{c|c||c|rrr||c|rrr}
    \bf Label   &  \multirow{2}{*}{\bf Dropout} & \multicolumn{4}{c||}{\bf Beam Size = 10}  &   \multicolumn{4}{c}{\bf Beam Size = 100}  \\
    \cline{3-10}
    \bf Smoothing & & \em BLEU & \em ECE & \em Over. & \em Under. & \em BLEU & \em ECE & \em Over. & \em Under. \\
    \hline
    \texttimes & \texttimes & 23.03 & 25.49 & 58.3\% & 9.6\%
                            & 22.90 & 26.46 & 59.4\% & 9.3\% \\
    \hdashline
    \checkmark & \texttimes & 24.51 & 14.99 & 42.3\% & 17.3\%
                            & 24.58 & 15.97 & 42.8\% & 16.9\% \\
    \texttimes & \checkmark & 27.52 & 20.75 & 52.3\% & 10.1\%
                            & 26.93 & 22.57 & 53.6\% & 9.8\%  \\
    \hdashline
    \checkmark & \checkmark & 27.65 & 14.26 & 39.7\% & 14.1\%
                            & 27.68 & 14.75 & 40.1\% & 14.2\%  \\
    \hline
    \textproc{Graduated} & \checkmark & \bf 27.76 & \bf 5.07 & 29.1\% & 31.6\%
                              & \bf 28.07 & \bf 5.23 & 29.5\% & 31.4\%
  \end{tabular}
  \caption{Results of label smoothing and dropout on the En-De task. ``Over.'' and ``Under.'' denote over-estimation and under-estimation, respectively.}
  \label{tab:ls-dropout}
\end{table*}

\begin{figure*}[ht]
    \centering
    \subfloat[None]{
    \includegraphics[height=0.24\textwidth]{figures/tech_none_bar.pdf}
    }
    \hspace{0.05\textwidth}
    \subfloat[Vanilla]{
    \includegraphics[height=0.24\textwidth]{figures/tech_constant_bar.pdf}
    }
    \hspace{0.05\textwidth}
    \subfloat[Graduated]{
    \includegraphics[height=0.24\textwidth]{figures/tech_grad_bar.pdf}
    }
\caption{Reliability diagrams of different label smoothing strategies: (a) no label smoothing; (b) vanilla label smoothing; (c) graduated label smoothing. The results are reported on the WMT14 En-De translation task.}
\label{fig:tech_smoothing}
\end{figure*}

\section{Revisiting Advances in Architecture and Regularization}

\citet{Guo:2017:ICML} have revealed that the miscalibration on classification tasks is closely related to lack of regularization and increased model size. In this section we check whether the conclusion holds on the inference of NMT models, which belong to a family of structured generation.

One criticism of NMT inference is that the translation performance inversely decreases with the increase of search space~\cite{Tu:2017:AAAI}. Quite recently,~\newcite{Kumar:2019:ICLR-Workshop} claimed that this problem can be attributed to miscalibration. Accordingly, we also report results on large beam size and find that reducing miscalibration can improve the NMT performance in large beam size.

\begin{table*}[t]
  \centering
  \begin{tabular}{c|c|r||c|rrr||c|rrr}
    \multirow{2}{*}{\bf Enc.} & \multirow{2}{*}{\bf Dec.} &  \multirow{2}{*}{\bf Para.}  & \multicolumn{4}{c||}{\bf Beam Size = 10}  &  \multicolumn{4}{c}{\bf Beam Size = 100} \\
   \cline{4-11}
    & & & \em BLEU & \em ECE & \em Over. & \em Under.
          & \em BLEU & \em ECE & \em Over. & \em Under. \\
    \hline
    \textproc{base} & \textproc{base} & 88M & 27.65 &  14.26 & 39.7\% & 14.1\% & 27.68 & 14.75 & 40.1\% & 14.2\% \\
    \hline \hline
    \textproc{deep} & \textproc{deep} & 220M & 28.86 & 14.99 & 40.3\% & 14.1\% & 28.64 & 15.55 & 41.8\% & 14.0\% \\
    \hdashline
    \textproc{deep} & \textproc{base} & 145M & \bf 29.09 & \bf 14.28 & 39.6\% & 14.1\% & \bf 29.29 & \bf 14.53 & 39.6\% & 14.2\% \\
    \hline \hline
    \textproc{wide} & \textproc{wide} & 264M & 28.66 & 16.09 & 42.3\% & 12.6\% & 28.42 & 17.22 & 43.2\% & 12.5\% \\
    \hdashline
    \textproc{wide} & \textproc{base} & 160M & \bf 28.97 & \bf 14.83 & 39.7\% & 13.6\% & \bf 29.09 & \bf 15.06 & 39.8\% & 13.7\% \\
  \end{tabular}
  \caption{Effect of model size by enlarging encoder (``Enc.'') and decoder (``Dec.'') on the En-De dataset.}
  \label{tab:capacity-architecture}
\end{table*}

\subsection{Regularization Techniques}
\label{sec:training}

We revisit two important regularization techniques that directly affect confidence estimation:

\begin{itemize}
    \item {\em Label Smoothing}~\cite{Szegedy:2016:CVPR}: distributing a certain percentage of confidence from the ground truth label to other labels uniformly in training.
    \item{\em Dropout}~\cite{Hinton:2012:arXiv}: randomly omitting a certain percentage of the neural networks on each training case, which has been shown effective to prevent the over-fitting problem for large neural networks.
\end{itemize}

\iffalse
We believe that label smoothing on structured prediction with large vocabulary (e.g., machine translation) can be further improved by solving the following two potential constraints:
\begin{itemize}
    \item It assigns a unified smoothing parameter $\epsilon$ to all generated tokens, regardless of the distinct properties of the tokens. For example, a miscalibrated prediction should be assigned a higher $\epsilon$ than a well-calibrated prediction.
    \item It only penalizes the groundtruth label, while ignore the incorrect labels during the training phase.
    Recent studies show that overlooking incorrect labels leads to the representation degeneration problem: most of the learnt word embeddings tend to degenerate and be distributed into a narrow cone~\cite{Gao:2019:ICLR}.
\end{itemize}
\fi

For comparison, we disable label smoothing or dropout to retrain the model on the whole training set. The results are shown in Table~\ref{tab:ls-dropout}. We find that label smoothing improves the performance by greatly reducing the over-estimation, at the cost of increasing the percentage of under-estimation error. Dropout alleviates the over-estimation problem, and does not aggravate under-estimation. Although label smoothing only marginally improves performance on top of dropout, it is essential for maintaining the translation performance in larger search space (i.e., Beam Size = 100).

As seen from Table~\ref{tab:ls-dropout}, reducing ECE can only lead to marginal BLEU gains. We attribute this phenomenon to the fact that ECE is another metric to evaluate NMT models, which is potentially complementary to BLEU. Accordingly, ECE is not necessarily strictly negatively related to BLEU.

\paragraph{Graduated Label Smoothing}
Inspired by this finding, we propose a novel {\em graduated label smoothing} approach, in which the smoothing penalty for high-confidence predictions is bigger than that for low-confidence predictions. We firstly use the model trained by vanilla label smoothing to estimate the confidence of each token in the training set, then we set the smoothing penalty to 0.3 for tokens with confidence above 0.7, 0.0 for tokens with confidence below 0.3, and 0.1 for the remaining tokens.

As shown in Table~\ref{tab:ls-dropout}, the graduated label smoothing can improve translation performance by alleviating inference miscalibration, and the improvement is more significant in large beam size.
Figure~\ref{fig:tech_smoothing} shows the reliability diagrams of different label smoothing strategies.
The graduated label smoothing can effectively calibrate the predictions with $0.4\leq$ confidence $\leq0.8$, while is less effective for low- (i.e., $<0.4$) and high-confidence (i.e., $>0.8$) predictions. We believe that the design of more advanced techniques to solve this problem is a worthwhile future direction of research.

\subsection{Increased Model Size}

The model size of NMT models has increased significantly recently~\cite{Bahdanau:2015:ICLR,Vaswani:2017:NeurIPS,Wang:2019:ACL}. We evaluated the inference calibration of models with different sizes.
We increase model size in the following two ways:
\begin{itemize}
    \item {\em Deeper model}: both the encoder and the decoder are deepened to 24 layers;
    \item {\em Wider model}: the hidden size of the encoder and the decoder is widened to 1024.
\end{itemize}
The BLEU score and inference ECE of different models are shown in Table~\ref{tab:capacity-architecture}.

{\em Increasing model size negatively affects inference calibration.}
We find that increasing both the encoder and the decoder increases the inference calibration error despite increasing the BLEU, confirming the finding of \citet{Guo:2017:ICML} that increased model size is closely related to model miscalibration. This leads to a performance drop in a larger search space (i.e., Beam Size = 100).

{\em Only enlarging the encoder improves translation quality while maintaining inference calibration.}
As the decoder is more directly related to the generation, it is more likely to result in miscalibration. In order to maintain the performance improvement and do not aggravate over-estimation, we propose to only increase the size of encoder and keep the decoder unchanged. Results in Table~\ref{tab:capacity-architecture} indicate that only enlarging the encoder can achieve better performance with fewer parameters compared to enlarging both the encoder and the decoder. In a larger search space (i.e., Beam Size = 100), models with high inference ECE will generate worse translations while models with low inference ECE can achieve improved translation performance.

\section{Conclusion}

Although NMT models are well-calibrated in training, we observe that they still suffer from miscalibration during inference because of the discrepancy between training and inference.
Through a series of in-depth analyses, we report several interesting findings which may help to analyze, understand and improve NMT models.
We revisit recent advances and find that label smoothing and dropout play key roles in calibrating modern NMT models. We further propose graduated label smoothing that can reduce the inference calibration error effectively.
Finally, we find that increasing model size can negatively affect the calibration of NMT models and this can be alleviated by only enlarging the encoder. As well-calibrated confidence estimation is more likely to establish trustworthiness with users, we plan to apply our work to interactive machine translation scenarios in the future.

\section*{Acknowledgments}
We thank all anonymous reviewers for their valuable comments and suggestions for this work.
This work was supported by the National Key R\&D Program of China (No. 2017YFB0202204), National Natural Science Foundation of China (No. 61925601, No. 61761166008, No. 61772302), Beijing Advanced Innovation Center for Language Resources (No. TYR17002),  and the NExT++ project supported by the National Research Foundation, Prime Ministers Office, Singapore under its IRC@Singapore Funding Initiative.

\bibliographystyle{acl_natbib}

redpajama_set_name: RedPajamaArXiv


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Crypto Exchange Bitfinex Seeks Case Dismissal Over Bitcoin Bubble
By Tony P.
Last Updated on Oct 8, 2020 at 14:07
~ 3 minutes
Hong Kong-based crypto exchange Bitfinex is pleading with court for the dismissal of a $1.4 Trillion case in which it allegedly manipulated the 2017 Bitcoin bubble. The exchange claims that the plaintiffs' cause of action has no legal basis, terming it 'frivolous.'
Earlier, the plaintiffs filed a class-action lawsuit against Tether, Bitfinex, and others accusing the companies of engaging in fraudulent market manipulation. The complainants claimed that the accused parties planned to inflate crypto prices through the printing of uncovered USDT tokens.
It is worth mentioning that community should pay more attention to reliable solutions, such as AML program, in order to easily detect suspicious transactions.
The scheme, according to the plaintiffs was not only sophisticated but also happens to be one of the biggest bubbles ever experienced in human history.
Bitfinex Case Dismissal Request
However, as things stand currently, Bitfinex states that the whole case should be dismissed on the merits of being frivolous. The crypto exchange further states that the case does not have any legal basis, and for that reason, there is no chance that the plaintiffs will win it.
Bitfinex also noted that the complainants' cause of action does not hold a legal basis that would allow the case to proceed past the very first stage. The stablecoin printer, therefore, believes that the right thing to do would be to have the case dismissed during the early stages.
"There are several reasons to look at the Plaintiffs' claims with a jaundiced eye," Bitfinex says.
As reported by a media outlet, Tether and its partner digital asset exchange have explained that the accusations advanced by the plaintiffs are mainly based on a price manipulation campaign that is based on a study that was never published, and which sought to explain the BTC's boom or rather, Bitcoin bubble experienced in 2017.
Unpublished Bitcoin Bubble Report
Apart from some of the methodological flaws that were present in the unpublished paper, Bitfinex adds that the claims, meritless as they were, had been amended to withdraw support for one of the complainant's core allegations.
The paper was a brainchild of Amin Shams, a graduate student, and John Griffin, an academician renowned for his ability to spot fraudulent financial transactions. It had established that the BTC price increases recorded in 2017, as well as that of other cryptocurrencies, could all be attributed to actions implemented by one player.
Bitfinex, in the past, claimed that it was not involved in any price manipulations. It has also gone on record to state that issuance of Tether can in no way be used as a way to prop BTC prices.
Featured Image Source: CoinCentral
Read more articles related to this subject:
BitfinexUSDT
Notice: The information in this article and the links provided are for general information purposes only and should not constitute any financial or investment advice. We advise you to do your own research or consult a professional before making financial decisions. Please acknowledge that we are not responsible for any loss caused by any information present on this website.
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Q: How to change frame source without reloading the page I am creating a Template Site, i want to change the content of the IFrame
based on the link clicked in javascript,
but the error i get is if i change the 'src' attribute of the iframe
the page gets reload.
So my question is
how to change iframe content without reloading the Page? below is my code
// HTML part

   HOME

   PRODUCTS

   SOLUTIONS

    SERVICES

    SUPPORT

//Javascript part

A: Just write hrefs for those links and add attribute target="content".
This can be done w/o javascript at all.

A: Your link_list is wrong, JavaScript is case-sensitive, it's new Array(), not new array():
var link_list = new Array(...);

Use the array literal [] and you're fine:
var link_list = ['homepage.html','product.html','solution.html',
            'services.html','support.html','event.html',
             'partner.html','company.html'];

If you check your browser's JavaScript error console it's easy to find such errors:
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This year's monsoon is truly never-ending and I'm Scottish – I have no romantic attachment to rain!
We were instantly drenched and wading through pond-sized puddles but we soon met a man who made it all worthwhile. We'd arrived, for once, with no particular plan in mind but decided to check out the breakfast fare in Sitaram Bazaar. We asked around for recommendations and were pointed in the direction of Ram Swaroop.
This is Anil Kumar, current owner of Ram Swaroop, named after his father, a shop which has been serving up mean breakfasts in the old city for over 70 years. In front of him in the picture is a pile of Nagori – crispy fried poori made from semolina, white flour and ghee. This is served with a warm, not too sweet, not too sickly semolina halwa, to the left of the picture. We also ordered plates of Bedmi Aloo; both dishes were exceptional and complemented each other fabulously. The savoury potato, cooked in its skin, laced with coriander, cumin, chilli powder and hing with its perfect bedmi for dunking and scooping; then the crumbling crispness of the nagori and soothing sweetness of the halwa. Now that I'm only 20 minutes away Mr Kumar is going to be seeing a lot of me in the coming months.
Mr Kumar himself was a gent, even sharing the recipe for his sensational nagori. The dough is made of 2 parts sooji (semolina) to one part maida (white flour). For every 1kg of dough, add 200g of ghee. Then fry to melting crispness.
I was longing to eat the whole thing all over again almost as soon as I'd devoured it. Always a good sign but on this occasion we had more breakfasts to try…….but that will have to be another post.
Before you go, though, look at these two….
Aren't they gorgeous? We spotted them outside Chaina Ram sweet shop in the Fatehpuri area. The rain was lashing down but they were oblivious as they devoured a crumbled laddoo – a moreish golf ball-sized sweet made from besan or semolina.
Hi Pamela..I have never had Nagori before but your delicious description is making me hungry!Lovely post and looking forward to more on the Delhi's delicious street food!
Glad you're back Pamela…with the Metro now able to whiz me, too, straight into Old Delhi, I need as many of your tips as I can get for street food excursions!
You are back , believe me i have been missing you Pamella.
I can imagine it must have been a beautiful experience with the breakfast after braving the puddles. This particular season of rain has busted all the romantic sentiments about rain and all Dehliites are miffed with it i am sure.
Nagori n halwa sounds great and if it is so dirt cheap i wonder how do they manage it ….
post the next episode soon… loved the younger child in the pic who is ready to lick his fingers..
Oops..I was skeptical n so jz Confirmed..You ve already been to Shyam Sweets!!
The other day I was wondering what is missing in my cyber being.
Today it dawned that it were your mmmmm delicious posts.
Hi Narender – Thank you so much I've been really useless the last few weeks – I'll try not to let it slip again!
I plan to eat enough for both of us!
Supratim – who could begrudge them – they were relishing every crumb!
Thanks Tennille – give me a shout when you're planning an Old Delhi trip, I'm always game!
Hi Madhuli – nagori was new to me, and several others too, but I have a feeling it's going to be a regular from now on!
You remind me and make me nostalgic about the years I spent in Delhi…Do try the kebabs and other street foods in Old Delhi. Will look forward to coming back to your Blog. (Arrived from Finely Chopped).